A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times

Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe

A simheuristic algorithm for solving an integrated resource allocation and scheduling problem

Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings

The Shortest Path Tour Problem and its variants

Scope of this thesis is to provide a treatment of the Shortest Path Tour Problem, and its variants. It presents a deep investigation of two variants of the SPTP, the Constrained Shortest Path Tour Problem and Shortest Path Tour Problem with Time Windows, respectively. Moreover, a GRASP meta-heuristic is applied to solve further hard combinatorial optimization problems

An efficient exact approach for the constrained shortest path tour problem

Given a directed graph with non-negative arc lengths, the Constrained Shortest Path Tour Problem ((Formula presented.)) is aimed at finding a shortest path from a single-origin to a single-destination, such that a sequence of disjoint and possibly different-sized node subsets are crossed in a given fixed order. Moreover, the optimal path must not include repeated arcs. In this paper, for the (Formula presented.) we propose a new mathematical model and a new efficient Branch & Bound method. Extensive computational experiments have been carried out on a significant set of test problems in order to evaluate empirically the performance of the proposed approach

Hybridizations of GRASP with path relinking for the far from most string problem

Among the sequence selection and comparison problems, the far from most string problem (FFMSP) is one of the computationally hardest with applications in several fields, including molecular biology where one is interested in creating diagnostic probes for bacterial infections or in discovering potential drug targets. In this paper, we describe several heuristics that hybridize GRASP with different path-relinking strategies, such as forward, backward, mixed, greedy randomized adaptive forward, and evolutionary path relinking. Experiments on a large set of both real-world and randomly generated test instances indicate that these hybrid heuristics are both effective and efficient. In particular, the hybrid GRASP with evolutionary path relinking finds slightly better quality solutions compared to the other variants when running for the same number of iterations, while the hybrid with backward path relinking finds better quality solution within a fixed running time

The constrained shortest path tour problem

In this paper, we study the constrained shortest path tour problem. Given a directed graph with non-negative arc lengths, the aim is to find a single-origin single-destination shortest path, which needs to cross a sequence of node subsets that are given in a fixed order. The subsets are disjoint and may be of different size. In addition, it is required that the path does not include repeated arcs. Theoretical properties of the problem are studied, proving that it belongs to the complexity class NP-complete. To exactly solve it, a Branch & Bound method is proposed. Given the problem hardness, a Greedy Randomized Adaptive Search Procedure is also developed to find near-optimal solutions for medium to large scale instances. Extensive computational experiments, on a significant set of test problems, are carried out in order to empirically evaluate the performance of the proposed approaches. The computational results show that the Greedy Randomized Adaptive Search Procedure is effective in finding optimal or near optimal solutions in very limited computational time