Metaheuristic for Solving Multi-Objective Job Shop Scheduling Problem in a Robotic Cell

Li, Xiaohui; Yang, Xi; Zhao, Yi; Teng, Ying; Yuan, Dong

doi:10.1109/access.2020.3015796

Cited by 9 publications

(2 citation statements)

References 35 publications

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“…This study aimed to investigate largescale problems by the Genetic Algorithm approach. Li et al (2020) developed a multi-objective disassembly planning problem with job-shop cells to minimize earliest and tardiness. Also, they solved their model with a meta-heuristic called Teaching Learning Based Optimization algorithm.…”

Section: Disassembly and Scheduling Problemsmentioning

confidence: 99%

An integrated multi-stage vehicle routing and mixed-model job-shop-type robotic disassembly sequence scheduling problem for e-waste management system

Malekkhouyan¹,

Aghsami

Rabbani

2021

International Journal of Computer Integrated Manufacturing

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The rapid development of technology has reduced the longevity of electronic products; this can be led to increase generation of electronic and electrical waste worldwide. The protection of the environment and the optimum use of non-renewable resources should be our priority in today's world. Therefore, e-waste management involves two crucial processes of the collection and recovery of e-waste that ought to be considered in an e-waste management. Thus, this paper presents a new integrated multi-stage model for a comprehensive e-waste management system in three levels of vehicle routing, disassembly sequence scheduling, and allocation of vehicles for transporting components. This model minimizes three objectives of total costs of collocation and transportation, total pollution emissions by trucks and vehicles, and carbon footprint by robots, and the total cost of disassembling products simultaneously. Moreover, our study is novel in some aspects such as integrated decisions related to vehicle routing and disassembly sequence scheduling, compatible truck for collecting e-waste, mixed-model disassembly by robots, and job-shoptype disassembly layout. Afterwards, several numerical examples are presented in different scales to examine the model's efficiency. An exact and meta-heuristic method are applied to solve the model. Finally, by changing some parameters, the useful sensitivity analysis is presented.

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Section: Disassembly and Scheduling Problemsmentioning

confidence: 99%

An integrated multi-stage vehicle routing and mixed-model job-shop-type robotic disassembly sequence scheduling problem for e-waste management system

Malekkhouyan¹,

Aghsami

Rabbani

2021

International Journal of Computer Integrated Manufacturing

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“…Fu et al [32] addressed a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan and the total tardiness. Li et al [33] proposed a mixed integer programming model and an improved multi-objective teaching learning-based optimization algorithm to minimize the makespan and total earliness & tardiness in job shop robotic cell scheduling problem. Yazdani et al [10] applied a new hybrid imperialist competitive algorithm(HICA) to the job shop scheduling problem with a single objective of minimizing the maximum earliness and tardiness.…”

Section: Literature Reviewmentioning

confidence: 99%

Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

Wei

Quan

et al. 2021

IEEE Access

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In recent years, people have paid more and more attention to traditional manufacturing's environmental impact, especially in terms of energy consumption and related emissions of carbon dioxide. Except for adopting new equipment, production scheduling could play an important role in reducing the total energy consumption of a manufacturing plant. Machine tools waste a considerable amount of energy because of their underutilization. Consequently, energy saving can be achieved by switching machines to standby or off when they lay idle for a comparatively long period. Herein, we first introduce the objectives of minimizing non-processing energy consumption, total weighted tardiness and earliness, and makespan into a typical production scheduling model-the job shop scheduling problem, based on a machine status switching framework. The multi-objective genetic algorithm U-NSGA-III combined with MME (a heuristic algorithm combined with the MinMax (MM) and Nawaz-Enscore-Ham (NEH) algorithms) population initialization method is used to solve the problem. The multi-objective optimization algorithm can generate a Pareto set of solutions so that production managers can flexibly select a schedule from these non-dominated schedules based on their priorities. Three sets of numerical experiments have been carried out on the extended Taillard benchmark to verify this three-objective model's effectiveness and the multi-objective optimization algorithm. The results show that U-NSGA-III has obtained better Pareto solutions in most test problem instances than NSGA-II and NSGA-III. Furthermore, the non-processing energy consumption is reduced by 46%-69%, which is 13-83% of the total energy consumption.INDEX TERMS Job shop scheduling, energy efficiency, unified multi-objective genetic algorithm, machine status switching.

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Cascaded Scheduling for Highly Autonomous Production Cells with AGVs

Miller

Kaupp

Schmitt

2023

Lecture Notes in Mechanical Engineering

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Highly autonomous production cells are a crucial part of manufacturing systems in industry 4.0 and can contribute to a sustainable value-adding process. To realize a high degree of autonomy in production cells with an industrial robot and a machine tool, an experimental approach was carried out to deal with numerous challenges on various automation levels. One crucial aspect is the scheduling problem of tasks for each resource (machine tool, tools, robot, AGV) depending on various data needed for a job-shop scheduling algorithm. The findings show that the necessary data has to be derived from different automation levels in a company: horizontally from ERP to shop-floor, vertically from the order handling department to the maintenance department. Utilizing that data, the contribution provides a cascaded scheduling approach for machine tool jobs as well as CNC and robot tasks for highly autonomous production cells supplied by AGVs.

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Metaheuristic for Solving Multi-Objective Job Shop Scheduling Problem in a Robotic Cell

Cited by 9 publications

References 35 publications

An integrated multi-stage vehicle routing and mixed-model job-shop-type robotic disassembly sequence scheduling problem for e-waste management system

An integrated multi-stage vehicle routing and mixed-model job-shop-type robotic disassembly sequence scheduling problem for e-waste management system

Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

Cascaded Scheduling for Highly Autonomous Production Cells with AGVs

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