A GRASPxELS with Depth First Search Split Procedure for the HVRP

Split procedures have been proved to be efficient within global framework optimization for routing problems by splitting giant tour into trips. This is done by generating optimal shortest path within an auxiliary graph built from the giant tour. An efficient application has been introduced for the first time by Lacomme et al. (2001) within a metaheuristic approach to solve the Capacitated Arc Routing Problem (CARP) and second for the Vehicle Routing Problem (VRP) by Prins (2004). In a further step, the Split procedure embedded in metaheuristics has been extended to address more complex routing problems thanks to a heuristic splitting of the giant tour using the generation of labels on the nodes of the auxiliary graph linked to resource management. Lately, Duhamel et al. (2010) defined a new Split family based on a depth first search approach during labels generation in graph. The efficiency of the new split method has been first evaluated in location routing problem with a GRASP metaheuristic. Duhamel et al. (2010) provided full numerical experiments on this topic

A GRASPxELS with Depth First Search Split Procedure for the HVRP

Split procedures have been proved to be efficient within global framework optimization for routing problems by splitting giant tour into trips. This is done by generating optimal shortest path within an auxiliary graph built from the giant tour. An efficient application has been introduced for the first time by Lacomme et al. (2001) within a metaheuristic approach to solve the Capacitated Arc Routing Problem (CARP) and second for the Vehicle Routing Problem (VRP) by Prins (2004). In a further step, the Split procedure embedded in metaheuristics has been extended to address more complex routing problems thanks to a heuristic splitting of the giant tour using the generation of labels on the nodes of the auxiliary graph linked to resource management. Lately, Duhamel et al. (2010) defined a new Split family based on a depth first search approach during labels generation in graph. The efficiency of the new split method has been first evaluated in location routing problem with a GRASP metaheuristic. Duhamel et al. (2010) provided full numerical experiments on this topic

The electric location-routing problem with heterogeneous fleet: Formulation and Benders decomposition approach

In this paper, we focus on a problem that requires the location of recharging stations and the routingof electric vehicles in a goods distribution system. The goods are disseminated from a depot anddistributed to the customers via a heterogeneous fleet of electric vehicles with limited capacity.Differently from the classical vehicle routing problem, the vehicles have battery restrictions thatneed to be recharged at some stations if a trip is longer than their range. The problem reducesto finding the optimal locations of the recharging stations and their number to minimize the totalcost, which includes the routing cost, the recharging cost, and the fixed costs of opening stationsand operating vehicles. We propose a novel mathematical formulation and an efficient Bendersdecomposition algorithm embedded into a two-phase general framework to solve this environmentallogistics problem. Phase I solves a restricted problem to provide an upper bound for the originalproblem which is later solved in Phase II. Between the two phases, an intermediate processingprocedure is introduced to reduce the computations of the Phase II problem. This is achieved bya combination of the Phase I upper bound and several lower bounds obtained via exploiting theunderlying network structure. Our approach solves the problem in a general setting with nonidentical stations and vehicles by allowing multiple visits to the stations and partial recharging.The computational study provides both managerial and methodological insights.Keywords: Recharging Station Location, Electric Vehicle Routing, Environmental Logistics,Integer Programming, Benders Decompositio

Etude du problème de localisation-routage périodique

International audienc

An evolutionary algorithm for the periodic location-routing problem

International audienc

Solving the capacitated location-routing problem

International audienceThis is a summary of the main results presented in the author’s Ph.D thesis, available at http://prodhonc.free.fr/homepage. This thesis, written in French, was supervised by Christian Prins and Roberto Wolfler-Calvo, and defended on 16 October 2006 at the Université de Technologie de Troyes. Several new approaches are proposed to solve the capacitated location-routing problem (CLRP): heuristic, cooperative and exact methods. Their performances are tested on various kinds of instances with capacitated vehicles and capacitated or uncapacitated depots

A Metaheuristic for the Periodic Location-Routing Problem

International audienceThe well-known Vehicle Routing Problem (VRP) has been generalized toward tactical or strategic decision levels of companies but not both. The tactical extension or Periodic VRP (PVRP) plans trips over a multi-period horizon, subject to frequency constraints. The strategic extension or Location-Routing Problem (LRP) tackles location and routing decisions simultaneously as in most distribution systems interdependence between these decisions leads to low-quality solutions if depots are located first, regardless the future routes. Our goal is to combine for the first time the PVRP and LRP into the Periodic LRP or PLRP. A metaheuristic is proposed to solve large size instances of the PLRP. It is based on our Randomized Extended Clarke and Wright Algorithm (RECWA) for the LRP and it tries to take into consideration several decision levels when making a choice during the construction of a solution. The method is evaluated on three sets of instances and results are promising. Solutions are compared to the literature on particular cases such as one-day horizon (LRP) or one available depot (PVRP)

A hybrid evolutionary algorithm for the periodic location-routing problem

International audienceThe well-known vehicle routing problem (VRP) has been studied in depth over the last decades. Nowadays, generalizations of VRP have been developed for tactical or strategic decision levels of companies but not both. The tactical extension or periodic VRP (PVRP) plans a set of trips over a multiperiod horizon, subject to frequency constraints. The strategic extension is motivated by interdependent depot location and routing decisions in most distribution systems. Low-quality solutions are obtained if depots are located first, regardless of the future routes. In the location-routing problem (LRP), location and routing decisions are tackled simultaneously. Here for the first time, except for some conference papers, the goal is to combine the PVRP and LRP into an even more realistic problem covering all decision levels: the periodic LRP or PLRP. A hybrid evolutionary algorithm is proposed to solve large size instances of the PLRP. First, an individual representing an assignment of customers to combinations of visit days is randomly generated. The evolution operates through an Evolutionary Local Search (ELS) on visit day assignments. The algorithm is hybridized with a heuristic based on the Randomized Extended Clarke and Wright Algorithm (RECWA) to create feasible solutions and stops when a given number of iterations is reached. The method is evaluated over three sets of instances, and solutions are compared to the literature on particular cases such as one-day horizon (LRP) or one depot (PVRP). This metaheuristic outperforms the previous methods for the PLRP

A hybrid evolutionary algorithm for the periodic location-routing problem

The well-known vehicle routing problem (VRP) has been studied in depth over the last decades. Nowadays, generalizations of VRP have been developed for tactical or strategic decision levels of companies but not both. The tactical extension or periodic VRP (PVRP) plans a set of trips over a multiperiod horizon, subject to frequency constraints. The strategic extension is motivated by interdependent depot location and routing decisions in most distribution systems. Low-quality solutions are obtained if depots are located first, regardless of the future routes. In the location-routing problem (LRP), location and routing decisions are tackled simultaneously. Here for the first time, except for some conference papers, the goal is to combine the PVRP and LRP into an even more realistic problem covering all decision levels: the periodic LRP or PLRP. A hybrid evolutionary algorithm is proposed to solve large size instances of the PLRP. First, an individual representing an assignment of customers to combinations of visit days is randomly generated. The evolution operates through an Evolutionary Local Search (ELS) on visit day assignments. The algorithm is hybridized with a heuristic based on the Randomized Extended Clarke and Wright Algorithm (RECWA) to create feasible solutions and stops when a given number of iterations is reached. The method is evaluated over three sets of instances, and solutions are compared to the literature on particular cases such as one-day horizon (LRP) or one depot (PVRP). This metaheuristic outperforms the previous methods for the PLRP.Location Routing Periodic Metaheuristics Evolutionary computations

A survey of recent research on location-routing problems

International audienceThe design of distribution systems raises hard combinatorial optimization problems. For instance, facility location problems must be solved at the strategic decision level to place factories and warehouses, while vehicle routes must be built at the tactical or operational levels to supply customers. In fact, location and routing decisions are interdependent and studies have shown that the overall system cost may be excessive if they are tackled separately. The location-routing problem (LRP) integrates the two kinds of decisions. Given a set of potential depots with opening costs, a fleet of identical vehicles and a set of customers with known demands, the classical LRP consists in opening a subset of depots, assigning customers to them and determining vehicle routes, to minimize a total cost including the cost of open depots, the fixed costs of vehicles used, and the total cost of the routes. Since the last comprehensive survey on the LRP, published by Nagy and Salhi (2007), the number of articles devoted to this problem has grown quickly, calling a review of new research works. This paper analyzes the recent literature (72 articles) on the standard LRP and new extensions such as several distribution echelons, multiple objectives or uncertain data. Results of state-of-the-art metaheuristics are also compared on standard sets of instances for the classical LRP, the two-echelon LRP and the truck and trailer problem