A simulation–optimization model for solving flexible flow shop scheduling problems with rework and transportation (original) (raw)
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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.
International Journal of Production Research (ISI-indexed)
In traditional flow shop scheduling problems (FSSP), the processing times are assumed to be pre-known and fixed parameters while in many practical environments, the processing times can be controlled by consumption of extra resources. In this paper, we propose resource-dependent processing times (RDPT) for permutation FSSP in which the processing time of a job depends on the amount of additional resources assigned to that job. To make a trade-off between makespan and required amount of resources, two conflict objective functions are considered: the minimization of maximum completion time and the minimization of total cost of resources. In order to solve the problem considered in this paper, a decomposition approach is suggested that strives to deal with the original model via two subproblems: (i) sequencing problem and (ii) resource allocation problem. A hybrid discrete differential evolution (HDDE) algorithm with an effective coordinate directions search and a variable neighborhood search (VNS) are combined to solve two subproblems. Furthermore, a statistical procedure is employed to adjust the significant parameters of proposed HDDE and VNS algorithms. This procedure is based on the stepwise regression (SR) technique. The effectiveness of suggested hybrid algorithm is investigated through a computational study and obtained results show the good performance of our approach with regard to the other algorithms.
The International Journal of Advanced Manufacturing Technology, 2007
This paper considers a flexible flow shop scheduling problem, where at least one production stage is made up of unrelated parallel machines. Moreover, sequence-and machine-dependent setup times are given. The objective is to find a schedule that minimizes a positively weighted convex sum of makespan and the number of tardy jobs in a static flexible flow shop environment. For this problem, a 0-1 mixed integer program is formulated. The problem is however 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. The proposed constructive heuristics for sequencing the jobs start with the generation of the representatives of the operation time for each operation. Then some dispatching rules and flow shop makespan heuristics are developed. To improve the solutions obtained by the constructive algorithms, polynomial heuristic improvement algorithms based on shift moves and pairwise interchanges of jobs are applied. In addition, metaheuristics are suggested, namely simulated annealing, tabu search and genetic algorithms. The basic parameters of each metaheuristic 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 and with an optimal solution for small-size problems. We have found that among the constructive algorithms the insertion based approach is superior to the others, whereas the proposed simulated algorithms are better than tabu search and genetic algorithms among the iterative metaheuristic algorithms.
Omega, 2014
The flow shop scheduling problem is finding a sequence given n jobs with same order at m machines according to certain performance measure(s). The job can be processed on at most one machine; meanwhile one machine can process at most one job. The most common objective for this problem is makespan. However, many real-world scheduling problems are multi-objective by nature. Over the years there have been several approaches used to deal with the multi-objective flow shop scheduling problems (MOFSP). Hence, in this study, we provide a brief literature review of the contributions to MOFSP and identify areas of opportunity for future research.
Multi-objective flow shop scheduling using hybrid simulated annealing
Measuring Business Excellence, 2010
Purpose -In order to achieve excellence in manufacturing, goals like lean, economic and quality production with enhanced productivity play a crucial role in this competitive environment. It also necessitates major improvements in generally three primary technical areas: variation reduction, equipment reliability, and production scheduling. Complexity of the real world scheduling problems also increases with interactive multiple decision-making criteria. This paper aims to deal with multi-objective flow shop scheduling problems, including sequence dependent set up time (SDST). The paper also aims to consider the objective of minimizing the weighted sum of total weighted tardiness, total weighted earliness and makespan simultaneously. It proposes a new heuristic-based hybrid simulated annealing (HSA) for near optimal solutions in a reasonable time.
Optimizing a multi-objectives flow shop scheduling problem by a novel genetic algorithm
International Journal of Industrial Engineering Computations, 2013
Flow-shop problems, as a typical manufacturing challenge, have become an interesting area of research. The primary concern is that the solution space is huge and, therefore, the set of feasible solutions cannot be enumerated one by one. In this paper, we present an efficient solution strategy based on a genetic algorithm (GA) to minimize the makespan, total waiting time and total tardiness in a flow shop consisting of n jobs and m machines. The primary objective is to minimize the job waiting time before performing the related operations. This is a major concern for some industries such as food and chemical for planning and production scheduling. In these industries, there is a probability of the decay and deterioration of the products prior to accomplishment of operations in workstation, due to the increase in the waiting time. We develop a model for a flowshop scheduling problem, which uses the planner-specified weights for handling a multi-objective optimization problem. These weights represent the priority of planning objectives given by managers. The results of the proposed GA and classic GA are analyzed by the analysis of variance (ANOVA) method and the results are discussed.
A Heuristic Search Algorithm for Flow-Shop Scheduling
Informaticasi, 2008
This article describes the development of a new intelligent heuristic search algorithm (IHSA*) which guarantees an optimal solution for flow-shop problems with an arbitrary number of jobs and machinesprovided the job sequence is constrained to be the same on each machine. The development is described in terms of 3 modifications made to the initial version of IHSA*. The first modification concerns thechoice of an admissible heuristic function. The second concerns the calculation of heuristic estimates as the search for an optimal solution progresses, and the third determines multiple optimal solutions whenthey exist. The first 2 modifications improve performance characteristics of the algorithm and experimental evidence of these improvements is presented as well as instructive examples which illustrate the use of initial and final versions of IHSA*.
Computers and Operations Research, Vol. 36, No.2, 2009, 358 - 378
This paper considers a flexible flow shop scheduling problem, where at least one production stage is made up of unrelated parallel machines. Moreover, sequence- and machine-dependent setup times are given. The objective is to find a schedule that minimizes a convex sum of makespan and the number of tardy jobs in a static flexible flow shop environment. For this problem, a 0-1 mixed integer program is formulated. The problem is however 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. The proposed constructive heuristics for sequencing the jobs start with the generation of the representatives of the operation time for each operation. Then some dispatching rules and flow shop makespan heuristics are developed. To improve the solutions obtained by the constructive algorithms, fast polynomial heuristic improvement algorithms based on shift moves and pairwise interchanges of jobs are applied. In addition, metaheuristics are suggested, namely simulated annealing, tabu search and genetic algorithms. The basic parameters of each metaheuristic 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 and with an optimal solution for small-size problems. We have found that among the constructive algorithms the insertion based approach is superior to the others, whereas the proposed simulated annealing algorithms are better than tabu search and genetic algorithms among the iterative metaheuristic algorithms.
An improved multiobjective memetic algorithm for permutation flow shop scheduling
This paper addresses a multiobjective scheduling problem in the permutation flow shop. The objectives are to minimize makespan and total flow time. The proposed approach is based on the framework of memetic algorithm, which is known as a hybrid of genetic algorithm and local search. The local search procedure is an iterative process repeating neighbor generation, neighbor evaluation, and neighbor selection. We take a problem-specific heuristic for neighbor generation and propose several strategies for neighbor evaluation and neighbor selection. Archive injection (adding non-dominated solutions to the population) is another issue under investigation. We examine the effects of the proposed strategies through experiments using forty widely used problem instances with different scales. We also evaluate the proposed approach by comparing it with other twenty-six ones in terms of three performance metrics. Our approach outperforms all benchmarks and updates a large portion of the sets of best known non-dominated solutions for large-scale instances.