Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm

Luo, Jia; Baz, Didier El; Xue, Rui; Hu, Jinglu

doi:10.1016/j.future.2020.02.019

Cited by 36 publications

(21 citation statements)

References 34 publications

(58 reference statements)

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“…A single solution form discovers the neighbors of a given initial solution. The rapid upgrade of Computing Processing Units (CPU) and Graphic Processing Units (GPU), plus the need for more hands to analyses data, attract both the evolutionary-and the mathematical model-based algorithms [31,32]. Hereby, both algorithms are capable of sharing information within multiple levels; the straightforward level as in mono-pool execution, and the plane level between the sub-populations-recognized as migration.…”

Section: Heuristicsmentioning

confidence: 99%

A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

Awad

Abd-Elaziz

2021

Processes

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In order to attain high manufacturing productivity, industry 4.0 merges all the available system and environment data that can empower the enabled-intelligent techniques. The use of data provokes the manufacturing self-awareness, reconfiguring the traditional manufacturing challenges. The current piece of research renders attention to new consideration in the Job Shop Scheduling (JSSP) based problems as a case study. In that field, a great number of previous research papers provided optimization solutions for JSSP, relying on heuristics based algorithms. The current study investigates the main elements of such algorithms to provide a concise anatomy and a review on the previous research papers. Going through the study, a new optimization scope is introduced relying on additional available data of a machine, by which the Flexible Job-Shop Scheduling Problem (FJSP) is converted to a dynamic machine state assignation problem. Deploying two-stages, the study utilizes a combination of discrete Particle Swarm Optimization (PSO) and a selection based algorithm followed by a modified local search algorithm to attain an optimized case solution. The selection based algorithm is imported to beat the ever-growing randomness combined with the increasing number of data-types.

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Section: Heuristicsmentioning

confidence: 99%

A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

Awad

Abd-Elaziz

2021

Processes

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“…As a solution, they developed a genetic programming hyper-heuristic with delayed routing (GPHH-DR) method for solving multi-objective DFJSS that optimizes the mean tardiness and energy efficiency simultaneously. Within this context and to deal with the new job arrival, [14] provided a dynamic energy aware job shop scheduling model which seeks a trade-off among the total tardiness, the energy cost and the disruption to the original schedule. An adequate renewed scheduling plan in a reasonable time, based on a parallel GA algorithm was presented.…”

Section: Flexible Job Shop Energy-efficient Schedulingmentioning

confidence: 99%

A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021

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The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

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“…break; (21) end if (22) end for (23) end while (24) Generate Chromosome X q ′ that corresponds to L q ′ (25) end function ALGORITHM 2: Chromosome repair. 8 Mathematical Problems in Engineering (6) Fitness evaluation.…”

Section: Basicmentioning

confidence: 99%

“…erefore, to solve the loop selection problem of multilevel nested loops, some heuristic methods can only be used to obtain approximate solutions, from which a satisfactory solution is then chosen. Compared with traditional heuristic methods, genetic algorithms (GAs) have a very strong search ability; they can find the global optimal solution of a problem with a high probability, and their inherent parallelism is more suitable for processing optimization problems [16][17][18][19][20][21][22][23]. erefore, a GA was adopted in this study to solve the loop selection problem.…”

Section: Introductionmentioning

confidence: 99%

Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm

Nie

Zhou

Huang

et al. 2021

Mathematical Problems in Engineering

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Loop selection for multilevel nested loops is a very difficult problem, for which solutions through the underlying hardware-based loop selection techniques and the traditional software-based static compilation techniques are ineffective. A genetic algorithm- (GA-) based method is proposed in this study to solve this problem. First, the formal specification and mathematical model of the loop selection problem are presented; then, the overall framework for the GA to solve the problem is designed based on the mathematical model; finally, we provide the chromosome representation method and fitness function calculation method, the initial population generation algorithm and chromosome improvement methods, the specific implementation methods of genetic operators (crossover, mutation, and selection), the offspring population generation method, and the GA stopping criterion during the GA operation process. Experimental tests with the SPEC2006 and NPB3.3.1 standard test sets were performed on the Sunway TaihuLight supercomputer. The test results indicated that the proposed method can achieve a speedup improvement that is superior to that by the current mainstream methods, which confirm the effectiveness of the proposed method. Solving the loop selection problem of multilevel nested loops is of great practical significance for exploiting the parallelism of general scientific computing programs and for giving full play to the performance of multicore processors.

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Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm

Cited by 36 publications

References 34 publications

A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm

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