A New Heuristic Method for Solving Joint Job Shop Scheduling of Production and Maintenance (original) (raw)
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Applying Improved Genetic Algorithm for Solving Job Shop Scheduling Problems
Tehnicki Vjesnik-technical Gazette, 2017
The Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling combinatorial problems with considerable importance in industry. When solving complex problems, search based on traditional genetic algorithms has a major drawback - high requirement for computational power. The goal of this research was to develop fast and efficient scheduling method based on genetic algorithm for solving the job-shop scheduling problems. In proposed GA initial population is generated randomly, and the relevant crossover and mutation operation is also designed. This paper presents an efficient genetic algorithm for solving job-shop scheduling problems. Performance of the algorithm is demonstrated in the real-world examples.
Genetic Algorithm based on Some Heuristic Rules for Job Shop Scheduling Problem
Job-shop scheduling problem (JSSP) is one of the most difficult scheduling problems, as it is classified as NP-hard problem. In this paper, a hybrid approach based on a genetic algorithm and some heuristic rules for solving (JSSP) is presented. The scheduling heuristic rules are integrated into the process of genetic evolution. the algorithm is designed and tested for the scheduling process in two cases in which the first generation the initial population is either random generation or the results obtained of some active heuristics rules. To speed up the generation of heuristics rules, a weighted priority rules are used as heuristic rules for achieving better performances for generating feasible schedules. The results of the purposed hybrid algorithm of this paper are promising where these results are compared to benchmark problems results.
Improved genetic algorithm for the job-shop scheduling problem
The International Journal of Advanced Manufacturing Technology, 2006
In this paper, an improved genetic algorithm, called the hybrid Taguchi-genetic algorithm (HTGA), is proposed to solve the job-shop scheduling problem (JSP). The HTGA approach is a method of combining the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimal offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to systematically select the better genes to achieve crossover, and consequently enhance the genetic algorithm. Therefore, the proposed HTGA approach possesses the merits of global exploration and robustness. The proposed HTGA approach is effectively applied to solve the famous Fisher-Thompson benchmarks of 10 jobs to 10 machines and 20 jobs to 5 machines for the JSP. In these studied problems, there are numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed GA-based approaches. The computational experiments show that the proposed HTGA approach can obtain both better and more robust results than other GA-based methods reported recently.
Genetic Algorithm for Job Shop Scheduling Problem: A Case Study
The job-shop scheduling (JSS) is a schedule planning for low volume systems with many variations in requirements. In job-shop scheduling problem (JSSP), there are k operations and n jobs to be processed on m machines with a certain objective function to be minimized. Due to complexity of transferring work in process product, this research add transfer time variable from one machine to another for each different operation. Performance measures are mean flow time and make span. In this paper we used genetic algorithm (GA) with some modifications to deal with problem of job shop scheduling. The result than is compared with dispatching rules such as longest processing time, shortest processing time and first come first serve. The numerical example showed that GA result can outperform the other three methods.
Solving job-shop scheduling problems by genetic algorithm
Proceedings of IEEE International Conference on Systems, Man and Cybernetics
Job-shop Scheduling Problem (JSP) is one of extremely hard problems because it requires very large combinatorial search space and the precedence constraint between machines. The traditional algorithm used t o solve the problem is the branch-and-bound method, which takes considerable computing time when the size of problem is large. W e propose a new method for solving JSP using Genetic Algorithm (G A) and demonstrate its efficiency by the standard benchmark of job-shop scheduling problems. Some important points of G A are how t o represent the schedules as an individuals and t o design the genetic operators for the representation in order t o produce better results.
E3S Web of Conferences, 2021
This paper presents optimization of makespan for Flexible Job Shop Scheduling Problems (FJSSP) using an Improved Genetic Algorithm integrated with Rules (IGAR). Machine assignment is done by Genetic Algorithm (GA) and operation selection is done using priority rules. Improvements in GA include a new technique of adaptive probabilities and a new forced mutation technique that positively ensures the generation of new chromosome. The scheduling part also proposed an improved scheduling rule in addition to four standard rules. The algorithm is tested against two well-known benchmark data set and results are compared with various algorithms. Comparison shows that IGAR finds known global optima in most of the cases and produces improved results as compared to other algorithms.
A new hybrid genetic algorithm for job shop scheduling problem
International Journal of Advanced Intelligence Paradigms, 2020
Job shop scheduling problem is an NP-hard problem. This paper proposes a new hybrid genetic algorithm to solve the problem in an appropriate way. In this paper, a new selection criterion to tackle premature convergence problem is introduced. To make full use of the problem itself, a new crossover based on the machines is designed. Furthermore, a new local search is designed which can improve the local search ability of proposed GA. This new approach is run on the some problems and computer simulation shows the effectiveness of the proposed approach.
Research Journal of Applied Sciences, Engineering and Technology, 2015
In this study, a metaheuristic based on the Non-dominated Sorting Genetic Algorithm type II (NSGA-II) is proposed to solve the Multi-Criterions Job Shop Scheduling Problem (MCJSSP) under resources availability constraints. Availability periods and starting time of maintenance activities are supposed to be flexible. The MCJSSP requires, simultaneous minimization several antagonistic criteria, such as the maximum completion time of all jobs (Makespan), production cost and maintenance cost. To validate the proposed approach we tested it on fortyfour instances references. The results show that our approach is experimentally promising to solve practical problems.
A hybrid genetic algorithm for the job shop scheduling problems
Computers & Industrial Engineering, 2003
This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.