A dynamic material distribution scheduling of automotive assembly line considering material-handling errors

Zhou, Binghai; Wen, Mingwu

doi:10.1108/ec-03-2022-0129

Cited by 5 publications

(1 citation statement)

References 46 publications

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“…Part feeding optimization involves distributing the correct quantity of materials from the central warehouse to the assembly line at the right time (Suleiman et al. , 2022; Zhou and Wen, 2023). Failure to do so may lead to material shortages and result in the assembly line being shut down.…”

Section: Introductionmentioning

confidence: 99%

Iterated-local-search-based chaotic differential evolution algorithm for hybrid-load part feeding scheduling optimization in mixed-model assembly lines

Wang,

Hu,

Liu

2023

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PurposeDriven by sustainable production, mobile robots are introduced as a new clean-energy material handling tool for mixed-model assembly lines (MMALs), which reduces energy consumption and lineside inventory of workstations (LSI). Nevertheless, the previous part feeding scheduling method was designed for conventional material handling tools without considering the flexible spatial layout of the robotic mobile fulfillment system (RMFS). To fill this gap, this paper focuses on a greening mobile robot part feeding scheduling problem with Just-In-Time (JIT) considerations, where the layout and number of pods can be adjusted.Design/methodology/approachA novel hybrid-load pod (HL-pod) and mobile robot are proposed to carry out part feeding tasks between material supermarkets and assembly lines. A bi-objective mixed-integer programming model is formulated to minimize both total energy consumption and LSI, aligning with environmental and sustainable JIT goals. Due to the NP-hard nature of the proposed problem, a chaotic differential evolution algorithm for multi-objective optimization based on iterated local search (CDEMIL) algorithm is presented. The effectiveness of the proposed algorithm is verified by dealing with the HL-pod-based greening part feeding scheduling problem in different problem scales and compared to two benchmark algorithms. Managerial insights analyses are conducted to implement the HL-pod strategy.FindingsThe CDEMIL algorithm's ability to produce Pareto fronts for different problem scales confirms its effectiveness and feasibility. Computational results show that the proposed algorithm outperforms the other two compared algorithms regarding solution quality and convergence speed. Additionally, the results indicate that the HL-pod performs better than adopting a single type of pod.Originality/valueThis study proposes an innovative solution to the scheduling problem for efficient JIT part feeding using RMFS and HL-pods in automobile MMALs. It considers both the layout and number of pods, ensuring a sustainable and environmental-friendly approach to production.

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Section: Introductionmentioning

confidence: 99%

Iterated-local-search-based chaotic differential evolution algorithm for hybrid-load part feeding scheduling optimization in mixed-model assembly lines

Wang,

Hu,

Liu

2023

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A hyper-heuristic algorithm-based automatic monorail shuttle system for material feeding optimization in mixed-model assembly lines

Zhou,

Zhao

2023

Soft Comput

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Research on Multi-objective Optimization Model of Power Storage Materials Based on NSGA-II Algorithm

Hu,

Liu,

Yang

et al. 2024

Int J Comput Intell Syst

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Aiming at the problems of slow convergence speed and low precision probability of multi-objective optimization of energy storage materials, a multi-objective optimization model of energy storage materials based on NSGA-II algorithm was proposed. The association rule set of storage materials in the joint supply chain operation performance management system is extracted, and the rough vector feature distribution set multi-objective optimization method is used to decompose and optimize the characteristics of storage materials in the joint supply chain operation performance management system. Using NSGA-II optimization analysis method, this paper summarizes the power storage materials under the joint supply chain operation performance management system, and summarizes three kinds of inventory control: periodic inventory, inventory coding, and computerized inventory. Combined with the positive regression learning method of organizational operational performance, the multi-objective optimization decision of electric storage materials under the joint supply chain operational performance management system is realized. The simulation results show that under the joint supply chain operation performance management system, the proposed method reaches the optimal convergence after 65 iterations, the convergence speed is fast, and the accuracy probability reaches 1.000 after 80 iterations, which solves the problems of slow convergence speed and low accuracy probability, and has a good scheduling ability of energy storage materials.

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A dynamic material distribution scheduling of automotive assembly line considering material-handling errors

Cited by 5 publications

References 46 publications

Iterated-local-search-based chaotic differential evolution algorithm for hybrid-load part feeding scheduling optimization in mixed-model assembly lines

Iterated-local-search-based chaotic differential evolution algorithm for hybrid-load part feeding scheduling optimization in mixed-model assembly lines

A hyper-heuristic algorithm-based automatic monorail shuttle system for material feeding optimization in mixed-model assembly lines

Research on Multi-objective Optimization Model of Power Storage Materials Based on NSGA-II Algorithm

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