Application of an Efficient Genetic Algorithm for Solving n× í µí²í µí² Flow Shop Scheduling Problem Comparing it with Branch and Bound Algorithm and Tabu Search Algorithm (original) (raw)
Related papers
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 Flow Shop Scheduling with Assembly Operations to Minimize Makespan
Journal Of The Institution Of Engineers (india): Series C, 2014
Manufacturing systems, in which, several parts are processed through machining workstations and later assembled to form final products, is common. Though scheduling of such problems are solved using heuristics, available solution approaches can provide solution for only moderate sized problems due to large computation time required. In this work, scheduling approach is developed for such flow-shop manufacturing system having machining workstations followed by assembly workstations. The initial schedule is generated using Disjunctive method and genetic algorithm (GA) is applied further for generating schedule for large sized problems. GA is found to give near optimal solution based on the deviation of makespan from lower bound. The lower bound of makespan of such problem is estimated and percent deviation of makespan from lower bounds is used as a performance measure to evaluate the schedules. Computational experiments are conducted on problems developed using fractional factorial orthogonal array, varying the number of parts per product, number of products, and number of workstations (ranging upto 1,520 number of operations). A statistical analysis indicated the significance of all the three factors considered. It is concluded that GA method can obtain optimal makespan.
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.
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.
IAEME PUBLICATION, 2020
In this paper the authors investigate the problem of minimizing the Makespan using Heuristic approach. The author compared the result with the existing algorithms namely Palmer’s Heuristic(a slope order index), CDS Heuristic, NEH Heuristic algorithm, Gupta heuristic, RA (rapid access) Heuristic found in the literature and it was found that our algorithm perform superior than the other algorithms found in the literature
Journal of Industrial Engineering International, 2014
Flow-shop scheduling problem (FSP) deals with the scheduling of a set of n jobs that visit a set of m machines in the same order. As the FSP is NP-hard, there is no efficient algorithm to reach the optimal solution of the problem. To minimize the holding, delay and setup costs of large permutation flow-shop scheduling problems with sequence-dependent setup times on each machine, this paper develops a novel hybrid genetic algorithm (HGA) with three genetic operators. Proposed HGA applies a modified approach to generate a pool of initial solutions, and also uses an improved heuristic called the iterated swap procedure to improve the initial solutions. We consider the make-to-order production approach that some sequences between jobs are assumed as tabu based on maximum allowable setup cost. In addition, the results are compared to some recently developed heuristics and computational experimental results show that the proposed HGA performs very competitively with respect to accuracy and efficiency of solution.
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.
Minimizing makespan for a no-wait flowshop using genetic algorithm
Sadhana, 2012
This research paper addresses the scheduling problems with the primary objective of minimizing the makespan in a flow shop with 'N' jobs through 'M' machines. The EPDT (Heuristic approach) and BAT (Meta-Heuristic approach) heuristics are proposed to solve the flow shop scheduling problem in a modern manufacturing environment. These two algorithms are applied along with the Genetic Algorithm (GA) for the further improvement of results in achieving the minimal makespan. The performances of these newer heuristics are evaluated by solving the Taillard benchmark problems in MATLAB environment with various sizes of problems. The proposed GA applied EPDT heuristic and GA applied BAT meta-heuristic for the flow shop problems have been found very effective in solving scheduling problems and finding a better sequence which can reduce the makespan to a great extent. The improvement of EPDT and BAT were obtained by applying the GA yields superior results as well as these results also very close to upper bound than NEH results. The results of the heuristics are tested statistically by ANOVA and it shows that the GA applied heuristics gives a quality solution.
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...
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.