Solving dynamic resource constraint project scheduling problems using new constraint programming tools

Timetabling problems have been studied a lot over the last decade. Due to the complexity and the variety of such problems, most work concern static problems in which activities to schedule and resources are known in advance, and constraints are fixed. However, every timetabling problem is subject to unexpected events (consider for example, for university timetabling problems, a missing teacher, or a slide projector breakdoawn). In such a situation, one has to quickly build a new solution which takes these events into account and which is preferably not too different form the current one. We introduce in this paper constraint-programming based tools for solving dynamic timetabling problems modelled as RCPSP (Resource-Constrained Project Scheduling Problems). This approach uses explanation-based constraint programming and operational research techniques

A dynamic approach for the vehicle routing problem with stochastic demands

The Vehicle Routing Problem with Stochastic Demands (VRPSD) is a variation of the classical Capacitated Vehicle Routing Problem (CVRP). In contrast to the deterministic CVRP, in the VRPSD the demand of each customer is modeled as a random variable and its realization is only known upon vehicle arrival to the customer site. Under this uncertain scenario, a possible outcome is that the demand of a customer ends up exceeding the remaining capacity of the vehicle, leading to a route failure. In this study we will focus on the single vehicle VRPSD in which the fleet is limited to one vehicle with finite capacity, that can execute various routes sequentially. The present work is based on an adaptation of an optimization framework developed initially for the vehicle routing problem with dynamic customers (i.e., customers appear while the vehicles are executing their routes)

On the Technician Routing and Scheduling Problem

ISBN 978-88-900984-3-7International audienceThe technician routing and scheduling problem consists in routing and scheduling a crew of technicians in order to attend a set of service requests, subject to skill, tool, and spare part constraints. In this study we propose a formal definition of the problem and present a constructive heuristic and a large neighborhood search optimization algorithm

The electric vehicle routing problem with partial charging and nonlinear charging function

SDOElectric vehicle routing problems (eVRPs) extend classical routing problems to consider the limited driving range of electric vehicles. In general, this limitation is overcome by introducing planned detours to battery charging stations. Most existing eVRP models rely on one (or both) of the following assumptions: (i) the vehicles fully charge their batteries every time they reach a charging station, and (ii) the battery charge level is a linear function of the charging time. In practical situations, however, the amount of charge is a decision variable, and the battery charge level is a concave function of the charging time.In this paper we extend current eVRP models to consider partial charging and nonlinear charging functions. We present a computational study comparing our assumptions with those commonly made in the literature. Our results suggest that neglecting partial and nonlinear charging may lead to infeasible or overly expensive solutions

The electric vehicle routing problem with non-linear charging functions

International audienceThe use of electric vehicles (EVs) in freight and passenger transportation gives birth to a new family of vehicle routing problems (VRPs), the so-called electric VRPs (e-VRPs). As their name suggests, e-VRPs extend classical VRPs to account (mainly) for two constraining EV features: the short driving range and the long battery charging time. As a matter of fact, routes performed by EVs usually need to include time-consuming detours to charging stations. Most of the existing literature on e-VRPs relies on one of the following assumptions: i) vehicles recharge to their battery to its maximum level every time they reach a charging station or ii) the amount of battery charge is a linear function of the charging time. In practical situations, however, the amount of charge (and thus the time spent at each charging point) is a decision variable and battery charge levels are a concave function of the charging times. In this research we introduce the electric vehicle routing problem with non-linear charging functions (e-VRP-NLCF). We propose a mixed-integer linear programming (MILP) formulation that, running on a commercial solver, is able to solve small instances of the problem. To tackle large-scale instances we propose a metaheuristic that uses a MILP formulation to find the optimal charging policy. We report on extensive computational experiments evaluating the performance of the proposed methods and analyzing the impact on the solutions of different charging policy assumptions

Dynamic Vehicle Routing Problems: State of the art and Prospects

This scientific report summarizes the results of a literature review on dynamic vehicle routing problems. After a brief description of vehicle routing problems in general, a classification is introduced to distinguish between static and dynamic problems. Then a more precise definition of dynamism is presented, supported by example of real-world applications of such problems. Finally, a detailed study of the current state of the art in dynamic vehicle routing optimization is drawn

Joint-optimization of inventory policies on a multi-product multi-echelon pharmaceutical system with batching and ordering constraints

Article de revue (Article scientifique dans une revue à comité de lecture)International audienceThis paper presents a methodology to find near-optimal joint inventory control policies for the real case of a one-warehouse, n-retailer distribution system of infusion solutions at a University Medical Center in France. We consider stochastic demand, batching and order-up-to level policies as well as aspects partic- ular to the healthcare setting such as emergency deliveries, required service level rates and a new con- straint on the ordering policy that fits best the hospital’s interests instead of abstract ordering costs. The system is modeled as a Markov chain with an objective to minimize the stock-on-hand value for the overall system. We provide the analytical structure of the model to show that the optimal reorder point of the policy at both echelons is easily derived from a simple probability calculation. We also show that the optimal policy at the care units is to set the order-up-to level one unit higher than the reorder point. We further demonstrate that optimizing the care units in isolation is optimal for the joint multi- echelon, n-retailer problem. A heuristic algorithm is presented to find the near-optimal order-up-to level of the policy of each product at the central pharmacy; all other policy parameters are guaranteed optimal via the structure provided by the model. Comparison of our methodology versus that currently in place at the hospital showed a reduction of approximately 45% in the stock-on-hand value while still respecting the service level requirements.&nbsp;</p