Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm

Jiang, Tianhua; Zhang, Chao; Zhu, Huiqi; Gu, Jiuchun; Deng, Guanlong

doi:10.3390/math6110220

Cited by 67 publications

(53 citation statements)

References 40 publications

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“…Whale optimization is a new swarm-based algorithm which mimics the hunting behavior of humpback whales in nature. It has also been successfully used to solve jobshop problems [22], as well flexible ones [23], with energy efficiency concerns [22,24]. We took particular interest in the particle swarm optimization method (PSO).…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Model and Evaluation Function for Conflict-Free Warranted Makespan Minimization of Mixed Blocking Constraint Job-Shop Problems

Sauvey

Trabelsi²,

Sauer

2020

Mathematics

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In this paper, we consider a job-shop scheduling problem with mixed blocking constraints. Contrary to most previous studies, where no blocking or only one type of blocking constraint was used among successive operations, we assume that, generally, we may address several different blocking constraints in the same scheduling problem depending on the intermediate storage among machines, the characteristics of the machines, the technical constraints, and even the jobs. Our objective was to schedule a set of jobs to minimize the makespan. Thus, we propose, for the first time, a mathematical model of the job-shop problem taking into account the general case of mixed blocking constraints, and the results were obtained using Mosel Xpress software. Then, after explaining why and how groups of jobs have to be processed, a blocking constraint conflict-free warranted evaluation function is proposed and tested with the particle swarm optimization and genetic algorithm methods. The results prove that we obtained a near-optimal solution to this problem in a very short time.

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Section: Literature Reviewmentioning

confidence: 99%

Mathematical Model and Evaluation Function for Conflict-Free Warranted Makespan Minimization of Mixed Blocking Constraint Job-Shop Problems

Sauvey

Trabelsi²,

Sauer

2020

Mathematics

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“…To test the effectiveness of the MMBO algorithm, it is compared with three existing algorithms, named the variable neighborhood structure (VNS) [19] and the improved whale optimization algorithm (IWOA) [38]. The VNS was developed to solve the DRCFJSP and can be directly employed to deal with the problem under study.…”

Section: Effectiveness Of the Proposed Mmbomentioning

confidence: 99%

Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem

Zhu

Jiang

2020

Algorithms

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In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware flexible job shop scheduling problem to reduce the total energy consumption in the workshop. For the considered problem, the energy consumption model is first built to formulate the energy consumption, such as processing energy consumption, idle energy consumption, setup energy consumption and common energy consumption. Then, a mathematical model is established with the criterion to minimize the total energy consumption. Secondly, a modified migrating birds optimization (MMBO) algorithm is proposed to solve the model. In the proposed MMBO, a population initialization scheme is presented to ensure the initial solutions with a certain quality and diversity. Five neighborhood structures are employed to create neighborhood solutions according to the characteristics of the problem. Furthermore, both a local search method and an aging-based re-initialization mechanism are developed to avoid premature convergence. Finally, the experimental results validate that the proposed algorithm is effective for the problem under study.

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“…In order to study the impact of the improved method (which is based on the probability density function of the Gaussian distribution mapping) on the optimization performance of the CGA, the ICGA was compared with CGA, bat algorithm (BA) [20] and whale optimization algorithm (WOA) [21]. These algorithms are the currently emerging intelligent optimization algorithms and are widely used in the field of optimization and scheduling [22][23][24]. In the BA, the number of individuals in the population NP = 30, pulse rate γ = 0.9, search pulse frequency range [F min , F max ] = [0, 2].…”

Section: Optimization Performance Testing On the Icgamentioning

confidence: 99%

An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer

et al. 2019

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Flow shop scheduling optimization is one important topic of applying artificial intelligence to modern bus manufacture. The scheduling method is essential for the production efficiency and thus the economic profit. In this paper, we investigate the scheduling problems in a flexible flow shop with setup times. Particularly, the practical constraints of the multi-queue limited buffer are considered in the proposed model. To solve the complex optimization problem, we propose an improved compact genetic algorithm (ICGA) with local dispatching rules. The global optimization adopts the ICGA, and the capability of the algorithm evaluation is improved by mapping the probability model of the compact genetic algorithm to a new one through the probability density function of the Gaussian distribution. In addition, multiple heuristic rules are used to guide the assignment process. Specifically, the rules include max queue buffer capacity remaining (MQBCR) and shortest setup time (SST), which can improve the local dispatching process for the multi-queue limited buffer. We evaluate our method through the real data from a bus manufacture production line. The results show that the proposed ICGA with local dispatching rules and is very efficient and outperforms other existing methods.

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Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm

Cited by 67 publications

References 40 publications

Mathematical Model and Evaluation Function for Conflict-Free Warranted Makespan Minimization of Mixed Blocking Constraint Job-Shop Problems

Mathematical Model and Evaluation Function for Conflict-Free Warranted Makespan Minimization of Mixed Blocking Constraint Job-Shop Problems

Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem

An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer

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