Stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling via fruit fly optimization algorithm

Fu, Yaping; Zhou, Meng Chu; Guo, Xiwang; Qi, Liang

doi:10.1016/j.jclepro.2020.123364

Cited by 57 publications

(15 citation statements)

References 60 publications

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“…is intractable to accurately know the information of EOL products beforehand as a result of their various usage processes in diverse customers [46][47][48][49][50]. Therefore, we need to cope with the noise from random disturbance.…”

Section: Proposed Methodsmentioning

confidence: 99%

An enhanced group teaching optimization algorithm for multi-product disassembly line balancing problems

Liang

Gao

et al. 2021

Complex Intell. Syst.

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Big data have been widely studied by numerous scholars and enterprises due to its great power in making highly reliable decisions for various complex systems. Remanufacturing systems have recently received much attention, because they play significant roles in end-of-life product recovery, environment protection and resource conservation. Disassembly is treated as a critical step in remanufacturing systems. In practice, it is difficult to know the accurate data of end-of-life products such as disassembly time because of their various usage processes, leading to the great difficulty of making effective and reliable decisions. Thus, it is necessary to model the disassembly process with stochastic programming method where the past collected data are fitted into stochastic distributions of parameters by applying big data technology. Additionally, designing and applying highly efficient intelligent optimization algorithms to handle a variety of complex problems in the disassembly process are urgently needed. To achieve the global optimization of disassembling multiple products simultaneously, this work studies a stochastic multi-product disassembly line balancing problem with maximal disassembly profit while meeting disassembly time requirements. Moreover, a chance-constrained programming model is correspondingly formulated, and then, an enhanced group teaching optimization algorithm incorporating a stochastic simulation method is developed by considering this model’s features. Via performing simulation experiments on real-life cases and comparing it with five popularly known approaches, we verify the excellent performance of the designed method in solving the studied problem.

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Section: Proposed Methodsmentioning

confidence: 99%

An enhanced group teaching optimization algorithm for multi-product disassembly line balancing problems

Liang

Gao

et al. 2021

Complex Intell. Syst.

Self Cite

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“…Also, they solved their model with a meta-heuristic called Teaching Learning Based Optimization algorithm. Fu et al (2021) presented a statistic multi-objective planning problem in three stages of disassembly, reprocessing, and reassembling simultaneously for end-of-life products. They used Fruit Fly Optimization to solve their problem.…”

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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“…Shi et al (2020) proposed an energy-aware remanufacturing scheduling method for the entire remanufacturing system with non-dedicated reprocessing lines. Fu et al (2021) focused on a stochastic multi-objective remanufacturing scheduling problem that integrates disassembly, reprocessing, and reassembly sub-systems.…”

Section: A Job Shop Scheduling Problemmentioning

confidence: 99%

A New Job Shop Scheduling Method for Remanufacturing Systems Using Extended Artificial Bee Colony Algorithm

et al. 2021

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With the emergence of the remanufacturing industry, scheduling problems related to remanufacturing systems have drawn considerable interest from scholars. There are usually two execution modes of remanufacturing for reprocessing components, i.e., replacement mode and repair mode. However, few studies have focused on the job shop scheduling problem that considers the trade-off between the two execution modes. Thus, a new job shop scheduling method with job families (JSS-JF) is proposed. It decomposes the scheduling problem into three sub-problems: (1) execution mode selection; (2) replacement job assignment and sequencing; and (3) repair machine assignment and operation sequencing. To solve the proposed JSS-JF model, an extended artificial bee colony algorithm with a new three-dimensional encoding scheme is presented to find a near-optimal solution of the proposed JSS-JF model. The crossover and mutation operators, local search, and elite replacement strategy are also integrated in the proposed algorithm. The experiments are conducted for verifying the practicality and effectiveness of the presented algorithm by comparing with six baseline algorithms.INDEX TERMS Job shop scheduling, job families, remanufacturing, execution mode, extended artificial bee colony algorithm.

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Stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling via fruit fly optimization algorithm

Cited by 57 publications

References 60 publications

An enhanced group teaching optimization algorithm for multi-product disassembly line balancing problems

An enhanced group teaching optimization algorithm for multi-product disassembly line balancing problems

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

A New Job Shop Scheduling Method for Remanufacturing Systems Using Extended Artificial Bee Colony Algorithm

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