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

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 desi...

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Vydáno v:Engineering computations Ročník 40; číslo 9/10; s. 2693 - 2729
Hlavní autoři: Wang, Zhu, Hu, Hongtao, Liu, Tianyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bradford Emerald Publishing Limited 05.12.2023
Emerald Group Publishing Limited
Témata:
Algorithms
Assembly lines
Automation
Automobiles
Clean energy
Clean technology
Design
Distribution costs
Effectiveness
Efficiency
Energy consumption
Evolutionary algorithms
Evolutionary computation
Integer programming
Layouts
Literature reviews
Manufacturing
Materials handling
Mixed integer
Multiple objective analysis
Optimization
Pareto optimization
Robots
Scheduling
Work stations
Workstations
Mixed-model assembly lines
Greening scheduling
Mobile robot
Part feeding
ISSN:0264-4401, 1758-7077
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Abstract 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.
AbstractList 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.
Author Wang, Zhu
Liu, Tianyu
Hu, Hongtao
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  email: liuty@shmtu.edu.cn
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Keywords Mixed-model assembly lines
Greening scheduling
Mobile robot
Part feeding
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Snippet PurposeDriven by sustainable production, mobile robots are introduced as a new clean-energy material handling tool for mixed-model assembly lines (MMALs),...
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SubjectTerms Algorithms
Assembly lines
Automation
Automobiles
Clean energy
Clean technology
Design
Distribution costs
Effectiveness
Efficiency
Energy consumption
Evolutionary algorithms
Evolutionary computation
Integer programming
Layouts
Literature reviews
Manufacturing
Materials handling
Mixed integer
Multiple objective analysis
Optimization
Pareto optimization
Robots
Scheduling
Work stations
Workstations
Title Iterated-local-search-based chaotic differential evolution algorithm for hybrid-load part feeding scheduling optimization in mixed-model assembly lines
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