Optimization of Total Holding Cost in Job Shop Scheduling by Using Hybrid Algorithm

2014 ◽  
Vol 591 ◽  
pp. 176-179
Author(s):  
S. Gobinath ◽  
C. Arumugam ◽  
G. Ramya ◽  
M. Chandrasekaran
Keyword(s):  
Hybrid Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems ◽  
Holding Cost ◽  
Hard Problems

The classical job-shop scheduling problem is one of the most difficult combinatorial optimization problems. Scheduling is defined as the art of assigning resources to tasks in order to insure the termination of these tasks in a reasonable amount of time. Job shop scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Mathematical and heuristic methods are the two major methods for resolving JSP. Job shop Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions. In this paper, a Hybrid algorithm combined artificial immune system and sheep flock heredity model algorithm is used for minimizing the total holding cost for different size benchmark problems. The results show that the proposed hybrid algorithm is an effective algorithm that gives better results than other hybrid algorithms compared in literature. The proposed hybrid algorithm is a good technique for scheduling problems.

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.

A PARTICLE SWARM OPTIMIZATION-BASED ALGORITHM FOR JOB-SHOP SCHEDULING PROBLEMS

2005 ◽  
Vol 02 (03) ◽  
pp. 419-430 ◽  
Author(s):  
H. W. GE ◽  
Y. C. LIANG ◽  
Y. ZHOU ◽  
X. C. GUO
Keyword(s):  
Particle Swarm Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Particle Swarm ◽  
Traditional Scheduling ◽  
Benchmark Problems ◽  
Scheduling Problems ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Novel Concept

A novel particle swarm optimization (PSO)-based algorithm is developed for job-shop scheduling problems (JSSP), which are the most general and difficult issues in traditional scheduling problems. Our goal is to develop an efficient algorithm based on swarm intelligence for the JSSP. Thereafter a novel concept for the distance and velocity of particles in the PSO is proposed and introduced to pave the way for the JSSP. The proposed algorithm effectively exploits the capabilities of distributed and parallel computing systems, with simulation results showing the possibilities of high quality solutions for typical benchmark problems.

scholarly journals Extended Hybrid Genetic Algorithm for Solving Job Shop Scheduling Problem

2021 ◽  
Vol 10 (02) ◽  
pp. 017-020
Author(s):  
Sumaia E. Eshim ◽  
Mohammed M. Hamed
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Solution Quality ◽  
Shop Scheduling

In this paper, a hybrid genetic algorithm (HGA) to solve the job shop scheduling problem (JSSP) to minimize the makespan is presented. In the HGA, heuristic rules are integrated with genetic algorithm (GA) to improve the solution quality. The purpose of this research is to investigate from the convergence of a hybrid algorithm in achieving a good solution for new benchmark problems with different sizes. The results are compared with other approaches. Computational results show that a hybrid algorithm is capable to achieve good solution for different size problems.

Using the Memetic Algorithm for Multi Objective Job Shop Scheduling Problems

2012 ◽  
Vol 544 ◽  
pp. 245-250 ◽  
Author(s):  
Guo Hui Zhang
Keyword(s):  
Search Strategy ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Shop Scheduling ◽  
Multi Objective

The multi objective job shop scheduling problem is well known as one of the most complex optimization problems due to its very large search space and many constraint between machines and jobs. In this paper, an evolutionary approach of the memetic algorithm is used to solve the multi objective job shop scheduling problems. Memetic algorithm is a hybrid evolutionary algorithm that combines the global search strategy and local search strategy. The objectives of minimizing makespan and mean flow time are considered while satisfying a number of hard constraints. The computational results demonstrate the proposed MA is significantly superior to the other reported approaches in the literature.

scholarly journals A Hybrid Artificial Bee Colony Algorithm for Flexible Job Shop Scheduling Problems

2011 ◽  
Vol 6 (2) ◽  
pp. 286 ◽  
Author(s):  
Jun-qing Li ◽  
Sheng-xian Xie ◽  
Quan-ke Pan ◽  
Song Wang
Keyword(s):  
Hybrid Algorithm ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Food Sources ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Bee Colony

<p>In this paper, we propose a hybrid Pareto-based artificial bee colony (HABC) algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each food sources is represented by two vectors, i.e., the machine assignment vector and the operation scheduling vector. The artificial bee is divided into three groups, namely, employed bees, onlookers, and scouts bees. Furthermore, an external Pareto archive set is introduced to record non-dominated solutions found so far. To balance the exploration and exploitation capability of the algorithm, the scout bees in the hybrid algorithm are divided into two parts. The scout bees in one part perform randomly search in the predefined region while each scout bee in another part randomly select one non-dominated solution from the Pareto archive set. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.</p>

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.

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