Genetic Algorithms for Shop Scheduling Problems

Computers & Industrial Engineering, 2016

Job shop scheduling has been the focus of a substantial amount of research over the last decade and most of these approaches are formulated and designed to address the static job shop scheduling problem. Dynamic events such as random job arrivals, machine breakdowns and changes in processing time, which are inevitable occurrences in production environment, are ignored in static job shop scheduling problem. As dynamic job shop scheduling problem is known NP-hard combinatorial optimization, this paper introduces efficient hybrid Genetic Algorithm (GA) methodologies for minimizing makespan in this kind of problem. Various benchmark problems including the number of jobs, the number of machines, and different dynamic events are generated and detailed numerical experiments are carried out to evaluate the performance of proposed methodologies. The numerical results indicate that the proposed methods produce superior solutions for well-known benchmark problems compared to those reported in the literature.

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.

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 .

International Journal of Quality and Innovation, 2017

This paper presents a genetic algorithm for a job shop scheduling problem with parallel machines. The objective is to minimise the makespan. After solving an example, the performance of the proposed algorithm was examined on a set of test problems. The computational test was performed with moderate benchmark instances given in the literature. Now, many industrial work centres are shifting their focus towards implementation of job shop scheduling model. Large throughput and just-in-time restrictions have augmented the requirement of additional parallel machines at various production stages. This generates the scope for research on optimising job shop problem with parallel machines. However, research on this topic is very limited as compared to other job shop problems. A genetic algorithm has been proposed in this research work which effectively finds the near optimal makespan schedules for the job shop problem with parallel machines. The algorithm was implemented on the modified benchmarks from the literature and the results were compared with heuristics methods available in literature for solving this problem. It is shown that the proposed GA performs reasonably well when compared to the other techniques under consideration.

2007 International Conference on Computer Engineering & Systems, 2007

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 .

International Journal of Production Management and Engineering, 2016

This paper discusses the application of heuristic-based evolutionary technique in search for solutions concerning the dynamic job-shop scheduling problems with dependent setup times and alternate routes. With a combinatorial nature, these problems belong to an NP-hard class, with an aggravated condition when in realistic, dynamic and therefore, more complex cases than the traditional static ones. The proposed genetic algorithm executes two important functions: choose the routes using dispatching rules when forming each individual from a defined set of available machines and, also make the scheduling for each of these individuals created. The chromosome codifies a route, or the selected machines, and also an order to process the operations. In essence , each individual needs to be decoded by the scheduler to evaluate its time of completion, so the fitness function of the genetic algorithm, applying the modified Giffler and Thomson’s algorithm, obtains a scheduling of the selected rou...

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.

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.

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.

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.

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.

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.

Sadhana, 1997

Scheduling is a key factor for manufacturing productivity. Effective scheduling can improve on-time delivery, reduce inventory, cut lead times, and improve the utilization of bottleneck resources. Because of the combinatorial nature of scheduling problems, it is often difficult to find optimal schedules, especially within a limited amount of computation time. Production schedules therefore are usually generated by using heuristics in practice. However, it is very difficult to evaluate the quality of these schedules, and the consistency of performance may also be an issue.

Natural Intelligence for …, 2009

Journal of Optimization, 2016

Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications. The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex andNP-hard problems. This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan. In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules. In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation. A machine based precedence preserving order-based crossover was proposed to generate the ...