A study of hybrid evolutionary algorithms for single machine scheduling problem with sequence-dependent setup times

Xu, Hongyun; Lu, Zhengding; Yin, Aijun; Shen, Liji; Buscher, Udo

doi:10.1016/j.cor.2014.04.009

Cited by 20 publications

(9 citation statements)

References 36 publications

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“…, 13} + swap and θ = 0.75. We compare the results found by ILS-RVND F ast not only with GVNS (Kirlik and Oguz, 2012), ILS-RVND SBP (Subramanian et al, 2014) and LOX⊕B (Xu et al, 2014), where in the latter the authors only reported the best of 100 executions, but also with the algorithms listed below along with the type of result presented.…”

Section: Graspmentioning

confidence: 99%

“…Some elimination rules were also suggested for the swap neighborhood. Xu et al (2014) proposed different versions of a Hybrid Evolutionary Algorithm (HEA) by combining two population updating strategies with three crossover operators, including the linear order crossover operator (LOX). The initial population is generated at random and a local search is applied using the neighborhood l-block as in Xu et al (2013).…”

Section: Literature Reviewmentioning

confidence: 99%

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Efficient local search limitation strategy for single machine total weighted tardiness scheduling with sequence-dependent setup times

Subramanian

Farias

2017

Computers & Operations Research

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This paper concerns the single machine total weighted tardiness scheduling with sequence-dependent setup times, usually referred as 1|sij | wj Tj. In this N P-hard problem, each job has an associated processing time, due date and a weight. For each pair of jobs i and j, there may be a setup time before starting to process j in case this job is scheduled immediately after i. The objective is to determine a schedule that minimizes the total weighted tardiness, where the tardiness of a job is equal to its completion time minus its due date, in case the job is completely processed only after its due date, and is equal to zero otherwise. Due to its complexity, this problem is most commonly solved by heuristics. The aim of this work is to develop a simple yet effective limitation strategy that speeds up the local search procedure without a significant loss in the solution quality. Such strategy consists of a filtering mechanism that prevents unpromising moves to be evaluated. The proposed strategy has been embedded in a local search based metaheuristic from the literature and tested in classical benchmark instances. Computational experiments revealed that the limitation strategy enabled the metaheuristic to be extremely competitive when compared to other algorithms from the literature, since it allowed the use of a large number of neighborhood structures without a significant increase in the CPU time and, consequently, high quality solutions could be achieved in a matter of seconds. In addition, we analyzed the effectiveness of the proposed strategy in two other well-known metaheuristics. Further experiments were also carried out on benchmark instances of problem 1|sij | Tj.

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Section: Graspmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Efficient local search limitation strategy for single machine total weighted tardiness scheduling with sequence-dependent setup times

Subramanian

Farias

2017

Computers & Operations Research

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“…Constraint (5) ensures that each real job occupies only one position of the scheduling sequence. Constraint (6) ensures that each position can only be occupied by one job. Constraints 7and (8) guarantee the sequencing similarity of the jobs in setup decision variable ' ' and job scheduling decision variable ' '; i.e., if the job i processed in position k ( = 1) and the job i also processed immediately after job j ( = 1), Constraints (7) and (8) guarantee that job j occupies position k-( ( −1) = 1).…”

Section: Problem Description and Mathematical Modelingmentioning

confidence: 99%

“…The authors clarified the sequence-dependent setup time when the setup time for any job depends on the immediately preceding job; i.e., the setup time for the job i after job j should not equal the setup time for the same job i after any other job. Different algorithms were proposed in [6][7][8][9][10][11][12][13][14][15] to optimize the SMSP with considering of the sequencedependent setup times constraints. An ant colony optimization was presented in [16] to minimize job tardiness for an SMSP with sequence-dependent setups.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling and Discrete Firefly Algorithm to Optimize Scheduling Problem with Release Date, Sequence-Dependent Setup Time, and Periodic Maintenance

Qamhan

Al-Harkan

et al. 2019

Mathematical Problems in Engineering

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An evolutionary discrete firefly algorithm (EDFA) is presented herein to solve a real-world manufacturing system problem of scheduling a set of jobs on a single machine subject to nonzero release date, sequence-dependent setup time, and periodic maintenance with the objective of minimizing the maximum completion time “makespan.” To evaluate the performance of the proposed EDFA, a new mixed-integer linear programming model is also proposed for small-sized instances. Furthermore, the parameters of the EDFA are regulated using full factorial analysis. Finally, numerical experiments are performed to demonstrate the efficiency and capability of the EDFA in solving the abovementioned problem.

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“…Pakzad-Moghaddam et al [22] presented a mixed-integer mathematical programming model for a single machine scheduling problem with deteriorating and learning effects. Xu et al [23] solved the single machine scheduling problem with sequence-dependent setup times and conducted a systematic comparison of hybrid evolutionary algorithms (HEAs), which independently used the six combinations of three crossover operators and two population updating strategies. More recent works that deal with advanced single machine scheduling problems can be found in [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

A Decomposition-Based Two-Stage Optimization Algorithm for Single Machine Scheduling Problems with Deteriorating Jobs

Liu

Zhang

Wang

et al. 2015

Mathematical Problems in Engineering

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This paper studies a production scheduling problem with deteriorating jobs, which frequently arises in contemporary manufacturing environments. The objective is to find an optimal sequence of the set of jobs to minimize the total weighted tardiness, which is an indicator of service quality. The problem belongs to the class of NP-hard. When the number of jobs increases, the computational time required by an optimization algorithm to solve the problem will increase exponentially. To tackle large-scale problems efficiently, a two-stage method is presented in this paper. We partition the set of jobs into a few subsets by applying a neural network approach and thereby transform the large-scale problem into a series of small-scale problems. Then, we employ an improved metaheuristic algorithm (called GTS) which combines genetic algorithm with tabu search to find the solution for each subproblem. Finally, we integrate the obtained sequences for each subset of jobs and produce the final complete solution by enumeration. A fair comparison has been made between the two-stage method and the GTS without decomposition, and the experimental results show that the solution quality of the two-stage method is much better than that of GTS for large-scale problems.

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A study of hybrid evolutionary algorithms for single machine scheduling problem with sequence-dependent setup times

Cited by 20 publications

References 36 publications

Efficient local search limitation strategy for single machine total weighted tardiness scheduling with sequence-dependent setup times

Efficient local search limitation strategy for single machine total weighted tardiness scheduling with sequence-dependent setup times

Mathematical Modeling and Discrete Firefly Algorithm to Optimize Scheduling Problem with Release Date, Sequence-Dependent Setup Time, and Periodic Maintenance

A Decomposition-Based Two-Stage Optimization Algorithm for Single Machine Scheduling Problems with Deteriorating Jobs

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