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.

This work investigates the space-limited aircraft assembly scheduling problem (SAASP) based on real-world cases. A computational model, minimizing the makespan, is developed to formulate the complex operational relationships and space constraints of the workstations in SAASP. To address SAASP, we propose a reinforcement and statistical learning-assisted evolutionary algorithm (RSLEA). First, hierarchical encoding and queue decoding methods are designed to capture the intricate sequencing and workstation space constraints. Second, a statistical learning strategy is implemented to accelerate the convergence of early exploration. Next, a reinforcement learning strategy is introduced to control the sampling size of the statistical learning process. Additionally, another reinforcement evolutionary learning strategy is developed for exploitation. Three crossover operators are employed, with their computational resources adaptively allocated by the agent. To validate the performance of the proposed algorithm, RSLEA is tested on 520 instances and three real-world cases from a Chinese aircraft manufacturing factory. These cases are the assembly workstations of wings, partial bodies, and tails of aircraft, which are critical and complex. The RSLEA is compared with state-of-the-art algorithms and obtains better performance on 89% instances, and reduces the average gap to the baseline by 2.72%. Moreover, RSLEA achieves the best solution and overall performance in real-world cases.

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.

This study addresses the lot-streaming hybrid job shop scheduling problem with variable sublots (LHJSV), inspired by a real-world aircraft tooling shop. A computational model is developed to represent the complex scheduling processes of the tooling shop. To solve this problem, we propose an automatic memetic algorithm enhanced by a heuristic designed with the assistance of a large language model (LLM). The approach is designed as follows: first, a memetic computing framework with automated algorithmic design is proposed for LHJSV. Second, a cooperative evolutionary heuristic framework based on problem decomposition is introduced, enabling the LLM to comprehend the LHJSV characteristics and generate feasible algorithms. Third, problem-specific prompts for LHJSV are carefully designed to guide the LLM. To evaluate the effectiveness of the proposed method, 20 benchmark instances derived from the Taillard dataset and a real-world case involving 575 operations are utilized. The proposed algorithm is compared against three swarm-based algorithms, an end-to-end method, and an LLM-based algorithm. Experimental results demonstrate that our method outperforms the compared algorithms on 85% of benchmark instances and exhibits significant superiority in real-world scenarios.

Dynamic flexible job shop scheduling (DFJSS) problem is an important scenario in intelligent manufacturing domain with the requirement of real-time decision-making under complex constraints. Existing approaches struggle to handle dynamic environments and heterogeneous job-resource relationships effectively while keeping optimum scheduling performance simultaneously. To address this challenge, a heterogeneous graph reinforcement learning framework combining question-aware neighborhood aggregation and interoption prompt attention (QIHGRL) is presented to address the DFJSS problem with new job insertions and variable processing times to minimize total tardiness. The optimization objectives are transformed into attention-guided signals by the question-aware neighborhood aggregation module to improve feature representation in the heterogeneous graph encoding stage of the QIHGRL. The competition and collaboration among scheduling actions are explicitly modeled by the interoption prompt attention layer in the policy optimization phase of the QIHGRL via adjusting action selection weights through a multihead attention mechanism to balance exploration and exploitation of the candidates in the population of the QIHGRL. The experimental results testified that the performance and efficiency of the QIHGRL outperforms that of the state of the arts algorithms.

The production process of aluminum profiles is a typical distributed flexible job shop environment. The scheduling problem in distributed systems with worker factors is a complex combinatorial optimization problem. In this article, the multiobjective distributed flexible job shop scheduling with worker factors (MO-DFJSPWF), including proficiency and the learning–forgetting effect, is studied to minimize makespan and total resource load (TRL). A mixed-integer linear programming (MILP) model is established according to the degree of proficiency and the rate of learning–forgetting effect of the workers. A tri-stage cooperative optimization algorithm (TSCOA) is designed for the MO-DFJSPWF. First, a knowledge-based initialization method considering worker proficiency, machine load, and job priority is proposed to generate the initial population of the problem. Second, a bi-population cooperative strategy with a dynamic adaptive search strategy (DASS) is developed to balance the convergence speed and candidate diversity of the algorithm. Eight perturbation operators in the second and third stages are introduced to explore and exploit the solution space of the TSCOA. Third, the perturbation operators are selected dynamically via the Q-learning mechanism by leveraging the historical performance data of local search operators. The effectiveness and efficiency of the TSCOA are tested on a benchmark test suite. The experimental results indicated that the performance of the TSCOA outperforms certain state-of-the-art algorithms in solving MO-DFJSPWF.

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.

The rapid expansion of the on-demand food delivery (OFD) market has led the service providers to manage large-scale dynamic order dispatching. Postponing the dispatch of orders is an effective strategy to alleviate the pressures from sudden order surges and to enhance decision-making quality. This paper addresses the OFD problem with order postponement to minimize travel distances and delayed deliveries, focusing on deciding which orders to postpone and which rider to assign for each remaining order at each decision point. We propose a prior and posterior postponement framework that separates the postponement decision-making process into two phases to balance computational efficiency and decision quality. In the prior phase, multiple knowledge-based postponement rules are designed to quickly filter out orders unsuitable for immediate dispatch. In the posterior phase, a data-driven postponement strategy using reinforcement learning is developed to further optimize long-term objectives. Particularly, an action-oriented phase-specific reward shaping method is designed by analyzing the intrinsic nature of the order postponement process, which helps customize the postponement duration for each order to achieve better postponement performance. Extensive numerical ablation and comparative experiments using real-world data demonstrate that the proposed postponement approach is able to improve customer satisfaction, delivery efficiency, and rider experience better than existing methods. Managerial insights are provided regarding the value of order postponement, key factors for designing effective postponement strategies, and practical ready-to-use postponement tactics.

This work addresses the flexible job shop scheduling with finite skilled workers, extending classical flexible job shop scheduling by incorporating operation decomposition, finite worker, and worker transfer. These new problem features significantly increase the complexity of solving, as several operations requiring multiple workers can lead to worker competition, causing delays in other operations that depend on the same workers. Previous studies focused on either operation decomposition or worker transfer but did not address the issue of worker competition. To tackle this challenging optimization problem, we propose a knowledge-guided hierarchical evolutionary algorithm (KHEA) with multiview cooperative neighborhood search. The key contributions of this work are as follows: 1) a hierarchical solving framework is proposed to reduce the solving difficulty. This problem is decomposed into three levels. The first level ignores the worker assignment and the second level starts optimizing it. The final level then refines the global solution; 2) a knowledge-guided crossover operator with a feedback schema is designed to improve the efficiency of crossover operations; and 3) a multiview cooperative neighborhood search strategy is proposed to reduce the idle time caused by worker competition. This involves designing a new disjunctive graph that accounts for worker competition to identify the critical path. The information from both machine-view and worker-view Gantt charts is cooperatively utilized to minimize idle time. Our method, KHEA, was tested on two benchmarks across 28 instances and 16 large-scale instances, with equal running time for comparisons. Compared to state-of-the-arts, KHEA obtains significant superiority.

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.

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 presents a novel migrating birds optimization (NMBO) algorithm for solving the lot-streaming flowshop scheduling problem with minimizing makespan. The proposed NMBO algorithm utilizes discrete job permutations to represent solutions, and applies multiple neighborhoods based on insert and swap operators to improve the leading solution. Two new crossover operators, i.e., similar job order with artificial chromosome crossover, and similar block order crossover are employed to obtain solutions for the rest migrating birds. An initialization scheme based on the problem-specific heuristics is presented to generate an initial population with a certain level of quality and diversity. A local search based on the insert neighborhood is embedded to improve the algorithm's local exploitation ability. NMBO is compared with the existing discrete invasive weed optimization, estimation of distribution algorithm and modified MBO algorithms based on the well-known lot-streaming flow shop benchmark. The computational results and comparison demonstrate the superiority of the proposed NMBO algorithm for the lot-streaming flow shop scheduling problems with makespan criterion.

This paper proposes a novel penalty function measure for constrained optimization using a new harmony search algorithm. In the proposed algorithm, a two-stage penalty is applied to the infeasible solutions. In the first stage, the algorithm can search for feasible solutions with better objective values efficiently. In the second stage, the algorithm can take full advantage of the information contained in infeasible individuals and avoid trapping in local optimum. In addition, for adapting to this method, a new harmony search algorithm is presented, which can keep a balance between exploration and exploitation in the evolution process. Numerical results of 13 benchmark problems show that the proposed algorithm performs more effectively than the ordinary methods for constrained optimization problems.

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.