Fausto Errico

Exact Algorithms for Electric Vehicle-Routing Problems with Time Windows

Effective route planning for battery electric commercial vehicle (ECV) fleets has to take into account their limited autonomy and the possibility of visiting recharging stations during the course of a route. In this paper, we consider four variants of the electric vehicle-routing problem with time windows: (i) at most a single recharge per route is allowed, and batteries are fully recharged on visit of a recharging station; (ii) multiple recharges per route, full recharges only; (iii) at most a single recharge per route, and partial battery recharges are possible; and (iv) multiple, partial recharges. For each variant, we present exact branch-price-and-cut algorithms that rely on customized monodirectional and bidirectional labeling algorithms for generating feasible vehicle routes. In computational studies, we find that all four variants are solvable for instances with up to 100 customers and 21 recharging stations. This success can be attributed to the tailored resource extension functions (REFs) that e...

A priori optimization with recourse for the vehicle routing problem with hard time windows and stochastic service times

The vehicle routing problem with hard time windows and stochastic service times (VRPTW-ST) introduced by Errico et al. (2013) in the form of a chance-constrained model mainly differs from other vehicle routing problems with stochastic service or travel times considered in the literature by the presence of hard time windows. This makes the problem extremely challenging. In this paper, we model the VRPTW-ST as a two-stage stochastic program and define two recourse policies to recover operations feasibility when the first stage plan turns out to be infeasible. We formulate the VRPTW-ST as a set partitioning problem and solve it by exact branch-cut-and-price algorithms. Specifically, we developed efficient labeling algorithms by suitably choosing label components, determining extension functions, and developing lower and upper bounds on partial route reduced cost to be used in the column generation step. Results on benchmark data show that our methods are able to solve instances with up to 50 customers for both recourse policies.

The vehicle routing problem with hard time windows and stochastic service times

In this paper we consider the vehicle routing problem with hard time windows and stochastic service times (VRPTW-ST); in this variant of the classic VRPTW the service times are random variables. In particular, given a set of vehicle routes, some of the actual service times might not lead to a feasible solution, given the customer time windows. We consider a chance-constrained program to model the VRPTW-ST and provide a new set partitioning formulation that includes a constraint on the minimum success probability of the set of vehicle routes. Under some mild conditions, we develop a method to exactly compute the success probability of the routes. We then solve the VRPTW-ST by a branch-price-and-cut algorithm, where the main challenges are in the solution of the subproblems of the column generation procedure. We adapt the dynamic programming algorithm to account for the probabilistic resource consumption by extending the label dimension and by providing new dominance rules. Extensive computational experiments prove the effectiveness of both the solution method and the stochastic model.

Modeling demand uncertainty in two-tier city logistics tactical planning

We consider the complex and not-yet-studied issue of building the tactical plan of a two-tiered city logistics system while explicitly accounting for the uncertainty in the forecast demand. We describe and formally define the problem and then propose a general modeling framework, which takes the form of a two-stage stochastic programming formulation, the first stage selecting the first-tier service network design and the general workloads of the intertier transfer facilities, and the second stage determines the actual vehicle routing on the second tier as well as some limited adjustments of the first-stage service design decisions. Four different strategies of adapting the plan to the observed demand are introduced together with the associated recourse formulations. These strategies are then experimentally compared through an evaluation procedure that, based on Monte Carlo principles, mimics the decision process of a priori planning followed by repetitively applying the adjusted plan to the periods of the...

A Benders Decomposition Approach for the Symmetric TSP with Generalized Latency Arising in the Design of Semiflexible Transit Systems

We present the symmetric traveling salesman problem with generalized latency (TSP-GL) a new problem arising in the planning of the important class of semiflexible transit systems. The TSP-GL can be seen as a very challenging variant of the symmetric traveling salesman problem (S-TSP), where the objective function combines the usual cost of the circuit with a routing component accounting for the passenger travel times. The main contributions of the paper include the formulation of the problems in terms of multicommodity flows, the study of its mathematical properties, and the introduction of a branch-and-cut approach based on Benders reformulation taking advantage of properties that relate the feasible region of the TSP-GL and the S-TSP polyhedron. An extensive computational experimentation compares a number of variants of the proposed algorithm, as well as a commercial solver. These experiments show that the method we propose significantly outperforms a well-known commercial solver and obtains good-qualit...