A Co-evolutionary Algorithm for Open-shop Scheduling with Disassembly Operations

Tanimizu, Yoshitaka; Sakamoto, Michisuke; Nonomiya, Hideyuki

doi:10.1016/j.procir.2017.03.138

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…As the table shows, the research on the multi-objective OSSP is still in its infancy. GA Minimize makespan [8] GA Minimize makespan [9] GA Minimize makespan [10] GA Minimize makespan [11] GA Minimize makespan [12] GA Minimize makespan [14] DE Minimize makespan [15] CEA Minimize makespan [17] MA Minimize makespan [18] MA Minimize makespan, total delay [19] MOEA Minimize makespan, , total machine workload [20] MOEA Minimize total flow time, minimize workload of people and machines [21] MOEA Minimize makespan and human error, maximize machine availability…”

Section: Evolution Algorithm For Solving Osspmentioning

confidence: 99%

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Research Survey on Open Shop Scheduling Strategies Based on Intelligent Algorithms

2022

Proceedings of WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications

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Open shop scheduling problem (OSSP) is one of the fundamental scheduling problems. Many scenarios such as vehicle maintenance, the medical system, and food processing can be modeled as OSSP in production and life. Therefore, this problem has essential application value and research significance. With the rapid development of artificial intelligence, the research of intelligent algorithms has increased explosively. Genetic algorithms, swarm intelligence algorithms, and artificial neural networks are several important intelligent algorithms that can solve OSSP. This paper summarizes the OSSP based on intelligent algorithms. Firstly, it briefly introduces the intelligent algorithm and OSSP. Then, the research on evolutionary algorithms and swarm intelligence algorithms to solve the OSSP is classified and summarized. Finally, the current algorithms for solving the problem are analyzed and discussed, the problems not solved by the existing algorithms are proposed. Furthermore, the development direction of intelligent algorithms for OSSP has been prospected.

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Section: Evolution Algorithm For Solving Osspmentioning

confidence: 99%

“…In the actual production scenario, customers often return goods. In view of this situation, Tanimizu [15] studied the OSSP including disassembly and post-processing operations, and proposed a co-evolutionary algorithm. However, the algorithm also takes too long in the face of large-scale problems.…”

Section: Emerging Evolutionary Algorithmmentioning

confidence: 99%

Research Survey on Open Shop Scheduling Strategies Based on Intelligent Algorithms

2022

Proceedings of WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications

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“…Xia et al (2016) introduced a multi-objective disassembly sequence scheduling electronic waste to meet objectives concerning maximizing the index of diminished toxicity, recovery value, and recovery weighted. Tanimizu, Sakamoto, and Nonomiya (2017) developed a planning method for open-shop sequence planning problem considering disassembly and reprocessing operations. The paper of Fang et al (2019) provided optimization of disassembly balancing and sequencing with robotic workstations.…”

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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“…They used the general dense scheduling algorithm (GDSA) for solving problems in a larger scale aimed at accelerating convergence. Tanimizu et al [29] presented a scheduling method for solving the open workshop problems including disassembly and post-processing operations. A co-evolutionary algorithm was designed to improve the sequence of operations and product loading.…”

Section: Introductionmentioning

confidence: 99%

An improved bat optimization algorithm to solve the tasks scheduling problem in open shop

Shareh

Bargh

Hosseinabadi

et al. 2020

Neural Comput & Applic

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The open shop scheduling problem involves a set of activities that should be run on a limited set of machines. The purpose of scheduling open shops problem is to provide a timetable for implementation of the entire operation so that the total execution time is reduced. The tasks scheduling problem in open shops is important in many applications due to the arbitrariness of the processing sequence of each job and lack of a prioritization of its operations. This is an NP-hard problem and obtaining an optimal solution to this problem requires a high time complexity. Therefore, heuristic techniques are used to solve these problems. In this paper, we investigate the tasks scheduling problem in open shops using the Bat Algorithm (BA) based on ColReuse and substitution meta-heuristic functions. The heuristic functions are designed to increase the rate of convergence to the optimal solution. To evaluate the performance of the proposed algorithm, standard open shop benchmarks were used. The results obtained in each benchmark are compared with those of the previous methods. Finally, after analyzing the results, it was found that the proposed BA had a better performance and was able to generate the best solution in all cases.

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A Co-evolutionary Algorithm for Open-shop Scheduling with Disassembly Operations

Cited by 8 publications

References 8 publications

Research Survey on Open Shop Scheduling Strategies Based on Intelligent Algorithms

Research Survey on Open Shop Scheduling Strategies Based on Intelligent Algorithms

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

An improved bat optimization algorithm to solve the tasks scheduling problem in open shop

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