Solving job-shop scheduling problems by genetic algorithm

University of Ulster at …, 2000

Most of the GA approaches for job shop scheduling problem (JSSP) represent a solution by a chromosome containing the sequence of all the operations and decode the chromosome to a real schedule from the first gene to the last gene. There are two common problems for this kind of GAs, namely, high redundancy at the tail of the chromosome and little significance of rear genes on the overall schedule quality. GAoperators (e.g. the 1-point, 2-point crossover, and some mutation operators, etc.) applied on the real part of the chromosome (only involving the change of the real part of a chromosome) are less likely to create good offsprings, i.e., most likely a waste of evolution (time). In this paper, we propose a genetic algorithm with an incomplete representation (the number of genes is less than the number of operations) and apply it to the JSSPs. In our approach, the most important and the largest part of a schedule is decoded from a chromosome and the rest of the schedule is completed by a simple heuristic rule.

Computers & Operations Research, 2008

In this paper, we present a genetic algorithm for the Flexible Job-shop Scheduling Problem (FJSP). The algorithm integrates different strategies for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. Computational result shows that the integration of more strategies in a genetic framework leads to better results, with respect to other genetic algorithms. Moreover, results are quite comparable to those obtained by the best-known algorithm, based on tabu search. These two results, together with the flexibility of genetic paradigm, prove that genetic algorithms are effective for solving FJSP. ᭧ Scheduling of operations is one of the most critical issues in the planning and managing of manufacturing processes. To find the best schedule can be very easy or very difficult, depending on the shop environment, the process constraints and the performance indicator [1]. One of the most difficult problems in this area is the Job-shop Scheduling Problem (JSP), where a set of jobs must be processed on a set of machines, each job is formed by a sequence of consecutive operations, each operation requires exactly one machine, machines are continuously available and can process one operation at a time without interruption. The decision concerns how to sequence the operations on the machines, such as a given performance indicator is optimized. A typical performance indicator for JSP is the makespan, i.e., the time needed to complete all the jobs. JSP is a well-known NP-hard problem .

Serials Publications, 2011

A Job-Shop Scheduling is a process-organized manufacturing facility. Its main characteristics are that a great diversity of jobs is performed. A Job-Shop produces goods (parts) and these parts have one or more alternatives process plans. Each process plan consists of a sequence of a operations and these operations require resources and have certain (predefined) duration on machines. The Job-Shop Scheduling is a problem of planning and organization of a set of tasks to be performed on a set of resources with variable performance. In this paper, two approaches Jobs Sequencing List Oriented Genetic Algorithm and Operations machines Coding Oriented Genetic Algorithm have been implemented and compared for solution of the Job-Shop scheduling problem. Each approach has its own coding, evaluation function, crossovers and mutations applicable in Job-Shop scheduling problem to minimize the makespan, the workload of the most loaded machine and the total workload of the machines. Jobs Sequencing List Oriented Genetic Algorithm has been found to be the best out of two approaches to minimize the objectives.

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.

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.

European Journal of Operational Research, 2005

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.

2007

Abstract—The Job-Shop Scheduling Problem (JSSP) is a well-known difficult combinatorial optimization problem. Many algorithms have been proposed for solving JSSP in the last few decades, including algorithms based on evolutionary techniques. However, there is room for improvement in ...

time 0 2 4 6 8 1 0 1 2 ,, ,, ,, J 3 Figure 1: A Gantt-Chart representation of a solution for a 3 × 3 problem a set of disjunctive arcs representing pairs of operations that must be performed on the same machines. The processing time for each operation is the weighted value attached to the corresponding nodes. shows this in a graph representation for the problem given in .

Job Shop Scheduling Problem (JSSP) is an optimization problem in which ideal jobs are assigned to resources at particular times. In recent years many attempts have been made at the solution of this problem using a various range of tools and techniques. This paper presents hybrid genetic algorithm (HGA) for JSSP. The hybrid algorithm is a combination between genetic algorithm (GA) and local search. Firstly, a new initialization method is proposed. A modified crossover and mutation operators are used. Secondly, local search based on the neighborhood structure is applied in the GA result. Finally, the approach is tested on a set of standard instances taken from the literature. The computation results have validated the effectiveness of the proposed algorithm.

Natural Intelligence for …, 2009

The present work aims to develop a genetic algorithm-based approach to solve the scheduling optimization problem in the Job Shop manufacturing environment. A new encoding scheme for chromosome representation has been developed for this purpose that denotes a priority sequence of operations, from which a schedule can be generated if the precedence constraints are known. The successful implementation of the proposed encoding scheme has been presented and its performance has been compared with the existing operation-based scheme found in literatures across different test cases by varying the number of jobs and machines in the shop floor.

Computers & Operations Research, 1995

Scope and Purpoee-Job shop scheduling is a strongly NP-hard problem of combinatorial optimization and one of the most well-known machine scheduling problems. Scope of this paper is to present some improvements obtained in dealing with this problem using a heuristic technique based on Genetic Algorithms.

2008

The primary objective of this research is to solve the job-shop scheduling problems by minimizing the makespan. In this paper, we first developed a genetic algorithm (GA) for solving JSSPs, and then improved the algorithm by integrating it with three priority rules. The performance of the developed algorithm was tested by solving 40 benchmark problems and comparing their results with that of a number of well-known algorithms. For convenience of implementation, we developed a decision support system (DSS). In the DSS, we built a graphical user interface (GUI) for user friendly data inputs, model choices, and output generation. An overview of the DSS and the analysis of experimental results are provided.

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.