With growing environmental awareness and increasing energy demands, sustainable manufacturing has become a focal point in the industry. Meanwhile, globalization has propelled distributed manufacturing systems as a dominant trend. This paper tackles the energy-efficient distributed heterogeneous hybrid flow-shop scheduling problem (EDHHFSP), aiming to minimize both makespan and total energy consumption. We first formulate a mixed-integer linear programming (MILP) model to provide a benchmark for small instances. More importantly, we propose a novel end-to-end deep reinforcement learning framework based on a heterogeneous graph neural network, which models the scheduling problem as a distributed decision-making process. A key innovation lies in the design of an action space composed of "job–factory" and "operation–machine" pairs, enabling fine-grained, decentralized scheduling decisions. Our approach starts with a novel heterogeneous graph representation of scheduling states, capturing complex interactions among jobs, factories, and machines. A three-stage embedding mechanism is developed to encode real-time scheduling environments. The agent then learns a parameterized policy using the proximal policy optimization (PPO) algorithm, guided by a reward function that balances makespan and energy efficiency. Experimental results demonstrate that our method generalizes well across different problem scales and significantly outperforms traditional heuristics and learning-based baselines in terms of both scheduling quality and energy savings.

Advances in multi-robot technology have accelerated the development of smart agriculture, enabling tasks to be executed collaboratively with higher efficiency. In heterogeneous agricultural robots collaborative operation scheduling, fuzzy time window and matching constraints significantly increase the problem complexity. This paper proposes a multi-objective heterogeneous agricultural robot operation scheduling model with fuzzy service time window and matching constraints (MHROS_FT&M), aiming to optimize the total operation cost and service level. Given the NP-hard property of MHROS_FT&M, the hierarchical learning large neighborhood search algorithm (HLLNS) is developed. HLLNS incorporates the hierarchical reinforcement learning to enhance adaptability, a dynamic programming-based approach to improve service levels, and a sub-problem collaboration and mutation strategy to escape local optimum. By employing automated algorithm design technique to optimize 12 key parameters, the automated HLLNS (AHLLNS) is realized. In practical smart-farming scenarios, AHLLNS supports the joint scheduling of heterogeneous robots such as spraying drones, weeding robots, and seeding drones under uncertain service times, and explicitly balances operation cost against farmer satisfaction. The obtained schedules reduce unnecessary travel and resource consumption while keeping service times within acceptable ranges for farmers. Through automatic parameter tuning and the use of problem-specific operators, AHLLNS effectively addresses fuzzy time windows and matching constraints, achieving better performance across different problem scales. Experimental comparisons with Gurobi and state-of-the-art algorithms demonstrate AHLLNS superior computational efficiency and solution quality, validating its effectiveness for MHROS_FT&M.

Integrated coal mine energy systems (ICMES) generate streaming constraints where the number of active constraints fluctuates over time due to equipment switching, safety driven operations, and maintenance events. These dynamics cause abrupt contraction, expansion, or fragmentation of the feasible region. To address this challenge, we propose a federated learning (FL) variational autoencoder (VAE) evolutionary algorithm (FVE). Each constraint is mapped to an FL client so that local VAEs can learn heterogeneous constraint specific feasible subspaces as clients observe differently structured constraint data. Federated aggregation fuses these local latent models into a global generative model that adapts quickly and robustly to constraint inflow and outflow. An adaptive population correction mechanism repairs infeasible individuals, and enhanced dynamic dynamic nondominated sorting genetic algorithm-II tracks pareto front evolution under structural shifts. Comparative experiments on benchmark functions and an ICMES scheduling case demonstrate that FVE achieves faster feasibility restoration, improved convergence, and higher diversity than state-of-the-art methods. These results confirm the practicality of FVE for real-time industrial optimization under streaming constraints.

In the realm of scheduling problems, metaheuristics have been widely embraced as superior solutions, appreciated for their ability to generate resolutions for non-deterministic polynomial-time hard (NP-hard) problems swiftly. This paper presents a novel parallel evolutionary algorithm (PEA), which marries metaheuristics and parallel computing to amplify computer performance utilization. Four operators and a restart strategy are incorporated into the proposed PEA to bolster both its global and local search capabilities. An accelerated calculation method for two operators is proposed. The algorithm also features an adaptive method that generates sub-threads and parameters based on computer performance, along with rotation for evaluating solutions. A random search sub-thread is established to update the solution. The algorithm is tested on the workshop automated guided vehicle (AGV) scheduling problem and compared against other optimization algorithms to ascertain its efficacy. The test results overwhelmingly highlight the superior performance of the proposed algorithm. Note to Practitioners—The paper introduces a novel parallel evolutionary algorithm (PEA) for scheduling problems, which combines metaheuristics and parallel computing to enhance computer performance utilization. The algorithm incorporates four operators and a restart strategy, along with an accelerated calculation method for two operators. It also includes an adaptive method to generate sub-threads and parameters based on computer performance, as well as rotation for evaluating solutions. A random search sub-thread is established to update the solution. The proposed algorithm is tested on the workshop automated guided vehicle (AGV) scheduling problem, producing superior results compared to other optimization algorithms. Its ability to swiftly generate resolutions for NP-hard problems can greatly benefit industries that rely on efficient scheduling, such as logistics and manufacturing. However, it is important to note that the algorithm has some limitations. Further research is needed to explore its application in different domains and evaluate its performance in more complex scheduling scenarios. Additionally, the algorithm’s scalability and adaptability need to be thoroughly examined to ensure its practicality in real-world settings.

An actual hybrid flow shop scheduling (HFSS) problem with operation skipping is investigated from the steelmaking continuous casting (SCC) process, which plays a vital role in the productive processing of a slab in iron and steel enterprises. Firstly, a mixed integer mathematical model is explored for this problem based on the previous survey. Secondly, a block heuristic method based on double-layer right shift is presented on the basis of the problem-specific characteristics, which can generate the better initial solution for the problem. Thirdly, an improved memetic algorithm (IMA) with double-vector-representation based on attribution feature is proposed for dealing with the problem, which includes the block heuristic for initialization, the novel mutation structure on the basis of the problem-specific characteristics, and the enhanced local research to improve the exploitation ability. Finally, to test the performance of the IMA, a large number of instances from a steel plant are adopted. By statistical analysis, the results of experiment evaluation indicate that the proposed IMA has highly effective performance and obvious advantages comparing with other well-known algorithms.Note to Practitioners—Due to the change of order and the technology requirements of SCC process, partial charges must be further refined to eliminate the impurities or heat molten steel. This study models a novel hybrid flow shop scheduling problem with operation skipping, in which all charges undergo the primary refining stage, and partial ones undergo the double or triple refining stage. The average sojourn time, the total earliness and the total tardiness are taken as the objective functions, and the constraint for operation skipping is added. We develop an IMA based on the attribution feature of charge, in which double-vector-representation is designed, and a novel mutation structure on the basis of the problem-specific characteristics is presented, and an enhanced local research is proposed to improve the exploitation ability. Furthermore, a block heuristic method with double-layer right shift is presented to generate good initial individuals. The performance of IMA is analyzed by comparing with six modern meta-heuristics algorithms, the results show that the IMA is more superior than others. Because of the complex of scheduling problem in SCC, some dynamic influence, such as machine breakdowns, the influence of transportation tools, should be considered. The paper’s work can be extended to the above actual dynamic problem. Moreover, the presented IMA can also be developed to other hybrid flow shop scheduling problem with operation skipping.

Due to the complexity of modern production processes and environments, most products must pass through multiple workshops from raw materials to finished goods. This article investigates a collaborative scheduling problem in a cascaded dual-shop production setting. Unlike single-shop scheduling or distributed multiworkshop scheduling, this problem emphasizes collaborative optimization between two interdependent workshops. In addition, real-world production often involves a mode where main and suborders must be integrated through mating operations. This study formulates an energy-efficient cascaded dual-shop collaborative scheduling problem with the mating operation (ECDCSP-M). The focus is on developing a mixed-integer linear programming (MILP) model for the ECDCSP-M and designing an end-to-end graph-based deep reinforcement learning (GDRL) approach. A dual-shop heterogeneous graph is constructed to capture the real-time state of the entire system, in which "job-factory" and "operation-machine" pairs are defined as agent actions. A heterogeneous graph neural network (HGNN) is then proposed, employing a three-stage embedding mechanism to model complex relationships, including mating operations. Experimental results show that the proposed method achieves strong generalization across varying problem complexities and provides robust solutions to challenging scheduling scenarios.

Driven by the evolution of smart and sustainable manufacturing paradigms under Industry 5.0, which emphasize adaptability, connectivity, and data-driven decision-making, the dynamic flexible job shop scheduling problem (DFJSSP) has emerged as a critical area of research. The DFJSSP involves scheduling jobs in a highly dynamic and uncertain manufacturing environment where new tasks are continually introduced, further complicating the scheduling process. In this study, the DFJSSP is extended to incorporate single crane transportation and sequence-dependent setup times, reflecting real-world manufacturing constraints. To tackle this multifaceted problem, we introduce a novel approach, i.e., a multipopulation-based evolutionary multitask optimization (EMTO) framework. In addition, the genetic programming algorithm is employed as a generative hyperheuristic to deal with the dynamic uncertainties in the shop floor. Two components are collaborated to optimize two objectives, i.e., minimizing the maximum completion time and the total tardiness. Furthermore, a dynamic transfer ratio is proposed, allowing the proportion of knowledge transfer to adapt throughout the iteration process, balancing convergence speed with population diversity. The results demonstrate that both the EMTO framework and the dynamic transfer ratio significantly enhance the performance of the algorithm. Compared to well-known constructive heuristics and reinforcement learning algorithm, the proposed approach enables parallel resolution of multiple optimization objectives, leading to enhanced scheduling efficiency and adaptability in dynamic manufacturing environments.

Production processes are inherently complex, often involving multiple production phases from raw materials to finished products, making joint scheduling problems a focal point of research. This article addresses the dynamic cascaded flowshop joint scheduling problem, which integrates a distributed permutation flowshop in Phase 1 and a hybrid flowshop in Phase 2. The challenge involves both initial scheduling and dynamic response mechanisms for new job insertions. We propose an evolutionary greedy algorithm (EGA) aimed at minimizing total flowtime. The EGA employs a multistart cooperative framework tailored to problem characteristics, alternating between a population-based EGA for Phase 1 and an elitist-based greedy algorithm for Phase 2 to generate a robust and complete schedule. Upon new job insertions, three heuristic-driven response strategies enhance solution stability and adaptability. In addition, phase-specific hybrid local search operators and an adaptive insertion strategy, leveraging knowledge-based problem properties, further improve solution quality and search efficiency. The experimental results indicate that the EGA outperforms five state-of-the-art algorithms, achieving an average improvement of 39% in RPI values across 480 instances. Moreover, the proposed local search mechanisms and dynamic response strategies significantly enhance its performance. Thus, the EGA is well-suited for addressing the studied problem.

The distributed hybrid flowshop scheduling problems (DHFSPs) widely exist in various industrial production processes, and thus have received widespread attention. However, the existing research mainly focuses on interfactory and intermachine collaboration, but ignores collaborative processing between jobs. Therefore, this article considers rescheduling DHFSP with job merging and reworking (DHFRPJM) and establishes a mixed-integer linear programming model. The objective is to minimize the makespan. Based on problem-specific knowledge, a decoding heuristic and initialization strategy considering job merging are designed. An acceleration strategy based on critical path is adopted to save the computational effort of the iterated greedy algorithm. A local search strategy based on a deep reinforcement learning algorithm further improves the performance of the algorithm. Experimental results based on actual production data show that the proposed algorithm outperforms other algorithms in closely related literature.

Traffic forecasting leverages multivariate time series analysis to predict traffic patterns. Real-world traffic data comprises two distinct types of latent time-series signals:diffuse signals, which refer to time-varying information propagated across the traffic network, andintrinsic signals, which capture unique, location-specific patterns. However, existing approaches often treat traffic signals solely as diffusion outcomes, overlooking the intrinsic characteristics that can significantly influence model performance. To address this issue, we propose the Position-invariant Graph Convolutional Recurrent Network (PGCRN), which decouples diffuse and intrinsic signals for improved traffic forecasting. Instead of relying on a predefined graph, PGCRN learns graph structures from spatio-temporal data through a learnable position-invariant node representation that forms an adaptive adjacency matrix. This is integrated into a Graph Convolutional Recurrent Network (GCRN) encoder–decoder to jointly capture spatial and temporal dependencies. Furthermore, we introduce a contrastive learning framework in which a node’s time-varying and position-invariant representations form positive pairs, while position-invariant representations from different nodes form negative pairs. The model is trained with a triplet loss. Experiments on four benchmark datasets show that PGCRN consistently outperforms strong baselines. Owing to its computational efficiency, PGCRN is also well suited for deployment on resource-constrained edge devices.

The sustainable scheduling of distributed manufacturing has received considerable attention from manufacturing researchers in developing sustainable manufacturing. The multi-objective sustainable distributed blocking flow shop scheduling problem with heterogeneous factories (HFMS-DBFSP) is studied in this paper. The model of the HFMS-DBFSP is proposed considering the three goals of total tardiness, total carbon emission, and negative social impact. A learning-based discrete Jaya algorithm (LDJaya) is presented to address the HFMS-DBFSP. The cooperative initialization method based on the characteristics of the HFMS-DBFSP is designed for population initialization. The self-learning operation selection strategy is introduced to guide the selection of operations, and local search operators are proposed to keep the population diverse. The carbon saving speed adjustment strategy is proposed to lower carbon emissions still further. The effectiveness of each of the strategies in the LDJaya is validated and benchmarked within the benchmark suite against the state-of-the-art algorithms. The simulation results of the experiment prove that the designed LDJaya is superior to other comparative algorithms in significance and efficiency in resolving the HFMS-DBFSP.

Sustainable scheduling within the manufacturing field has garnered substantial attention from both academia and industry. The escalating market demands have heightened requirements on the flexibility of production modes, multi-zone, and multi-objective. In this context, our study explores the intricacies of the multi-objective distributed flow shop group scheduling problem with sequence-dependent setup times, aiming to concurrently optimize makespan and total energy consumption (DFm|group, sdst|#(Cmax, TEC) ). Firstly, a mathematical model is constructed to analyze problem characteristics. Subsequently, we introduce a collaborative multi-objective evolutionary algorithm driven by indicators (CMOEA/I). In CMOEA/I, an indicator-driven approach is proposed for solution selection, which approximates the Pareto front based on the convergence indicator, while screening potential solutions based on the spread indicator. Furthermore, a collaborative model and local search are developed by incorporating the intrinsic linkages of factories, groups, and jobs. Additionally, to further explore the potential non-dominated solutions, a speed variation strategy is devised based on the pivots of decreasing speed to save energy and increasing speed to reduce makespan. An extensive set of simulation experiments is conducted on a diverse range of test instances. Through meticulous statistical analysis, the outcomes demonstrate that the CMOEA/I exhibits efficacy when contrasted with other advanced algorithms.

With the advent of agriculture 4.0 era, the combination of agriculture and unmanned technology has promoted the development of intelligent agriculture. However, there are relatively few studies on the agricultural robot task allocation problem to optimize the cost and efficiency of smart farms. To make up this deficiency, this paper addresses a multi-picking-robot task allocation (MPRTA) problem with two objectives of minimizing the maximum completion time and minimizing the total travel length of all robots. An effective multi-objective discrete artificial bee colony (MODABC) algorithm is proposed to solve this problem. At first, a heuristic allocation method based on robot load balancing is designed to generate high-quality initial solutions. And then, a multi-objective self-adaptive strategy is proposed to enhance the exploitation and exploration of the algorithm. In addition, a multi-objective local search strategy for the non-dominated solutions is presented to help the population find better solutions. At last, extensive experiments based on different task sizes and robot scales of an intelligent orchard demonstrate the effectiveness and high performance of the proposed algorithm for solving the MPRTA problem.

With the increasingly serious ecological problems, energy-efficient scheduling, an effective approach to achieve sustainable development and green manufacturing, has attracted much attention by taking both economic effect and energy conservation into account. This paper addresses an energy-efficient scheduling of the distributed blocking flow-shop problem (EDBFSP) to minimize both makespan and total energy consumption. The mixed-integer linear programming (MILP) model of EDBFSP is designed. A multi-action reinforcement learning algorithm based on problem-specific knowledge called multi-greedy policy optimization (multi-GPO) is proposed to solve the EDBFSP. In addition, after analyzing the characteristics of the problem, an energy-saving strategy and an acceleration strategy are designed to further optimize the solution. Experiments in a large number of benchmark tests have testified that the multi-GPO is superior to the state-of-the-art algorithms in terms of efficiency and importance in solving EDBFSP.

The flowshop sequence-dependent group scheduling problem (FSDGSP) with the production efficiency measures has been extensively studied due to its wide industrial applications. However, energy efficiency indicators are often ignored in the literature. This article considers the FSDGSP to minimize makespan, total flow time, and total energy consumption, simultaneously. After the problem-specific knowledge is extracted, a mixed-integer linear programming model and a critical path-based accelerated evaluation method are proposed. Since the FSDGSP includes multiple coupled subproblems, a greedy cooperative co-evolutionary algorithm (GCCEA) is designed to explore the solution space in depth. Meanwhile, a random mutation operator and a greedy energy-saving strategy are employed to adjust the processing speeds of machines to obtain a potential nondominated solution. A large number of experimental results show that the proposed algorithm significantly outperforms the existing classic multiobjective optimization algorithms, which is due to the usage of problem-related knowledge.

Automated guided vehicles (AGVs) have become indispensable transportation tools in intelligent production workshops. The current AVG scheduling system has almost no processing capacity for temporary special cases and mostly depends on the path planning part to solve them, which can only reduce the cost waste caused to a certain extent. In this article, a dynamic AGV scheduling model is proposed, including an aperiodic departure method and a real-time task list update method. Compared with the static AGV scheduling model, the new model can reassign the AGVs for new tasks and special cases. A discrete invasive weed optimization (DIWO) algorithm with parameter adaptation and computing time adaptation is used to prove the effectiveness of the new model. The proposed model is verified by the cases from actual production workshops, which proves the effectiveness of the proposed dynamic AGV scheduling model for the special cases.

The present work expects to explore the application effect of biologically inspired Plasticity Neural Network in the industrial intelligent dispatching energy storage system, and highlight the intelligence and fault detection performance of the control system. To address the faults in intelligent dispatching energy storage system, the present work implements a fault diagnosis model of intelligent dispatching energy storage system based on Deep Belief Network (DBN), and simulates and analyzes the model. The results show that the transmission probability of the fault diagnosis model of the constructed intelligent energy storage scheduling system is 100% and when the parameters$\lambda $is between 0.01 and 0.05, the real-time performance of data transmission is the highest. Compared with other classical algorithm models, the success rate and detection accuracy of the proposed algorithm are about 85%, the energy consumption is lower, and the detection effect is more obvious. Therefore, the constructed system obviously has higher real-time performance and more accurate fault detection performance, and significantly better system detection and protection performance. The results provide an experimental basis for the operation and fault detection of intelligent dispatching energy storage system.

This paper deals with the distributed event-triggered formation control problem of networked nonholonomic mobile robots (NNMRs) in a leader-follower-based frame. The event-triggered mechanism (ETM) is first introduced for the design of the kinematic controller by a suitable auxiliary (or virtual) reference vector, and a unified integrated dynamic controller is then proposed in combination with backstepping technique and sliding mode approach. The designed event-triggered condition is derived based on the local communication among robots by the best use of nonholonomic property of NNMRs, which can fully guarantee to exclude the Zeno behavior before the desired formation configuration is achieved. Finally, the theoretical results are validated through simulation analysis, and then implemented on experimental platform for real-time physical NNMRs. Moreover, both simulation and experimental results demonstrate the key feature of the ETM integral formation scheme, it can effectively reduce communication resource usage and save energy while still providing comparable performance compared with the conventional periodic communication mechanism (PCM).

In this study, we propose an efficient optimization algorithm that is a hybrid of the iterated greedy and simulated annealing algorithms (hereinafter, referred to as IGSA) to solve the flexible job shop scheduling problem with crane transportation processes (CFJSP). Two objectives are simultaneously considered, namely, the minimization of the maximum completion time and the energy consumptions during machine processing and crane transportation. Different from the methods in the literature, crane lift operations have been investigated for the first time to consider the processing time and energy consumptions involved during the crane lift process. The IGSA algorithm is then developed to solve the CFJSPs considered. In the proposed IGSA algorithm, first, each solution is represented by a 2-D vector, where one vector represents the scheduling sequence and the other vector shows the assignment of machines. Subsequently, an improved construction heuristic considering the problem features is proposed, which can decrease the number of replicated insertion positions for the destruction operations. Furthermore, to balance the exploration abilities and time complexity of the proposed algorithm, a problem-specific exploration heuristic is developed. Finally, a set of randomly generated instances based on realistic industrial processes is tested. Through comprehensive computational comparisons and statistical analyses, the highly effective performance of the proposed algorithm is favorably compared against several efficient algorithms.Note to Practitioners—The flexible job shop scheduling problem (FJSP) can be extended and applied to many types of practical manufacturing processes. Many realistic production processes should consider the transportation procedures, especially for the limited crane resources and energy consumptions during the transportation operations. This study models a realistic production process as an FJSP with crane transportation, wherein two objectives, namely, the makespan and energy consumptions, are to be simultaneously minimized. This study first considers the height of the processing machines, and therefore, the crane lift operations and lift energy consumptions are investigated. A hybrid iterated greedy algorithm is proposed for solving the problem considered, and several problem-specific heuristics are embedded to balance the exploration and exploitation abilities of the proposed algorithm. In addition, the proposed algorithm can be generalized to solve other types of scheduling problems with crane transportations.

This article studies an operational optimization problem of the fluid catalytic cracking (FCC) unit under uncertainty. The objective of this problem is to quickly reoptimize the operating variables that control the operational condition of the FCC unit when fossil fuel yield constraints or prices change. To solve this problem, based on the challenges caused by the varied constraints, we establish a mathematical model and propose a fast adaptive differential evolution algorithm with an adaptive mutation strategy, a parameter adaptation strategy, a repaired strategy, and an enhanced strategy. In the proposed algorithm, we integrate the status information of each solution into the mutation strategy and parameter adaptation scheme to search for the best solution in the irregular feasible region of the operating variables. In addition, a repaired strategy is proposed to repair the infeasible operating variables with unknown bounds, and an enhanced strategy is presented to further improve the objective function value of the best solution. The experimental results on ten test scenarios with different fossil fuel yield constraints and prices demonstrate the robustness of the proposed algorithm for optimizing the operating variables of the FCC unit under uncertainty.

This article addresses a novel scheduling problem, a distributed flowshop group scheduling problem, which has important applications in modern manufacturing systems. The problem considers how to arrange a variety of jobs subject to group constraints at a number of identical manufacturing cellulars, each one with a flowshop structure, with the objective of minimizing makespan. We explore the problem-specific knowledge and present a mixed-integer linear programming model, a counterintuitive paradox, and two suites of accelerations to save computational efforts. Due to the complexity of the problem, we consider a decomposition strategy and propose a cooperative co-evolutionary algorithm (CCEA) with a novel collaboration model and a reinitialization scheme. A comprehensive and thorough computational and statistical campaign is carried out. The results show that the proposed collaboration model and reinitialization scheme are very effective. The proposed CCEA outperforms a number of metaheuristics adapted from closely related scheduling problems in the literature by a significantly considerable margin.

A new scheduling problem, the distributed permutation flowshop scheduling problem with uncertain processing times and carryover sequence-dependent setup time (DPUC), is addressed. The DPUC is an important application problem in modern electronics manufacturing. A robust model is established for the DPUC with makespan criterion. A counter-intuitive paradox is found, that is, adding a new job to one of the production lines can reduce the completion time of the production line. Two acceleration methods are provided to save computational efforts. An iterated greedy algorithm called IG_FS is proposed to solve the DPUC. A heuristic based on the well-known NEH is proposed to generate the initial solution for the IG_FS. In the destruction phase of the IG_FS, dynamic sizes based on both adaptability and randomness are provided to improve the exploration capability. During the local search phase of the IG_FS, a hybrid local search method consisting of shift and swap operators is presented to exploit more diverse search areas. Extensive experiments show that the proposed IG_FS performs significantly better than the six competing algorithms adapted from the closely related scheduling literature.

In this study, a novel general variable neighborhood search through Q-learning (GVNS-QL) algorithm is proposed to solve the no-idle flowshop scheduling problem with the makespan objective. In the outer loop of the GVNS-QL, insertion, and exchange operators are used to shaking the permutation. On the other hand, in the inner loop of variable neighborhood descent procedure, variable iterated greedy and variable block insertion heuristic algorithms are employed with two effective insertion local search procedures. The proposed GVNS-QL defines the parameters of the algorithm using a Q-learning mechanism. The developed GVNS-QL algorithm is compared with the traditional iterated greedy (IG) algorithm using the well-known benchmark set. The comprehensive computational experiments show that the GVNS-QL outperforms the traditional IG algorithm. The results of the IG and GVNS-QL algorithms are also compared with the current best-known solutions reported in the literature. The computational results show that the proposed GVNS-QL algorithm improves the current best-known solutions for 104 out of 250 instances.

No-wait flowshop scheduling problem (NWFSP) is a well-known strongly NP-hard problem, where in-process waiting is not allowed between any two consecutive machines in such a way that once a job is started, subsequent processing must be carried out on all machines until completion. In this paper, we propose an energy-efficient NWFSP in order to investigate the trade-off between makespan and total energy consumption. The energy-efficient NWFSP aims to seek to obtain Pareto solution sets to minimize the makespan and the total energy consumption conflicting with each other. Unlike the classical NWFSP, there are different speed levels for each job on machines and the processing times of jobs can differ according to the assigned speed levels. Therefore, we modify the formulation of NWFSP by introducing a speed scaling strategy in order to approximate Pareto solution sets, i.e., non-dominated solution sets. In this paper, we propose a mixed-integer linear programming model (MILP), an energy-efficient variable block insertion heuristic (EE-VBIH), an energy-efficient iterated greedy algorithm (IG) and an energy-efficient & IG-ALL) to solve the energy-efficient NWFSP. Extensive computational analyses on Taillard's benchmark suite show that the proposed algorithms are very effective for approximating Pareto solution sets.

In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.

This paper investigates an energy-efficient hybrid flowshop scheduling problem with the consideration of machines with different energy usage ratios, sequence-dependent setups, and machine-to-machine transportation operations. To minimize the makespan and total energy consumption simultaneously, a mixed-integer linear programming (MILP) model is developed. To solve this problem, a three-stage multiobjective approach based on decomposition (TMOA/D) is suggested, in which each solution is bound with a main weight vector and a set of its neighbors. Accordingly, a variable direction strategy is developed to ensure each solution along its main direction is thoroughly exploited and can jump to the neighboring directions using a proximity principle. To ensure an active schedule of arranging jobs to machines, a two-level solution representation is employed. In the first phase, each solution attempts to improve itself along its current weight vector through a developed neighborhood-based local search. In the second phase, the promising solutions are selected through the technique for order preference by similarity to an ideal solution. Then, they attempt to update themselves with a proposed global replacement strategy via incorporation with their closing solutions. In the third phase, a solution conducts a large perturbation when it goes through all its assigned weight vectors. Extensive experiments are conducted to test the performance of TMOA/D, and the results demonstrate that TMOA/D has a very competitive performance.

Distributed permutation flowshop scheduling problem (DPFSP) has become a very active research area in recent years. However, minimizing total flowtime in DPFSP, a very relevant and meaningful objective for today's dynamic manufacturing environment, has not captured much attention so far. In this paper, we address the DPFSP with total flowtime criterion. To suit the needs of different CPU time demands and solution quality, we present three constructive heuristics and four metaheuristics. The constructive heuristics are based on the well-known LR and NEH heuristics. The metaheuristics are based on the high-performing frameworks of discrete artificial bee colony, scatter search, iterated local search, and iterated greedy, which have been applied with great success to closely related scheduling problems. We explore the problem-specific knowledge and accelerations to evaluate neighboring solutions for the considered problem. We introduce advanced and effective technologies like a referenced local search, a strategy to escape from local optima, and an enhanced intensive search method for the presented metaheuristics. A comprehensive computational campaign against the closely related and well performing algorithms in the literature is carried out. The results show that both the presented constructive heuristics and metaheuristics are very effective for solving the DPFSP with total flowtime criterion.

This paper studies a hot rolling batch scheduling problem in compact strip production (CSP), which is decomposed into a two-stage problem. The first stage is the strip combination problem aimed at determining the strip combination of each rolling turn and the number of rolling turns with the objective of minimizing the number of virtual strips, and the second is the strip allocation and sequencing problem aimed at optimizing the allocation and rolling sequence of the strips in each rolling turn. We first model this two-stage problem considering a set of production constraints and then design an optimal approach to solve the strip combination problem. Subsequently, we design an evolutionary algorithm (i.e., artificial bee colony algorithm) with a novel search strategy for employed bees, a dynamic strategy for onlooker bees, a variable neighborhood search strategy for a scout bee, and an enhanced strategy to solve the problem in the second stage. Computational experiments demonstrate the effectiveness of the proposed algorithms.Note to Practitioners—The hot rolling batch scheduling process is crucial in linking the casting and rolling processes of iron and steel productions. In the rolling batch scheduling problem of CSP, there is no buffer between the casting and rolling processes, and virtual strips must be added to satisfy production constraints. Most rolling batch scheduling methods do not consider the addition of virtual strips. In this paper, we mathematically characterize the hot rolling batch scheduling problem in CSP with flexible production constraints. We then show how the optimal approach and artificial bee colony algorithm are designed. Finally, the effectiveness of the proposed algorithms is demonstrated by comparisons with other well-known metaheuristic algorithms. This paper can be extended to other hot rolling batch scheduling problems with buffers and hybrid flowshop scheduling problems.

Rescheduling is a necessary procedure for a flexible job shop when newly arrived priority jobs must be inserted into an existing schedule. Instability measures the amount of change made to the existing schedule and is an important metrics to evaluate the quality of rescheduling solutions. This paper focuses on a flexible job-shop rescheduling problem (FJRP) for new job insertion. First, it formulates FJRP for new job insertion arising from pump remanufacturing. This paper deals with bi-objective FJRPs to minimize: 1) instability and 2) one of the following indices: a) makespan; b) total flow time; c) machine workload; and d) total machine workload. Next, it discretizes a novel and simple metaheuristic, named Jaya, resulting in DJaya and improves it to solve FJRP. Two simple heuristics are employed to initialize high-quality solutions. Finally, it proposes five objective-oriented local search operators and four ensembles of them to improve the performance of DJaya. Finally, it performs experiments on seven real-life cases with different scales from pump remanufacturing and compares DJaya with some state-of-the-art algorithms. The results show that DJaya is effective and efficient for solving the concerned FJRPs.

In various flow shop scheduling problems, it is very common that a machine suffers from breakdowns. Under this situation, a robust and stable suboptimal scheduling solution is of more practical interest than a global optimal solution that is sensitive to environmental changes. However, blocking lot-streaming flow shop (BLSFS) scheduling problems with machine breakdowns have not yet been well studied up to date. This paper presents, for the first time, a multiobjective model of the above problem including robustness and stability criteria. Based on this model, an evolutionary multiobjective robust scheduling algorithm is suggested, in which solutions obtained by a variant of single-objective heuristic are incorporated into population initialization and two novel crossover operators are proposed to take advantage of nondominated solutions. In addition, a rescheduling strategy based on the local search is presented to further reduce the negative influence resulted from machine breakdowns.The proposed algorithm is applied to 22 test sets, and compared with the state-of-the-art algorithms without machine breakdowns. Our empirical results demonstrate that the proposed algorithm can effectively tackle BLSFS scheduling problems in the presence of machine breakdowns by obtaining scheduling strategies that are robust and stable.

Process planning and scheduling are modeled sequentially in the traditional manufacturing system. However, because of their complementarity, the increasing need to integrate them has emerged to enhance the manufacturing productivity significantly. Therefore, the integrated process planning and scheduling (IPPS) is becoming a hotspot in providing a blueprint for efficient manufacturing system. This paper proposes a novel algorithm hybridizing the genetic algorithm with strong global searching ability and variable neighborhood search with strong local searching ability for the IPPS problem. To improve the searching ability, a novel procedure, encoding method, and local search method have been designed. Effective operators have been adopted. Three experiments with totally 37 well-known benchmark problems are employed to evaluate the performance of the proposed method. Based on the results, the proposed algorithm outperforms the state-of-the-art methods and finds the new solutions (the best solutions found so far) for some problems. The proposed method has also been applied on a real-world case from a nonstandard equipment production workshop for the packaging machine of a machine tool company in China. The solution demonstrates that it can solve real-world cases very well.

The hybrid flowshop scheduling problem (HFSP) has been extensively studied in the literature, due to its complexity and real-life applicability. Various exact and heuristic algorithms have been developed for the HFSP, and most consider makespan as the only criterion. The studies on HFSP with the objective of minimizing total flow time have been rather limited. This paper presents a mathematical model and efficient iterated greedy algorithms, IG and IGALL, for the HFSP with total flow time criterion. In order to evaluate the performance of the proposed IG algorithms, the well-known HFSP benchmark suite from the literature is used. As the problem is NP-hard, the proposed mathematical model is solved for all 87 instances under a time limit on CPLEX. Optimal results are obtained for some of these instances. The performance of the IG algorithms is measured by comparisons with these time-limited CPLEX results of the mathematical model. Computational results show that the proposed IG algorithms perform very well in terms of solution time and quality. To the best of our knowledge, for the first time in the literature, the results of flow time criterion have been reported for the HFSP benchmark suite.

This paper proposes a populated variable block insertion heuristic (PVBIH) algorithm for solving the permutation flowshop scheduling problem with the makespan criterion. The PVBIH algorithm starts with a minimum block size being equal to one. It removes a block from the current solution and inserts it into the partial solution randomly with a predetermined move size. A local search is applied to the solution found after several block moves. If the new solution generated after the local search is better than the current solution, it replaces the current solution. It retains the same block size as long as it improves. Otherwise, the block size is incremented by one and a simulated annealing-type of acceptance criterion is used to accept the new solution. This process is repeated until the block size reaches at the maximum block size. In addition, we present a randomized profile fitting heuristic with excellent results. Extensive computational results on the Taillard's well-known benchmark suite show that the proposed PVBIH algorithm substantially outperforms the differential evolution algorithm (NS-SGDE) recently proposed in the literature.

The aim of this paper is to apply the variable block insertion heuristic (VBIH) algorithm recently proposed in the literature for solving the quadratic assignment problem (QAP). The VBIH algorithm is concerned with making block moves in a given solution. As a local search in this paper, the VNST is employed from the literature to be applied to a solution obtained after several block moves. Besides the single-solution based VBIH, we also propose a populated VBIH (PVBIH) in this paper. The proposed algorithms were evaluated on quadratic assignment problem instances arising from real life problems as well as on a number of benchmark instances from the QAPLIB. The computational results show that the proposed algorithms are very effective in solving both types of instances. All PCB instances are further improved.

Three dimensional (3D) point clouds are typically used in computer vision and pattern recognition areas. In general, the raw point cloud has large numbers of redundant points which require excessively large storage space and lots of time for post-processing. This paper presents a synthetic point cloud simplification method to obtain computationally manageable point sets. First, a coarse-to-fine feature extraction manner is designed with normal vectors deviation and k-means clustering methods, which can concentrate more sample points in regions of high curvature. Additionally, the directed Hausdorff distance is performed directly on the point cloud which samples the point cloud judiciously with an edge-preserving manner. Experimental results demonstrate that the proposed method is effective for point cloud simplification, and it exhibited superior performance compared to existing techniques.

The aim of the research is to find a feasible set of theatre hall design alternatives for two objectives, which are the total cost and the reverberation time, subject to several constraints. We formulate the problem as a multi-objective realparameter constrained optimization problem. To handle this problem, we investigated two different optimization algorithms, namely, a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and a multi-objective Harmony Search algorithm (MOHS) in order to gather Pareto front approximation with a set of non-dominated solutions. We demonstrate that the MOHS yields slightly better results than the NSGA-II algorithm.

This paper presents a multi-objective self-adaptive differential evolution algorithm to solve the form-finding problem of high-rise building design in the conceptual phase. The aim of the research is to reach suitable high-rise design alternatives for hard and soft objectives, which are construction cost per square meter, structural displacement, and visual perception of the spaces from the inside out subject to several constraints that are related with both high-rise construction regulations, and profitability of the spaces. We formulate the problem as a multi-objective realparameter constrained optimization problem for three objectives that are inherently conflicting. To tackle this problem, we developed two different optimization algorithms, namely, a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and a Self-Adaptive Differential Evolution Algorithm (jDE) in order to obtain Pareto fronts with diversified non-dominated solutions. The extensive computational results show that the jDE algorithm yields much more desirable Pareto front than the NSGA-II algorithm.

A discrete artificial bee colony (DABC) algorithm for the permutation flowshop scheduling problem with sequence-dependent setup times (PFSP-SDST) is presented in this paper. PFSP-SDST is an important problem that has practical applications in production facilities. The proposed DABC algorithm uses destruction and construction procedure to generate neighboring food sources. In addition, a local search algorithm with insert and swap neighborhoods is used to enhance the solution quality. The main contribution of this work is providing a speedup algorithm for the swap neighborhood. Computational experiments are carried out to test the performance of the algorithm on a benchmark problem set from the literature. Experimental results show that the proposed DABC algorithm utilizing swap neighborhood is very competitive to the best performing algorithms from the literature.

In this paper, an ensemble of differential evolution algorithms based on a variable neighborhood search algorithm (EDE-VNS) is proposed so as to solve the constrained real parameter-optimization problems. The performance of DE algorithms heavily depends on the mutation strategies, crossover operators and control parameters employed. The proposed EDE-VNS algorithm employs multiple mutation operators and control parameters in its VNS loops to enhance the solution quality. In addition, we utilize opposition-based learning (OBL) to take advantages of opposite solutions to find a candidate solution which might be close to the global optimum. In addition, we also present an idea of injecting some good dimensional values from promising areas in the population to the trial individual through the injection procedure. The computational results show that the EDE-VNS algorithm is very competitive to some of the best performing algorithms from the literature.

In this paper, a memetic algorithm with a variable block insertion heuristic is presented to solve the single machine total weighted tardiness problem with sequence dependent setup times. Together with the traditional insertion neighborhood structure, the memetic algorithm is combined with a variable block insertion heuristic in which a block of jobs are removed from a sequence and then inserted into all possible positions of the partial sequence. For this purpose, we devise a variable neighborhood descent algorithm to incorporate different block insertion heuristics having different block sizes. We also employ a simulated annealing type of acceptance criterion to diversify the population. To evaluate its performance, the memetic algorithm is tested on a set of benchmark instances from the literature. The analyses of experimental results have shown highly effective performance of the memetic algorithm against the best performing algorithms from the literature. The proposed memetic algorithm was able to find 98 out 120 optimal solutions within reasonable CPU times.

Range image registration is a popular problem in pattern recognition and computer vision, and it has a wide range of applications in real life. The objective of registration is to match two models as close as possible. In this area, the best known iterative closest point (ICP) method is sensitive to the initial position of two models and it is easy stuck in local minima. In recent years, heuristic algorithms have been used for registration with good ability for global searching. However, the features of models are ignored generally in the iterative evolution process, so the tailored methods are lack of versatility for different models registration. In this paper, normal angle histogram is added into the fruit fly optimization algorithm for registration. The searching step of each individual is relative to the initial position of two models. The versatility and effectiveness of proposed algorithm are illustrated by a series of experiments.

Range image registration is used to align two or more three dimensional (3D) point sets into a common coordinate system. In the matching process, however, the influence of the resolution of 3D scans is ignored generally. In this paper, an enhanced differential evolution (DE) algorithm is proposed to align two different scaling 3D point sets. Specifically, Generalized Procrustes Analysis (GPA) method is employed to accelerate the DE in population initialization. In addition, a novel mutation technique is introduced to improve the accuracy for registration. The proposed method can evaluate all of the transformation parameters synchronously and it is effective for both isotropic and anisotropic scaling registration problem. Experiment results reveal that the proposed algorithm is much superior to other methods in terms of accuracy and robustness for scaling registration problem.

In this study, we propose a hybrid fruit fly optimization algorithm (HFOA) to solve the hybrid flowshop rescheduling problem with flexible processing time in steelmaking casting systems. First, machine breakdown and processing variation disruptions are considered simultaneously in the rescheduling problem. Second, each solution is represented by a fruit fly with a well-designed solution representation. Third, two novel decoding heuristics considering the problem characteristics, which can significantly improve the solution quality, are developed. Several routing and scheduling neighborhood structures are proposed to balance the exploration and exploitation abilities. Finally, we propose an effective HFOA with well-designed smell and vision search procedures. In addition, an iterated greedy (IG) local search is embedded in the proposed algorithm to further enhance its exploitation ability. The proposed algorithm is tested on sets of instances generated from industrial data. Through comprehensive computational comparisons and statistical analyses, the performance of the proposed HFOA algorithm is favorably compared against several algorithms in terms of both solution quality and efficiency. Note to Practitioners-The steelmaking rescheduling process is critical to the effective operation of iron and steel production. This study models the steelmaking rescheduling problem with flexible processing time as a complex hybrid flowshop in which two types of disruptions, machine breakdown and processing variation, are considered concurrently. A weighted sum of the five objectives, including minimization of the average sojourn time, earliness penalty, tardiness penalty, cast-break penalty, and system instability penalty, is considered in the proposed algorithm. We develop an effective hybrid fruit fly optimization algorithm (HFOA) that applies two vectors to represent individuals and presents routing and scheduling neighborhood structures. An IG-based local search procedure is embedded to enhance the exploitation ability of the proposed algorithm. Two decoding heuristics considering the problem characteristics are developed. The effectiveness of the proposed HFOA is demonstrated through comparisons to other well-known and recently developed meta-heuristics. This work can be extended to practical problems by considering other types of disruptions. In addition, the proposed HFOA can also be generalized, and to other hybrid flowshop rescheduling problems.

In this paper, we propose an improved discrete artificial bee colony (DABC) algorithm to solve the hybrid flexible flowshop scheduling problem with dynamic operation skipping features in molten iron systems. First, each solution is represented by a two-vector-based solution representation, and a dynamic encoding mechanism is developed. Second, a flexible decoding strategy is designed. Next, a right-shift strategy considering the problem characteristics is developed, which can clearly improve the solution quality. In addition, several skipping and scheduling neighborhood structures are presented to balance the exploration and exploitation ability. Finally, an enhanced local search is embedded in the proposed algorithm to further improve the exploitation ability. The proposed algorithm is tested on sets of the instances that are generated based on the realistic production. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed DABC algorithm is favorably compared against several presented algorithms, both in solution quality and efficiency.

This paper presents a differential evolution algorithm with a variable neighborhood search to solve the multidimensional knapsack problem. Unlike the studies employing check and repair operators, we employ some sophisticated constraint handling methods to enrich the population diversity by taking advantages of infeasible solution within a predetermined threshold. We propose to a variable neighborhood search employing different mutation strategies to generate the trial population. The proposed algorithm in fact works on a continuous domain, but these real-values are converted to 0-1 binary values by using the sigmoid function. In order to enhance the solution quality, the differential evolution algorithm with a variable neighborhood search is combined with a binary swap local search algorithm. To the best of our knowledge, this is the first reported application of the differential evolution algorithm to solve the multidimensional knapsack problem in the literature. The proposed algorithm is tested on a benchmark instances from the OR-Library. Computational results show its efficiency in solving benchmark instances and its superiority to the best performing algorithms from the literature.

This paper presents a populated local search algorithm through a differential evolution algorithm for solving the blocking flowshop scheduling problem under makespan criterion. Iterated greedy and iterated local search algorithms are simple but extremely effective in solving scheduling problems. However, these two algorithms have some parameters to be tuned for which it requires a design of experiments with expensive runs. In this paper, we propose a novel multi-chromosome solution representation for both local search and differential evolution algorithm which is responsible for providing the parameters of IG and ILS algorithms. In other words, these parameters are learned by the differential evolution algorithm in order to guide the local search process. We also present the greedy randomized adaptive search procedure (GRASP) for the problem on hand. The performance of the populated local search algorithm with differential evolution algorithm and the GRASP heuristic is tested on Taillard's benchmark suite and compared to the best performing algorithms from the literature. Ultimately, 90 out of 120 problem instances are further improved.

The steelmaking-continuous-casting (SCC) process, which includes steelmaking, refining and continuous casting, is one of the major bottlenecks of iron and steel production. Efficient and effective scheduling of this process is essential to improve the productivity and reduce the production costs of the entire production system. We present a time-index formulation for this scheduling problem and a Lagrangian relaxation (LR) approach based on the relaxation of the machine capacity constraints. The relaxed problem is solved using an efficient polynomial dynamic programming algorithm. The corresponding Lagrangian dual (LD) problem is solved using a deflected conditional subgradient level method. Unlike the conventional subgradient algorithms for the LD problem, our method guarantees convergence using the Brannlund's level control strategy to replace the strict convergence condition that the optimum of the dual problem is known a priori. Furthermore, our method enhances the efficiency by introducing a deflected conditional subgradient to weaken the zigzagging phenomena that slows the convergence of conventional subgradient algorithms. The computational results demonstrate that the approaches can quickly obtain high-quality solutions and are notably promising for the SCC scheduling. Note to Practitioners-Efficient and effective SCC schedule is vital for the manufacturing system of iron and steel production. Unfortunately, the scheduling is extremely difficult because of its combinatorial nature and practical complex constraints such as job grouping constraints, precedence constraints, different transport time, and setup times. To obtain high-quality solutions within an acceptable computational time, we can use a problem-oriented approach, which can be the LR. However, there are two deficiencies in this approach: its empirical termination criteria, such as maximal iteration number or running time, which make it difficult to find a golden rule for various problems, and the inefficiency, which is caused by the so-called zigzagging phenomena. To overcome these deficiencies, this paper develops an effective subgradient method for SCC scheduling based on the machine capacity relaxation. This method gives an objective termination criterion based on the convergence condition of the method, and improves the efficiency based on a new search direction or a new subgradient. Then, the work shows how this method can be applied to solve an SCC scheduling problem. The computational results confirm their effectiveness and efficiency. The approaches can also be applied to other similar production scheduling problems.

This paper aims to provide a solution method for the real-world hybrid flowshop scheduling problem resulting from a steelmaking process, which has important applications in modern iron and steel industry. We first present a mixed integer mathematic model based on a comprehensive investigation. Then, we develop a heuristic method and two improvement procedures for a given schedule based on the problem-specific characteristics. Finally, we propose an effective artificial bee colony (ABC) algorithm with the job-permutation-based representation for solving the scheduling problem. The proposed ABC algorithm incorporates the heuristic and improvement procedures as well as new characteristics including a neighboring solution generation method and two enhanced strategies. To evaluate the proposed algorithm, we present several adaptations of other well-known and recent metaheuristics to the problem and conduct a serial of experiments with the instances generated according to real-world production process. The results show that the proposed ABC algorithm is more effective than all other adaptations after comprehensive computational comparisons and statistical analysis.

This paper considers minimizing makespan for a blocking flowshop scheduling problem, which has important application in a variety of modern industries. A constructive heuristic is first presented to generate a good initial solution by combining the existing profile fitting (PF) approach and Nawaz-Enscore-Ham (NEH) heuristic in an effective way. Then, a memetic algorithm (MA) is proposed including effective techniques like a heuristic-based initialization, a path-relinking-based crossover operator, a referenced local search, and a procedure to control the diversity of the population. Afterwards, the parameters and operators of the proposed MA are calibrated by means of a design of experiments approach. Finally, a comparative evaluation is carried out with the best performing algorithms presented for the blocking flowshop with makespan criterion, and with the adaptations of other state-of-the-art MAs originally designed for the regular flowshop problem. The results show that the proposed MA performs much better than the other algorithms. Ultimately, 75 out of 120 upper bounds provided by Ribas [“An iterated greedy algorithm for the flowshop scheduling with blocking”, OMEGA, vol. 39, pp. 293-301, 2011.] for Taillard flowshop benchmarks that are considered as blocking flowshop instances are further improved by the presented MA.

In this paper, an effective artificial bee colony (ABC) algorithm is proposed for solving the flexible job shop scheduling problems. The total flow time criterion was considered. In the proposed algorithm, tabu search (TS) heuristic is introduced to perform local search for employed bee, onlookers, and scout bees. Meanwhile, an external Pareto archive set is employed to record enough non-dominated solutions for the problem considered. Experimental results on five well-known benchmarks show the efficiency of the proposed hybrid algorithm. It is concluded that the proposed algorithm is superior to the very recent algorithms in term of both search quality and computational efficiency.

This paper presents a hybrid Pareto-based local search (PLS) algorithm for solving the multi-objective flexible job shop scheduling problem. Three minimization objectives-the maximum completion time (makespan), the total workload of all machines, and the workload of the critical machine are considered simultaneously. In this study, several well-designed local search approaches are proposed, which consider the problem characteristics and thus can hold fast convergence ability while keep rich population diversity. Then, an external Pareto archive is developed to memory the Pareto optimal solutions found so far. In addition, to improve the efficiency of the scheduling algorithm, a speed-up method is devised to decide the domination status of a solution with the archive set. Experimental results on two well-known benchmarks show the efficiency of the proposed hybrid algorithm. It is concluded that the PLS algorithm is superior to the very recent algorithms in term of both search quality and computational efficiency.

The harmony search (HS) algorithm is one of the recent evolutionary computation techniques to solve optimization problems. To make it applicable for lot-streaming flow shop problems, a discrete variant of the HS algorithm (DHS) with job permutations representation is proposed. In the proposed DHS algorithm, a new improvisation scheme is designed to generate feasible job sequences. A local search algorithm based on the insert neighborhood structure is fused to stress the further enhancement capability of the algorithm proposed whereas a restart scheme is employed to avoid the stagnation of the evolution. Extensive computational simulations and comparisons are provided, which demonstrate the effectiveness of the proposed DHS against the best performing algorithms from the literature.

Very recently, Jarboui et al. (Computers & Operations Research 36 (2009) 2638-2646) and Tseng and Lin (European Journal of Operational Research 198 (2009) 84-92) presented a novel estimation distribution algorithm (EDA) and a hybrid genetic local search (hGLS) algorithm for the permutation flowshop scheduling (PFSP) with the total flowtime (TFT) criterion, respectively. Both algorithms generated excellent results, thus improving all the best known solutions reported in the literature so far. However, in this paper, we present a discrete artificial bee colony (DABC) algorithm hybridized with an iterated greedy (IG) and iterated local search (ILS) algorithms embedded in a variable neighborhood search (VNS) procedure based on swap and insertion neighborhood structures. We also present a hybrid version of our previous discrete differential evolution (hDDE) algorithm employing the IG and VNS structure too. The performance of the DABC and hDDE is highly competitive to the EDA and hGLS algorithms in terms of both solution quality and CPU times. Ultimately, 43 out of 60 best known solutions provided very recently by the EDA and hGLS algorithms are further improved by the DABC and hDDE algorithms with short-term search.

This paper presents an ensemble of differential evolution algorithms employing the variable parameter search and two distinct mutation strategies in the ensemble to solve real-parameter constrained optimization problems. It is well known that the performance of DE is sensitive to the choice of mutation strategies and associated control parameters. For these reasons, the ensemble is achieved in such a way that each individual is assigned to one of the two distinct mutation strategies or a variable parameter search (VPS). The algorithm was tested using benchmark instances in Congress on Evolutionary Computation 2010. For these benchmark problems, the problem definition file, codes and evaluation criteria are available in http://www.ntu.edu.sg/home/EPNSugan. Since the optimal or best known solutions are not available in the literature, the detailed computational results required in line with the special session format are provided for the competition.

This paper presents a harmony search algorithm with ensemble of parameter sets, named EHS algorithm, for solving continuous optimization problems. In the proposed algorithm, an ensemble of parameter sets is adopted to self-adaptively choose the best control parameters during the evolution process. This method not only eliminates the need to perform the trail-and-error search for the best single parameter set, but enables us to benefit from the match between the parameter sets, the different search phases, and the specific problems as well. Extensive computational simulations and comparisons are carried out by employing a set of 10 benchmark problems from the literature. The computational results show that the proposed EHS algorithm is more effective in finding better solutions than the state-of-the-art harmony search (HS) variants [1,2,3].

This paper presents a novel differential evolution algorithm based on variable parameter search to solve realparameter continuous function optimization problems. In order to provide differential evolution algorithm with local intensification capability, each trial individual is generated by a variable parameter search procedure using variable mutation scale factor and crossover rate as well as (possibly) variable mutation strategies. The novelty stems from the fact that while a pure differential evolution algorithm achieves global exploration during the search process, variable parameter search procedure intensifies the search around local minima by using traditional DE mutation and crossover operators as well as variable mutation strategies. The algorithm was tested using benchmark instances designed for a special session in CEC05 and other instaces from the literature. The experimental results show its highly competitive performance against the very recent differential evolution algorithm with local search by Noman and Iba in [1] (IEEE Transaction on Evolutionary Computation, Vol. 12, No. 1, pp. 107-125, February 2008).

In this paper, the discrete particle swarm optimization (DPSO) algorithm is employed to solve the no-wait flowshop scheduling problem with the makespan criterion for Taillard’s benchmark suite [1]. As known, there exist 31 benchmark instances provided by Carlier [2], Heller [3], and Revees [4] for the makespan criterion. However, these benchmarks are relatively small in size and easy to be solved even by a simple descent algorithm. Since there is a lack of a sound benchmark suite for the no-wait flowshop scheduling problem with the makespan criterion, the DPSO algorithm presented by the authors [5] is applied to the 110 benchmark instances of Taillard by treating them as the no-wait flowshop problem instances with the makespan criterion. The DPSO algorithm is hybridized with the variable neighborhood descent (VND) algorithm to further improve the solution quality. Ultimately, we carried out extensive runs and provide the upper bounds for the future researchers to test their algorithms.

In this paper, a discrete differential evolution algorithm with the reference local search is presented to solve the single machine total weighted tardiness problem with sequence dependent setup times. In addition, To facilitate the greedy job insertion into a partial solution, newly designed speed-up methods are presented for the insertion move as a further and novel contribution to the single machine tardiness related scheduling with sequence dependent setup times literature. To evaluate its performance, the discrete differential evolution algorithm is tested on a set of benchmark instances from the literature. Through the analyses of experimental results, highly effective performance of the discrete differential evolution algorithm is shown against the best known solutions from the literature, especially, against the very recent newly designed particle swarm optimization algorithm and ant colony algorithm of Anghinolfi & Paolucci [European Journal of Operational Research 2007; don: 10.1016/j.ejor.2007.10.044, Available Online] and Anghinolfi & Paolucci [to appear in the International Journal of Operations Research 2007], respectively. Ultimately, 46 out of 120 aggregated best known solutions so far in the literature are further improved.

In a traveling salesman problem, if the set of nodes is divided into clusters so that a single node from each cluster can be visited, then the problem is known as the generalized traveling salesman problem where the objective is to find a tour with minimum cost passing through only a single node from each cluster. In this paper, a genetic algorithm is presented to solve the problem on a set of benchmark instances. The genetic algorithm is hybridized with an iterated local search to further improve the solution quality. Some speed-up methods are presented to accelerate the greedy node insertions. The genetic algorithm is tested on a set of benchmark instances with symmetric distances ranging from 51 to 442 nodes from the literature. Computational results show that the proposed genetic algorithm is the best performing algorithm so far in the literature in terms of solution quality.

In this paper, a novel discrete differential evolution (DDE) algorithm is presented to solve the permutation flowhop scheduling problem with the makespan criterion. The DDE algorithm is simple in nature such that it first mutates a target population to produce the mutant population. Then the target population is recombined with the mutant population in order to generate a trial population. Finally, a selection operator is applied to both target and trial populations to determine who will survive for the next generation based on fitness evaluations. As a mutation operator in the discrete differential evolution algorithm, a destruction and construction procedure is employed to generate the mutant population. We propose a referenced local search, which is embedded in the discrete differential evolution algorithm to further improve the solution quality. Computational results show that the proposed DDE algorithm with the referenced local search is very competitive to the iterated greedy algorithm which is one of the best performing algorithms for the permutation flowshop scheduling problem in the literature.

Dividing the set of nodes into clusters in the well-known traveling salesman problem results in the generalized traveling salesman problem which seeking a tour with minimum cost passing through only a single node from each cluster. In this paper, a discrete particle swarm optimization is presented to solve the problem on a set of benchmark instances. The discrete particle swarm optimization algorithm exploits the basic features of its continuous counterpart. It is also hybridized with a local search, variable neighborhood descend algorithm, to further improve the solution quality. In addition, some speed-up methods for greedy node insertions are presented. The discrete particle swarm optimization algorithm is tested on a set of benchmark instances with symmetric distances up to 442 nodes from the literature. Computational results show that the discrete particle optimization algorithm is very promising to solve the generalized traveling salesman problem.

In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the single machine total earliness and tardiness penalties with a common due date. A modified version of HRM heuristic presented by Hino et al. in [7], here we call it M_HRM, is also presented to solve the problem. In addition, the DPSO algorithm is hybridized with the neighborhood search algorithm to further improve the solution quality. The performance of the proposed DPSO algorithm is tested on 280 benchmark instances up to 1000 jobs from the OR Library. The computational experiments showed that the proposed DPSO algorithm has generated better results, in terms of both percent deviations from the upper bounds in Biskup and Feldmann [1] and computational time, than the existing approaches in the literature.