An efficient evolutionary grey wolf optimizer for multi-objective flexible job shop scheduling problem with hierarchical job precedence constraints

Zhu, Zhenwei; Zhou, Xionghui

doi:10.1016/j.cie.2020.106280

Cited by 78 publications

(23 citation statements)

References 50 publications

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“…Other manufacture related objectives are also considered in the previous work, such as production cost (Wang et al, 2018a), total energy cost (TEC) (Mokhtari and Hasani, 2017;Meng et al, 2019;Wang et al, 2018b), energy efficiency (Tang et al, 2016), and workload (Zhu and Zhou, 2020). Energy related objectives have attracted increasing attention recently because of the trend of green manufacturing and sustainable manufacturing (Gahm et al, 2016).…”

Section: Different Objectives In Fjssmentioning

confidence: 99%

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

Mei

Wang

et al. 2021

Evolutionary Computation

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Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e. the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this paper, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and more accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multi-objective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.

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Section: Different Objectives In Fjssmentioning

confidence: 99%

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

Mei

Wang

et al. 2021

Evolutionary Computation

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“…the processing time of each process has been determined, and each job must be processed in the order of the process, the task of scheduling is to arrange the processing scheduling sequence of all jobs, so that the performance indicators are optimized under the premise of satisfying the constraints [68]. A common mathematical description of the n/m/Cmax (where Cmax is the maximum completion time, called makespan [69]) scheduling problem is as follows [70]:…”

Section: The Jssp Modelmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems

Gao

Wang

et al. 2020

Mathematics

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Job shop scheduling problem (JSSP) has high theoretical and practical significance in academia and manufacturing respectively. Therefore, scholars in many different fields have been attracted to study this problem, and many meta-heuristic algorithms have been proposed to solve this problem. As a meta-heuristic algorithm, particle swarm optimization (PSO) has been used to optimize many practical problems in industrial manufacturing. This paper proposes a hybrid PSO enhanced with nonlinear inertia weight and and Gaussian mutation (NGPSO) to solve JSSP. Nonlinear inertia weight improves local search capabilities of PSO, while Gaussian mutation strategy improves the global search ability of NGPSO, which is beneficial to the population to maintain diversity and reduce probability of the algorithm falling into the local optimal solution. The proposed NGPSO algorithm is implemented to solve 62 benchmark instances of JSSP, and the experimental results are compared with other algorithms. The results obtained by analyzing the experimental data show that the algorithm is better than other comparison algorithms in solving JSSP.

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“…Zhenwei Zhu and Xionghui Zhou [9] proposed the grey wolf optimizer for solving the multi-objective FJSP. The grey wolf optimizer algorithm is inspired from the social hierarchy and the hunting mechanism of grey wolves in nature.…”

Section: Introductionmentioning

confidence: 99%

The hybrid firefly algorithm with the fuzzy movement method for solving a complex scheduling problem

Phu-ang

2021

ECTI-CIT

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This paper proposes a hybrid algorithm that applied the firefly algorithm (FA) with the new idea labeled as the fuzzy movement method for solving a complex scheduling problem called the flexible job shop scheduling. The step of the proposed algorithm is similar to the original FA, which is based on the concept of flashing behavior to attract the other fireflies. In order to improve the efficiency of the FA algorithm for the FJSP, the proposed algorithm introduces three new ideas. In the first idea, the genetic algorithm (GA) is used to generate the high quality of the initial population. Next, the self-adaptive roulette wheel selection which embedded in the GA introduced to increase the diversity of the machine selection process. Finally, the fuzzy movement method is presented to enhance the work balancing ability between the high workload machines and the low workload machines. The proposed algorithm has been evaluated with a benchmark data set and compared to the other algorithm. The experimental results demonstrate that the proposed algorithm can effectively solve the flexible job shop scheduling problem.

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An efficient evolutionary grey wolf optimizer for multi-objective flexible job shop scheduling problem with hierarchical job precedence constraints

Cited by 78 publications

References 50 publications

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems

The hybrid firefly algorithm with the fuzzy movement method for solving a complex scheduling problem

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