Heuristic algorithms for scheduling hybrid flow shops with machine blocking and setup times (original) (raw)
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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.
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.
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.
An efficient bi-objective heuristic for scheduling of hybrid flow shops
The International Journal of Advanced Manufacturing Technology, 2011
This paper considers the problem of scheduling n independent jobs in hybrid flow shop environment with sequence-dependent setup times to minimize the makespan and total tardiness. For the optimization problem, an algorithm namely; bi-objective heuristic (BOH) is proposed for searching Pareto-optimal frontier. The aim of the proposed algorithm is to generate a good approximation of the set of efficient solutions. The BOH procedure initiates by generating a seed sequence. Since the output results are strongly dependent on the initial solution and in order to increase the quality of output results algorithm, we have considered how the generation of seed sequence with random way and particular sequencing rules. Two methods named Euclidean distance and percent error have been proposed to compare non-dominated solution sets obtain of each seed sequence. It is perceived from these methods that the generation of seed sequence using earliest due date rule is more effective. Then, the performance of the proposed BOH is compared with a simulated annealing proposed in the literature and a VNS heuristic on a set of test problems. The data envelopment analysis is used to evaluate the performance of approximation methods. From the results obtained, it can be seen that the proposed algorithm is efficient and effective.
Scheduling Methods for Hybrid Flow Shops with Setup Times
Future Manufacturing Systems, 2010
The purpose of this chapter is to present a class of deterministic scheduling problems in a multi-stage parallel machine environment called hybrid flow shop with setup times and appropriate methods for its resolution. The chapter includes a description of model with necessary definitions and notations; concepts of product family and batch, which are important elements of setup time analysis as well as a classification of setup times and problems that each category produces. The last section is focused on problems with sequence-depended setup times in hybrid flow shops. A review of investigated cases is explained, including the application of genetic algorithms for this kind of scheduling problems: structure of a genetic algorithm and description of several crossover operators appropriated to use based on previous investigations of authors. This section includes an algorithm and an example of a complex problem solution. A conclusion is presented at the chapter end.
Two-stage hybrid flow shop with precedence constraints and parallel machines at second stage
Computers & Operations Research, 2012
This study deals with the two-stage hybrid flow shop (HFS) problem with precedence constraints. Two versions are examined, the classical HFS where idle time between the operations of the same job is allowed and the no-wait HFS where idle time is not permitted. For solving these problems an adaptive randomized list scheduling heuristic is proposed. Two global bounds are also introduced so as to conservatively estimate the distance to optimality of the proposed heuristic. The evaluation is done on a set of randomly generated instances. The heuristic solutions for the classical HFS in average are provably situated below 2% from the optimal ones, and on the other hand, in the case of the no-wait HFS the average deviation is below 5%.
Scheduling of Permutation Flow Shops Using a New Hybrid Algorithm
2018
In the current situation, modern engineering and industrial built-up units are encountering a jumble of issues in a variety of areas, including machining time, electricity, manpower, raw materials, and client restraints. One of the most important industrial behaviors, particularly in manufacturing planning, is job-shop scheduling. This study provides a new updated suggested approach of johnson's algorithm as well as the gupta's heuristic algorithm to solve the permutation flow shop sequencing problem with the goal of making the makespan as little as possible. This work is about determining the processing order of n tasks in m machines. Although, because the problem is np-hard for three or more computers, this results in a near-optimal solution to the given issue. The suggested approach is straightforward and easy to comprehend, and it is accompanied with a numerical example.
Flow Shop Scheduling Problem: a Computational Study
2002
A computational study has been developed to obtain optimal / near optimal solution for the flow shop scheduling problem with make-span minimization as the primary criterion and the minimization of either the mean completion time, total waiting time or total idle time as the secondary criterion. The objective is to determine a sequence of operations in which to process 'n' jobs on 'm' machines in same order (flow shop environment) where skipping is allowed. The Simulation approach for deterministic and stochastic flow shop scheduling has been developed. It reads and manipulates data for 500 jobs on 500 machines. Different factorial experiments present a comparative study on the performance of different dispatching rules, such as FCFS, SPT, LPT, SRPT and LRPT with respect to the objectives of minimizing makespan, mean flow time, waiting time of jobs, and idle time of machines. The proposed model is evaluated and found to be relatively more effective in finding optimal/ near optimal solutions in many cases. The influence of the problem size in computational time for this model is discussed and recommendations for further research are presented.
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.
The 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. The problem is a combinatorial optimization problem which is too difficult to be solved optimally for large problem sizes, and hence heuristics are used to obtain good solutions in a reasonable time. Some dispatching rules and flow shop makespan heuristics are developed. Then this solution may be improved by fast polynomial heuristic improvement algorithms based on shift moves and pairwise interchanges. In addition, metaheuristic proposed is a tabu search algorithm. Three basic parameters (i.e., number of neighbors, neighborhood structure, and size of tabu list in each iteration) of a tabu search 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.