Hybrid metaheuristics for the Clustered Vehicle Routing Problem

Vidal, Thibaut; Battarra, Maria; Subramanian, Anand; Erdoğan, Güneş

doi:10.1016/j.cor.2014.10.019

Cited by 74 publications

(50 citation statements)

References 23 publications

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“…In previous works, such as those mentioned in Section 1, the value of I ILS was tuned based on the instance size. In VRPs (e.g., Penna et al (2013); Silva et al (2015); Subramanian (2012), and Vidal et al (2015)), this parameter is usually set as a function of the number of customers and vehicles. In TSP-like (or single machine scheduling) problems (e.g., Silva et al, 2012;Subramanian and Battarra, 2013;and Subramanian et al, 2014), I ILS was set only as a function of the number of customers (or jobs).…”

Section: Parameter Tuningmentioning

confidence: 99%

A heuristic algorithm for a single vehicle static bike sharing rebalancing problem

Cruz

Subramanian

Bruck

et al. 2017

Computers & Operations Research

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129

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The static bike rebalancing problem (SBRP) concerns the task of repositioning bikes among stations in self-service bike-sharing systems. This problem can be seen as a variant of the one-commodity pickup and delivery vehicle routing problem, where multiple visits are allowed to be performed at each station, i.e., the demand of a station is allowed to be split. Moreover, a vehicle may temporarily drop its load at a station, leaving it in excess or, alternatively, collect more bikes from a station (even all of them), thus leaving it in default. Both cases require further visits in order to meet the actual demands of such station. This paper deals with a particular case of the SBRP, in which only a single vehicle is available and the objective is to find a least-cost route that meets the demand of all stations and does not violate the minimum (zero) and maximum (vehicle capacity) load limits along the tour. Therefore, the number of bikes to be collected or delivered at each station should be appropriately determined in order to respect such constraints. We propose an iterated local search (ILS) based heuristic to solve the problem. The ILS algorithm was tested on 980 benchmark instances from the literature and the results obtained are quite competitive when compared to other existing methods. Moreover, our heuristic was capable of finding most of the known optimal solutions and also of improving the results on a number of open instances.

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Section: Parameter Tuningmentioning

confidence: 99%

A heuristic algorithm for a single vehicle static bike sharing rebalancing problem

Cruz

Subramanian

Bruck

et al. 2017

Computers & Operations Research

Self Cite

129

View full text Add to dashboard Cite

show abstract

“…In this section we explain how we embedded the proposed approach in the ILS-RVND metaheuristic . The reason for choosing this metaheuristic to test our local search limitation strategy is that it was found capable of generating highly competitive results not only for problem 1|s ij | w j T j (Subramanian et al, 2014), as mentioned in Section 2, but also for other combinatorial optimization problems (Subramanian et al, 2010;Silva et al, 2012;Penna et al, 2013;Subramanian and Battarra, 2013;Martinelli et al, 2013;Vidal et al, 2015). Moreover, the referred method relies on very few parameters and it can be considered relatively simple, which makes it quite practical and also easy to implement.…”

Section: Embedding the Proposed Approach In The Ils-rvnd Metaheuristicmentioning

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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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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“…Combinatorial optimization problems have been solved using a wide variety of hybrid strategies [2][3][4][5][6][7][8][9][10]. However, the literature reveals that less importance is attached to modelling the problem's solution while solving combinatorial optimization problems using hybrid strategies [1].…”

Section: Motivationmentioning

confidence: 99%

“…When these strategies proposed are implemented the approaches used were classified into algorithm based approaches or framework based approaches. These algorithm or framework based approaches were implemented by applying one or more hybrid meta-heuristic [2][3][4][5][6][7][8][9][10] algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Qualitative Study of Model based Approach with the Existing Approaches for Solving Combinatorial Optimization Problems Using Hybrid Strategies

Muthuraman¹,

Venkatesan²

2017

IJMECS

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In literature, combinatorial optimization problems have been solved using several hybrid strategies. From the principles of software engineering, it is explicit that modelling enables better understanding of the problem's solution as well as the various parts that constitute the solution. However, the literature reveals that there is less importance attached to modelling the problem's solution while solving combinatorial optimization problems using hybrid strategies. Therefore, in order to better understand the advantages and significance of using a model based approach in solving such problems, a survey on model based approach and the various properties achieved by modelling has been carried out. A comparison of the algorithm or technique based approach, framework based approach and model based approach is done to better understand the differences between the approaches and their outcomes. From the comparison made between the approaches and the analysis made on the advantages of using a model based approach in solving combinatorial optimization problems using hybrid strategies, it is found that a model based approach gives clear and better understanding of complex problems by making their representation easily modular, understandable, adaptable, verifiable, reliable, customizable, reusable etc. Further, when hybrid strategies are used, and the problems solution is depicted in the form of a model, every part of the model could be implemented using different algorithms and frameworks, thus aiding to identify the optimal algorithm or framework for every part of the model, as well as the most efficient hybrid combination that solves the whole problem in an optimal manner. Index Terms-Model based approach, Solving combinatorial optimization problems, Hybrid strategies. productivity, verifiability, reliability, reusability, intra and interoperability, thereby increasing the overall quality of the problem's solution representation.An analysis of the advantages obtained by using model based approach with that of the algorithm and framework based approaches in solving the combinatorial optimization problems using hybrid strategies is presented. This analysis highlights that the model based approach is useful when compared to the algorithm or technique and framework based approaches in solving the combinatorial optimization problems using hybrid strategies as it suitably portrays the different hybrid strategies used to solve the same problem from an architectural perspective, aiding in identifying the best strategy for every part of the model and the optimal hybrid strategy for the entire problem. He has over 27 years of teaching and research experience in the field of computer science and engineering, and banking technology. He has developed an architectural reference model for multilingual software. His research interests include software engineering, service oriented architecture, pervasive computing, bankruptcy prediction methods, and business intelligence.

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Hybrid metaheuristics for the Clustered Vehicle Routing Problem

Cited by 74 publications

References 23 publications

A heuristic algorithm for a single vehicle static bike sharing rebalancing problem

A heuristic algorithm for a single vehicle static bike sharing rebalancing problem

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

A Qualitative Study of Model based Approach with the Existing Approaches for Solving Combinatorial Optimization Problems Using Hybrid Strategies

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