Javier Faulin

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.

Simulation Methods for Reliability and Availability of Complex Systems

Complex systems have become ubiquitous and are essential to todays society. The design of reliable complex systems and the determination of their availability are therefore very important tasks for managers and engineers. These tasks, however, can be extremely difficult to achieve, due to the fact that current analytical methods are often too complicated, time-consuming, inefficient, or even inappropriate, when dealing with real-life systems. Simulation Methods for Reliability and Availability of Complex Systems discusses the use of computer simulation-based techniques and algorithms to determine reliability and availability (RA forecasting emerging technologies and trends in the use of computer simulation for RA and proposing hybrid approaches to the development of efficient methodologies designed to solve R&A-related problems in real-life systems. Dealing with practical issues, Simulation Methods for Reliability and Availability of Complex Systems is designed to support managers and system engineers in the improvement of R&A, as well as providing a thorough exploration of the techniques and algorithms available for researchers, and for advanced undergraduate and postgraduate students.

Applying MIXALG procedure in a routing problem to optimize food product delivery

This work seeks to develop the MIXALG method as a way of solving routing problems with moderated size. This method avoids the applications of some burdensome procedures in unnecessary situations. The resolution of real cases serves as touchstone for testing the properties of our method. The application of MIXALG procedure in the logistic problems of a specific company, situated in Navarra (Spain), permits the verification of good properties of method in real world. Having this aim, some heuristic algorithms and linear programming routines have been used. A Clarke-Wright variant method (ALGACEA) presents some starting solutions that will be improved by linear programming tools. Final outcomes are focused on costs reduction and on the suitable knowledge for logistic decision making. The general outline of the MIXALG procedure can easily adapted to other small-sized distribution problems.

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.

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 biased‐randomized algorithm for the two‐dimensional vehicle routing problem with and without item rotations

This paper proposes an efficient algorithm, with a reduced number of parameters, for solving the two-dimensional loading-capacitated vehicle routing problem (2L-CVRP). This problem combines two of the most important issues in logistics, that is, vehicle routing and packing problems. Our approach contemplates unrestricted loading including the possibility of applying 90° rotations to each rectangular-shaped item while loading it into the vehicle, which is a realistic assumption seldom considered in the existing literature. The algorithm uses a multistart approach that is designed to avoid local minima and also to make the algorithm an easily parallelizable one. At each restart, a biased randomization of a savings-based routing algorithm is combined with an enhanced version of a classical packing heuristic to produce feasible good solutions for the 2L-CVRP. The proposed algorithm has been compared with the classical benchmarks for two different 2L-CVRP variants, that is, with and without item rotations. Experimental results show that our approach outperforms several best-known solutions from previous work, both in terms of quality and the computational time needed to obtain them.

The ALGACEA-1 method for the capacitated vehicle routing problem

We base the ALGACEA-1 method on Clarke–Wrights savings estimates, using Monte Carlo techniques for node selection. This procedure controls the assigned probabilities for node selection using a bounded Entropy function. Similarly, we have discussed the parameter tuning for this method with the purpose of obtaining a good algorithm performance. Finally, some computational results are given to show the quality of the solutions provided by ALGACEA-1 in a real case and in some benchmarking instances.

Developing an Information System for Monitoring Student's Activity in Online Collaborative Learning

In this paper we address the issue of monitoring studentspsila and groupspsila activity in online collaborative learning environments. This issue is especially important in the collaborative e-learning context, since an efficient monitoring process can provide valuable information to online instructors who may guide and support the development of collaborative learning projects. We have developed and tested an information system model which facilitates the automatic generation of weekly monitoring reports derived from data contained in server log files. These reports provide online instructors with visual information regarding studentspsila and groupspsila activity, thus allowing for a quick and easy classification of students and groups according to their activity level. Therefore, entities with a low activity level are identified as soon as possible and just-in-time assistance can be established for them. Furthermore, instructors can use these monitoring reports to forecast potential problems -such as studentspsila dropouts or possible conflicts inside the groups due to unbalanced distribution of tasks- and take operational and tactical decisions oriented to avoid them.