Two stage reentrant hybrid flow shop with setup times and the criterion of minimizing makespan (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.
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.
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.
The journal of Mathematic and Computer Science
In This paper a two stages Hybrid Flow Shop (HFS) problem with sequence dependent set up times is considered in which the preemption is also allowed. The objective is to minimize the weighted sum of completion time and maximum tardiness. Since this problem is categorized as an NP-hard one, meta-heuristic algorithms can be utilized to obtain high quality solutions in a reasonable amount of time. In this paper a Genetic algorithm (GA) approach is used and for parameter tuning the Response Surface Method (RSM) is applied to increase the performance of the algorithm. Computational results show the high performance of the proposed algorithm to solve the generated problems.
Hybrid Genetic Algorithms for Solving Reentrant Flow-shop Scheduling with Time Windows
The semiconductor industry has grown rapidly, and subsequently production planning problems have raised many important research issues. The reentrant flow-shop (RFS) scheduling problem with time windows constraint for harddisk devices (HDD) manufacturing is one such problem of the expanded semiconductor industry. The RFS scheduling problem with the objective of minimizing the makespan of jobs is considered. Meeting this objective is directly related to maximizing the system throughput which is the most important of HDD industry requirements. Moreover, most manufacturing systems have to handle the quality of semiconductor material. The time windows constraint in the manufacturing system must then be considered. In this paper, we propose a hybrid genetic algorithm (HGA) for improving chromosomes/offspring by checking and repairing time window constraint and improving offspring by leftshift routines as a local search algorithm to solve effectively the RFS scheduling problem with time windows constraint. Numerical experiments on several problems show that the proposed HGA approach has higher search capability to improve quality of solutions.
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...
The hybrid flow shop scheduling problem
2010
The scheduling of flow shops with multiple parallel machines per stage, usually referred to as the Hybrid Flow Shop (HFS), is a complex combinatorial problem encountered in many real world applications. Given its importance and complexity, the HFS problem has been intensively studied. This paper presents a literature review on exact, heuristic and metaheuristic methods that have been proposed for its solution. The paper discusses several variants of the HFS problem, each in turn considering different assumptions, constraints and objective functions. Research opportunities in HFS are also discussed.
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.
Invited Review The hybrid flow shop scheduling problem
The scheduling of flow shops with multiple parallel machines per stage, usually referred to as the hybrid flow shop (HFS), is a complex combinatorial problem encountered in many real world applications. Given its importance and complexity, the HFS problem has been intensively studied. This paper presents a literature review on exact, heuristic and metaheuristic methods that have been proposed for its solution. The paper briefly discusses and reviews several variants of the HFS problem, each in turn considering different assumptions, constraints and objective functions. Research opportunities in HFS are also discussed.
SSRN Electronic Journal
The hybrid flowshop problem is prevalent in manufacturing plants with high volume production. Its a flowshop problem in which at least one of the stages has multiple machines operating in parallel, one of which must be used to process the job at that stage. The problem may even get more difficult if constraints and interesting machine layout are introduced. In this paper, a hybrid flowshop problem with unrelated parallel machines, sequence dependent setup times and machines eligibility constraints through a prominent machine bottleneck is considered. This is an NP-hard problem. The solution space is explored by using NEH to obtain the initial solution sequence followed by improvement meta-heuristics such as simulated annealing, genetic algorithm, and particle swarm optimisation algorithms. The NEH heuristic and the improvement meta-heuristics make use of a hyper-heuristic to further optimise the job allocation on parallel machines within a stage. From the simulated results, particle swarm optimisation seemed to perform the best on small scale problems, but was later outperformed by the genetic algorithm on larger problems. Lastly, the hyper-heuristic was also shown to be effective in constructing a efficient schedule.