The Time-Dependent Multiple-Vehicle Prize-Collecting Arc Routing Problem

In this paper, we introduce a multi vehicle version of the Time-Dependent Prize-Collecting Arc Routing Problem (TD-MPARP). It is inspired by a situation where a transport manager has to choose between a number of full truck load pick-ups and deliveries to be performed by a fleet of vehicles. Real-life traffic situations where the travel times change with the time of day are taken into account. Two metaheuristic algorithms, one based on Variable Neighborhood Search and one based on Tabu Search, are proposed and tested for a set of benchmark problems, generated from real road networks and travel time information. Both algorithms are capable of finding good solutions, though the Tabu Search approach generally shows better performance for large instances whereas the VNS is superior for small instances. We discuss the structural differences of the implementation of the algorithms which explain these results

Throwing out food before expiration and still reducing food waste: online vs. offline retail

Online retailers throw out food that has not yet expired. This gives rise to the question whether online retailers generate more food waste than offline retailers, who typically throw out food only after it has expired. We focus on the food waste at the retailer which inherently ensues from the logistical set-up. We first provide a theoretical analysis to establish whether throwing out food before expiration indeed results in an increase in food waste, putting online retailers at a disadvantage compared to offline retailers. We show the relevance of this question by providing a theoretical example, showing an inventory control policy which counter-intuitively results in a decrease in food waste. Nonetheless, we show for well-behaved inventory control policies, including the optimal policy, that food waste increases when food is thrown out before expiration. Next, we compare the food waste of the online retailer with that of an offline retailer in numerical experiments. Note that the online retailer has some advantages over offline retailers as well. Online retailers benefit from full control of order picking, which is instead often done by the consumer in offline retail. Moreover, the online retailer often benefits from the pooling effect, as offline retailers might use multiple stores to satisfy the same demand volume as an online retailer from a single warehouse. Our numerical experiments with a base-stock policy suggests that online retail actually yields less food waste for many products, despite throwing out food before expiration

The time-dependent prize-collecting arc routing problem

A new problem is introduced named the Time-Dependent Prize-Collecting Arc Routing Problem (TDPARP). It is particularly relevant to situations where a transport manager has to choose between a number of full truck load pick-ups and deliveries on a road network where travel times change with the time of day. Two metaheuristic algorithms, one based on Variable Neighborhood Search and one based on Tabu Search, are proposed and tested for a set of benchmark problems, generated from real road networks and travel time information. Both algorithms are capable of finding good solutions, though the VNS approach generally shows better performance

A Lower Bound for the Node, Edge, and Arc Routing Problem

The Node, Edge, and Arc Routing Problem (NEARP) was defined by Prins and Bouchenoua in 2004, although similar problems have been studied before. This problem, also called the Mixed Capacitated General Routing Problem (MCGRP), generalizes the classical Capacitated Vehicle Routing Problem (CVRP), the Capacitated Arc Routing Problem (CARP), and the General Routing Problem. It captures important aspects of real-life routing problems that were not adequately modeled in previous Vehicle Routing Problem (VRP) variants. The authors also proposed a memetic algorithm procedure and defined a set of test instances called the CBMix benchmark. The NEARP definition and investigation contribute to the development of rich VRPs. In this paper we present the first lower bound procedure for the NEARP. It is a further development of lower bounds for the CARP. We also define two novel sets of test instances to complement the CBMix benchmark. The first is based on well-known CARP instances; the second consists of real life cases of newspaper delivery routing. We provide numerical results in the form of lower and best known upper bounds for all instances of all three benchmarks. For three of the instances, the gap between the upper and lower bound is closed. The average gap is 25.1%. As the lower bound procedure is based on a high quality lower bound procedure for the CARP, and there has been limited work on approximate solution methods for the NEARP, we suspect that a main reason for the rather large gaps is the quality of the upper bound. This fact, and the high industrial relevance of the NEARP, should motivate more research on approximate and exact methods for this important problem.acceptedVersio

A Lower Bound for the Node, Edge, and ArcRouting Problem

-The Node, Edge, and Arc Routing Problem (NEARP) was defined by Prins and Bouchenoua in 2004. They also proposed a memetic algorithm procedure and defined a set of test instances: the so-called CBMix benchmark. The NEARP generalizes the classical CVRP, the CARP, and the General Routing Problem. It captures important aspects of real-life routing problems that were not adequately modelled in previous VRP variants. Hence, its definition and investigation contribute to the development of rich VRPs. In this paper we present the first lower bound for the NEARP. It is a further development of lower bounds for the CARP. We also define two novel sets of test instances to complement the CBMix benchmark. The first is based on well-known CARP instances; the second consists of real life cases of newspaper delivery routing. We provide numerical results in the form of lower and best known upper bounds for all instances of all three benchmarks. For two of the instances, the gap is close

Solution of the maximal covering tour problem for locating recycling drop-off stations

Tactical decisions on the location of recycling drop-off stations and the associated collection system are essential in order to increase recycling amounts while keeping operational costs at a minimum. The conflicting nature of the objectives of the problem can be modelled as a bi-objective location-routeing problem. In this paper, we address the location-routeing problem of recycling drop-off stations by solving the Maximal Covering Tour Problem. To this aim, we propose a heuristic inspired by a variable neighbourhood search. The heuristic is tested on a set of benchmark instances from the TSPLIB and applied to a set of real-life instances from both urban and rural areas in Denmark. Based on the results of the real-life cases, we provide insights on the trade-off between recycling rates and transportation costs