A multi-space sampling heuristic for the vehicle routing problem with stochastic demands

The vehicle routing problem with stochastic demands consists in designing transportation routes of minimal expected cost to satisfy a set of customers with random demands of known probability distributions. This paper proposes a simple yet effective heuristic approach that uses randomized heuristics for the traveling salesman problem, a tour partitioning procedure, and a set partitioning formulation to sample the solution space and find high-quality solutions for the problem. Computational experiments on benchmark instances from the literature show that the proposed approach is competitive with the state-of-the-art algorithm for the problem in terms of both accuracy and efficiency. In experiments conducted on a set of 40 instances, the proposed approach unveiled four new best-known solutions (BKSs) and matched another 24. For the remaining 12 instances, the heuristic reported average gaps with respect to the BKS ranging from 0.69 to 0.15 % depending on its configuration

Solving real-world vehicle routing problems in uncertain environments

This is a summary of the Ph.D. thesis defended by the author in December 2009 at École des Mines de Nantes and Universidad de los Andes in Bogotá. The thesis was advised by Christelle Guéret and Andrés L. Medaglia and co-advised by Bruno Castanier and Nubia Velasco. The manuscript is written in English and it is available from the author upon request. The focus of the dissertation is to study real-world vehicle routing problems (VRPs) in uncertain environments. First, the thesis proposes a set of new methods for the VRP faced by a public utility and reports how these methods were embedded into a decision support system. Second, the thesis introduces a stochastic VRP widely found in practice but never studied in the literature before: the multi-compartment VRP with stochastic demands (MC-VRPSD). To solve the problem the dissertation proposes a set of solution methods that offer different tradeoffs between accuracy, speed, simplicity and flexibility. Lastly, the thesis proposes two multiobjective approaches to address the risk behavior of decision makers towards the cost spread in stochastic routing problems and applies them to the MC-VRPSD

A comparative study of charging assumptions in electric vehicle routing problems

International audienceElectric 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 research 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

A parallel matheuristic for the technician routing problem with electric and conventional vehicles

The technician routing problem with conventional and electric vehicles (TRP-CEV) consists in designing service routes taking into account the customers’ time windows and the technicians’ skills, shifts, and lunch breaks. In the TRP-CEV routes are covered using a fixed and heterogeneous fleet of conventional and electric vehicles (EVs). Due to their relatively limited driving ranges, EVs may need to include in their routes one or more recharging stops. In this talk we present a parallel matheuristic for the TRP-CEV. The approach works in two phases. In the first phase it decomposes the problem into a number of “easier to solve” vehicle routing problems with time windows and solves these problems in parallel using a GRASP. During the execution of this phase, the routes making up the local optima are stored in a long-term memory. In the second phase, the approach uses the routes stored in the long-term memory to assemble a solution to the TRP-CEV. We discuss computational experiments carried on real-world TRP-CEV instances provided by a French public utility and instances for the closely-related electric fleet size and mix vehicle routing problem with time windows and recharging stations taken from the literature.

Scheduling Maintenance in Wind Farms

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A MIP Model and Several Approaches to Schedule Maintenance in Wind Farms on a Short-term Horizon

Taking into account wind prediction when scheduling maintenance on wind turbines can lead to potential gains. Preemption, transfer times for resources, and outsourcing are considered in this problem. The objective is concerned with maximizing the difference between the profits of wind farms related to the estimated production and the costs associated with outsourcing and resources transfers. A MIP model, a benders decomposition technique and a constraint programming approach are proposed

A Benders-based branch-and-cut approach to solve a wind turbine maintenance scheduling problem

We deal with a maintenance scheduling problem rising in the onshore wind power industry. We address the problem on a short-term horizon considering an individual management of the technicians through a space-time tracking. The objective is to find a maintenance planning that maximizes the production of the turbines while taking into account wind predictions, multiple task execution modes, and task-technician assignment constraints. We introduce an exact method to solve this challenging problem. We first propose integer linear programming (ILP) formulations of this problem. Then, on this basis, we build up a Benders-based branch-and-cut approach making use of Benders cuts as well as problem-specific cuts. Our method solves to optimality most of the instances or delivers solutions with a small average gap with respect to upper bounds. The results suggest that our method significantly outperforms the direct resolution of ILP models

A branch-and-check approach to solve a short-term maintenance scheduling problem faced by the onshore wind industry

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Maintenance scheduling in the electricity industry: A literature review

The reliability of the power plants and transmission lines in the electricity industry is crucial for meeting demand. Consequently, timely maintenance plays a major role reducing breakdowns and avoiding expensive production shutdowns. By now, the literature contains a sound body of work focused on improving decision making in generating units and transmission lines maintenance scheduling. The purpose of this paper is to review that literature. We update previous surveys and provide a more global view of the problem: we study both regulated and deregulated power systems and explore some important features such as network considerations, fuel management, and data uncertainty