The Simulation Optimization for Job-Shop Scheduling Based on Plant Simulation Using Genetic Algorithm

2012 ◽  
Vol 217-219 ◽  
pp. 1444-1448
Author(s):  
Xiang Ke Tian ◽  
Jian Wang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems ◽  
Plant Simulation

The job-shop scheduling problem (JSP), which is one of the best-known machine scheduling problems, is among the hardest combinatorial optimization problems. In this paper, the key technology of building simulation model in Plant Simulation is researched and also the build-in genetic algorithm of optimizing module is used to optimize job-shop scheduling, which can assure the scientific decision. At last, an example is used to illustrate the optimization process of the Job-Shop scheduling problem with Plant Simulation genetic algorithm modules.

scholarly journals Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-32 ◽  
Author(s):  
Muhammad Kamal Amjad ◽  
Shahid Ikramullah Butt ◽  
Rubeena Kousar ◽  
Riaz Ahmad ◽  
Mujtaba Hassan Agha ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Future Research ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.

A modified biogeography-based optimization algorithm with improved mutation operator for job shop scheduling problem with time lags

2020 ◽  
Author(s):  
Madiha Harrabi ◽  
Olfa Belkahla Driss ◽  
Khaled Ghedira
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Time Lag ◽  
Initial Population ◽  
Time Lags ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems

Abstract This paper addresses the job shop scheduling problem including time lag constraints. This is an extension of the job shop scheduling problem with many applications in real production environments, where extra (minimum and maximum) delays can be introduced between successive operations of the same job. It belongs to a category of problems known as NP-hard problem due to large solution space. Biogeography-based optimization is an evolutionary algorithm which is inspired by the migration of species between habitats, recently proposed by Simon in 2008 to optimize hard combinatorial optimization problems. We propose a hybrid biogeography-based optimization (HBBO) algorithm for solving the job shop scheduling problem with additional time lag constraints with minimization of total completion time. In the proposed HBBO, the effective greedy constructive heuristic is adapted to generate the initial population of habitat. Moreover, a local search metaheuristic is investigated in the mutation step in order to ameliorate the solution quality and enhance the diversity of the population. To assess the performance of HBBO, a series of experiments on well-known benchmark instances for job shop scheduling problem with time lag constraints is performed.

The Hybrid Approach of LCO and SA to Solve Job-Shop Scheduling Problem

2012 ◽  
Author(s):  
Yasumasa Tamura ◽  
Ikuo Suzuki ◽  
Masahito Yamamoto ◽  
Masashi Furukawa
Keyword(s):  
Manufacturing System ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
Computational Time ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems

A Job-shop Scheduling Problem (JSP) is one of the combinatorial optimization problems. JSP appears as a basic scheduling problem in many situations of a manufacturing system and many methods for JSP have been invented. This study examines two effective methods, SA and LCO, for JSP and propose a hybrid method based on them. As a result of the experiments, the proposed method can find a good solution with short computational time. And the proposed method can hold the variance of the solutions less than the fundamental methods. Summarizing this study, the proposed method can find a good solution efficiently and stably.

scholarly journals Job Shop Scheduling Problem Optimization by Means of Graph-Based Algorithm

2021 ◽  
Vol 11 (4) ◽  
pp. 1921
Author(s):  
Jiri Stastny ◽  
Vladislav Skorpil ◽  
Zoltan Balogh ◽  
Richard Klein
Keyword(s):  
Genetic Algorithm ◽  
Operations Research ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
A Algorithm ◽  
Shop Scheduling

In this paper we introduce the draft of a new graph-based algorithm for optimization of scheduling problems. Our algorithm is based on the Generalized Lifelong Planning A* algorithm, which is usually used for path planning for mobile robots. It was tested on the Job Shop Scheduling Problem against a genetic algorithm’s classic implementation. The acquired results of these experiments were compared by each algorithm’s required time (to find the best solution) as well as makespan. The comparison of these results showed that the proposed algorithm exhibited a promising convergence rate toward an optimal solution. Job shop scheduling (or the job shop problem) is an optimization problem in informatics and operations research in which jobs are assigned to resources at particular times. The makespan is the total length of the schedule (when all jobs have finished processing). In most of the tested cases, our proposed algorithm managed to find a solution faster than the genetic algorithm; in five cases, the graph-based algorithm found a solution at the same time as the genetic algorithm. Our results also showed that the manner of priority calculation had a non-negligible impact on solutions, and that an appropriately chosen priority calculation could improve them.

SOLVING A MULTI-OBJECTIVE FLEXIBLE JOB SHOP SCHEDULING PROBLEM WITH TIMED PETRI NETS AND GENETIC ALGORITHM

2010 ◽  
Vol 02 (02) ◽  
pp. 221-237 ◽  
Author(s):  
HEJIAO HUANG ◽  
TAIPING LU
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Crossover And Mutation

The method presented in this paper is used to solve flexible job shop scheduling problem (JSP) with multiple objectives, which is much more complex than the classical JSP. Based on timed Petri net model, genetic algorithm is applied to solve the scheduling problems. The chromosomes are composed by sequences of transitions, the crossover and mutation operations are based on transition sequences. The experiment result shows that a definite solution to a specific flexible job shop scheduling problem can be found.

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