Flow Shop Scheduling Problem with Missing Operations: Genetic Algorithm and Tabu Search (original) (raw)
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American Scientific Research Journal for Engineering, Technology, and Sciences, 2018
Emergence of advance manufacturing systems such as CAD/CAM, FMS and CIM etc. has increased the importance of the flow shop scheduling. Flow shop scheduling problem is considered NP-hard for m machines and n jobs. In this paper, we develop an efficient genetic algorithm (EGA) for solving n flow shop scheduling problem with makespan as the criterion. The objective of this proposed EGA is to obtain a sequence of jobs and the minimization of the total completion time and waiting time. For finding optimal solution, this EGA is very effective. In large scale problems, the result of the proposed algorithm shows that the efficient genetic algorithm gives high performance comparing with Branch and Bound algorithm and Tabu search algorithm.
American Scientific Research Journal for Engineering, Technology and Science (ASRJETS), 2018
Emergence of advance manufacturing systems such as CAD/CAM, FMS and CIM etc. has increased the importance of the flow shop scheduling. Flow shop scheduling problem is considered NP-hard for m machines and n jobs. In this paper, we develop an efficient genetic algorithm (EGA) for solving n × í µí±í µí± flow shop scheduling problem with makespan as the criterion. The objective of this proposed EGA is to obtain a sequence of jobs and the minimization of the total completion time and waiting time. For finding optimal solution, this EGA is very effective. In large scale problems, the result of the proposed algorithm shows that the efficient genetic algorithm gives high performance comparing with Branch and Bound algorithm and Tabu search algorithm.
Bohr Publishers, 2024
Flow Shop Scheduling (FSS) Problems are examples of combinatorial optimization issues that are classified as NP-hard. Because of the NP-hard structure of FSS problems, it can be extremely challenging to find mathematical modeling methodologies that will result in an optimal solution for these problems. The Genetic Algorithm (GA), which is a metaheuristic approach, is one of the most important factors in the process of locating near-optimal answers to NP-hard optimization issues. In this research, a GA model for addressing an FSS problem was developed with the goal of lowering the overall weighted tardiness time and placing a constraint on the operation changeover time. When compared with the performance of the standard heuristics EDD, being used in the company under study, the GA model's performance was shown to be superior. Based on the findings, it can be shown that the objective value was cut by 43%, going from 215.95 (h) to 123.07 (h). This demonstrates that the GA model is an effective strategy for addressing FSS problems.
FLOW SHOP SCHEDULING USING GENETIC ALGORITHM: HISTORICAL REVIEW AND CATEGORIZATION OF PROCEDURES
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), will give different forms. The different forms of crossover and mutation process in GA method can be combined to give various GAs that can be impact on the quality of the solution. In this paper we present a comprehensive review of different GAs built up-to-date for solving the flow shop scheduling problems. Also, we show the suitable default GA parameters mentioned in literature at different problem size.
Tabu Search and Genetic Algorithm for Scheduling with Total Flow Time Minimization
Proceedings of the …, 2010
In this paper we confront the job shop scheduling problem with total flow time minimization. We start extending the disjunctive graph model used for makespan minimization to represent the version of the problem with total flow time minimization. Using this representation, we adapt local search neighborhood structures originally defined for makespan minimization. The proposed neighborhood structures are used in a genetic algorithm hybridized with a simple tabu search method, outperforming state-of-the-art methods in solving problem instances from several datasets.
Impact of Genetic Algorithm Parameters on its performance for Solving Flow Shop Scheduling Problem
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...
Improved heuristically guided genetic algorithm for the flow shop scheduling problem
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.
Impact of Genetic Algorithm Operators on Its Performance in Solving Flow Shop Scheduling Probl
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.
An application of effective genetic algorithms for Solving Hybrid Flow Shop Scheduling Problems
International Journal of Computational Intelligence Systems, 2008
This paper addresses the Hybrid Flow Shop (HFS) scheduling problems to minimize the makespan value. In recent years, much attention is given to heuristic and search techniques. Genetic algorithms (GAs) are also known as efficient heuristic and search techniques. This paper proposes an efficient genetic algorithm for hybrid flow shop scheduling problems. The proposed algorithm is tested by Carlier and Neron's (2000) benchmark problem from the literature. The computational results indicate that the proposed efficient genetic algorithm approach is effective in terms of reduced total completion time or makespan (C max) for HFS problems.
A novel approach for no-wait flow-shop scheduling problem with hybrid genetic algorithm (HGA
No-wait flowshop scheduling problem (NWFSSP) is a constrained for flowshop scheduling problem that exists widely in manufacturing field. Such problems are NP-hard and hence the optimal solutions are not guaranteed. In this paper, a hybrid genetic algorithm (HGA) methodology is proposed to solve the no-wait flowshop scheduling problem with the objective of minimizing the makespan, for a number of jobs to be processed on a number of machines using a spreadsheet based genetic algorithm. This technique hybridizes the genetic algorithm and a novel local search scheme to generate population of initial chromosomes and also improve the operators of genetic algorithm (GA). The present approach is compared with existing literature for the efficiency of this method which performs better working solutions. Additionally, it is also shown that any objective function can be minimized with the same model without changing the logic of GA routine.