A genetic algorithm for the job shop problem

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.

2011

Genetic algorithms are a very popular heuristic which have been successfully applied to many optimization problems within the last 30 years. In this chapter, we give a survey on some genetic algorithms for shop scheduling problems. In a shop scheduling problem, a set of jobs has to be processed on a set of machines such that a specific optimization criterion is satisfied. According to the restrictions on the technological routes of the jobs, we distinguish a flow shop (each job is characterized by the same technological route), a job shop (each job has a specific route) and an open shop (no technological route is imposed on the jobs). We also consider some extensions of shop scheduling problems such as hybrid or flexible shops (at each processing stage, we may have a set of parallel machines) or the inclusion of additional processing constraints such as controllable processing times, release times, setup times or the no-wait condition. After giving an introduction into basic genetic...

2013

Genetic algorithms are a very popular heuristic which have been suc-cessfully applied to many optimization problems within the last 30 years. In this chapter, we give a survey on some genetic algorithms for shop scheduling problems. In a shop scheduling problem, a set of jobs has to be processed on a set of machines such that a specific optimization crite-rion is satisfied. According to the restrictions on the technological routes of the jobs, we distinguish a flow shop (each job is characterized by the same technological route), a job shop (each job has a specific route) and an open shop (no technological route is imposed on the jobs). We also consider some extensions of shop scheduling problems such as hybrid or flexible shops (at each processing stage, we may have a set of parallel machines) or the inclusion of additional processing constraints such as controllable processing times, release times, setup times or the no-wait condition. After giving an introduction into basic genet...

2008

International Journal of Services and Operations Management, 2007

This paper deals with the problem of scheduling on makespan criterion in the flow shop environment. We have presented a new heuristic genetic algorithm (NGA) that combines the good features of both the genetic algorithms and heuristic search. The NGA is run on a large number of problems and its performance is compared with that of the Standard Genetic Algorithm (SGA) and the well-known Nawaz-Enscore-Ham (NEH) heuristic. The NGA is seen to perform better in almost all instances. The complexity of the NGA is found to be better than that of the SGA. The NGA also performs superior results when compared with the simulated annealing from the literature.

Studies in Computational Intelligence, 2009

The Job-Shop Scheduling Problem (JSSP) is one of the most difficult NPhard combinatorial optimization problems. In this chapter, we consider JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. First, we develop a genetic algorithm (GA) based approach for solving JSSPs. We then introduce a number of priority rules to improve the performance of GA, such as partial re-ordering, gap reduction, and restricted swapping. The addition of these rules results in a new hybrid GA algorithm that is clearly superior to other wellknown algorithms appearing in the literature. Results show that this new algorithm obtained optimal solutions for 27 out of 40 benchmark problems. It thus makes a significantly new contribution to the research into solving JSSPs.

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.

2002

This paper addresses an attempt to evolve genetic algorithms by a particular genetic programming method to make it able to solve the classical Job Shop Scheduling problem (JSSP), which is a type of very well known hard combinatorial optimisation problems. The aim is to look for a better GA such that solves JSSP with preferable scores. This looking up procedure is done by evolving GA with GP. First we solve a set of job shop scheduling benchmarks by using a conventional GA and then an association of GP to evolve a GA. The instance of JSSP tackled are available in OR literature.

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 ...

2001

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.

2006

Abstract This paper presents a novel memetic genetic algorithm (GA) for the flow shop scheduling problem by combining mutation-based local search with traditional genetic algorithm. The local search is based on the depth-first mutation-based searching process and the depth, ie, the number of total mutation within each generation is according to the number of jobs to be scheduled.

2015

The primary objective of flow shop scheduling is to obtain the best sequence which optimizes various objectives such as makespan, total flow time, total tardiness, or number of tardy jobs, etc. Due to the combinatorial nature of the flow shop problem (FSP) there is a lot of artificial intelligence methods proposed to solve it. The Genetic Algorithm (GA), one of these methods, is considered a valuable search algorithm capable of finding a reasonable solution in a short computational time. GAparameters, (population size, crossover probability and mutation probability) give different values that can be combined to give various GAs. In this paper we investigate the impact of the GA parameters (population size, crossover probability and mutation probability)on the quality of the GA solution in solving the flow shop scheduling problems. In this paperfourpopulation size (Ps), fivecrossover probability (Pc) andten mutation probability (Pm) are investigated. The computational results show th...

Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), 2008

In scheduling, the two machine flow shop problem F 2|| C i is to find a schedule that minimizes the sum of finishing times of an arbitrary number of jobs that need to be executed on two machines, such that each job must complete processing on machine 1 before starting on machine 2. Finding such a schedule is N P-hard [6]. We propose a heuristic for approximating the solution for the F 2|| C i problem using a genetic algorithm. We calibrate the algorithm using optimal results obtained by a branch-and-bound technique. Genetic algorithms simulate the survival of the fittest among individuals over consecutive generations for solving a problem. Prior work has shown that genetic algorithms generally do not perform well for shop problems [21]. However, the fact that in the case of two machines the search space can be restricted to permutations makes the construction of effective genetic operators more feasible.

Egyptian Journal for Engineering Sciences and Technology

The primary objective of flow shop scheduling is to obtain the best sequence which optimizes various objectives such as makespan, total flow time, total tardiness, or number of tardy jobs, etc. Due to the combinatorial nature of the flow shop problem (FSP) there is a lot of artificial intelligence methods proposed to solve it. The Genetic Algorithm (GA), one of these methods, is considered a valuable search algorithm capable of finding a reasonable solution in a short computational time. GA operators, (selection, crossover and mutation process), give different forms that can be combined to give various GAs. In this paper we investigate the impact of selection, crossover and mutation process on the quality of the GA solution in solving the flow shop scheduling problems. In this study, four selection methods, seventeen crossover methods and eight mutation methods are investigated. The computational results show that there are significant differences among the investigated methods on the performance of the proposed GA.