Iterated local search based on multi-type perturbation for single-machine earliness/tardiness scheduling

Qin, Tao; Peng, Bo; Benlic, Una; Cheng, T.C.E.; Wang, Yang; Lu, Zhengding

doi:10.1016/j.cor.2015.03.005

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…Iterated local search algorithm is applied to many combinatorial optimization problem successfully. Iterated local search algorithm is proposed for the scheduling problem [32], vehicle routing problem [33][34][35][36] Figure 1. Algorithm of the iterated local search One of the important features of the proposed algorithm is to start the search process multiple times.…”

Section: Multi-start Iterated Tabu Search Algorithmmentioning

confidence: 99%

A multi-start iterated tabu search algorithm for the multi-resource agent bottleneck generalized assignment problem

Bektur¹

2019

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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In this study, a multi-resource agent bottleneck generalized assignment problem (MRBGAP) is addressed. In the bottleneck generalized assignment problem (BGAP), more than one job can be assigned to an agent, and the objective function is to minimize the maximum load over all agents. In this problem, multiple resources are considered and the capacity of the agents is dependent on these resources and it has minimum two indices. In addition, agent qualifications are taken into account. In other words, not every job can be assignable to every agent. The problem is defined by considering the problem of assigning jobs to employees in a firm. BGAP has been shown to be NP- hard. Consequently, a multi-start iterated tabu search (MITS) algorithm has been proposed for the solution of large-scale problems. The results of the proposed algorithm are compared by the results of the tabu search (TS) algorithm and mixed integer linear programming (MILP) model.

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Section: Multi-start Iterated Tabu Search Algorithmmentioning

confidence: 99%

A multi-start iterated tabu search algorithm for the multi-resource agent bottleneck generalized assignment problem

Bektur¹

2019

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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“…The JiT problem is usually modeled as a single objective optimization task that minimizes the weighted sum of total earliness and total tardiness of jobs. In most works on JiT scheduling a branch an bound ([8,12,16,17]) or meta-heuristic ( [8,11,13]) is used to search though the jobs permutations while a timing algorithm is applied to compute the job execution times and thus the sequence cost [18].…”

Section: Introductionmentioning

confidence: 99%

An exact algorithm for the bi-objective timing problem

2018

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The timing problem in the bi-objective just-in-time single-machine jobshop scheduling problem (JiT-JSP) is the task to schedule N jobs whose order is fixed, with each job incurring a linear earliness penalty for finishing ahead of its due date and a linear tardiness penalty for finishing after its due date. The goal is to minimize the earliness and tardiness simultaneously. We propose an exact greedy algorithm that finds the entire Pareto front in O(N 2 ) time. This algorithm is asymptotically optimal.

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“…The iterated local search (ILS) metaheuristic presented by Lourenço et al (2003) [21] and the iterated greedy (IG) introduced by Ruiz and Stützle [22] are two trajectory-based metaheuristics that have resulted in top-notch performance for many scheduling problems despite their simplicity [23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times

Pan

Gao

2017

Applied Mathematics and Computation

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This paper proposes a total of nine algorithms to minimize the makespan for the hybrid flowshop scheduling problem with sequence-dependent setup times. The first six algorithms are trajectory-based metaheuristics, including three variants of iterated local search and three variants of iterated greedy. The remaining three algorithms are population-based metaheuristics, namely, the improved fruit fly optimization, the improved migrating birds optimization, and the discrete artificial bee colony optimization. We present some advanced and effective technologies, including three mixed neighborhood structures, an enhanced perturbation method, and an enhanced destruction and construction procedure for the trajectory-based metaheuristics. We propose a path-relinking-based cooperative search, a diversity control scheme, and a diversified initialization approach for the improved fruit fly optimization. We calibrate the parameters and operators for the proposed algorithms by means of a design of experiments approach. To evaluate the proposed algorithms, we present several adaptations of other recent well-known meta-heuristics for the problem and conduct a comprehensive set of computational and statistical experiments to demonstrate the effectiveness of the presented algorithms. Among them, the discrete artificial bee colony optimization is the best-performing algorithm and it is able to improve 126 out of the 240 best known solutions for the benchmarks in the literature.

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Iterated local search based on multi-type perturbation for single-machine earliness/tardiness scheduling

Cited by 9 publications

References 23 publications

A multi-start iterated tabu search algorithm for the multi-resource agent bottleneck generalized assignment problem

A multi-start iterated tabu search algorithm for the multi-resource agent bottleneck generalized assignment problem

An exact algorithm for the bi-objective timing problem

Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times

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