Flexible job shop scheduling with overlapping in operations

In this paper, a new approach to overlapping operations in job shop scheduling is presented. In many job shops, a customer demand can be met in more than one way for each job, where demand determines the quantity of each finished job ordered by a customer. In each job, embedded operations can be performed due to overlapping considerations in which each operation may be overlapped with the others because of its nature. The effects of the new approach on job shop scheduling problems are evaluated. Since the problem is well known as NP-Hard class, a simulated annealing algorithm is developed to solve large scale problems. Moreover, a mixed integer linear programming (MILP) method is applied to validate the proposed algorithm. The approach is tested on a set of random data to evaluate and study the behavior of the proposed algorithm. Computational experiments confirmed superiority of the proposed approach. To evaluate the effect of overlapping considerations on the job shop scheduling p...

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.

To investigate the efficiency of a discretization procedure utilizing a time-indexed mathematical optimization model for finding accurate solutions to flexible job shop scheduling problems considering objectives comprising makespan and tardiness, respectively. Design/methodology/approach A time-indexed mixed integer programming model is used to find solutions by iteratively employing time steps of decreasing length. The solutions and computation times are compared with results from a known benchmark formulation and an alternative model. Findings The proposed method finds significantly better solutions for the largest instances within the same time frame. Both the other models are better choices for some smaller instances, which is expected since the new method is designed for larger problems. Only our alternative model is able to solve two of the largest instances when minimizing the tardiness. Research limitations/implications Interesting future research topics include the introduction of constraints representing other relevant entities such as the availability of tools and fixtures, and the scheduling of maintenance activities and personnel. Practical implications Real cases of flexible job shop problems typically yield very large models. Since the new procedure quickly finds solutions of good quality to such instances, our findings imply that the new procedure is beneficially utilized for scheduling real flexible job shops. Original/value We show that real flexible job shop problems can be solved through the solution of a series of carefully formulated discretized mathematical optimization models.

Mathematika, 2004

Kertas ini membincangkan dua penyelesaian untuk menyelesaikan masalah penjadualan bengkel kerja iaitu kaedah cabang dan batas serta kaedah simulasi penye\-puhlindapan. Objektifnya adalah untuk menjadualkan kerja-kerja kepada mesin-mesin supaya jumlah masa penyelesaian adalah minimum. Dalam kaedah cabang dan batas, masalah penjadualan bengkel kerja telah diwakili oleh graf disjungtif, selepas itu, jadual optima boleh didapati dengan menggunakan algoritma cabang dan batas manakala simulasi penyepuhlindapan merupakan algoritma pencarian tempatan yang akan membuat usikan kecil kepada penyelesaian awal tersaur untuk mengurangkan ``makespan.'' Katakunci: Penjadualan bengkel kerja; cabang dan batas; formulasi graf disjungtif; simulasi penyepuhlindapan. This paper presents two approaches to the solution of the job shop scheduling problem, namely the branch and bound, and simulated annealing approach. The objective is to schedule the jobs on the machines so that the total completion ...

2011

Flexibility in job shop scheduling is a main problem that has been considered by many of researchers in recent years.So Scheduling for the flexible job shop is very important in the field of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem in medium and actual size problems with traditional optimization approaches because of it's high computational complexity. In this paper a new mathematical model based on considering overlapping in operation in the flexible job shop scheduling problem(FJSP) is developed and a hybrid metaheuristic algorithm called memetic algorithm (MA) is proposed to solve this problem.Also the objective function that we consider is minimizing the makespan time. The Numerical experiments are used to evaluate the performance and efficiency of the proposed algorithm. Results show that the proposed algorithm is capable to achieve the optimal solution for small size problems and nea...

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

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...

The International Journal of Advanced Manufacturing Technology, 2014

This paper deals with the flexible job shop scheduling problem with the objective of minimizing the makespan. An efficient heuristic based on a constructive procedure is developed to obtain high-quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic can obtain effective solutions in very short and nearly zero time and is comparable with even metaheuristic algorithms and promising for practical problems.

An efficient heuristic algorithm for solving the Flexible Job Shop Scheduling Problem is presented and it is called artificial intelligence (A1) algorithm. AI is a new construction heuristic technique. It is mainly depending upon a new heuristic rule that has the capability for establishing feasible solutions. First Out First In, the new heuristic rule, is designed for minimizing the makespan of the Flexible Job Shop Scheduling Problem. The Flexible Job Shop Scheduling Problem is known as NP-hard combinatorial optimization problem that has long challenged researchers. Makespan is the maximum completion time of achieving all the jobs. Some problems from references are solved using the proposed algorithm and an implementation study is presented. The implementation study shows the efficiency of the proposed algorithm with respect to the tested problem.

Flexible job-shop scheduling is a type of scheduling which is extension of Job-shop scheduling problem. In FJSP, operations are processed on different machines, which means operations are break down to sublots, and these sublots are processed by machines independently. In previous research, mathematical model was developed along with implementation of Genetic Algorithm. This paper gives an overview of improved methods to Flexible Job-Shop scheduling Problem with overlapping in operation[1].