Distributed Services with Foreseen and Unforeseen Tasks: The Mobile Re-allocation Problem

In this paper we deal with a common problem found in the operations of security and preventive/corrective maintenance services: that of routing a number of mobile resources to serve foreseen and unforeseen tasks during a shift. We define the (Mobile Re-Allocation Problem) MRAP as the problem of devising a routing strategy to maximize the expected weighted number of tasks served on time. For obtaining a solution to the MRAP, we propose to solve successively a multi-objective optimization problem called the stochastic Team Orienteering Problem with Multiple Time Windows (s-TOP-MTW) so as to consider information about known tasks and the arrival process of new unforeseen tasks. Solving successively the s-TOP-MTW we find that considering information about the arrival process of new unforeseen tasks may aid in maximizing the expected proportion of tasks accomplished on time.location;reliability;routing;distributed services

A Critical Analysis of a Tourist Trip Design Problem with Time-Dependent Recommendation Factors and Waiting Times

Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: http://github.com/cporrasn/TTDP_TDRF_WT_NWT.git.Acknowledgments: C.P. has been supported by a scholarship from AUIP Association coordinated with the Universidad de Granada. B.P.-C. was supported by the Erasmus+ programme of the European Union. The authors are grateful to the editors and the anonymous reviewers for their constructive comments and suggestions.The tourist trip design problem (TTDP) is a well-known extension of the orienteering problem, where the objective is to obtain an itinerary of points of interest for a tourist that maximizes his/her level of interest. In several situations, the interest of a point depends on when the point is visited, and the tourist may delay the arrival to a point in order to get a higher interest. In this paper, we present and discuss two variants of the TTDP with time-dependent recommendation factors (TTDP-TDRF), which may or may not take into account waiting times in order to have a better recommendation value. Using a mixed-integer linear programming solver, we provide solutions to 27 real-world instances. Although reasonable at first sight, we observed that including waiting times is not justified: in both cases (allowing or not waiting times) the quality of the solutions is almost the same, and the use of waiting times led to a model with higher solving times. This fact highlights the need to properly evaluate the benefits of making the problem model more complex than is actually needed.Projects PID2020-112754GB-I0, MCIN/AEI/10.13039/501100011033FEDER/Junta de Andalucía, Consejería de Transformación Económica, Industria, Conocimiento y Universidades/ Proyecto (B-TIC-640-UGR20

Distributed Services with Foreseen and Unforeseen Tasks: The Mobile Re-allocation Problem

In this paper we deal with a common problem found in the operations of security and preventive/corrective maintenance services: that of routing a number of mobile resources to serve foreseen and unforeseen tasks during a shift. We define the (Mobile Re-Allocation Problem) MRAP as the problem of devising a routing strategy to maximize the expected weighted number of tasks served on time. For obtaining a solution to the MRAP, we propose to solve successively a multi-objective optimization problem called the stochastic Team Orienteering Problem with Multiple Time Windows (s-TOP-MTW) so as to consider information about known tasks and the arrival process of new unforeseen tasks. Solving successively the s-TOP-MTW we find that considering information about the arrival process of new unforeseen tasks may aid in maximizing the expected proportion of tasks accomplished on time

DISCRETE PARTICLE SWARM OPTIMIZATION FOR THE ORIENTEERING PROBLEM

Discrete particle swarm optimization (DPSO) is gaining popularity in the area of combinatorial optimization in the recent past due to its simplicity in coding and consistency in performance.  A DPSO algorithm has been developed for orienteering problem (OP) which has been shown to have many practical applications.  It uses reduced variable neighborhood search as a local search tool.  The DPSO algorithm was compared with ten heuristic models from the literature using benchmark problems.  The results show that the DPSO algorithm is a robust algorithm that can optimally solve the well known OP test problems

Exploring algorithms to score control points in metrogaine events

Metrogaining is an urban outdoor navigational sport that uses a street map to which scored control points have been added. The objective is to collect maximum score points within a set time by visiting a subset of the scored control points. There is currently no metrogaining scoring standard, only guidelines on how to allocate scores. Accordingly, scoring approaches were explored to create new score sets by using scoring algorithms based on a simple relationship between the score of, and the number of visits to a control point. A spread model, which was developed to evaluate the score sets, generated a range of routes by solving a range of orienteering problems, which belongs to the class of NP-hard combinatorial optimisation problems. From these generated routes, the control point visit frequencies of each control point were determined. Using the visit frequencies, test statistics were subsequently adapted to test the goodness of scoring for each score set. The ndings indicate that the score-visits relationship is not a simple one, as the number of visits to a control point is not only dependent on its score, but also on the scores of the surrounding control points. As a result, the scoring algorithms explored were unable to cope with the complex scoring process uncovered.Decision SciencesM. Sc. (Operations Research