Solving multi-objective Modified Distributed Parallel Machine and Assembly Scheduling Problem (MDPMASP) with eligibility constraints using metaheuristics

Amallynda, Ikhlasul; Santosa, Budi

doi:10.1080/21693277.2022.2070559

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…Constraint (14) indicates that each part can only be processed by one machine and can only be processed once. Constraint (15) imposes that each machine needs to process parts.…”

Section: Optimization Of Kittingmentioning

confidence: 99%

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Multi-Objective Parallel Machine Scheduling with Eligibility Constraints for the Kitting of Metal Structural Parts

Zhu

et al. 2022

Machines

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This paper studied a class of coupling problems of material assignment, part nesting, kit delivery and parallel machine scheduling. The aim of this paper was to solve the scheduling problem of metal structural part processing and welding assembly with eligibility constraints. A two-stage mixed-integer programming model was constructed. The eligibility constraints took into account the material type of parts and nesting. The objectives were to minimize the makespan, maximize material utilization and minimize the kit delivery metrics (kitting time and numbers of earliness and tardiness of kits). A hierarchical optimization approach was proposed. The scheduling model was solved by using the Gurobi solver in the first stage, and the results were used to constrain the second stage. The second stage of the scheduling model was solved using an improved multi-objective genetic algorithm. Due to the strong coupling relationships among the sorting of parts, the sorting of each profile and the sorting of each material, a hybrid encoding and decoding mode was designed for part sorting with eligibility constraints. Finally, the proposed scheduling approach was applied to actual production cases. The data showed that when the number of components exceeded 300 (the number of parts was about 1500), the material utilization reached 95%. Choosing a suitable number of machines, machine utilization reached 90%. The results demonstrated the effectiveness of the proposed scheduling model and algorithm.

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“…Constraint (14) indicates that each part can only be processed by one machine and can only be processed once. Constraint (15) imposes that each machine needs to process parts.…”

Section: Optimization Of Kittingmentioning

confidence: 99%

“…A product could be assembled in the assembly stage only after all the jobs in the production stage were completed. The objectives were to minimize the mean flow time and the number of tardy jobs [14]. The above studies addressed the PMSP of production and assembly or delivery with different optimization objectives.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Parallel Machine Scheduling with Eligibility Constraints for the Kitting of Metal Structural Parts

Zhu

et al. 2022

Machines

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“…Over the past two decades, multi-objective optimization (MO) has aroused increasing research interest due to its widespread real-world applications, such as energy dispatch [1], [2], job assembly [3] and controller optimization [4]. However, the conflicts among different objectives lead the issue of simultaneously gaining global optimum to each objective to be challenging or even impossible [5].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Optimization Approach Based on an Enhanced Particle Swarm Optimization Algorithm With Evolutionary Game Theory

Yin

Tang

et al. 2023

IEEE Access

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Due to conflicts among objectives of multi-objective optimization (MO) problems, it remains challenging to gain high-quality Pareto fronts for different MO issues. Attempt to handle this challenge and obtain high-performance Pareto fronts, this paper proposes a novel MO optimizer via leveraging particle swarm optimization (PSO) with evolutionary game theory (EGT). Firstly, a modified self-adaptive PSO (MSAPSO) adopting a novel self-adaptive parameter adaption rule determined by the evolutionary strategy of EGT to tune the three key parameters of each particle is proposed in order to well balance the exploration and exploitation abilities of MSAPSO. Then, a parameter selection principle is provided to sufficiently guarantee convergence of MSAPSO followed after the analytical convergence investigation of this optimizer so as to assure convergence of the searched Pareto front toward the true Pareto front as far as possible. Subsequently, a MSAPSO-based MO optimizer is developed, in which an external archive is applied to preserve the searched non-dominated solutions and a circular sorting method is amalgamated with the elitistsaving method to update the external archive. Lastly, the performance of the proposed method is examined by 16 benchmark test functions against 4 well-known MOO methods. The simulation results reveal that the proposed method dominates its peers regarding the quality of the Pareto fronts for most of the studied benchmarks. Furthermore, the results of the non-parametric analysis confirm that the proposed method significantly outperforms its contenders at the confidential level of 95% over the 16 benchmarks.

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Research on assembly scheduling problem with nested operations

Hao

Zhu

Shen

et al. 2023

Computers & Industrial Engineering

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Solving multi-objective Modified Distributed Parallel Machine and Assembly Scheduling Problem (MDPMASP) with eligibility constraints using metaheuristics

Cited by 6 publications

References 35 publications

Multi-Objective Parallel Machine Scheduling with Eligibility Constraints for the Kitting of Metal Structural Parts

Multi-Objective Parallel Machine Scheduling with Eligibility Constraints for the Kitting of Metal Structural Parts

A Multi-Objective Optimization Approach Based on an Enhanced Particle Swarm Optimization Algorithm With Evolutionary Game Theory

Research on assembly scheduling problem with nested operations

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