A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop

Pinedo, Michael; Singer, Marcos

doi:10.1002/(sici)1520-6750(199902)46:1<1::aid-nav1>3.3.co;2-r

Cited by 23 publications

(36 citation statements)

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“…The due date range factor RDD is set to 0.2 to increase the contention around the average due date. In order to assign the weights for the TWT instances, we follow a strategy similar to that in Pinedo and Singer (1999). The weights are set randomly to one of three possible values 3, 2, and 1, which represent high, medium, and low job priority levels, respectively.…”

Section: Random Relative Perturbationsmentioning

confidence: 99%

Minimizing value-at-risk in single-machine scheduling

2016

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The vast majority of the machine scheduling literature focuses on deterministic problems in which all data is known with certainty a priori. In practice, this assumption implies that the random parameters in the problem are represented by their point estimates in the scheduling model. The resulting schedules may perform well if the variability in the problem parameters is low. However, as variability increases accounting for this randomness explicitly in the model becomes crucial in order to counteract the ill effects of the variability on the system performance. In this paper, we consider single-machine scheduling problems in the presence of uncertain parameters. We impose a probabilistic constraint on the random performance measure of interest, such as the total weighted completion time or the total weighted tardiness, and introduce a generic risk-averse stochastic programming model. In particular, the objective of the proposed model is to find a non-preemptive static job processing sequence that minimizes the value-at-risk (VaR) of the random performance measure at a specified confidence level. We propose a Lagrangian relaxation-based scenario decomposition method to obtain lower bounds on the optimal VaR and provide a stabilized cut generation algorithm to solve the Lagrangian dual problem. Furthermore, we identify promising schedules for the original problem by a simple primal heuristic. An extensive computational study on two selected performance measures is presented to demonstrate the value of the proposed model and the effectiveness of our solution method.

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Section: Random Relative Perturbationsmentioning

confidence: 99%

Minimizing value-at-risk in single-machine scheduling

2016

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“…In most of the successful neighborhood structures, a feasible solution is represented by a disjunctive graph and neighborhood is defined by the changes of operation sequences processed on the same machine. In this section, disjunctive graph representation for a tardiness minimization by Pinedo and Singer (1999) is presented. Neighborhoods are defined with the disjunctive graph model and the local search procedure of Essafi et al (2008) which is used in our proposed method is explained.…”

Section: Local Search Proceduresmentioning

confidence: 99%

“…There are roughly two kinds of approaches for the minimization of total weighted tardiness. First one is using iterated local search procedures proposed by Kreipl (2000) and Essafi et al (2008), which are quite efficient in terms of performance on standard benchmark problems (Pinedo and Singer, 1999). The large step random walk proposed by Kreipl (2000) consists of iterative improvement to intensify search and random walk to escape from the local optima.…”

Section: Introductionmentioning

confidence: 99%

“…The genetic local search (GLS) proposed by Essafi et al (2008) optimizes initial solutions for the iterative local search by genetic algorithm and is considered to be one of the most effective methods. They are also superior to the shifting bottleneck heuristic (Pinedo and Singer, 1999) which decomposes JSP into multiple sub-problems of determining Priority rule-based reconstruction for total weighted tardiness minimization of job-shop scheduling problem assignments on a single machine and are considered to be ones of the most efficient approaches to JSP with total weighted tardiness criterion. Another approach is based on priority rules that have been extensively studied especially for tardiness objective.…”

Section: Introductionmentioning

confidence: 99%

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Priority rule-based reconstruction for total weighted tardiness minimization of job-shop scheduling problem

Yokoyama

Iizuka

Yamamoto

2014

JAMDSM

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We propose an efficient heuristic method for job-shop scheduling problems (JSP) with the objective of total weighted tardiness minimization. The proposed method uses schedule reconstructions by priority rules to guide a local search towards promising solutions. Typically priority rules determine the whole schedule and thus incorporating it with local search procedure is difficult. In our proposed method, a priority rule decides a schedule within an arbitrary selected time window and the rest of the schedule is determined by the schedule obtained by a conventional local search method. The priority rule is given by a linear combination of simple priority rules. To improve a schedule efficiently, an appropriate set of time window and gains of the linear combined priority rule is required. Therefore, we optimize the set of time window and rule gains with genetic algorithm. This rule-based reconstruction procedure and the conventional local search procedure are alternately applied to a current solution.In the experiments, the efficiency of rule-based reconstruction procedure is verified and the proposed method is compared with one of the most effective existing methods. The results show that the proposed method outperforms the existing method on large problems with sufficient computational time. The average performance is particularly improved due to the ability of rule-based reconstruction procedure to escape from local optima of the conventional local search procedure efficiently.

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“…On the one hand, the approximate and heuristic methods make a tradeoff between solution quality and computational cost. These methods include dispatching priority rules 8 , shifting bottleneck approach 9 , and Lagrangian relaxation 10 . More recently, with the emergence of new techniques from the field of artificial intelligence, much attention has been devoted to meta-heuristics.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Artificial Fish Swarm Model with Estimation of Distribution for Flexible Job Shop Scheduling

Ge¹,

Sun²,

Chen³

et al. 2016

IJCIS

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The flexible job shop scheduling problem (FJSP) is one of the most important problems in the field of production scheduling, which is the abstract of some practical production processes. It is a complex combinatorial optimization problem due to the consideration of both machine assignment and operation sequence. In this paper, an efficient artificial fish swarm model with estimation of distribution (AFSA-ED) is proposed for the FJSP with the objective of minimizing the makespan. Firstly, a pre-principle and a post-principle arranging mechanism that operate by adjusting machine assignment and operation sequence with different orders are designed to enhance the diversity of population. Following this, the population is divided into two sub-populations and then two arranging mechanisms are applied. In AFSA-ED, a preying behavior based on estimation of distribution is proposed to improve the performance of algorithm. Moreover, an attracting behavior is proposed to improve the global exploration ability and a public factor based critical path search strategy is proposed to enhance the local exploitation ability. Simulated experiments are carried on BRdata, BCdata and HUdata benchmark sets. The computational results validate the performance of the proposed algorithm in solving the FJSP, as compared with some other state of the art algorithms.

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A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop

Cited by 23 publications

References 0 publications

Minimizing value-at-risk in single-machine scheduling

Minimizing value-at-risk in single-machine scheduling

Priority rule-based reconstruction for total weighted tardiness minimization of job-shop scheduling problem

An Efficient Artificial Fish Swarm Model with Estimation of Distribution for Flexible Job Shop Scheduling

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