Adaptation of some vehicle fleet mix heuristics

Salhi, Saı̈d; Sari, Mehmet Umut; Saidi, D; Touati, Nac

doi:10.1016/0305-0483(92)90009-v

Cited by 42 publications

(25 citation statements)

References 11 publications

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“…Salhi et al (1992) were the first who considered unit variable cost. They presented a mixed integer formulation of the problem and modified the saving based methods of Golden et al (1984) accordingly.…”

Section: Introductionmentioning

confidence: 99%

A variable neighborhood-based heuristic for the heterogeneous fleet vehicle routing problem

Imran

Salhi

Wassan

2009

European Journal of Operational Research

123

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The heterogeneous fleet vehicle routing problem is investigated using some adaptations of the variable neighborhood search (VNS). The initial solution is obtained by Dijkstra's algorithm based on a cost network constructed by the sweep algorithm and the 2-opt. Our VNS algorithm uses several neighborhoods which are adapted for this problem. In addition, a number of local search methods together with a diversification procedure are used. Two VNS variants, which differ in the order the diversification and Dijkstra's algorithm are used, are implemented. Both variants appear to be competitive and produce new best results when tested on the data sets from the literature. We also constructed larger data sets for which benchmarking results are provided for future comparison.

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Section: Introductionmentioning

confidence: 99%

A variable neighborhood-based heuristic for the heterogeneous fleet vehicle routing problem

Imran

Salhi

Wassan

2009

European Journal of Operational Research

123

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“…So far, the unit running cost has been considered as a constant parameter with a value of one, which means that the driving cost has been considered as equal irrespective of the size of the vehicle. However, Salhi et al [129] present a MIP formulation for the FSMVRP where they introduce variable unit running costs. They show how simple modifications of some well-known methods can cater for variable running costs and also show the effect of neglecting such variability.…”

Section: Standard Fleet Size and MIX Vehicle Routing Problemmentioning

confidence: 99%

Industrial aspects and literature survey: Fleet composition and routing

Hoff

Andersson

Christiansen

et al. 2010

Computers & Operations Research

301

150

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The purpose of this paper is to describe industrial aspects of combined fleet composition and routing in maritime and road-based transportation, and to present the current status of research in the form of a comprehensive literature review. With a backdrop of industrial aspects, a categorized survey of relevant literature since the first published papers in the 1950's is given. First, the literature review discusses some early seminal and application-oriented papers, presents a classification of problems, and then focuses on a basic definition of combined fleet composition and routing: the fleet size and mix vehicle routing problem. Three basic mathematical formulations from the literature are presented and compared. Further, the literature of extended and related problems is described and categorized. Surveys of application oriented research in road-based and maritime transportation conclude the review. Finally, we contrast the literature with aspects of industrial applications from a critical, but constructive stance. Major issues for future work are suggested.

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“…This algorithm is based on the concept of multiple giant tours as extended by Salhi, Sari, Saidi and Touati (1992). We shall look at both the original vehicle routing algorithm and how it was modified to cater for the case of pickups and deliveries.…”

Section: The Route Construction Heuristic (Phase 1)mentioning

confidence: 99%

“…In our experiments we constructed 5 giant tours; one using the nearest neighbour, the other the least insertion cost rule and the remaining three tours are generated randomly. A detailed description on how to generate these giant tours and how to construct the associated cost networks can be found in Salhi et al (1992).…”

Section: The Route Construction Heuristic (Phase 1)mentioning

confidence: 99%