A variable neighborhood search for job shop scheduling with set-up times to minimize makespan

Roshanaei, Vahid; Naderi, Bahman; Jolai, Fariborz; Khalili, Majid

doi:10.1016/j.future.2009.01.004

Cited by 84 publications

(32 citation statements)

References 25 publications

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“…VNS has manifested its successful application to other problems including [53][54][55] . The rationale behind the employment of VNS is that meta-heuristic algorithms get trapped in local optima.…”

Section: Continuous General Variable Neighborhood Search (Cgvns) Metamentioning

confidence: 99%

See 1 more Smart Citation

A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study

Vahdani¹,

Mousavi²,

Tavakkoli–Moghaddam³

et al. 2017

IJCIS

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In sustainable supply chain networks, companies are obligated to have a systematic decision support system in place to help it adopt right decisions at right times. Among strategic decisions, supplier selection and evaluation outranks other decisions in terms of importance due to its long-term impacts. Besides, the adoption of such strategic decision entails exploring several factors that contribute to the complexity of decision making in the supply chain. For the purpose of solving non-linear regression problems, a novel neural network technique known as least squaresupport vector machine (LS-SVM) with maximum generalization ability has successfully been implemented. However, the performance quality of the LS-SVM is recognized to notoriously vary depending on the rigorous selection of its parameters. Therefore, in this paper, a continuous general variable neighborhood search (CGVNS) which is an effective meta-heuristic algorithm to solve the real world engineering continuous optimization problems is proposed to be integrated with LS-SVM. The CGVNS is hybridized in our novel integrated LS-SVM and CGVNS model, to tune the parameters of the LS-SVM to better estimate performance rating of supplier selection and evaluation problem. To demonstrate the improved performance of our proposed integrated model, a real data set from a case study of a supplier selection and evaluation problem is presented in a cosmetics industry. Additionally, comparative evaluations between our proposed model and the conventional techniques, namely nonlinear regression, multi-layer perceptron (MLP) neural network and LS-SVM is provided. The experimental results simply manifest the outperformance of our proposed model in terms of estimation accuracy and effective prediction.

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Section: Continuous General Variable Neighborhood Search (Cgvns) Metamentioning

confidence: 99%

“…(2) A global optimum is regarded as a locally optimal with respect to all neighborhood structures 54 . Basic steps of the VNS meta-heuristic as seen in discrete optimization problems are given in Fig.…”

Section: Continuous General Variable Neighborhood Search (Cgvns) Metamentioning

confidence: 99%

A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study

Vahdani¹,

Mousavi²,

Tavakkoli–Moghaddam³

et al. 2017

IJCIS

View full text Add to dashboard Cite

show abstract

“…VNS can be categorized as a local search-based algorithm, armed with systematic neighborhood search structures. Roshanaei et al (2009) introduced implementations of VNS that improve the notorious myopic behavior of local search-based metaheuristic algorithms by the means of several systematic insertion neighborhood search structures. Roshanaei et al (2009) uses the Taillard's benchmark to evaluate the efficiency and effectiveness of their proposed VNS approach against some effective algorithms.…”

Section: Artificial Intelligence (Ai) Techniquesmentioning

confidence: 99%

“…Roshanaei et al (2009) introduced implementations of VNS that improve the notorious myopic behavior of local search-based metaheuristic algorithms by the means of several systematic insertion neighborhood search structures. Roshanaei et al (2009) uses the Taillard's benchmark to evaluate the efficiency and effectiveness of their proposed VNS approach against some effective algorithms. The obtained results strongly support the high performance of VNS with respect to other well-known heuristic and metaheuristic algorithms.…”

Section: Artificial Intelligence (Ai) Techniquesmentioning

confidence: 99%

Jackson Network

2013

Encyclopedia of Operations Research and Management Science

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“…VNS algorithms have been successfully applied to both scheduling and routing problems, e.g. nurse scheduling [10,11], job shop scheduling [12], vehicle routing problem with time windows [13] and the multi-depot version of the vehicle routing problem with time windows [14], a problem inherently similar to the WSRP.…”

Section: Introductionmentioning

confidence: 99%

A Variable Neighbourhood Search for Nurse Scheduling with Balanced Preference Satisfaction

Constantino

Tozzo

Pinheiro

et al. 2015

Proceedings of the 17th International Conference on Enterprise Information Systems

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Abstract. The workforce scheduling and routing problem (WSRP) is a combinatorial optimisation problem where a set of workers must perform visits to geographically scattered locations. We present a Variable Neighbourhood Search (VNS) metaheuristic algorithm to tackle this problem, incorporating two novel heuristics tailored to the problem-domain. The first heuristic restricts the search space using a priority list of candidate workers and the second heuristic seeks to reduce the violation of specific soft constraints. We also present two greedy constructive heuristics to give the VNS a good starting point. We show that the use of domain-knowledge in the design of the algorithm can provide substantial improvements in the quality of solutions. The proposed VNS provides the first benchmark results for the set of real-world WSRP scenarios considered.

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A variable neighborhood search for job shop scheduling with set-up times to minimize makespan

Cited by 84 publications

References 25 publications

A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study

A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study

Jackson Network

A Variable Neighbourhood Search for Nurse Scheduling with Balanced Preference Satisfaction

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