Tackling a VRP challenge to redistribute scarce equipment within time windows using metaheuristic algorithms

This paper reports on the results of the VeRoLog Solver Challenge 2016–2017: the third solver challenge facilitated by VeRoLog, the EURO Working Group on Vehicle Routing and Logistics Optimization. The authors are the winners of second and third places, combined with members of the challenge organizing committee. The problem central to the challenge was a rich VRP: expensive and, therefore, scarce equipment was to be redistributed over customer locations within time windows. The difficulty was in creating combinations of pickups and deliveries that reduce the amount of equipment needed to execute the schedule, as well as the lengths of the routes and the number of vehicles used. This paper gives a description of the solution methods of the above-mentioned participants. The second place method involves sequences of 22 low level heuristics: each of these heuristics is associated with a transition probability to move to another low level heuristic. A randomly drawn sequence of these heuristics is applied to an initial solution, after which the probabilities are updated depending on whether or not this sequence improved the objective value, hence increasing the chance of selecting the sequences that generate improved solutions. The third place method decomposes the problem into two independent parts: first, it schedules the delivery days for all requests using a genetic algorithm. Each schedule in the genetic algorithm is evaluated by estimating its cost using a deterministic routing algorithm that constructs feasible routes for each day. After spending 80 percent of time in this phase, the last 20 percent of the computation time is spent on Variable Neighborhood Descent to further improve the routes found by the deterministic routing algorithm. This article finishes with an in-depth comparison of the results of the two approaches

An corrigendum on the paper: Solving the job-shop scheduling problem optimally by dynamic programming

In [1] an algorithm is proposed for solving the job-shop scheduling problem optimally using a dynamic programming strategy. This is, according to our knowledge, the first exact algorithm for the Job Shop problem which is not based on integer linear programming and branch and bound. Despite the correctness of the dynamic programming algorithm presented in [1], the proof of correctness given there is unfortunately flawed. The contribution of the present note is to point out that flaw, and refer the reader to [2], where the flaw is corrected. Particularly, in [2], we recall the main idea of the proof proposed in [1] and present a counterexample that shows where the problem of that proof lies. Taking into account the nature of the problem, we propose a new approach for proving the correctness of the algorithm. This requires the introduction of new concepts and notation. It is important to remark that the new proof modifies our understanding of the algorithm that, in fact, works in a different way than the one explained in the original article. We also recommend [3], where all the elements for understanding the algorithm, the new proof of its correctness and the estimations of its complexity are fully developed.Fil: van Hoorn, Jelke J.. Vrije Universiteit Amsterdam; Países BajosFil: Nogueira, Agustín. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; ArgentinaFil: Ojea, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas ; ArgentinaFil: Gromicho, Joaquim A. S.. Vrije Universiteit Amsterdam; Países Bajo