Daniel Palhazi Cuervo

An iterated local search algorithm for the vehicle routing problem with backhauls

The Vehicle Routing Problem with Backhauls (VRPB) is an extension of the VRP that deals with two types of customers: the consumers (linehaul) that request goods from the depot and the suppliers (backhaul) that send goods to the depot. In this paper, we propose a simple yet effective iterated local search algorithm for the VRPB. Its main component is an oscillating local search heuristic that has two main features. First, it explores a broad neighborhood structure at each iteration. This is efficiently done using a data structure that stores information about the set of neighboring solutions. Second, the heuristic performs constant transitions between feasible and infeasible portions of the solution space. These transitions are regulated by a dynamic adjustment of the penalty applied to infeasible solutions. An extensive statistical analysis was carried out in order to identify the most important components of the algorithm and to properly tune the values of their parameters. The results of the computational experiments carried out show that this algorithm is very competitive in comparison to the best metaheuristic algorithms for the VRPB. Additionally, new best solutions have been found for two instances in one of the benchmark sets. These results show that the performance of existing metaheuristic algorithms can be considerably improved by carrying out a thorough statistical analysis of their components. In particular, it shows that by expanding the exploration area and improving the efficiency of the local search heuristic, it is possible to develop simpler and faster metaheuristic algorithms without compromising the quality of the solutions obtained.

A variable neighborhood search algorithm to generate piano fingerings for polyphonic sheet music

A piano fingering indicates which finger should play each note in a piece. Such a guideline is very helpful for both amateur and experienced players in order to play a piece fluently. In this paper, we propose a variable neighbourhood search algorithm to generate piano fingerings for complex polyphonic music, a frequently encountered case that was ignored in previous research. The algorithm takes into account the biomechanical properties of the pianist’s hand in order to generate a fingering that is user-specific and as easy to play as possible. An extensive statistical analysis was carried out in order to tune the parameters of the algorithm and evaluate its performance. The results of computational experiments show that the algorithm generates good fingerings that are very similar to those published in sheet music books.

A critical analysis of the “improved Clarke and Wright savings algorithm”

In their paper “An improved Clarke and Wright savings algorithm for the capacitated vehicle routing problem,” published in ScienceAsia (38, 3, 307–318, 2012), Pichpibul and Kawtummachai developed a simple stochastic extension of the well-known Clarke and Wright savings heuristic for the capacitated vehicle routing problem. Notwithstanding the simplicity of the heuristic, which they call the “improved Clarke and Wright savings algorithm” (ICW), the reported results are among the best heuristics ever developed for this problem. Through a careful reimplementation, we demonstrate that the results published in the paper could not have been produced by the ICW heuristic. Studying the reasons how this paper could have passed the peer review process to be published in an ISI-ranked journal, we have to conclude that the necessary conditions for a thorough examination of a typical paper in the field of optimization are generally lacking. We investigate how this can be improved and come to the conclusion that disclosing source code to reviewers should become a prerequisite for publication.

Getting Business People on the Coach: A Stated Preference Experiment for Intercity Long Distance Coach Travel

From the middle of the 1990s, the traditional coach industry in Western Europe has been in decline. However, recent regulatory changes have created new opportunities in the sector of scheduled intercity services, resulting in fast growth of both coach lines and passengers traveling on these lines. Until now, operators have persuaded a public consisting mainly of students and people traveling for leisure purposes. In this paper we analyze whether business travelers could also be an interesting target group and what service characteristics are most convincing for them. For this purpose, we organized a stated preference experiment in which we gathered data from 63 Belgian business travelers. Analysis of the data revealed that for business travelers, price is the dominant factor in seducing customers. However, journey length, higher commercial travel speeds, ample leg space, on-board Wi-Fi and the entertainment system also play a role. Moreover, business travelers are prepared to pay for extra services. We conclude that when an adjusted service is offered, business travelers form an interesting (additional) target group for the intercity coach business. Our findings could be used by coach operators for product development and help to understand travel market segmentation, and eventually also have impact on developing a more sustainable travel policy.

An algorithmic framework for generating optimal two-stratum experimental designs

Two-stratum experiments are widely used in the event a complete randomization is not possible. In some experimental scenarios, there are constraints that limit the number of observations that can be made under homogeneous conditions. In other scenarios, there are factors whose levels are hard or expensive to change. In both of these scenarios, it is necessary to arrange the observations in different groups. Moreover, it is important that the analysis performed accounts for the variation in the response variable due to the differences between the groups. The most common strategy for the design of these kinds of experiments is to consider groups of equal size. The number of groups and the number of observations per group are usually defined by the constraints that limit the experimental scenario. It is argued, however, that these constraints do not define the design itself, but should be considered only as upper bounds. The number of groups and the number of observations per group should be chosen not only to satisfy the experimental constraints, but also to maximize the quality of the experiment. An algorithmic framework for generating optimal designs for two-stratum experiments, in which the number of groups and the number of observations per group are limited only by upper bounds, is proposed. Computational results show that this additional flexibility in the design generation process can significantly improve the quality of the experiments. Additionally, the results also show that the grouping configuration of an optimal design depends on the characteristics of the two-stratum experiment, namely, the type of experiment, the model to be estimated and the optimality criterion considered. This is a strong argument in favor of using algorithmic techniques that are able to identify not only the best factor-level configuration for each experimental run, but also the best grouping configuration.

Generating Fingerings for Polyphonic Piano Music with a Tabu Search Algorithm

A piano fingering is an indication of which finger is to be used to play each note in a piano composition. Good piano fingerings enable pianists to study, remember and play pieces in an optimal way. In this paper, we propose a tabu search algorithm to find a good piano fingering automatically and in a short amount of time. An innovative feature of the proposed algorithm is that it implements an objective function that takes into account the characteristics of the pianist’s hand and that it can be used for complex polyphonic music.