A multi-agent system for FJSP with setup and transportation times

Pal, Manojkumar; Mittal, Murari Lal; Soni, Gunjan; Chouhan, Satyendra S.; Kumar, Manish

doi:10.1016/j.eswa.2022.119474

Cited by 16 publications

(8 citation statements)

References 50 publications

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“…(6) The processing equipment requires adjustment before processing different types of workpieces; conversely, if processing the same type of workpiece, no readjustment is needed. (7) The carrying capacity of each handling equipment is not limited, and the number of handling equipment is not limited. (8) The transportation time is not considered after the last working procedure of the job is completed.…”

Section: Assumptionsmentioning

confidence: 99%

“…Zhang et al [6] considered an FJSP with shipping and setup times while optimizing the maximum completion time, total energy consumption, critical machine workload, and early/late penalties, and established a multi-objective optimization mathematical model. Pal et al [7] proposed a multi-agent approach to address the FJSP with setup and transportation times, aiming to minimize makespan. Zhang et al [8] established a mixed-integer linear programming model, aiming at minimizing makespan and total energy consumption for the FJSP with setup and transportation time.…”

Section: Introductionmentioning

confidence: 99%

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Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm

Kong,

Wang

2024

Applied Sciences

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With the gradual emergence of customized manufacturing, intelligent manufacturing systems have experienced widespread adoption, leading to a surge in research interests in the associated problem of intelligent scheduling. In this paper, we study the flexible job shop scheduling problem (FJSP) with setup time, handling time, and processing time in a multi-equipment work center production environment oriented toward smart manufacturing and make-to-order requirements. A mathematical model with the optimization objectives of minimizing the maximum completion time, the total number of machine adjustments, the total number of workpieces handled and the total load of the machine is constructed, and an improved discrete particle swarm algorithm based on Pareto optimization and a nonlinear adaptive inertia weighting strategy is proposed to solve the model. By integrating the model characteristics and algorithm features, a hybrid initialization method is designed to generate a higher-quality initialized population. Next, three cross-variance operators are used to implement particle position updates to maintain information sharing among particles. Then, the performance effectiveness of this algorithm is verified by testing and analyzing 15 FJSP test instances. Finally, the feasibility and effectiveness of the designed algorithm for solving multi-objective FJSPs are verified by designing an FJSP test example that includes processing time, setup time and handling time.

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Section: Assumptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm

Kong,

Wang

2024

Applied Sciences

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“…This section presents the modified multi-agent based approach proposed by Pal et al (2023). The next section demonstrates the proposed multi-agent based system.…”

Section: The Proposed Approachmentioning

confidence: 99%

“…The best makespan of three problem instances are generated using the proposed approach. Table 4 shows the comparison of results with three state of the art solution approaches: FJSP with maintenance planning only by Rajkumar et al (2010), FJSP with setup and transportation time only (Pal et al 2023;Zhang et al 2020). The Gantt chart for the best makespan for 4x5 problem instance is shown in Figure 4.…”

Section: Computational Experiencementioning

confidence: 99%

Solving Complex FJSP Using a Multi-Agent System with Heuristic

Pal¹,

Mittal²,

Soni³

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Flexible Job-shop Scheduling Problem (FJSP), which closely represent contemporary manufacturing systems, is regarded as one of the most challenging and crucial scheduling problems. A job-shop scheduling problem that attempts to mimic real-life manufacturing is incomplete without consideration of machine related issues such as setting up and maintenance; and job related issues such as transportation of jobs between machines. Consideration of these factors for FJSP has gained attention in recent times. Majority of the papers, however, deals with only a few of these issues. The proposed work considers the FJSP with maintenance, setup and transportation time, termed as the complex FJSP. This work proposes a multi-agent based approach for solving the complex FJSP.

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“…Govi et al [8] considered the FJSP with the work center, which is a group of machines performing the same type of operations. Pal et al [9] studied the FJSP with preparation time and transportation time, and established a mathematical model by integrating these two kinds of adjustment time and processing time. The multi-agent system approach has also been found to converge faster than the comparison algorithms.…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-Objective Flexible Job Shop Scheduling Problem with Setup and Handling Based on an Improved Shuffled Frog Leaping Algorithm

Kong,

Yang

2024

Applied Sciences

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Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact on the scheduling plan. Furthermore, there is a trend for a cluster of machines with similar functionalities to form a work center. Considering the above constraints, a new order-driven multi-equipment work center FJSP model with setup and handling including multiple objectives encompassing the minimization of the makespan, the number of machine shutdowns, and the number of handling batches is established. An improved shuffled frog leading algorithm is designed to solve it through the optimization of the initial solution population, the improvement of evolutionary operations, and the incorporation of Pareto sorting. The algorithm also combines the speed calculation method in the gravity search algorithm to enhance the stability of the solution search. Some standard FJSP data benchmarks have been selected to evaluate the effectiveness of the algorithm, and the experimental results confirm the satisfactory performance of the proposed algorithm. Finally, a problem example is designed to demonstrate the algorithm’s capability to generate an excellent scheduling plan.

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A multi-agent system for FJSP with setup and transportation times

Cited by 16 publications

References 50 publications

Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm

Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm

Solving Complex FJSP Using a Multi-Agent System with Heuristic

Research on Multi-Objective Flexible Job Shop Scheduling Problem with Setup and Handling Based on an Improved Shuffled Frog Leaping Algorithm

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