Comparison of Three Meta Heuristics to Optimize Hybrid Flow Shop Scheduling Problem with Parallel Machines (original) (raw)
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Metaheuristic methods in hybrid flow shop scheduling problem
Memetic algorithms are hybrid evolutionary algorithms that combine global and local search by using an evolutionary algorithm to perform exploration while the local search method performs exploitation. This paper presents two hybrid heuristic algorithms that combine particle swarm optimization (PSO) with simulated annealing (SA) and tabu search (TS), respectively. The hybrid algorithms were applied on the hybrid flow shop scheduling problem. Experimental results reveal that these memetic techniques can effectively produce improved solutions over conventional methods with faster convergence.
A novel metaheuristic approach for the flow shop scheduling problem
Engineering Applications of Artificial Intelligence, 2004
Advances in modern manufacturing systems such as CAD/CAM, FMS, CIM, have increased the use of intelligent techniques for solving various combinatorial and NP-hard sequencing and scheduling problems. Production process in these systems consists of workshop problems such as grouping similar parts into manufacturing cells and proceeds by passing these parts on machines in the same order. This paper presents a new hybrid simulated annealing algorithm (hybrid SAA) for solving the flow-shop scheduling problem (FSSP); an NP-hard scheduling problem with a strong engineering background. The hybrid SAA integrates the basic structure of a SAA together with features borrowed from the fields of genetic algorithms (GAs) and local search techniques. Particularly, the algorithm works from a population of candidate schedules and generates new populations of neighbor schedules by applying suitable small perturbation schemes. Further, during the annealing process, an iterated hill climbing procedure is stochastically applied on the population of schedules with the hope to improve its performance.
A hybridisation of metaheuristics for flow shop scheduling
2004
The present paper deals with the formation of an optimal sequence of flow shop scheduling (FSS) for efficient operation. The primary concern of FSS is to obtain the optimal sequence, which minimises the idle time, tardiness, makespan, etc. Among these, the criteria of minimising the makespan plays a vital part. Thus, in this paper, the sequencing of the FSS for minimising the makespan is addressed. An effective hybrid has been formed with the metaheuristics, namely an ant system and a genetic algorithm (GA). A number of illustrative examples with different combinations of machines and jobs have been solved using the proposed hybrid method.
2014
This paper addresses the problem of no-wait twostage flexible flow shop scheduling problem (NWTSFFSSP) considering unrelated parallel machines, sequence-dependent setup times, probable reworks and different ready times to actualize the problem. The performance measure used in this study is minimizing maximum completion time (makespan). Because of the complexity of addressed problem, we propose a novel intelligent hybrid algorithm [called hybrid algorithm (HA)] based on imperialist competitive algorithm (ICA) which are combined with simulated annealing (SA), variable neighborhood search (VNS) and genetic algorithm (GA) for solving the mentioned problem. The hybridization is carried out to overcome some existing drawbacks of each of these three algorithms and also for increasing the capability of ICA. To achieve reliable results, Taguchi approach is used to define robust parameters' values for our proposed algorithm. A simulation model is developed to study the performance of our proposed algorithm against ICA, SA, VNS, GA and ant colony optimization (ACO). The results of the study reveal the relative superiority of HA studied. In addition, potential areas for further researches are highlighted.
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.
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.
Most scheduling problems are combinatorial optimization problems which are too difficult to be solved optimally, and hence heuristics are used to obtain good solutions in reasonable times. The specific goal of this paper is to investigate scheduling heuristics to seek the minimum of a positively weighted convex sum of makespan and the number of tardy jobs in a static hybrid flow shop environment, where at least one production stage is made up of unrelated parallel machines. In addition, sequence -and machine -dependent setup times are considered. Some simple dispatching rules and flow shop makespan heuristics are adapted for the sequencing problem under consideration. The improvement heuristic algorithm proposed is a reinsertion algorithm. A simulated annealing algorithm is presented in this paper. Three basic parameters (i.e., cooling schedules, neighborhood structures, and initial temperatures) of a simulated annealing algorithm are briefly discussed in this paper. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages.
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.
A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop
Engineering Applications of Artificial Intelligence, 2010
In this paper we address a hybrid flow shop scheduling problem considering the minimization of the sum of the total earliness and tardiness penalties. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. So, we propose an ant colony optimization method to deal with this problem. Our proposed method has several features, including some heuristics that specifically take into account both earliness and tardiness penalties to compute the heuristic information values. The performance of our algorithm is tested by numerical experiments on a large number of randomly generated problems. A comparison with solutions performance obtained by some constructive heuristics is presented. The results show that the proposed approach performs well for this problem.
2007
Most scheduling problems are combinatorial optimization problems which are too difficult to be solved optimally, and hence heuristics are used to obtain good solutions in a reasonable time. The specific goal of this paper is to investigate scheduling heuristics, to seek the minimum of a positively weighted convex sum of makespan, and the number of tardy jobs, in a static hybrid flow shop environment, where at least one production stage is made up of unrelated parallel machines. In addition, sequence-and machine-dependent setup times are considered. Some simple dispatching rules and flow shop makespan heuristics are adapted for the sequencing problem under consideration. Then, this solution may be improved by a fast polynomial reinsertion algorithm. Moreover, a simulated annealing algorithm is presented in this paper. Three basic parameters (i.e., cooling schedules, neighborhood structures, and initial temperatures) of a simulated annealing algorithm are briefly discussed in this paper. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages.