Buletin GASA-JOSH: A Hybrid Evolutionary-Annealing Approach for Job-Shop Scheduling Problem (original) (raw)

An Efficient Approach to Job Shop Scheduling Problem using Simulated Annealing

The Job-Shop Scheduling Problem (JSSP) is a well-known and one of the challenging combinatorial optimization problems and falls in the NP-complete problem class. This paper presents an algorithm based on integrating Genetic Algorithms and Simulated Annealing methods to solve the Job Shop Scheduling problem. The procedure is an approximation algorithm for the optimization problem i.e. obtaining the minimum makespan in a job shop. The proposed algorithm is based on Genetic algorithm and simulated annealing. SA is an iterative well known improvement to combinatorial optimization problems. The procedure considers the acceptance of cost-increasing solutions with a nonzero probability to overcome the local minima. The problem studied in this research paper moves around the allocation of different operation to the machine and sequencing of those operations under some specific sequence constraint.

A Hybrid Simulated Annealing for Job Shop Scheduling Problem

Int. J. Comb. Optim. Probl. Informatics, 2019

The Job Shop Scheduling Problem (JSSP) arises in the context of high-performance computing and belongs to the NP-hard combinatorial optimization problems. The purpose of JSSP is to find the order of execution of a set of jobs on a group of machines, subject to certain precedence and resource availability constraints. The objective in this problem is minimizing the makespan that is the time elapsed from the starting time of the first job until the completion time of the last job. In this paper, a novel hybrid algorithm named AntGenSA for solving JSSP is proposed. AntGenSA uses Ant Colony System (ACS), Simulated Annealing (SA), and Genetic Algorithm (GA). To assess the performance of this algorithm, it is executed in a parallel computer, using a set of instances proposed by Fisher-Thompson, Yamada-Nakano, Taillard, Lawrence, and Applegate-Cook. The evaluation of this algorithm was performed mainly by the quality of the solution but the execution time was measuring as well. The experim...

Hybrid Genetic Algorithm for Solving Job-Shop Scheduling Problem

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 ...

Hybrid algorithm for job-shop scheduling problem

Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527)

The Job Shop Scheduling Problem (JSSP) is regarded as one of the most challenging issues by the research community in this field due to its complexity. This paper presents a hybrid algorithm called H-PSO-SA for JSSP which is a mixture of two computational artificial intelligence algorithms: Particle Swarm Optimization and Simulated Annealing. In order to demonstrate efficiency of the proposed hybrid algorithm, a series of tests are conducted using a set of classical JSSP benchmarks. The schedule results are compared with outcomes well known in the scientific literature.

Job Shop Scheduling by Simulated Annealing

Operations Research, 1992

We describe an approximation algorithm for the problem of finding the minimum makespan in a job shop. The algorithm is based on simulated annealing, a generalization of the well known iterative improvement approach to combinatorial optimization problems. The generalization involves the acceptance of cost-increasing transitions with a nonzero probability to avoid getting stuck in local minima. We prove that our algorithm asymptotically converges in probability to a globally minimal solution, despite the fact that the Markov chains generated by the algorithm are generally not irreducible. Computational experiments show that our algorithm can find shorter makespans than two recent approximation approaches that are more tailored to the job shop scheduling problem. This is, however, at the cost of large running times.

A HYBRID GENETIC ALGORITHM FOR JOB SHOP SCHEDULING PROBLEMS

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.

A Genetic Algorithm with Priority Rules for Solving Job-Shop Scheduling Problems

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.

Job Shop Scheduling Using Modified Simulated Annealing Algorithm

Timely and cost factor is increasingly important in today’s global competitive market. The key problem faced by today’s industries are feasible allocation of various jobs to available resources i.e., machines (Scheduling) and optimal utilization of the available resources. Among the various problems in scheduling, the job shop scheduling is the most complicated and requires a large computational effort to solve it. A typical job shop scheduling problem has a set of jobs to be processed in a set of machines, with certain constraints and objective function to be achieved. The most commonly considered objectives are the minimization of make span, minimization of tardiness which leads to minimization of penalty cost, and to maximize machine utilization. Machine shop scheduling can be done using various techniques like standard dispatching rules, heuristic techniques like Simulated annealing, Tabu Search, Genetic algorithm, etc,.here a typical job shop shop scheduling problem is solved using simulated annealing(SA) technique, a heuristic search algorithm. SA is generic neighbourhood search algorithm used to locate optimal solution very nearer to global optimal solution. A software based program is developed in VB platform for a typical job shop problem and test instances were performed over it. Experimental results obtained were further tuned by varying parameters and optimal results were obtained

A REVIEW ON NON TRADITIONAL ALGORITHMS FOR JOB SHOP SCHEDULING

iaeme

A great deal of research has been focused on solving the job-shop problem, over the last fifty years, resulting in a wide variety of approaches. Recently, much effort has been concentrated on hybrid methods to solve job shop scheduling problem. JSSP is stated as a NP Hard problem [36, 37] so that as a single technique cannot solve this stubborn problem. As a result much effort has recently been concentrated on techniques that combine the specific methods and a meta-strategy which guides the search out of local optima. These approaches currently provide the best results. Such hybrid techniques are known as iterated local search algorithms or meta-heuristics. In this paper we seek to assess the work done in the job-shop domain by providing a review of many of the techniques used. The impact of the major contributions is indicated by applying these techniques to a set of standard benchmark problems. It is established that methods such as Tabu Search, Genetic Algorithms, Simulated Annealing should be considered complementary rather than competitive. In addition this work suggests guide-lines on features that should be incorporated to create a good job shop scheduling system. Finally the possible direction for future work is highlighted so that current barriers within job shop scheduling problem may be surmounted as we approach the 21st Century.

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.