Barry B. Barrios

On the use of Monte Carlo simulation, cache and splitting techniques to improve the clarke and wright savings heuristics

This paper presents the SR-GCWS-CS probabilistic algorithm that combines Monte Carlo simulation with splitting techniques and the Clarke and Wright savings heuristic to find competitive quasi-optimal solutions to the Capacitated Vehicle Routing Problem (CVRP) in reasonable response times. The algorithm, which does not require complex fine-tuning processes, can be used as an alternative to other metaheuristics—such as Simulated Annealing, Tabu Search, Genetic Algorithms, Ant Colony Optimization or GRASP, which might be more difficult to implement and which might require non-trivial fine-tuning processes—when solving CVRP instances. As discussed in the paper, the probabilistic approach presented here aims to provide a relatively simple and yet flexible algorithm which benefits from: (a) the use of the geometric distribution to guide the random search process, and (b) efficient cache and splitting techniques that contribute to significantly reduce computational times. The algorithm is validated through a set of CVRP standard benchmarks and competitive results are obtained in all tested cases. Future work regarding the use of parallel programming to efficiently solve large-scale CVRP instances is discussed. Finally, it is important to notice that some of the principles of the approach presented here might serve as a base to develop similar algorithms for other routing and scheduling combinatorial problems.

The SR-GCWS hybrid algorithm for solving the capacitated vehicle routing problem

The capacitated vehicle routing problem (CVRP) is a well known problem which has long been tackled by researchers for several decades now, not only because of its potential applications but also due to the fact that CVRP can be used to test the efficiency of new algorithms and optimization methods. The objective of our work is to present SR-GCWS, a hybrid algorithm that combines a CVRP classical heuristic with Monte Carlo simulation using state-of-the-art random number generators. The resulting algorithm is tested against some well-known benchmarks. In most cases, our approach is able to compete or even outperform much more complex algorithms, which is especially interesting if we consider that our algorithm does not require any previous parameter fine-tuning or set-up process. Moreover, our algorithm has been able to produce high-quality solutions almost in real-time for most tested instances. Another important feature of the algorithm worth mentioning is that it uses a randomized constructive heuristic, capable of generating hundreds or even thousands of alternative solutions with different properties. These alternative solutions, in turn, can be really useful for decision-makers in order to satisfy their utility functions, which are usually unknown by the modeler. The presented methodology may be a fine framework for the development of similar algorithms for other complex combinatorial problems in the routing arena as well as in some other research fields.

Using biased randomization for solving the two-dimensional loading vehicle routing problem with heterogeneous fleet

This paper discusses the two-dimensional loading capacitated vehicle routing problem (2L-CVRP) with heterogeneous fleet (2L-HFVRP). The 2L-CVRP can be found in many real-life situations related to the transportation of voluminous items where two-dimensional packing restrictions have to be considered, e.g.: transportation of heavy machinery, forklifts, professional cleaning equipment, etc. Here, we also consider a heterogeneous fleet of vehicles, comprising units of different capacities, sizes and fixed/variable costs. Despite the fact that heterogeneous fleets are quite ubiquitous in real-life scenarios, there is a lack of publications in the literature discussing the 2L-HFVRP. In particular, to the best of our knowledge no previous work discusses the non-oriented 2L-HFVRP, in which items are allowed to be rotated during the truck-loading process. After describing and motivating the problem, a literature review on related work is performed. Then, a multi-start algorithm based on biased randomization of routing and packing heuristics is proposed. A set of computational experiments contribute to illustrate the scope of our approach, as well as to show its efficiency.

Horizontal cooperation in road transportation: a case illustrating savings in distances and greenhouse gas emissions

This is the accepted version of the following article: Perez-Bernabeu, E., Juan, A. A., Faulin, J. and Barrios, B. B. (2015), Horizontal cooperation in road transportation: a case illustrating savings in distances and greenhouse gas emissions. Intl. Trans. in Op. Res., 22: 585–606. doi:10.1111/itor.12130, which has been published in final form at http://dx.doi.org/10.1111/itor.12130.

A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times

Abstract This paper describes a simulation–optimization algorithm for the Permutation Flow shop Problem with Stochastic processing Times (PFSPST). The proposed algorithm combines Monte Carlo simulation with an Iterated Local Search metaheuristic in order to deal with the stochastic behavior of the problem. Using the expected makespan as initial minimization criterion, our simheuristic approach is based on the assumption that high-quality solutions (permutations of jobs) for the deterministic version of the problem are likely to be high-quality solutions for the stochastic version – i.e., a correlation will exist between both sets of solutions, at least for moderate levels of variability in the stochastic processing times. No particular assumption is made on the probability distributions modeling each job-machine processing times. Our approach is able to solve, in just a few minutes or even less, PFSPST instances with hundreds of jobs and dozens of machines. Also, the paper proposes the use of reliability analysis techniques to analyze simulation outcomes or historical observations on the random variable representing the makespan associated with a given solution. This way, criteria other than the expected makespan can be considered by the decision maker when comparing different alternative solutions. A set of classical benchmarks for the deterministic version of the problem are adapted and tested under several scenarios, each of them characterized by a different level of uncertainty – variance level of job-machine processing times.

Combining biased randomization with iterated local search for solving the multidepot vehicle routing problem

This paper proposes a hybrid approach for solving the multidepot vehicle routing problem (MDVRP) with a limited number of identical vehicles per depot. Our approach, which only uses a few parameters, combines “biased randomization”—use of nonsymmetric probability distributions to generate randomness—with the iterated local search (ILS) metaheuristic. Two biased-randomized processes are employed at different stages of the ILS framework in order to (a) assign customers to depots following a randomized priority criterion—this allows for fast generation of alternative allocation maps and (b) improving routing solutions associated with a “promising” allocation map—this is done by randomizing the classical savings heuristic. These biased-randomized processes rely on the use of the geometric probability distribution, which is characterized by a single and bounded parameter. Being an approach with few parameters, our algorithm does not require troublesome fine-tuning processes, which tend to be time consuming. Using standard benchmarks, the computational experiments show the efficiency of the proposed algorithm. Despite its hybrid nature, our approach is relatively easy to implement and can be parallelized in a very natural way, which makes it an interesting alternative for practical applications of the MDVRP.

Using Oriented Random Search to Provide a Set of Alternative Solutions to the Capacitated Vehicle Routing Problem

In this paper we present SR-GCWS, a simulation-based algorithm for the Capacitated Vehicle Routing Problem (CVRP). Given a CVRP instance, the SR-GCWS algorithm incorporates a randomness criterion to the classical Clarke and Wright Savings (CWS) heuristic and starts an iterative process in order to obtain a set of alternative solutions, each of which outperforms the CWS algorithm. Thus, a random but oriented local search of the space of solutions is performed, and a list of “good alternative solutions” is obtained. We can then consider several properties per solution other than aprioristic costs, such as visual attractiveness, number of trucks employed, load balance among routes, environmental costs, etc. This allows the decision-maker to consider multiple solution characteristics other than just those defined by the aprioristic objective function. Therefore, our methodology provides more flexibility during the routing selection process, which may help to improve the quality of service offered to clients. Several tests have been performed to discuss the effectiveness of this approach.

A successive approximations method for the heterogeneous vehicle routing problem: analysing different fleet configurations

In this paper, we propose a relatively simple-to-implement procedure for solving the heterogeneous-fleet vehicle routing problem (HeVRP), in which different types of vehicle loading capacities are considered. Our approach is based on the so called successive approximations method (SAM), which is a multi-round process. At each round, a new subset of nodes and a new type of vehicle are selected following some specific criteria. Then, assuming an unlimited fleet of vehicles of this type, the associated homogeneous-fleet vehicle routing problem (HoVRP) is solved. After several rounds, a global solution for the HeVRP is obtained by merging routes from different HoVRP solutions. In the first part of the paper, we analyse how distance-based costs vary when slight deviations from the homogeneous fleet assumption are considered. In the second part of the article, the SAM approach is adapted so it can simultaneously deal with both fixed and variable costs in HeVRPs. An experimental comparison is then made with other HeVRP algorithms.

On the use of biased randomization and simheuristics to solve vehicle and arc routing problems

This paper reviews the main concepts and existing literature related to the use of biased randomization of classical heuristics and the combination of simulation with meta-heuristics (Simheuristics) in order to solve complex combinatorial optimization problems, both of deterministic and stochastic nature, in the popular field of Vehicle and Arc Routing Problems. The paper performs a holistic approach to these concepts, synthesizes several cases of successful application from the existing literature, and proposes a general simulation-based framework for solving richer variants of Vehicle and Arc Routing Problems. Also examples of algorithms based on this framework successfully applied to concrete cases of Vehicle and Arc Routing Problems are presented.