A dynamic approach for the vehicle routing problem with stochastic demands (original) (raw)

Stochastic Vehicle Routing Problem: A Literature Survey

The purpose of this paper is to develop structural classi¯cation of Stochastic Vehicle Routing Problem (SVRP) by di®erent domains and attributes. This research used a systematic review and meta-analysis on SVRP literatures. This includes browsing relevant researches and publications, screening related articles, identifying domains, attributes and categorising the articles based on the identi¯ed domains and attributes. Thē ndings of the study show clear di®erences on the number of studies under each domain and attribute. Most studied attributes are stochastic customer demand, capacitated vehicle, synthesis data and objective function with cost minimization. Whereas the least studied are maximisation objective function, stochastic service time, and an applied model using stochastic with recurs. The research helps to summarise and map a comprehensive survey on SVRP literatures so that various contributions in the¯eld are organised in a manner that provide a clear view for the readers and identify future research directions. This paper is the¯rst of its kind in the¯eld of SVRP that develop a classi¯cation scheme for articles published since 1993 to enhances the development of this newly emerging discipline.

A simulation-based algorithm for solving the Vehicle Routing Problem with Stochastic Demands

2013

This paper proposes a flexible solution methodology for solving the Vehicle Routing Problem with Stochastic Demands (VRPSD). The logic behind this methodology is to transform the issue of solving a given VRPSD instance into an issue of solving a small set of Capacitated Vehicle Routing Problem (CVRP) instances. Thus, our approach takes advantage of the fact that extremely efficient metaheuristics for the CVRP already exists. The CVRP instances are obtained from the original VRPSD instance by assigning different values to the level of safety stocks that routed vehicles must employ to deal with unexpected demands. The methodology also makes use of Monte Carlo Simulation (MCS) to obtain estimates of the expected costs associated with corrective routing actions (recourse actions) after a vehicle runs out of load before completing its route.

Vehicle Fleet Sizing, Positioning and Routing Problem with Stochastic Customers

2021

The Vehicle Fleet Sizing, Positioning and Routing Problem with Stochastic Customers (VFSPRP-SC) consists on pairing strategic decisions of depot positioning and fleet sizing with operational vehicle routing decisions while taking into account the inherent uncertainty of demand. We successfully solve the VFSPRP-SC with a methodology comprised of two main blocks: i) a scenario generation phase and ii) a two–stage stochastic program. For the first block, a set of scenarios is selected with a simulation–based approach that captures the behavior of the demand and allows us to come up with different solutions that could match different risk profiles. The second block is comprised of a facility location and allocation model and a Multi Depot Vehicle Routing Problem (MDVRP) assembled under a two–stage stochastic program. We propose several novel ideas within our methodology: problem specific cuts that serve as an approximation of the expected second stage costs as a function of first stage ...

The vehicle routing problem with stochastic demand and duration constraints

Transportation Science, 2010

Time considerations have been largely ignored in the study of vehicle routing problems with stochastic demands, even though they are crucial in practice. We show that tour duration limits can effectively and efficiently be incorporated in solution approaches that build fixed, or a priori, tours for such problems. We do so by assuming that each tour must be duration feasible for all demand realizations, and determine the maximum duration of a given delivery tour by solving the optimization problem of an adversary. A computational ...

Vehicle routing with stochastic demands and restricted failures

ZOR Zeitschrift f�r Operations Research Methods and Models of Operations Research, 1993

This paper considers a class of stochastic vehicle routing problems (SVRPs) with random demands, in which the number of potential failures per route is restricted either by the data or the problem constraints. These are realistic cases as it makes little sense to plan vehicle routes that systematically fail a large number of times. First, a chance constrained version of the problem is considered which can be solved to optimality by algorithms similar to those developed for the deterministic vehicle routing problem (VRP). Three classes of SVRP with recourse are then analyzed. In all cases, route failures can only occur at one of the last k customers of the planned route. Since in general, SVRPs are considerably more intractable than the deterministic VRPs, it is interesting to note that these realistic stochastic problems can be solved as a sequence of deterministic traveling salesman problems (TSPs). In particular, when k = 1 the SVRP with recourse reduces to a single TSP.

A Multiple Vehicles Routing Problem Algorithm with Stochastic Demand

2006 6th World Congress on Intelligent Control and Automation, 2006

A heuristic algorithm for multiple vehicles routing problem with stochastic demand is proposed and the goal is to minimize the total traveling cost. Two-phase method is adopted to deal with this problem. In the first phase, an algorithm proposed to partition customers into clusters, and the main task of the second phase is to design effective routing through each cluster of customers to minimize the total expected traveling cost. he a priori strategy and reoptimization strategy used to obtain the optimal routing. The experiment results indicate that this method can produce solutions of good quality and is an effective algorithm for the multiple vehicles routing problem with stochastic demand.

New Exact Algorithm and Solution Properties for the Vehicle Routing Problem with Stochastic Demands

arXiv: Optimization and Control, 2018

This paper considers the vehicle routing problem with stochastic demands (VRPSD) under optimal restocking. We develop an exact algorithm that is effective for solving instances with many vehicles and few customers per route. In our experiments, we show that in these instances solving the stochastic problem is most relevant (i.e., the potential gains over the deterministic equivalent solution are highest). The proposed branch-price-and-cut algorithm relies on an efficient labeling procedure, exact and heuristic dominance rules, and completion bounds to price profitable columns. Instances with up to 76 nodes could be solved in less than 5 hours, and instances with up to 148 nodes could be solved in long-runs of the algorithm. The experiments also allowed new findings on the problem. Solving the stochastic problem leads to solutions up to 10% superior to the deterministic equivalent solution. When the number of routes is not fixed, the optimal solutions under detour-to-depot and optima...