Fuel emissions optimization in vehicle routing problems with time-varying speeds

The problem considered in this paper is to produce routes and schedules for a fleet of delivery vehicles that minimize the fuel emissions in a road network where speeds depend on time. In the model, the route for each vehicle must be determined, and also the speeds of the vehicles along each road in their paths are treated as decision variables. The vehicle routes are limited by the capacities of the vehicles and time constraints on the total length of each route. The objective is to minimize the total emissions in terms of the amount of Greenhouse Gas (GHG) produced, measured by the equivalent weight of CO2 (CO2e). A column generation based tabu search algorithm is adapted and presented to solve the problem. The method is tested with real traffic data from a London road network. The results are analyzed to show the potential saving from the speed adjustment process. The analysis shows that most of the fuel emissions reduction is able to be attained in practice by ordering the customers to be visited on the route using a distance-based criterion, determining a suitable path between customers for each vehicle and travelling as fast as is allowed by the traffic conditions up to a preferred speed

Variable-depth adaptive large meighbourhood search algorithm for Open Periodic Vehicle Routing Problem with time windows

The Open Periodic Vehicle Routing Problem with Time Windows (OPVRPTW) is a practical transportation routing and scheduling problem arising from real-world scenarios. It shares some common features with some classic VRP variants. The problem has a tightly constrained large-scale solution space and requires well balanced diversification and intensification in search. In Variable Depth Neighbourhood Search, large neighbourhood depth prevents the search from trapping into local optima prematurely, while small depth provides thorough exploitation in local areas. Considering the multi-dimensional solution structure and tight constraints in OPVRPTW, a Variable-Depth Adaptive Large Neighbourhood Search (VD-ALNS) algorithm is proposed in this paper. Contributions of four tailored destroy operators and three repair operators at variable depths are investigated. Comparing to existing methods, VD-ALNS makes a good trade-off between exploration and exploitation, and produces promising results on both small and large size benchmark instances

Developing vehicle routing and outbound fulfillment systems for an E-grocery company

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2009.Includes bibliographical references (p. 60-61).This paper outlines areas for improvement within the outbound fulfillment network of an emerging online grocery ("e-grocery") company offering home delivery to the customer. In particular, the research focuses on developing an efficient, scalable home delivery network, as a result of the known challenges and relatively high fulfillment costs associated with this business model. Last-mile home delivery accounts for a substantial portion of total e-grocery fulfillment costs. The Vehicle Routing Problem (VRP), a well-known NP-hard combinatorial optimization problem, is examined in the context of e-grocery and its impact on last-mile delivery costs. The paper emphasizes an integration of scalable vehicle routing systems with efficient order fulfillment operations. Practical analytical approaches, as well as new case experiments, serve as a framework of recommendations for an emerging e-grocer or similar last-mile delivery provider. The paper presents analysis using a real case study, serving as a basis for example, as well as more broad recommendations in the field. Moreover, it directs the reader to a wealth of literature in the fields of logistics, grocery fulfillment operations and the VRP class.by Nicholas Barker.S.M.M.B.A

Column generation with dynamic duty selection for railway crew rescheduling

The Dutch railway network experiences about three large disruptions per day on average. In this paper, we present an algorithm to reschedule the crews when such a disruption occurs. The algorithm is based on column generation techniques combined with Lagrangian heuristics. Since the number of duties is very large in practical instances, we first define a core problem of tractable size. If some tasks remain uncovered in the solution of the core problem, we perform a neighborhood exploration to improve the solution. Computational experiments with real-life instances show that our method is capable of producing good solutions within a couple of minutes of Computation time

Partitionnement d’une zone géographique en territoires homogènes et contigus

« RÉSUMÉ : Le problème de régionalisation ou de "districting" consiste à diviser une zone géographique en un nombre prédéfini de sous-zones contigües tout en minimisant un critère de partitionnement fonction de données non géographiques. Le problème de régionalisation peut être vu comme un processus de regroupement d'unités élémentaires, les unités géographiques (UG), en groupes appelés territoires qui, une fois assemblés, reconstituent la carte ou la configuration donnée. Ce problème a surtout été étudié dans le cadre du découpage électoral. Le problème qui nous intéresse consiste à grouper les UG selon une valeur associée à chacune d'elle en des territoires homogènes respectant un poids minimum. Pour cela nous utilisons comme critère d'agrégation la variance intra-territoire qui est la somme pondérée de la variance de chaque territoire en solution. La variance d'un territoire est la variance de la valeur associée à chaque UG lui appartenant, et la pondération d'un territoire dans l'objectif est la somme des poids de chaque UG qui lui est associé. Ce problème est difficile à résoudre et une technique d'énumération de tous les territoires n'est donc pas envisageable pour de grandes instances. La difficulté par rapport à quelques travaux déjà réalisés est la présence simultanée d'une fonction objectif quadratique et d'une contrainte de contiguïté, ainsi que la taille des instances (500 UG). Ce travail présente une méthode heuristique de génération de colonnes couplée avec une méthode de branchement pour résoudre un tel problème de partitionnement avec contrainte de contiguïté. Dans la méthode de génération de colonnes, le sous-problème génère de nouveaux territoires et il est résolu par un algorithme heuristique de type glouton avec plusieurs points de départ. La méthode de branchement est aussi heuristique car les décisions prises sont fixées de façon permanente, i.e., aucun retour en arrière n'est permis dans l'arbre de branchement. A notre connaissance une telle méthode de résolution avec des instances de l'ordre de 500 UG n'a pas encore été appliquée. Cette méthode a été développée dans un contexte industriel et permet d'obtenir des solutions réalisables de bonne qualité sur les instances testées dans des temps relativement courts (15 min. à 40 min.). Abstract: The regionalization or districting problem consists of dividing a geographical area into a predefined number of contiguous territories, while optimizing a clustering criterion. The regionalization problem can be seen as a process of aggregating elementary geographical units (GU) into clusters called territories, that combined, cover the entire map or given configuration. The most studied variant of this problem is the electoral districting problem. The variant of the regionalization problem studied here, referred to as the PPHCT, consists of aggregating the GU according to their value into homogeneous and contiguous territories, satisfying a minimum weight constraint. For this matter, an aggregation criterion, namely the within-territory variance, is used, which is the weighted sum of the variance of each territory in the solution. The variance of a territory is the variance of the value assigned to each GU in that territory, and the weight of a territory is the sum of the weights of each GU in that territory.» et «-----------ABSTRACT : The PPHCT is difficult to solve optimally and an enumeration of all the feasible territories cannot be applied for large instances. The main difficulty of this variant, in comparison to other variants previously studied, is the simultaneous presence of a contiguity constraint and a quadratic objective function, together with large instances (500 GU). The purpose of this paper is to present a heuristic column-generation model and branch-and-bound algorithm designed to solve the PPHCT. In the column-generation method, the sub-problem generates new feasible territories and is solved by a greedy multi-start algorithm. The branching method is also heuristic, as branching decisions are taken permanently, that is, no back -tracking is possible in the branching tree. This solution method was developed in an industrial context and is able to produce good quality feasible solutions on the tested data, in relatively short computing times (15 min. to 40 min.).