An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems (original) (raw)
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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.
2020
This paper presents an effective memetic algorithm (MA) for a hybrid flow shop scheduling problem with multiprocessor tasks (HFSMT) to minimize the makespan. The problem is modeled as deterministic by a mixed graph. This problem has at least two production stages, each of which has several machines, operating in parallel. Two sub-problems are considered for solving this problem: determining the sequence of jobs in the first stage and reducing the idle time of the processors in the next stages. The developed algorithm uses an operator called Bad Selection Operator. This operator holds the worst chromosomes of each generation and uses them to search in the space of other problems at predetermined timescales. Besides, this algorithm uses a dynamic adjustment structure to improve the ratio of crossover and mutation operators that could reduce the execution time. The efficiency of the proposed MA is investigated by testing it on well-known benchmark instances and also compared with other...
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.
2014
The hybrid flow shop with parallel batching (HFSPB) is a kind of flow shop production system wherein some stages may be populated by parallel processors that simultaneously process groups of jobs. This paper addresses the makespan minimization problem for a HFSPB system whose machines are characterized by both capacity and eligibility restrictions. Firstly, a mixed integer linear programming model concerning the proposed problem is presented. Then, a specific genetic algorithm (GA) that makes use of a permutation encoding scheme as well as a crossover operator specifically designed for effectively managing the batch processing is discussed. The relevant parameters of the developed algorithm were calibrated by means of a full factorial design of experiments, and an extensive comparison campaign has been carried out with the aim to statistically assess the performance of the proposed GAwith respect to five alternative procedures, four of which arisen from the relevant literature. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the effectiveness of the proposed GAbased metaheuristics in tackling the HFSPB problem investigated, under both quality of solutions and computational burden viewpoints.
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 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.
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.
Multiprocessor task scheduling in multistage hybrid flow-shops: a genetic algorithm approach
Journal of the Operational Research …, 2004
This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The objective is to minimize the make-span, i.e. the completion time of all the tasks in the last stage. This problem is of practical interest in the textile and process industries. A genetic algorithm (GA) is developed to solve the problem. The GA is tested against a lower bound from the literature as well as against heuristic rules on a test bed comprised of 400 problems with up to 100 jobs, 10 stages, and with up to 5 processors on each stage. For small problems, solutions found by the GA are compared to optimal solutions, which are obtained by total enumeration. For larger problems, optimum solutions are estimated by a statistical prediction technique. Computational results show that the GA is both effective and efficient for the current problem. Test problems are provided in a web site at www.benchmark.ibu.edu.tr/mpt-hfsp
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.
SN Applied Sciences, 2020
Hybrid flow shop (HFS) scheduling problem is combining of the flow shop and parallel machine scheduling problem. Hybrid flow shop with multiprocessor task (HFSMT) scheduling problem is a special structure of the hybrid flow shop scheduling problem. The HFSMT scheduling is a well-known NP-hard problem. In this study, a new memetic algorithm which combined the global and local search methods is proposed to solve the HFSMT scheduling problems. The developed new memetic global and local search (MGLS) algorithm consists of four operators. These are natural selection, crossover, mutation and local search methods. A preliminary test is performed to set the best values of these developed new MGLS algorithm operators for solving HFSMT scheduling problem. The best values of the MGLS algorithm operators are determined by a full factorial experimental design. The proposed new MGLS algorithm is applied the 240 HFSMT scheduling instances from the literature. The performance of the generated new MGLS algorithm is compared with the genetic algorithm (GA), parallel greedy algorithm (PGA) and efficient genetic algorithm (EGA) which are used in the previous studies to solve the same set of HFSMT scheduling benchmark instances from the literature. The results show that the proposed new MGLS algorithm provides better makespan than the other algorithms for HFSMT scheduling instances. The developed new MGLS algorithm could be applicable to practical production environment.