The flexible job shop scheduling problem with limited automatic guided vehicles (FJSP-AGV) is prevalent in manufacturing enterprises. To improve production efficiency and reduce energy consumption, this paper investigates the energy-efficient FJSP-AGV (EFJSP-AGV), aiming to minimize both the makespan and total energy consumption. To address EFJSP-AGV, both exact and approximate methods were developed. The exact method employs a novel mixed integer linear programming (MILP) model, capable of producing optimal Pareto solutions for small-sized instances using the epsilon method. EFJSP-AGV is an NP-hard problem that involves three subproblems: operation sequencing, machine selection, and AGV selection. To overcome these challenges, a novel approximate method called imitation learning (IL)-assisted multi-population evolutionary algorithm (ILMPEA) was proposed. The multi-population evolutionary framework assigns distinct search regions to populations to improve the efficiency of solution space exploration. To further enhance search accuracy, IL is applied to select search operators, guiding the Pareto front toward a better approximation of the true front. Experimental results demonstrated the effectiveness of both the MILP model and ILMPEA.

The distributed flexible job shop scheduling problem (DFJSP) has emerged as a critical challenge in the field of scheduling optimization due to its intricate resource allocation and the demand for production–logistics collaboration across multiple factories. However, most existing studies related to DFJSP only focus on the production and transportation process of jobs within a single factory, while neglecting the cross-factory logistics and the heterogeneous characteristics of transportation resources. Therefore, this article first investigates the distributed flexible job shop scheduling problem with heterogeneous transportation (DFJSPHT) resource constraints and proposes an end-to-end deep reinforcement learning (DRL) scheduling method to minimize the makespan. An innovative heterogeneous disjunctive graph model is constructed to uniformly represent the states of factories, machines, operations, and transportation resources in DFJSPHT, and the scheduling process is modeled as a Markov decision process (MDP). Next, a resource release strategy is developed to enhance the efficiency of transportation resources. To enhance the feature expression ability of the model, a graph neural network (GNN) is employed to capture the problem characteristics, and the policy network is trained using the proximal policy optimization. Comparative experiments are conducted on synthetic and benchmark instances demonstrate that the proposed method outperforms the classical priority scheduling rules and two popular DRL-based scheduling methods in solving DFJSPHT, with performance improvements exceeding 10% in most instances.

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.

In complex navigational environments, effective unmanned surface vehicle (USV) scheduling is critical. However, the obstacle avoidance problems are often ignored in the literature. This study addresses the multiobjective heterogeneous USV scheduling problems with obstacle avoidance. The objective is to minimize the maximum completion time and total carbon emissions. First, a mathematical model is developed to describe the concerned problems. Second, an A* algorithm is employed to obtain a path between task points with avoiding obstacles. Third, to obtain a high-quality scheduling scheme, an improved artificial bee colony (ABC) algorithm with deep Q-network (DQN) is designed. According to the problem nature, six novel rules are designed for finding high-quality solutions in initialized population. Five local search strategies are designed based on the structure of solution space. The scales of the instances and different objectives are utilized to designed a DQN, which recommends suitable local search strategies during iterations for improving convergence speed. Then, the Gurobi solver is employed to verify the proposed model. The effectiveness of the proposed strategies is verified by 13 instances with different scales. Finally, the experimental results and discussions show that the designed ABC with DQN has the strongest competitiveness among all compared algorithms.

With the acceleration of urbanization, the traffic congestion issue is becoming more and more prominent in large cities. The effective scheduling of urban traffic signals becomes critical. This study proposes three novel prediction and feedback assisted evolutionary algorithms (PFAEAs) to address the urban traffic signal scheduling problem (UTSSP) with minimizing vehicle delays. First, we construct a mathematical model of UTSSP and design an improved evolutionary algorithm (EA) framework that integrates an eight-phase control strategy based on a vehicle movement relationship graph. Then, by combining a back-propagation neural network (BPNN) and meta-heuristics, we improve the prediction accuracy of the vehicle turning rate for generating high-quality initial solutions. Further, 12 problem-specific search operators (PSSOs) are designed to enhance the exploration capability of EA. Reinforcement learning (RL) algorithms, especially the Q-learning and Sarsa algorithms, are employed to select premium PSSOs dynamically for guiding the search direction of EA. Finally, for 18 cases with different scales, the proposed PFAEAs show significant advantages in reducing vehicle delays compared with the state-of-the-art algorithms. The results validate the competitiveness and practicality of the PFAEAs for UTSSP.

In response to escalating market demands, we extend the distributed assembly flowshop problems (DAFSPs) by incorporating batch delivery, optimizing both total energy consumption (TEC) and total completion time, simultaneously. First, a mathematical model for DAFSP with batch delivery is constructed. Second, the artificial bee colony (ABC) algorithm is enhanced to solve the concerned problems. Two dispatch rules are designed to enhance the quality and diversity of initial solutions. Third, seven local search operators tailored to problem characteristics and two objective-oriented machine speed adjustment strategies are designed for improving the performance of ABC. Two reinforcement learning (RL) algorithms, SARSA andQ-learning, are used to select the appropriate local search operators and speed adjustment strategies during iterations. Two pairs of state-action strategies are developed for local search selection and speed adjustment, respectively. Finally, extensive simulation experiments and detailed analysis demonstrate that the SARSA-assisted ABC has a better performance than its peers for DAFSP with batch delivery.

This article studies the flexible job shop scheduling problem with a certain number of automatic guided vehicles (FJSP-AGVs), aiming to minimize the makespan. First, a novel constraint programming (CP) model is formulated to obtain optimal solutions. Specifically, the proposed CP model addresses the shortcomings of the existing CP model, which cannot solve instances with a machine processing two consecutive operations of the same job. Additionally, redundant and symmetry-breaking constraints are designed to accelerate constraint propagation and break problem symmetry, respectively. Then, to more effectively solve FJSP-AGVs, a CP-assisted meta-heuristic algorithm framework is designed, with a CP-assisted dual-population collaborative genetic algorithm (DCGA-CP) being developed as an example. Finally, experiments are performed on benchmark instances to demonstrate the effectiveness and superiority of the proposed CP model and DCGA-CP. Experimental results show that the proposed CP models first prove 29 new optimal solutions and improve 27 best-known solutions. Meanwhile, DCGA-CP first proves 29 new optimal solutions and improves 32 best-known solutions for benchmark instances.

Amid the global push for sustainable development, rising market demands have necessitated a multiregional, multiobjective, and flexible production model. Against this backdrop, this article investigates the multiobjective distributed flow shop group scheduling problem by formulating a mathematical model and introducing an advanced memetic algorithm integrated with reinforcement learning (RLMA). The RLMA involves a novel cooperative crossover operation in conjunction with the nature of the coupled problems to extensively explore the solution space. Additionally, the Sarsa algorithm enhanced with eligibility traces guides the selection of optimal schemes during the local enhancement phase. To ensure a balance between convergence and diversity, a solution selection strategy based on penalty-based boundary intersection decomposition is utilized. Furthermore, the increasing-efficiency and reducing-consumption strategies integrating a rapid evaluation mechanism are designed by dynamically changing the machine speed to balance economic and sustainability metrics. Comprehensive numerical experiments and comparative analyses demonstrate that the proposed RLMA surpasses existing state-of-the-art algorithms in addressing this complex problem.

This study addresses an energy-efficient multiobjective distributed assembly permutation flowshop scheduling problem with sequence dependent setup time. The objectives are to minimize the maximum completion time (makespan), mean of earliness and tardiness, and total carbon emission, simultaneously. First, a mathematical model is established. Second, the double-learning-strategy-based Jaya algorithms are developed to address the problems. According to problem-specific nature, one Q-learning state-action strategy is designed to guide nondominated solutions choosing appropriate machine speed adjustment strategies for achieving a satisfactory tradeoff among the three objectives. Third, eight neighborhood structures are designed and embedded in the proposed Jaya algorithms to discover high-quality solutions in local spaces. Fourth, another three novel Q-learning state-action design strategies are proposed to dynamically select the appropriate neighborhood structures during iterations, which introduce the searching directions and improve the convergence of the proposed Jaya. Finally, 81 benchmark instances are solved and the effectiveness of improved strategies is demonstrated. The proposed Jaya algorithm with the best double Q-learning strategies is compared to a solver, Gurobi, to verify the developed mathematical model. The experimental analysis demonstrates that the improved Jaya algorithm with both the Q-learning based machine speed adjustment and the Q-learning-based neighborhood selection strategies shows the best performance.

Flexible job shop scheduling problem (FJSP) is one of the challenging issues in industrial systems. In this study, we propose a bi-population balancing multi-objective evolutionary algorithm, to solve the distributed FJSPs from a steelmaking system, with considering the fuzzy processing time and crane transportation processes. Two objectives are considered simultaneously, including minimization of the maximum fuzzy completion time and the energy consumption during machine processing and crane transportation. Firstly, the mathematical model is formulated for the considered problem. Then, an efficient problem-specific initialization heuristic is developed. To balance the convergence and diversity abilities, a novel crossover operator and two cooperative population environmental selection mechanisms are developed. In addition, an efficient population size adaptive adjustment mechanism is designed. Then, an enhanced local search heuristic is developed to further improve the searching abilities. Finally, a set of randomly generated instances based on realistic industrial processes are tested, and through comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several presented algorithms.Note to Practitioners—In practical manufacturing processes, the processing times for each job should not be considered as deterministic values because of the disruption events, such as machine breakdown, resource limitation, and machine maintenance. Therefore, the fuzzy scheduling should be considered in many industrial procedures. This study considered multi-objective optimization flexible job shop with energy and robotic transportations, where the fuzzy makespan and energy consumptions are minimized simultaneously. Two populations balancing the convergence and diversity abilities are developed. Efficient problem-specific heuristics are designed to enhance the searching performance. The proposed methods can be generalized and applied to many applications considering both the realistic constraints and objectives.

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.

Currently, the healthcare of elderly people arouses widespread concerns since the sharp increase of aging population puts severe stress on public medical resources. Home health care (HHC) is regarded as an alternative answer to hospitalization, while it plays an important role in reducing healthcare cost and improving service satisfaction. This work addresses a service resource routing and scheduling problem with sharing strategy among multiple HHC centers for given customers. Two objective functions are involved: minimizing the total operation cost including the fixed usage cost of centers, caregiver usage cost and service cost, and minimizing the total tardiness caused by delay service. Firstly, a mixed integer programming model is formulated to describe the concerned problem. Secondly, a multi-objective artificial bee colony algorithm with problem-specific knowledge (MABC-PK) is proposed. Three problem-specific knowledge-based heuristics are designed to initialize population. A crossover operation and a self-learning neighborhood selection method are developed to prompt collaborative search of population and external archive. Furthermore, two knowledge-based local search methods are proposed for refining solutions in the external archive via employing some observations and priority properties derived from the problem characteristics. Finally, extensive experiments are conducted by comparing the proposed approach with four widely-acknowledged multi-objective optimization methods and a mathematical programming solver CPLEX. The comparative results and statistical analysis confirm the strong competitiveness of MABC-PK for solving the concerned problem.

In complex and variable traffic environments, efficient multi-objective urban traffic light scheduling is imperative. However, the carbon emission problem accompanying traffic delays is often neglected in most existing literature. This study focuses on multi-objective urban traffic light scheduling problems (MOUTLSP), concerning traffic delays and carbon emissions simultaneously. First, a multi-objective mathematical model is firstly developed to describe MOUTLSP to minimize vehicle delays, pedestrian delays, and carbon emissions. Second, three well-known meta-heuristics, namely genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), are improved to solve MOUTLSP. Six problem-feature-based local search operators (LSO) are designed based on the solution structure and incorporated into the iterative process of meta-heuristics. Third, the problem nature is utilized to design two novel Q-learning-based strategies for algorithm and LSO selection, respectively. The Q-learning-based algorithm selection (QAS) strategy guides non-dominated solutions to obtain a good trade-off among three objectives and generates high-quality solutions by selecting suitable algorithms. The Q-learning-based local search selection (QLSS) strategies are employed to seek premium neighborhood solutions throughout the iterative process for improving the convergence speed. The effectiveness of the improvement strategies is verified by solving 11 instances with different scales. The proposed algorithms with Q-learning-based strategies are compared with two classical multi-objective algorithms and some state-of-the-art algorithms for solving urban traffic light scheduling problems. The experimental results and comparisons demonstrate that the proposed GA$+$QLSS, a variant of GA, is the most competitive one. This research proposes new ideas for urban traffic light scheduling with three objectives by Q-learning assisted evolutionary algorithms firstly. It provides strong support for achieving more efficient and environmentally friendly urban traffic management.

An integrated container scheduling problem (ICSP) is a significant challenge to improve the overall efficiency of (un)loading, transshipment, and reduce energy consumption in container terminals. In this study, we address a ICSP with two transshipment routes (ICSP$\_$TR) for sea-road containers. First, a multi-objective optimization mathematical model is formulated for the ICSP$\_$TR. The objectives are to minimize the maximum completion time, the total load waiting time of automatic guided vehicles (AGVs), and the total energy consumption of quay cranes (QCs) and yard cranes (YCs). Second, an improved non-dominated sorting genetic algorithm II (INSGAII) is proposed to solve the ICSP$\_$TR. The order crossover and two-point mutation are employed for container sequence. A following rule is designed for transshipment routes and external trucks. The early complete time rule is adopted in equipment allocation to configure QCs, AGVs, and YCs. Third, an external archive technology and a variable neighborhood local search strategy are developed to improve the exploitation ability. Finally, 80 instances based on ICSP$\_$TR and the single route ICSP are solved, respectively. Two storage situations of containers are compared between ICSP$\_$TR and the single route ICSP. The results based on ICSP$\_$TR show higher feasibility and effectiveness for hybrid transshipment. Furthermore, the results of the algorithm analysis show that INSGAII outperforms the original NSGAII and two other prominent multi-objective algorithms in convergence, diversity, and distribution for the ICSP_TR. Moreover, experimental results verify that INSGAII is capable of generating higher-quality scheduling schemes, offering container terminal managers a broader array of superior options.

Recently, energy conservation in manufacturing industry, particular in energy-intensive industries, receives much attention in order to meet the environmental protection and sustainable development needs. Optimal job scheduling is of great importance in reducing unnecessary energy consumption. To this end, both energy and time-related criteria need to be taken into consideration to achieve an efficient and sustainable production process. Generally, it is difficult to obtain the accurate processing time of jobs in advance due to various uncertainties in open shop scheduling problems arising from manufacturing and service systems. This work formulates a stochastic multiobjective hybrid open shop scheduling problem that consists of open shop and parallel-machine models. First, a multiobjective chance-constrained program is established to minimize total tardiness and energy consumption while meeting makespan requirements. Second, we newly develop a multiobjective framework integrating a brain storm optimizer and a simulation system to solve this problem. We combine population evolution to enhance exploration and external archive evolution to strengthen exploitation into the brain storm optimizer to seek for promising solutions. A simulation system is accordingly designed by using stochastic simulation and discrete-event simulation to assess the searched solutions. Finally, by conducting experiments and comparing the proposed method with several existing algorithms and an exact solver, our results confirm that it significantly outperforms its peers in tackling the considered problem.

Prefabricated construction has attracted research interest as it can significantly save energy consumption. In this study, a distributed hybrid flow shop with family setup time in a typical prefabricated system is investigated. A hybrid graph-based imitation learning from multiple experts (hereafter called IML) is developed to minimize the makespan. Efficient input features with operation processing times are presented. Next, to enhance the training speed of the network, a less parameter encoder mechanism is developed. Subsequently, a multiexpert learning method is proposed, in which the solutions obtained by these experts are used as the ground truth values to enhance the convergence and searching capabilities. Moreover, a variable neighborhood search (VNS)-based local search method is embedded to further improve the performance. Finally, based on a realistic prefabricated component production horizon, a set of instances is generated to test the performance of the proposed algorithm. The comprehensive computational comparison and statistical analysis reveal that the proposed IML algorithm, when compared to two recently published efficient algorithms, yields an average improvement of about 8.66% and 13.78%, respectively. This highlights the efficiency of the proposed algorithm to solve large-scale instances.

This study focuses on distributed no-wait permutation flow shop scheduling problems that have many practical engineering backgrounds. The objective is to dispatch jobs optimally to multiple processing centers and ordering them for minimizing the maximum completion time (makespan). First, to solve the problems, a mathematical model is established. Second, a novel evolutionary algorithm is proposed, in which a two-dimensional (2-D) array is designed for solution representation. Based on the problem-specific knowledge, a factory assign strategy and jigsaw puzzle inspired algorithm (JPA) are employed for initializing the population of the evolutionary algorithm. Furthermore, a relative local search is used to improve the performance of the proposed algorithm. Finally, 120 instances with different scales are solved and the results are recorded. Comparisons and discussions show the proposed algorithm has computational competitiveness in solving the concerned problems with makespan criteria.

This work addresses multiobjective dynamic surgery scheduling problems with considering uncertain setup time and processing time. When dealing with them, researchers have to consider rescheduling due to the arrivals of urgent patients. The goals are to minimize the fuzzy total medical cost, fuzzy maximum completion time, and maximize average patient satisfaction. First, we develop a mathematical model for describing the addressed problems. The uncertain time is expressed by triangular fuzzy numbers. Then, four meta-heuristics are improved, and eight variants are developed, including artificial bee colony, genetic algorithm, teaching-learning-base optimization, and imperialist competitive algorithm. For improving initial solutions’ quality, two initialization strategies are developed. Six local search strategies are proposed for fine exploitation and a$Q$-learning algorithm is used to choose the suitable strategies among them in the iterative process of the meta-heuristics. The states and actions of$Q$-learning are defined according to the characteristic of the addressed problems. Finally, the proposed algorithms are tested for 57 instances with different scales. The analysis and discussions verify that the improved artificial bee colony with$Q$-learning is the most competitive one for scheduling the dynamic surgery problems among all compared algorithms.

This paper addresses a distributed lot-streaming permutation flow shop scheduling problem that has various applications in real-life manufacturing systems. We aim to optimally assign jobs to multiple distributed factories and sequence them to minimize the maximum completion time (Makespan). A mathematic model is first developed to describe the considered problem. Then, five meta-heuristics are executed to solve it, including particle swarm optimization, genetic algorithm, harmony search, artificial bee colony, and Jaya algorithm. To improve the performance of these meta-heuristics, we employ Nawaz-Enscore-Ham (NEH) heuristic to initialize populations and propose improved strategies based on the problem’s feature. Finally, experiments are carried out based on 120 instances. The performance of improved strategies is verified. Comparisons and discussions show that the artificial bee colony algorithm with improved strategies has the best competitiveness for solving the proposed problem with makespan criteria. Note to Practitioners—In contemporary manufacturing industry, the traditional single-factory environment is being replaced by a distributed multi-factory environment, as a distributed pattern can effectively improve the production efficiency through the reasonable resource allocation strategies. The distributed lot-streaming permutation flow shop scheduling problem in such a pattern is of significance to practitioners. Although intelligent optimization can provide an effective tool to solve such problems, most of the algorithms are parameter-sensitive. A challenge for engineers is parameter selection, which greatly impacts the algorithm performance. To ensure the robustness of the algorithms, we develop five improved meta-heuristics by employing some strategies. Furthermore, parameter setting test is carried out to select the appropriate parameter values. As a result, the proposed algorithms can obtain resource allocation schemes with high-quality. It is shown that the artificial bee colony algorithm with improved strategies outperforms other algorithms well. The proposed methodology can be readily applied to real distributed scheduling problems.

In the actual production, the insertion of new job and machine preventive maintenance (PM) are very common phenomena. Under these situations, a flexible job-shop rescheduling problem (FJRP) with both new job insertion and machine PM is investigated. First, an imperfect PM (IPM) model is established to determine the optimal maintenance plan for each machine, and the optimality is proven. Second, in order to jointly optimize the production scheduling and maintenance planning, a multiobjective optimization model is developed. Third, to deal with this model, an improved nondominated sorting genetic algorithm III with adaptive reference vector (NSGA-III/ARV) is proposed, in which a hybrid initialization method is designed to obtain a high-quality initial population and a critical-path-based local search (LS) mechanism is constructed to accelerate the convergence speed of the algorithm. In the numerical simulation, the effect of parameter setting on the NSGA-III/ARV is investigated by the Taguchi experimental design. After that, the superiority of the improved operators and the overall performance of the proposed algorithm are demonstrated. Next, the comparison of two IPM models is carried out, which verifies the effectiveness of the designed IPM model. Last but not least, we have analyzed the impact of different maintenance effects on both the optimal maintenance decisions and integrated maintenance-production scheduling schemes.

A distributed flow-shop scheduling problem with lot-streaming that considers completion time and total energy consumption is addressed. It requires to optimally assign jobs to multiple distributed factories and, at the same time, sequence them. A biobjective mathematic model is first developed to describe the considered problem. Then, an improved Jaya algorithm is proposed to solve it. The Nawaz-Enscore-Ham (NEH) initializing rule, a job-factory assignment strategy, the improved strategies for makespan and energy efficiency are designed based on the problem's characteristic to improve the Jaya's performance. Finally, experiments are carried out on 120 instances of 12 scales. The performance of the improved strategies is verified. Comparisons and discussions show that the Jaya algorithm improved by the designed strategies is highly competitive for solving the considered problem with makespan and total energy consumption criteria.

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.

This paper addresses urban traffic light scheduling problems (UTLSP) with eight phases. The objective is to minimize the total vehicle delay time by assigning traffic phases and phase-timing optimally. A novel hybrid algorithm framework by combining meta-heuristics with Q-learning is proposed to solve the UTLSP for the first time. First, a mathematical model is developed to describe UTLSP. Second, five meta-heuristics are employed and improved to solve the concerned problems. Based on the feature of UTLSP, five local search operators are developed to improve the exploitation performance of the meta-heuristics. Third, two Q-learning-based ensemble strategies are designed to select the premium local search operators during the meta-heuristics’ iterations. Finally, experiments are conducted on 10 cases with different scales. A total of 26 algorithms are compared for validation. Experimental results verify the effectiveness of the proposed ensemble strategies. Comparisons and discussions show that the improved water cycle algorithm with the first Q-learning strategy has the best competitiveness for solving the considered problems.

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 provides a novel intelligent scheduling strategy for a real-world transportation dynamic scheduling case from an engine workshop of general motor company (GMEW), which is a key production line throughout the manufacturing process. In order to reduce the carbon emission in the scheduling process and make up for ignoring the energy consumption of each part in the scheduling when optimizing the carbon emission of the workshop and the factory. This paper first formulates a fuzzy random chance-constrained programming model of inverse scheduling problem (ISP) with energy consumption. A multi-strategy parallel genetic algorithm based on machine learning (RL-MSPGA) is proposed, which uses machine learning to improve the genetic algorithm. First, the parallel idea is developed to accelerate the process of evolution of genetic algorithm, and the initial population is divided into clusters by$k$-means clustering algorithm. Second, similar individuals are evenly distributed to different sub-populations to ensure the diversity and uniformity of sub-populations. Third, in the process of evolution, the sub-populations communicate with each other, and extend the excellent individuals to replace the poor ones in other populations, so as to improve the overall quality of the population. Fourth, the self-learning of the crossover probability is realized by the self-learning of the self-sensing environment, which makes the crossover probability adapt to the evolutionary process according to experience. Finally, the real instance is used to validate the different algorithms. It can effectively adjust the completion time and the proportion of energy consumption, thus providing the possibility for the production of energy-saving enterprises. This implies that the suggested model is reasonable and the provided algorithm can effectively solve the inverse shop scheduling problem.

A permutation flow-shop scheduling problem (PFSP) has been studied for a long time due to its significance in real-life applications. This work proposes an improved artificial bee colony (ABC) algorithm with$Q$-learning, named QABC, for solving it with minimizing the maximum completion time (makespan). First, the Nawaz–Enscore–Ham (NEH) heuristic is employed to initialize the population of ABC. Second, a set of problem-specific and knowledge-based neighborhood structures are designed in the employ bee phase.$Q$-learning is employed to favorably choose the premium neighborhood structures. Next, an all-round search strategy is proposed to further enhance the quality of individuals in the onlooker bee phase. Moreover, an insert-based method is applied to avoid local optima. Finally, QABC is used to solve 151 well-known benchmark instances. Its performance is verified by comparing it with the state-of-the-art algorithms. Experimental and statistical results demonstrate its superiority over its peers in solving the concerned problems.

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.

The rapid development of urban cities coupled with the rise in population has led to an exponentially growing number of vehicles on the roads for the latter to commute. This is adding to the already overbearing problem of traffic congestion. Short term, costly and short-sighted solutions of road infrastructure expansions are no longer suitable. One effective method of road resource allocation is focusing on the widely used traffic signal controllers’ timing schedules. Searching for a suitable or an optimal schedule for the prior via brute force to ease traffic congestion might not be the most elegant or feasible solution. Nature-inspired algorithms including evolutionary and swarm intelligence algorithms are gaining a lot of momentum. Many of these algorithms have been used in the last two decades to address different applications in the smart city era including traffic signal control (TSC). This paper conducts a comprehensive literature review on applications of evolutionary and swarm intelligence algorithms to TSC. Surveyed work is categorized based on the set of decision variables, optimization objective(s), problem modeling and solution encoding. The paper, based on gaps identified by the conducted review, identifies promising future research directions and discusses where the future research is headed.

In this article, an optimized tracking control scheme is studied for the quadrotor unmanned aerial vehicle (QUAV) system by combining both reinforcement learning (RL) and the backstepping technique. The RL aims to overcome the difficulty coming from solving the Hamilton–Jacobi–Bellman (HJB) equation, and it is performed via iterating both critic and actor each other, where the critic is for improving the control performance and the actor is for executing the control behavior. In mathematics, a QUAV system is composed of two connected subsystems that are, respectively, modeled by the translational and rotational dynamic equations, which are coupled via a rotation matrix; hence, the optimized tracking scheme is composed of two interconnected individual controls corresponding to the position and attitude, respectively. To achieve the two optimized position and attitude controls, the RL is constructed on the basis of the neural network (NN) approximation of the HJB equation’s solution by utilizing NN’s outstanding function approximation ability. Particularly, the position control is accomplished by introducing an intermediate control because the translational dynamic is an underactuated system. Since the proposed RL algorithm is significantly simple in comparison with the published methods, the optimized QUAV control can be easily executed in practical applications. Finally, the results are demonstrated by a Lyapunov stability analysis and a numerical simulation.

Improving the performance of transportation network is a crucial task in traffic management. In this paper, we start with a cooperative routing problem, which aims to minimize the chance of road network breakdown. To address this problem, we propose a subgradient method, which can be naturally implemented as a semi-centralized pricing approach. Particularly, each road link adopts the pricing scheme to calculate and adjust the local toll regularly, while the vehicles update their routes to minimize the toll costs by exploiting the global toll information. To prevent the potential oscillation brought by the subgradient method, we introduce a heavy-ball method to further improve the performance of the pricing approach. We then test both the basic and improved pricing approaches in a real road network, and simultaneously compare them with several baselines. The experimental results demonstrate that, our approaches significantly outperform others, by comprehensively evaluating them in terms of various metrics including average travel time and travel distance, winners and losers, potential congestion occurrence, last arrival time, toll costs and average traffic flows, with two different O-D profiles.

Addressing the positioning problem of a mobile robot remains challenging to date despite many years of research. Indoor robot positioning strategies developed in the literature either rely on sophisticated computer vision techniques to handle visual inputs or require strong domain knowledge for nonvisual sensors. Although some systems have been deployed, the former may be lacking due to the intrinsic limitation of cameras (such as calibration, data association, system initialization, etc.) and the latter usually only works under certain environment layouts and additional equipment. To cope with those issues, we design a lightweight indoor robot positioning system which operates on cost-effective WiFi-based received signal strength (RSS) and could be readily pluggable into any existing WiFi network infrastructures. Moreover, a novel deep fuzzy forest is proposed to inherit the merits of decision trees and deep neural networks within an end-to-end trainable architecture. Real-world indoor localization experiments are conducted and results demonstrate the superiority of the proposed method over the existing approaches.

In wafer manufacturing, extensive research on the operations of cluster tools under the steady state has been reported. However, with the shrinking down of wafer lot size, such tools are frequently required to switch from handling one lot of wafers to another, resulting in more transient processes, including start-up and close-down ones. Also, wafer residency time constraint is critical for many wafer fabrication processes. To cope with the transient scheduling problem of time-constrained single-arm cluster tools with parallel chambers, based on a generalized backward strategy, this paper first builds timed Petri net models for these two transient processes. Then, two linear programs are derived for the first time to search a feasible schedule with a minimal makespan. Two industrial examples are given to demonstrate the effectiveness of the obtained results at last.

A novel many-objective particle swarm optimization (PSO) algorithm called IDMOPSO is presented in this study to robustly and effectively address many-objective optimization problems (MaOPs). IDMOPSO is based on a performance indicator and direction vectors. A selection strategy based on the quality indicator Iε+ and Pareto dominance for personal best (pbest) particles is proposed to ensure the convergence and diversity of the algorithm and enhance the capability of local exploration. An external archive based on Iε+ and direction vectors is used to preserve the diversity of non-dominated solutions found in the search process. A multi-global optimal (gbest) particle selection method is developed to increase global search ability and ensure the particles' diversity. This method allows each particle to be assigned to a different gbest particle. This method differs from the traditional method, wherein only one gbest particle is allocated for the whole population of PSO. We aim to design a robust multi-objective evolutionary algorithm to deal with MaOPs. Extensive comparative experiments on DTLZ and DTLZ-1problems with varied numbers of objectives show that IDMOPSO is effective and flexible in addressing MaOPs. The influences and effectiveness of the proposed strategies are also analyzed in detail.

This work addresses on flexible job shop rescheduling problem with machine recovery. The goal is to minimize the maximum machine workload and instability simultaneously. As an almost parameter-free metaheuristic, Jaya is used and developed to solve it. A local search operator and an initializing rule are developed for improving Jaya’s performance. Ten cases from a remanufacturing company are solved to verify the proposed Jaya’s performance. The comparisons and discussions show the effectiveness of the proposed Jaya for rescheduling flexible job shop with machine recovery.

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.

This paper addresses a traffic signal scheduling (TSS) problem in a heterogeneous traffic network with signalized and non-signalized intersections. The objective is to minimize the total network-wise delay time of all vehicles within a given finite-time window. First, a novel model is proposed to describe a heterogeneous traffic network with signalized and non-signalized intersections. Second, five meta-heuristics are implemented to solve the TSS problem. Based on the problem characteristics, three local search operators and their ensemble are proposed. Then, five meta-heuristics with such an ensemble are proposed to solve the TSS problem. Third, experiments are carried out based on the real traffic data in the Jurong area of Singapore. The performance of the ensemble of local search operators is verified. Ten algorithms, including five meta-heuristics with and without the ensemble, are evaluated by solving 18 cases with different scales. Finally, the algorithm with the best performance is compared against the currently used traffic signal control strategies. The comparisons and discussions show the competitiveness of the proposed model and meta-heuristics.

This paper addresses a bi-objective urban traffic light scheduling problem (UTLSP), which requires minimizing both the total network-wise delay time of all vehicles and total delay time of all pedestrians within a given finite-time window. First, a centralized model is employed to describe the UTLSP, where the cost functions and constraints of the two objectives are presented. A non-domination strategy-based metric is used to compare and rank solutions based on the two objectives. Second, metaheuristics, such as harmony search (HS) and artificial bee colony (ABC), are implemented to solve the UTLSP. Based on the characteristics of the UTLSP, a local search operator is utilized to improve the search performance of the developed optimization algorithms. Finally, experiments are carried out based on the real traffic data in Jurong area of Singapore. The HS, ABC, and their variants with the local search operator are evaluated in 19 case studies with different scales and time windows. To the best of our knowledge, this paper is the first of its kind to solve bi-objective traffic light scheduling problems in the literature. To demonstrate the effectiveness of the proposed algorithms in dealing with bi-objective optimization in traffic light scheduling, they are compared to the classical non-dominated sorting genetic algorithm II (NSGAII) with and without the local search operation. The comparisons indicate that our algorithms outperform the NSGAII algorithm with and without the local search operator for solving the UTLSP.

This paper presents a macroscopic model for pedestrian-vehicle mixed-flow network and a traffic signal scheduling strategy for both pedestrians and vehicles. We first propose a novel mathematical model of pedestrians crossing a junction. By combining a link-based vehicle network model, a traffic light scheduling problem is formulated with the aim to strike a good balance between pedestrians' needs and vehicle drivers' needs. The problem is first converted into a mixed-integer linear programming (MILP) problem via a novel transformation procedure, which is solvable by several existing solvers, e.g., GUROBI. Then a meta-heuristic method called discrete harmony search (DHS) algorithm is also adopted to reduce the computational complexity in MILP. Numerical simulation results are provided to illustrate the effectiveness of our real-time traffic light scheduling strategy for pedestrians and vehicles, and the potential impact of the pedestrian movement to the vehicle traffic flows.

In this paper, a novel centralized traffic network model is proposed to describe the urban traffic light scheduling problem (UTLSP) in a traffic network. The objective is to minimize the network-wise total delay time of all vehicles in a fixed time window. To overcome the potentially high computational complexity involved in UTLSP, an improved artificial bee colony (IABC) algorithm is proposed. A new solution generating strategy and three local search operators corresponding to different neighbourhood structures of UTLSP are proposed to improve the performance of IABC. Extensive computational experiments are carried out using sixteen instances with different problem-scales. The IABC with and without three local search operators are evaluated and compared. The comparisons and discussions show the competitiveness of IABC for solving UTLSP.

This paper presents a novel approximation technique based on metaheuristics and weighted residual function (WRF) for tackling singular boundary value problems (BVPs) arising in engineering and science. With the aid of certain fundamental concepts of mathematics, Fourier series expansion, and metaheuristic optimisation algorithms, singular BVPs can be approximated as an optimisation problem with boundary conditions as constraints. The target is to minimise the WRF (i.e. error function) constructed in approximation of BVPs. The scheme involves generational distance metric for quality evaluation of the approximate solutions against exact solutions (i.e. error evaluator metric). Four test problems including two linear and two non-linear singular BVPs are considered in this paper to check the efficiency and accuracy of the proposed algorithm. The optimisation task is performed using three different optimisers including the particle swarm optimisation, the water cycle algorithm, and the harmony search algorithm. Optimisation results obtained show that the suggested technique can be successfully applied for approximate solving of singular BVPs.

Differential equations are at the heart of physics and much of chemistry. In this paper, differential equations of convective-radiative longitudinal fins with convex and exponential profiles have been solved approximately using a Fourier-based optimization approach. Using the concepts of mathematics, Fourier series expansion, and metaheuristics, ordinary differential equations (ODEs) can be modeled as an optimization problem. The optimization's target is to minimize the weighted residual function (cost function) of the ODEs. Boundary and initial conditions of ODEs are considered as constraints for the optimization model. Generational distance metric has been used for evaluation and assessment of the approximate solutions against the exact (numerical) solutions. The optimization task has been performed using two well-known optimizers including the harmony search and particle swarm optimization. Approximate solutions obtained by the applied method have been compared with numerical and approximate methods in literature. The optimization results obtained show that the applied approach can be successfully utilized for approximately solving of longitudinal fins with convex and exponential profiles.

This paper studies a large-scale urban traffic signal control problem (LUTSCP). A centralized model is developed for describing the LUTSCP in a scheduling framework. The objective is to minimize the total network-wise delay in a fixed time window. We have implemented a recently developed algorithm, so called Jaya, to solve the LUTSCP. The population initialization is based on the four stages of traffic signal in Singapore. A simple new solution generation strategy is proposed to improve the performance of the Jaya. A neighborhood search operator is proposed based on the characteristics of LUTSCP to improve the search performance in local search space. Experiments are carried out using the traffic signal data from Singapore traffic network. The performance of the new strategy for generating feasible solution and the neighborhood search operator are evaluated and discussed. The optimization results obtained by standard Jaya algorithm and its variants are compared to those by existing traffic signal control system. The comparisons and discussions verify that the Jaya algorithm and its variants are superior over the existing traffic light control. In future work, we will compare the performance of Jaya algorithm to existing intelligent algorithms in literature.

This paper researches on the flexible job shop scheduling problem (FJSP) with new job insertion. FJSP with new job insertion includes two phases: initializing schedules and rescheduling after new job(s) insertion. Initializing schedules is the standard FJSP problem while rescheduling is an FJSP with different job start time and different machine start time. The objective is to minimize maximum machine workload. A recently developed algorithm, so called Jaya, is employed to solve the FJSP with new job insertion and a discrete version of Jaya is proposed. Extensive computational experiments are carried out on eight real instances from remanufacturing enterprise. The discrete Jaya is compared to several existing heuristics and ensemble of them for FJSP with new job insertion. The results and comparisons verify that the discrete Jaya algorithm is superior over the existing methods. In future work, we will future improve the performance of discrete Jaya and compare it to more existing intelligent algorithms in literature.

Online shopping has become an essential part of our life, which provides a suitable, cheap, and quick way for customers to enjoy a wide variety of products. However, due to the large number of online stores, a customer usually faces difficulties to review all available offers manually in order to find a favorite item. The Internet shopping optimization problem (ISOP) is a multiple-item multiple-shop optimization problem, which targets to minimize the total cost for a costumer to purchase a given set of products over all available offers. In this paper, the mathematical model of existing ISOP has been improved. In the improved model of ISOP different constraints and assumptions such as the maximum budget, discounts offered by internet shops have been taken into account. Several metaheuristic optimization methods such as the genetic algorithm are implemented. The obtained numerical results illustrate the effectiveness of the improved model and metaheuristics applied.

This paper proposes a hybrid discrete harmony search algorithm for solving the re-processing scheduling problem in pump remanufacturing. The process of pump remanufacturing and the scheduling problem of re-processing for pump subassembly are modeled. An experience based strategy is proposed for solving the unpredictability of subassembly re-processing time in remanufacturing. Hybrid discrete harmony search algorithm and local search are employed for scheduling re-processing of pump subassembly. The objectives of pump subassembly re-processing scheduling are minimization of the maximum completion time (makespan), and the mean of earliness and tardiness (E/T). These objectives are considered individually as well as together as a multi-objective problem. Computational experiments are carried out using real data from a pump remanufacturing enterprise. Computational results show that the objectives makespan and E/T can be optimized and the resulting schedules can be used in practice.

Heuristics that explore specific characteristics of the problem are essential to find good solutions in limited computational time for many practical systems. This paper first presents a constructive heuristic, namely improved standard deviation heuristic (ISDH), by combining the standard deviation heuristic (SDH) with the procedure of effective double-job-insert-operator. Then, a composite heuristic, improved standard deviation heuristic with iteration (ISDHI), is proposed using the iteration operator to improve the solutions of the ISDH. Extensive computational experiments are carried out based on a set of well-known flow shop benchmark instances that are considered as no-wait flow shop scheduling instances. Computational results and comparisons show that the ISDHI performs significantly better than the existing ones, and the ISDHI heuristic further improves the proposed constructive heuristics for no-wait flow shop scheduling problem with total flow time criterion.

This paper proposes a pareto-based discrete harmony search (PDHS) algorithm to solve multi-objective FJSP. The objectives are the minimization of two criteria namely, the maximum of the completion time (Makespan) and the mean earliness and tardiness. Firstly, we develop a new method for the initial the machine assignment task. Some existing heuristics are also employed for initializing the harmony memory. Hence, harmony memory is filled with discrete machine permutation for machine assignment and job permutation for operation sequence. Secondly, we develop a new rule for the improvisation to produce a new harmony for FJSP. The machine assignment and operation sequence are processed respectively. Thirdly, several local search methods are embedded to enhance the algorithm's local exploitation ability. Finally, extensive computational experiments are carried out using well-known benchmark instances. Computational results and comparisons show the efficiency and effectiveness of the proposed pareto-based discrete harmony search algorithm for solving the multi-objective flexible job-shop scheduling problem.