A Two‐Level Metaheuristic for the Job‐Shop Scheduling Problem with Multipurpose Machines

Pongchairerks, Pisut

doi:10.1155/2022/3487355

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…Peng et al [12] proposed a hybrid PSO algorithm to overcome the limitations of conventional PSO that cannot escape from local optima. Pongchairerks [13] established a two-level metaheuristic algorithm to solve the JSSP with multipurpose machines. Wang et al [14] used a hybrid gray wolf weed algorithm (GIWO) to obtain a minimal makespan by solving the flexible job shop scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

Choi¹,

Hwang²,

Kim³

2023

Computers, Materials &Amp; Continua

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A small and medium enterprises (SMEs) manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities. The optimal job shop scheduling is generated by utilizing the scheduling system of the platform, and a minimum production time, i.e., makespan decides whether the scheduling is optimal or not. This scheduling result allows manufacturers to achieve high productivity, energy savings, and customer satisfaction. Manufacturing in Industry 4.0 requires dynamic, uncertain, complex production environments, and customer-centered services. This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform. The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors. The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors: early delivery date and fulfillment of processing as many orders as possible. The genetic algorithm (GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem (JSSP) by comparing with the real-world data from a textile weaving factory in South Korea. The proposed platform will provide producers with an optimal production schedule, introduce new producers to buyers, and eventually foster relationships and mutual economic interests.

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Section: Introductionmentioning

confidence: 99%

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

Choi¹,

Hwang²,

Kim³

2023

Computers, Materials &Amp; Continua

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Quantum-inspired African vultures optimization algorithm with elite mutation strategy for production scheduling problems

Liang

Zhou

Luo

et al. 2023

Journal of Computational Design and Engineering

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The Production Scheduling (PS) problem is a challenging task that involves assigning manufacturing resources to jobs while ensuring that all constraints are satisfied. The key difficulty in PS is determining the appropriate order of operations. In this study, we propose a novel optimization algorithm called the Quantum-inspired African Vultures Optimization Algorithm with an Elite Mutation Strategy (QEMAVOA) to address this issue. QEMAVOA is an enhanced version of the African Vulture Optimization Algorithm (AVOA) that incorporates three new improvement strategies. Firstly, to enhance QEMAVOA's diversification ability, the population diversity is enriched by the introduction of Quantum Double-Chain Encoding (QDCE) in the initialization phase of QEMAVOA. Secondly, the implementation of the Quantum Rotating Gate (QRG) will balance QEMAVOA's diversification and exploitation capabilities, leading the vulture to a better solution. Finally, with the purpose of improving the exploitability of QEMAVOA, the Elite Mutation (EM) strategy is introduced. To evaluate the performance of QEMAVOA, we apply it to two benchmark scheduling problems: Flexible Job Shop Scheduling (FJSP) and Parallel Machine Scheduling (PMS). The results are compared to those of existing algorithms in the literature. The test results reveal that QEMAVOA surpasses comparison algorithms in accuracy, stability, and speed of convergence.

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A Probabilistic Hill-Climbing Algorithm for the Single-Source Transportation Problem

Pongchairerks

2023

Sustainability

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This paper proposes a probabilistic hill-climbing algorithm, called PH, for the single-source transportation problem (STP). PH is a tree search algorithm in which each node contains an assignment problem (AP) transformed from the STP being solved. The transformation converts each source’s product units into product lots; a product lot equals multiple product units. The AP aims to find the optimal assignment of product lots to destinations to minimize the total assignment cost. PH uses the Hungarian method to find the optimal solution of the AP in every node, which is a solution of the STP. For the AP of the root node (as the initial current node), the number of each source’s product lots is set to be small enough to guarantee the generation of a feasible solution for the STP. To generate every subsequent level, the current node is branched into multiple child nodes, in which the number of child nodes equals the number of sources in the STP. The AP of each child node is modified from the AP of the current node by adding one more product lot into a specific different source. Consequently, each child node provides a solution that is better than or the same as the current node’s solution; however, some child nodes’ solutions may be infeasible for the STP due to the insufficiency of a source’s capacity. If all of the child nodes cannot find a better feasible solution than the current node’s solution, PH stops its procedure. To diversify the search, PH selects one of the child nodes as the new current node in a probabilistic way, instead of always selecting the best child node. The experiment’s results in this paper reveal the performance of the three variants of PH.

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A Two‐Level Metaheuristic for the Job‐Shop Scheduling Problem with Multipurpose Machines

Cited by 3 publications

References 37 publications

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

Quantum-inspired African vultures optimization algorithm with elite mutation strategy for production scheduling problems

A Probabilistic Hill-Climbing Algorithm for the Single-Source Transportation Problem

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