Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm

Luan, Fei; Cai, Zongyan; Wu, Shuqiang; Liu, Shi Qiang; He, Yao

doi:10.3390/math7080688

Cited by 36 publications

(16 citation statements)

References 56 publications

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“…The LTMA was designed to be implemented easily and used in combination with different EA frameworks or in combination with standalone EAs. The focus in this implementation was on continuous optimization problems, but it can be adapted to discrete optimization problems such as job shop scheduling, the traveling salesman problem, or the knapsack problem [58,59].…”

Section: Methodsmentioning

confidence: 99%

Long Term Memory Assistance for Evolutionary Algorithms

et al. 2019

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Short term memory that records the current population has been an inherent component of Evolutionary Algorithms (EAs). As hardware technologies advance currently, inexpensive memory with massive capacities could become a performance boost to EAs. This paper introduces a Long Term Memory Assistance (LTMA) that records the entire search history of an evolutionary process. With LTMA, individuals already visited (i.e., duplicate solutions) do not need to be re-evaluated, and thus, resources originally designated to fitness evaluations could be reallocated to continue search space exploration or exploitation. Three sets of experiments were conducted to prove the superiority of LTMA. In the first experiment, it was shown that LTMA recorded at least 50 % more duplicate individuals than a short term memory. In the second experiment, ABC and jDElscop were applied to the CEC-2015 benchmark functions. By avoiding fitness re-evaluation, LTMA improved execution time of the most time consuming problems F 03 and F 05 between 7% and 28% and 7% and 16%, respectively. In the third experiment, a hard real-world problem for determining soil models’ parameters, LTMA improved execution time between 26% and 69%. Finally, LTMA was implemented under a generalized and extendable open source system, called EARS. Any EA researcher could apply LTMA to a variety of optimization problems and evolutionary algorithms, either existing or new ones, in a uniform way.

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Section: Methodsmentioning

confidence: 99%

Long Term Memory Assistance for Evolutionary Algorithms

et al. 2019

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“…Many other subtypes exist, many of them extending the basic types with different constraints and Objective Functions, for instance, machine blocking constraints [17]. Recent research even covers the so-called "low-carbon" JSSPs by pursuing the goal to minimize the sum of completion time cost and energy consumption cost [18].…”

Section: Related Work Jssp Classes and Typesmentioning

confidence: 99%

A JSSP solution for production planning optimization combining industrial engineering and evolutionary algorithms

Srsen

Mernik

2021

ComSIS

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A Job Shop Scheduling Problem (JSSP), where p processes and n jobs should be processed on m machines so that the total completion time is minimal, is a well-known problem with many industrial applications. Many researchers focus on the JSSP classification and algorithms that address the different JSSP classes. In this research work, the production times, a very well-known theme covered in Industrial Engineering (IE), are integrated into an Evolutionary Algorithm (EA) to present a solution for real-world manufacturing JSSP problems solving. Since a drawback of classical IE is a manual determination of the technological times, an Internet of Things (IoT) architecture is proposed as a possible solution.

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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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Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm

Cited by 36 publications

References 56 publications

Long Term Memory Assistance for Evolutionary Algorithms

Long Term Memory Assistance for Evolutionary Algorithms

A JSSP solution for production planning optimization combining industrial engineering and evolutionary algorithms

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

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