A Multistage Stochastic Programming Approach to the Dynamic and Stochastic VRPTW - Extended version

We consider a dynamic vehicle routing problem with time windows and stochastic customers (DS-VRPTW), such that customers may request for services as vehicles have already started their tours. To solve this problem, the goal is to provide a decision rule for choosing, at each time step, the next action to perform in light of known requests and probabilistic knowledge on requests likelihood. We introduce a new decision rule, called Global Stochastic Assessment (GSA) rule for the DS-VRPTW, and we compare it with existing decision rules, such as MSA. In particular, we show that GSA fully integrates nonanticipativity constraints so that it leads to better decisions in our stochastic context. We describe a new heuristic approach for efficiently approximating our GSA rule. We introduce a new waiting strategy. Experiments on dynamic and stochastic benchmarks, which include instances of different degrees of dynamism, show that not only our approach is competitive with state-of-the-art methods, but also enables to compute meaningful offline solutions to fully dynamic problems where absolutely no a priori customer request is provided.Comment: Extended version of the same-name study submitted for publication in conference CPAIOR201

Heuristics for dynamic and stochastic routing in industrial shipping

Maritime transportation plays a central role in international trade, being responsible for the majority of long-distance shipments in terms of volume. One of the key aspects in the planning of maritime transportation systems is the routing of ships. While static and deterministic vehicle routing problems have been extensively studied in the last decades and can now be solved effectively with metaheuristics, many industrial applications are both dynamic and stochastic. In this spirit, this paper addresses a dynamic and stochastic maritime transportation problem arising in industrial shipping. Three heuristics adapted to this problem are considered and their performance in minimizing transportation costs is assessed. Extensive computational experiments show that the use of stochastic information within the proposed solution methods yields average cost savings of 2.5% on a set of realistic test instances

Dynamic vehicle routing problems: Three decades and counting

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

Management policies in a dynamic multi-period routing problem

Summary: In this paper we analyze the Dynamic Multi-Period Routing Problem (DMPRP), where a fleet of uncapacitated vehicles has to satisfy customers' pick-up requests. The service of each customer can take place the day the request is issued or the day after. At the beginning of a day a set of requests are already known and have to be served during the day. Additional requests may arrive during the day while the vehicles are traveling. In this context we perform different types of analysis, each one characterized by the comparison of alternative management policies. The first analysis compares a policy which decides, at the time the request is issued, whether to accept or reject it to a policy that accepts all the requests and decides, at a later time, which ones to forward to a back-up service company. The second evaluates the advantages of a collaborative service policy where a fleet of vehicles is managed by a unique decision maker with respect to a policy where the same vehicles are managed independently. Finally, in the last analysis a policy where each new request is taken into account as soon as it is issued is compared to a policy where all the requests issued during a day are analyzed at the end of the day. Extensive computational results evaluating the number of lost requests and the distance traveled provide interesting insights

Management policies in a dynamic multi-period routing problem

Summary: In this paper we analyze the Dynamic Multi-Period Routing Problem (DMPRP), where a fleet of uncapacitated vehicles has to satisfy customers' pick-up requests. The service of each customer can take place the day the request is issued or the day after. At the beginning of a day a set of requests are already known and have to be served during the day. Additional requests may arrive during the day while the vehicles are traveling. In this context we perform different types of analysis, each one characterized by the comparison of alternative management policies. The first analysis compares a policy which decides, at the time the request is issued, whether to accept or reject it to a policy that accepts all the requests and decides, at a later time, which ones to forward to a back-up service company. The second evaluates the advantages of a collaborative service policy where a fleet of vehicles is managed by a unique decision maker with respect to a policy where the same vehicles are managed independently. Finally, in the last analysis a policy where each new request is taken into account as soon as it is issued is compared to a policy where all the requests issued during a day are analyzed at the end of the day. Extensive computational results evaluating the number of lost requests and the distance traveled provide interesting insights

Thermally induced structural transformations of multicomponent Fe72Cu1V4Si15B8 alloy

Thermally induced structural transformations of Fe72Cu1V4Si15B8alloy were examined. Thermal analysis revealed multistep structural stabilization process starting at around 470oC, manifested by two complex exothermic DTA peaks, which were deconvoluted. Microstructure of the as-prepared and thermally treated alloy was studied using XRD. Kinetic triplets of individual steps were determined and further checked by comparing simulated and experimental DTA curves.Physical chemistry 2016 : 13th international conference on fundamental and applied aspects of physical chemistry; Belgrade (Serbia); 26-30 September 201