References of "François, Véronique"
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See detailLarge neighborhood search for multi-trip vehicle routing
François, Véronique ULg; Arda, Yasemin ULg; Crama, Yves ULg et al

in European Journal of Operational Research (2016), 255(2), 422-441

We consider the multi-trip vehicle routing problem, in which each vehicle can perform several routes during the same working shift to serve a set of customers. The problem arises when customers are close ... [more ▼]

We consider the multi-trip vehicle routing problem, in which each vehicle can perform several routes during the same working shift to serve a set of customers. The problem arises when customers are close to each other or when their demands are large. A common approach consists of solving this problem by combining vehicle routing heuristics with bin packing routines in order to assign routes to vehicles. We compare this approach with a heuristic that makes use of specific operators designed to tackle the routing and the assignment aspects of the problem simultaneously. Two large neighborhood search heuristics are proposed to perform the comparison. We provide insights into the configuration of the proposed algorithms by analyzing the behavior of several of their components. In particular, we question the impact of the roulette wheel mechanism. We also observe that guiding the search with an objective function designed for the multi-trip case is crucial even when exploring the solution space of the vehicle routing problem. We provide several best known solutions for benchmark instances. [less ▲]

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See detailNeighborhood search approaches for multi-trip vehicle routing problems
François, Véronique ULg; Arda, Yasemin ULg; Crama, Yves ULg

Conference (2016, July 06)

We introduce two large neighborhood search approaches for solving the multi-trip vehicle routing problem (MTVRP), where each vehicle can perform several routes to serve a set of customers. The problem ... [more ▼]

We introduce two large neighborhood search approaches for solving the multi-trip vehicle routing problem (MTVRP), where each vehicle can perform several routes to serve a set of customers. The problem specifically arises when travel times between customers are short and/or when demands are large. It has been commonly solved in the literature by mixing vehicle routing heuristics and bin packing techniques aimed at assigning routes to vehicles. As an alternative, we propose specific operators that tackle the routing and the assignment aspects of the problem simultaneously. The introduced methods are compared both for the MTVRP and the version with time windows, in which the assignment part of the problem becomes more challenging. In the latter, besides considering a time window at the depot, the working time of each vehicle may not exceed a maximum duration, while its start time is a decision variable. Beyond providing several best known solutions for benchmark instances of the MTVRP, we focus on understanding the behavior of the algorithms. An automatic configuration tool is used, not only to improve the quality of the results, but also as a mean to gain knowledge about algorithm design options and their interactions. We question the impact of several heuristic components and in particular those of the roulette wheel mechanism and of the adaptive memory of routes. [less ▲]

Detailed reference viewed: 22 (6 ULg)
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See detailLarge neighborhood search for multi-trip vehicle routing
François, Véronique ULg; Arda, Yasemin ULg; Crama, Yves ULg et al

E-print/Working paper (2015)

We consider two large neighborhood search approaches for the multi-trip vehicle routing problem, where each vehicle can perform several routes during the working shift to serve a set of customers. The ... [more ▼]

We consider two large neighborhood search approaches for the multi-trip vehicle routing problem, where each vehicle can perform several routes during the working shift to serve a set of customers. The problem specifically arises when customers are close to each other and/or when the demands are large. A common approach in the literature consists in solving this problem by mixing vehicle routing heuristics with bin packing routines to assign routes to vehicles. We compare this approach with the use of specific operators designed to tackle the routing and the assignment aspects of the problem simultaneously. We provide several best known solutions for benchmark instances. At the end of the work, we give insights about the proposed algorithm configurations by analyzing the behavior of several method components. [less ▲]

Detailed reference viewed: 140 (18 ULg)
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See detailSpecific Multi-trip Operators for Vehicle Routing Problems
Arda, Yasemin ULg; Crama, Yves ULg; François, Véronique ULg et al

Conference (2014, July 17)

In vehicle routing problems with multiple trips (VRPM), each vehicle is allowed to perform more than one trip during its working period. Classical solution techniques for this problem use existing VRP ... [more ▼]

In vehicle routing problems with multiple trips (VRPM), each vehicle is allowed to perform more than one trip during its working period. Classical solution techniques for this problem use existing VRP heuristics to create trips, together with bin packing methods aimed at assigning these trips to the available vehicles. In this work, specific local search operators for the VRPM are proposed. Heuristics using these operators are compared with classical solution techniques mentioned above. [less ▲]

Detailed reference viewed: 57 (4 ULg)
See detailVehicle routing problems with multiple trips: using specific local search operators
Arda, Yasemin ULg; Crama, Yves ULg; François, Véronique ULg et al

Conference (2014, June 25)

In vehicle routing problems with multiple trips (VRPM), each vehicle is allowed to perform more than one trip during its working period. Classical solution techniques for this problem make use of existing ... [more ▼]

In vehicle routing problems with multiple trips (VRPM), each vehicle is allowed to perform more than one trip during its working period. Classical solution techniques for this problem make use of existing VRP heuristics to create trips, together with bin packing methods aimed at assigning these trips to the available vehicles. The first contribution of this work is to propose specific local search operators for the VRPM. The operators directly integrate the multi-trip structure of the problem within well-known VRP operators. As a second contribution, heuristics using these operators are compared with classical solution techniques mentioned above. The comparison is performed by using the adaptive large neighborhood search metaheuristic as a common basis for both methods. The most classical version of the problem is studied as well as a variant involving time windows [less ▲]

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See detailAn adaptive large neighborhood search for a vehicle routing problem with multiple trips and driver shifts
Arda, Yasemin ULg; Crama, Yves ULg; François, Véronique ULg

Conference (2013, February 13)

This study analyzes a rich vehicle routing problem with multiple trips and driver shifts. The considered problem features are inspired from the practical case of a Belgian distribution company. Along with ... [more ▼]

This study analyzes a rich vehicle routing problem with multiple trips and driver shifts. The considered problem features are inspired from the practical case of a Belgian distribution company. Along with the multi-trip component, characteristics of this particular problem include time windows, pickup and delivery customers, and site-vehicle dependencies. Internal and external fleets are considered with different cost structures and driver shifts constraints. An adpative large neighborhood search is used to treat the problem. [less ▲]

Detailed reference viewed: 213 (8 ULg)
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See detailVariable Neighborhood Search applied to the Vehicle Routing Problem
François, Véronique ULg

Learning material (2011)

Detailed reference viewed: 77 (18 ULg)
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See detailAimms - Tutoriel en une heure à l’usage des débutants
Bay, Maud ULg; François, Véronique ULg

Learning material (2009)

Detailed reference viewed: 116 (16 ULg)