Hybrid metaheuristic algorithms for the tardiness blocking flow shop problem (original) (raw)
Related papers
2012
This paper deals with the blocking flow shop problem and proposes an Iterated Local Search (ILS) procedure combined with a variable neighbourhood search (VNS) for the total tardiness minimization. The proposed ILS makes use of a NEH-based procedure to generate the initial solution, uses a local search to intensify the exploration which combines the insertion and swap neighbourhood and uses a perturbation mechanism that applies, d times, three neighbourhood operators to the current solution to diversify the search. The computational evaluation has shown that the insertion neighbourhood is more effective than the swap one, but it also has shown that the combination of both is a good strategy to improve the obtained solutions. Finally, the comparison of the ILS with an Iterated greedy algorithm and with a greedy randomized adaptive search procedure has revealed its good performance.
Local search heuristics for two-stage flow shop problems with secondary criterion
Computers & Operations Research, 2002
This paper develops and compares different local search heuristics for the twostage flow shop problem with makespan minimization as the primary criterion and the minimization of total flow time, total weighted flow time and total weighted tardiness as the secondary criterion. We consider simulated annealing, threshold accepting, tabu search and multi-level search. The latter type of algorithms is characterized by the use of different neighbourhoods within the search. In the comparison we also include a genetic algorithm from the literature. In this paper we analyze the influence of the parameters of these heuristics and the starting solution. The algorithms have been tested on problems with up to 80 jobs.
Sequence-dependent flow shop scheduling problem minimising the number of tardy jobs
International Journal of Production Research, 2011
Flow shop scheduling problems with sequence-dependent setup times and minimising the number of tardy jobs as the criterion (Fm|prmu, Sijk|ΣUj) are considered in this research. A mixed-integer linear programming model is developed for the research problem. Since the proposed research problem has been proven to be NP-hard, several meta-heuristic algorithms based on tabu search (TS) and the imperialist competitive algorithm (ICA) are proposed to heuristically solve the problem. In order to find the best meta-heuristic algorithm, random test problems, ranging in size from small, medium, to large, are generated and solved by the meta-heuristic algorithms. Then, a detailed statistical experiment based on the split-plot design is performed to find the best meta-heuristic algorithm. The results of the experiment show that the performance of ICA is worse than the other algorithms for small- and medium-sized problems. The hybrid of ICA and the TS algorithm provides better performance than the other proposed algorithms for large-sized problems.
An effective iterated greedy algorithm for blocking hybrid flow shop problem with due date window
RAIRO - Operations Research, 2021
Nowadays many industry consider an interval time as a due date instead of precise points in time. In this study, the hybrid flow shop scheduling problem with basic blocking constraint is tackled. Where jobs, if done within a due window, are deemed on time. Therefore, the criterion is to minimize the sum of weighted earliness and tardiness. This variant of the hybrid flowshop problem is not investigated to the best of our knowledge. we introduced a new metaheuristic centered on the iterated greedy approach. to evaluate the proposed method we start by the re-implementation and the comparison of seven well-selected procedures that treat the hybrid flowshop problem. In order to prove the robustness of our method, we evaluated it using a new benchmark of more than 1000 instances. The experimental results demonstrated that the proposed algorithm is effective and produces a very high solution.
Efficient heuristics for the parallel blocking flow shop scheduling problem
Expert Systems with Applications, 2017
We consider the NP-hard problem of scheduling n jobs in F identical parallel flow shops, each consisting of a series of m machines, and doing so with a blocking constraint. The applied criterion is to minimize the makespan, i.e., the maximum completion time of all the jobs in F flow shops (lines). The Parallel Flow Shop Scheduling Problem (PFSP) is conceptually similar to another problem known in the literature as the Distributed Permutation Flow Shop Scheduling Problem (DPFSP), which allows modeling the scheduling process in companies with more than one factory, each factory with a flow shop configuration. Therefore, the proposed methods can solve the scheduling problem under the blocking constraint in both situations, which, to the best of our knowledge, has not been studied previously. In this paper, we propose a mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines. The proposed constructive procedures use two approaches that are totally different from those proposed in the literature. These methods are used as initial solution procedures of an iterated local search (ILS) and an iterated greedy algorithm (IGA), both of which are combined with a variable neighborhood search (VNS). The proposed constructive procedure and the improved methods take into account the characteristics of the problem. The computational evaluation demonstrates that both of them -especially the IGAperform considerably better than those algorithms adapted from the DPFSP literature. Abstract We consider the NP-hard problem of scheduling n jobs in F identical parallel flow shops, each consisting of a series of m machines, and doing so with a blocking constraint. The applied criterion is to minimize the makespan, i.e., the maximum completion time of all the jobs in F flow shops (lines). The Parallel Flow Shop Scheduling Problem (PFSP) is conceptually similar to another problem known in the literature as the Distributed Permutation Flow Shop Scheduling Problem (DPFSP), which allows modeling the scheduling process in companies with more than one factory, each factory with a flow shop configuration. Therefore, the proposed methods can solve the scheduling problem under the blocking constraint in both situations, which, to the best of our knowledge, has not been studied previously. In this paper, we propose a mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines. The proposed constructive procedures use two approaches that are totally different from those proposed in the literature. These methods are used as initial solution procedures of an iterated local search (ILS) and an iterated greedy algorithm (IGA), both of which are combined with a variable neighborhood search (VNS). The proposed
Computers & Industrial Engineering, 2015
This paper proposes two constructive heuristics, i.e. HPF1 and HPF2, for the blocking flow shop problem in order to minimize the total flow time. They differ mainly in the criterion used to select the first job in the sequence since, as it is shown, its contribution to the total flow time is not negligible. Both procedures were combined with the insertion phase of NEH to improve the sequence. However, as the insertion procedure does not always improve the solution, in the resulting heuristics, named NHPF1 and NHPF2, the sequence was evaluated before and after the insertion to keep the best of both solutions. The structure of these heuristics was used in Greedy Randomized Adaptive Search Procedures (GRASP) with variable neighborhood search in the improvement phase to generate greedy randomized solutions. The performance of the constructive heuristics and of the proposed GRASPs was evaluated against other heuristics from the literature. Our computational analysis showed that the presented heuristics are very competitive and able to improve 68 out of 120 best known solutions of Taillard's instances for the blocking flow shop scheduling problem with the total flow time criterion.
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.
A new heuristic for the flowshop scheduling problem to minimize makespan and maximum tardiness
International Journal of Production Research, 2009
In this paper a new heuristic for solving the flowshop scheduling problem which aims to minimising makespan and maximum tardiness is presented. The algorithm is then able to take into account the aforementioned performance measures, finding a set of non-dominated solutions representing the Pareto front. This method is based on the integration of two different techniques: a multi criteria decision making method and a constructive heuristic procedure developed for makespan minimisation in flowshop scheduling problems. In particular, the Technique for Order Preference by Similarity of Ideal Solution (TOPSIS) algorithm is integrated with the Nawaz-Enscore-Ham (NEH) heuristic to generate a set of potential scheduling solutions. To assess the proposed heuristic's performance a comparison with the best performing Multi Objective Genetic Local Search (MOGLS) algorithm proposed in literature is carried out. The test is executed on a large number of random problems characterized by different numbers of machines and jobs. The results show that the new heuristic frequently exceeds the MOGLS results in terms of both non-dominated solutions set quality and CPU time. In particular, the improvement becomes more and more significant as the number of jobs in the problem increases.
Computers & Operations Research, 2009
This paper proposes a hybrid metaheuristic for the minimization of makespan in permutation flow shop scheduling problems. The solution approach is robust, fast, and simply structured, and comprises three components: an initial population generation method based on a greedy randomized constructive heuristic, a genetic algorithm (GA) for solution evolution, and a variable neighbourhood search (VNS) to improve the population. The hybridization of a GA with VNS, combining the advantages of these two individual components, is the key innovative aspect of the approach. Computational experiments on benchmark data sets demonstrate that the proposed hybrid metaheuristic reaches high-quality solutions in short computational times. Furthermore, it requires very few user-defined parameters, rendering it applicable to real-life flow shop scheduling problems.