A novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem (original) (raw)
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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.
A Novel Hybrid Algorithm for Permutation Flow Shop Scheduling
In the present scenario the recent engineering and industrial built-up units are facing hodgepodge of problems in a lot of aspects such as machining time, electricity, man power, raw material and customer's constraints. The job-shop scheduling is one of the most significant industrial behaviours, particularly in manufacturing planning. This paper proposes the permutation flow shop sequencing problem with the objective of makespan minimization using the new modified proposed method of johnson's algorithm as well as the gupta's heuristic algorithm. This paper involves the determination of the order of processing of n jobs in m machines. Although since the problem is known to be np-hard for three or more machines, that produces near optimal solution of the given problem. The proposed method is very simple and easy to understand followed by a numerical illustration is given.
2015
Among typical production scheduling problems, job shop scheduling is one of the strong NP-complete combinatorial optimization problems. Using an enhanced genetic algorithm, an effective crossover operation for real coded job-based representation can be used to guarantee the feasibility of solutions, which are decoded into active schedules during the search process. This paper attempts to assign an optimum job sequence on a machine considering the objective of minimizing total makespan time in a flow shop environment based on the philosophy of Genetic Algorithms. The setup time or changeover time is taken as the main factor involved in the makespan time where setups are sequence dependent. For analyzing the results simulations are performed with various population sizes and crossover probabilities.
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.
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.
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.
A genetic algorithm for permutation flow shop scheduling under make to stock production system
Computers & Industrial Engineering, 2015
The permutation flow shop scheduling is a well-known combinatorial optimization problem that arises in many manufacturing systems. Over the last few decades, permutation flow shop problems have widely been studied and solved as a static problem. However, in many practical systems, permutation flow shop problems are not really static, but rather dynamic, where the challenge is to schedule n different products that must be produced on a permutation shop floor in a cyclical pattern. In this paper, we have considered a make-to-stock production system, where three related issues must be considered: the length of a production cycle, the batch size of each product, and the order of the products in each cycle. To deal with these tasks, we have proposed a genetic algorithm based lot scheduling approach with an objective of minimizing the sum of the setup and holding costs. The proposed algorithm has been tested using scenarios from a real-world sanitaryware production system, and the experimental results illustrates that the proposed algorithm can obtain better results in comparison to traditional reactive approaches.
Scheduling of Permutation Flow Shops Using a New Hybrid Algorithm
2018
In the current situation, modern engineering and industrial built-up units are encountering a jumble of issues in a variety of areas, including machining time, electricity, manpower, raw materials, and client restraints. One of the most important industrial behaviors, particularly in manufacturing planning, is job-shop scheduling. This study provides a new updated suggested approach of johnson's algorithm as well as the gupta's heuristic algorithm to solve the permutation flow shop sequencing problem with the goal of making the makespan as little as possible. This work is about determining the processing order of n tasks in m machines. Although, because the problem is np-hard for three or more computers, this results in a near-optimal solution to the given issue. The suggested approach is straightforward and easy to comprehend, and it is accompanied with a numerical example.
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.