A novel genetic algorithm for the hybrid flow shop scheduling with parallel batching and eligibility constraints (original) (raw)
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An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems
The hybrid flow shop scheduling with multiprocessor task (HFSMT) problem is a substantial production scheduling problem for minimizing the makespan, and there exist many difficulties in solving large scale HFSMT problems which include many jobs, machines and tasks. The HFSMT problems known as NP-hard and the proposal of an efficient genetic algorithm (GA) were taken into consideration in this study. The numerical results prove that the computational performance of a GA depends on the factors of initial solution, reproduction, crossover, and mutation operators and probabilities. The implementation details, including a new mutation operator, were described and a full factorial experimental design was determined with our GA program by using the best values of the control parameters and the operators. After a comparison was made with the studies of Oguz [1], Oguz and Ercan [2] and Kahraman et al. related to the HFSMT problems, the computational results indicated that the proposed genetic algorithm approach is very effective in terms of reduced total completion time or makespan (C max ) for the attempted problems.
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.
2015
This article considers a makespan minimization batch sizing and scheduling problem in a flexible flow shop scheduling problem with unrelated parallel machines and sequence dependent setup time. Because of NP-completeness of this problem, it is necessary to use the heuristics method. Therefore, this article presents a new mixed simulated-genetic algorithm (MSGA) to tackle this problem. In the comparison, this research reports optimality gaps which are calculated with respect to MSGA method and optimal solution for small instances and the average objective function for large instances. Computational studies indicate that the MSGA is computationally efficient and effective even for small and large instances.
This paper deals with the development and analysis of hybrid genetic algorithms for flow shop scheduling problems with sequence dependent setup time. A constructive heuristic called setup ranking algorithm is used for generating the initial population for genetic algorithm. Different variations of genetic algorithm are developed by using combinations of types of initial populations and types of crossover operators. For the purpose of experimentation, 27 group problems are generated with ten instances in each group for flow shop scheduling problems with sequence dependent setup time. An existing constructive algorithm is used for comparing the performance of the algorithms. A full factorial experiment is carried out on the problem instances developed. The best settings of genetic algorithm parameters are identified for each of the groups of problems. The analysis reveals the superior performance of hybrid genetic algorithms for all the problem groups.
World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 2010
This study compares three meta heuristics to minimize makespan (Cmax) for Hybrid Flow Shop (HFS) Scheduling Problem with Parallel Machines. This problem is known to be NP-Hard. This study proposes three algorithms among improvement heuristic searches which are: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). SA and TS are known as deterministic improvement heuristic search. GA is known as stochastic improvement heuristic search. A comprehensive comparison from these three improvement heuristic searches is presented. The results for the experiments conducted show that TS is effective and efficient to solve HFS scheduling problems. Keywords—Flow Shop, Genetic Algorithm, Simulated Annealing, Tabu Search.
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.
Journal of Optimization in Industrial Engineering, 2013
This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. The objective is to minimize the weighted sum of makespan and maximum tardiness. Three meta-heuristic methods based on genetic algorithm (GA), imperialist competitive algorithm (ICA) and a hybrid approach of GA and ICA are proposed to solve the generated problems. The performances of algorithms are evaluated by computational time and Relative Percentage Deviation (RPD) factors. The results indicate that ICA solves the problems faster than other algorithms and the hybrid algorithm produced best solution based on RPD.
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...
Solving flow shop scheduling problem using a parallel genetic algorithm
Procedia Technology, 2012
The effort of searching an optimal solution for scheduling problems is important for real-world industrial applications especially for mission-time critical systems. In this paper, a parallel GA is employed to solve flow shop scheduling problems to minimize the makespan.According to our experimental results, the proposed parallel genetic algorithm (PPGA) considerably decreases the CPU time without adversely affecting the makespan.
This paper discusses the application of Robust Hybrid Genetic Algorithm to solve a flow-shop scheduling problem. The proposed algorithm attempted to reach minimum makespan. PT. FSCM Manufacturing Indonesia Plant 4's case was used as a test case to evaluate the performance of the proposed algorithm. The proposed algorithm was compared to Ant Colony, Genetic-Tabu, Hybrid Genetic Algorithm, and the company's algorithm. We found that Robust Hybrid Genetic produces statistically better result than the company's, but the same as Ant Colony, Genetic-Tabu, and Hybrid Genetic. In addition, Robust Hybrid Genetic Algorithm required less computational time than Hybrid Genetic Algorithm