The Technician Routing and Scheduling Problem (TRSP) consists in routing staff to serve requests for service, taking into account time windows, skills, tools, and spare parts. Typical applications include maintenance operations and staff routing in telecoms, public utilities, and in the health care industry. In this paper we tackle the Dynamic TRSP (D-TRSP) in which new requests appear over time. We propose a fast reoptimization approach based on a parallel Adaptive Large Neighborhood Search (pALNS) and a Multiple Plan Approach (MPA). Finally, we present computational experiments on randomly generated instances

Andrés L. Medaglia

Christelle Guéret

Victor Pillac

Pillac, Victor

Guéret, Christelle

Medaglia, Andrés

HAL-Univ-Nantes

On the Dynamic Technician Routing and Scheduling Problem

HAL Mines Nantes

International audienceThe Technician Routing and Scheduling Problem (TRSP) deals with a limited crew of technicians that serves a set of requests. In the TRSP, each technician has a set of skills, tools, and spare parts, while requests require a subset of each. The problem is then to design a set of tours of minimal total duration such that each request is visited exactly once, within its time window, by a technician with the required skills, tools, and spare parts. The TRSP naturally arises in a wide range of applications, including telecoms, public utilities, and maintenance operations. To the best of our knowledge, no work considers neither tools, nor spare parts, nor the arrival of new requests, three important components of real-world applications. The present work addresses this aspect and proposes an optimization approach for the dynamic version of the problem, noted D-TRSP, where new requests arrive during the execution of the routes

Gueret, Christelle

On the dynamic technician routing and schedulingproblemVictor Pillac, Christelle Gueret, Andre´s MedagliaTo cite this version:Victor Pillac, Christelle Gueret, Andre´s Medaglia. On the dynamic technician routing andscheduling problem. ODYSSEUS 2012 - 5th International Workshop on Freight Transportationand Logistics, May 2012, Mikonos, Greece. pp.id: 194, 2012. <hal-00674408>HAL Id: hal-00674408https://hal.archives-ouvertes.fr/hal-00674408Submitted on 27 Feb 2012HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestine´e au de´poˆt et a` la diffusion de documentsscientifiques de niveau recherche, publie´s ou non,e´manant des e´tablissements d’enseignement et derecherche franc¸ais ou e´trangers, des laboratoirespublics ou prive´s.On the dynamic technician routing andscheduling problemVictor PillacE´cole des Mines de Nantes, IRCCyN UMR 6597, Nantes, FranceUniversidad de los Andes, Industrial Engineering Department, Bogota´, ColombiaEmail: vpillac@mines-nantes.frChristelle Gue´retE´cole des Mines de Nantes, IRCCyN UMR 6597, Nantes, FranceEmail: gueret@mines-nantes.frAndre´s L. MedagliaUniversidad de los Andes, Industrial Engineering Department, Bogota´, ColombiaEmail: amedagli@uniandes.edu.co1 IntroductionThe Technician Routing and Scheduling Problem (TRSP) deals with a limited crew oftechnicians K that serves a set of requests R. In the TRSP, each technician has a set ofskills, tools, and spare parts, while requests require a subset of each. The problem is thento design a set of tours of minimal total duration such that each request is visited exactlyonce, within its time window, by a technician with the required skills, tools, and spareparts. The TRSP naturally arises in a wide range of applications, including telecoms,public utilities, and maintenance operations.A distinctive feature of this problem is that it introduces several compatibility con-straints between technicians and requests. While skills are intrinsic attributes, techniciansmay carry different tools and spare parts over the planning horizon. Technicians oftenstart their tour from home, with a set of tools (renewable resources) and spare parts (con-sumed once the technician serves a request) that allows them to serve an initial set ofrequests. Technicians may have the opportunity to replenish their tools and spare partsat a central depot at any time to service more requests.Despite its numerous practical applications and its challenging features, static techni-cian routing and scheduling problems have received limited attention until recently. Xuand Chiu [1] studied a variant of the TRSP in which the objective is to maximize the num-ber of requests served while accounting for skill constraints and request priorities. Tanget al. [2] considered priorities and formulated a multi-period maximum collection problemin which time-dependent rewards are granted for the completion of a request. Tsang andVoudouris [3] solved a problem faced by British Telecom where technician skills affect thetime required to service a request.On the other hand, a number of technological advances have led to a renewed interestin dynamic vehicle routing problems, leading to the development of new optimizationapproaches. We refer the interested reader to the recent survey by Pillac et al. [4] for adetailed review of such approaches.To the best of our knowledge, no work considers neither tools, nor spare parts, northe arrival of new requests, three important components of real-world applications. Thepresent work addresses this aspect and proposes an optimization approach for the dynamicversion of the problem, noted D-TRSP, where new requests arrive during the execution ofthe routes.2 The proposed approachThe proposed approach is based on an parallel Adaptative Large Neighborhood Search(pALNS) algorithm which is used first to compute an initial solution, and then to reopti-mize the solution whenever a new customer request arrives.2.1 Computation of an initial solutionA regret heuristic [5] is first used to build an initial solution: at each iteration the algorithminserts (at the best position) the request with the lowest regret value. The regret value isthe difference between the cost of the first and second best insertions. It is a measure ofhow desirable it is to insert a request now that is used to prevent myopic decisions.This initial solution is then improved by a pALNS algorithm. The ALNS algorithm [6]works by successively destroying (removing requests) and repairing (reinserting requests)a current solution. In the parallel version we propose, the algorithm maintains a poolof promising solutions that are optimized in p subprocesses. For each master iteration,a subset of p promising solutions is selected randomly and distributed to the availablesubprocesses. Then for l iterations, each subprocess selects destroy and repair operatorswith a roulette wheel that adaptively reflects their past performance. The resulting newsolution is either accepted as the subprocess current solution or rejected. Finally, eachsubprocess reports the best solution found to the master process and the pool of promisingsolutions is updated. In addition, the weights of the destroy and repair operators areupdated depending on their performance. The algorithm stops after n master iterations,which corresponds to N = nl ALNS iterations. We adapted three destroy operators(random, critical, and related) and two repair heuristics (best insertion and regret).2.2 Re-optimization procedureEach time a new request arrives, we fix the currently executed portion of the solution, andre-run the pALNS for a limited number of iterations, leading to a reoptimized solution.If at the end of this procedure the new request could not be inserted, then we mark it asrejected.3 Computational resultsIn this section we report computational results of the approach on 36 randomly generatedinstances of the D-TRSP, based on the Solomon [7] benchmark1. For each instance, wegenerated skill, tool, and spare part information, and associated a depot (home) to each ofthe 25 vehicles (technicians). Additionally, we generated release dates for a proportion ofthe requests, corresponding to the time at which they are made known to the algorithm.We compare the proposed approach with a regret-2 heuristic, that consists in insertingnew requests at the best possible position in the current solution, and rejecting requeststhat cannot be inserted, the initial solution being the same as the one used in the pALNS.All experiments were run with a limit of N = 25000 iterations for the calculation ofthe initial solution, and N = 5000 for subsequent reoptimizations.pALNS regret-2DOD VI (%) R VI (%) R10 0.07 0.00 18.52 0.2550 2.09 0.33 36.58 1.0890 2.50 0.92 44.98 2.00Average 1.55 0.42 33.36 1.11Table 1: Computational results for 36 randomly generated D-TRSP instances.Table 1 reports preliminary results for the 36 instances. The first column contains thedegree of dynamism (DOD) defined as the proportion of dynamic requests. The secondand third columns reports the average Value of Information [8] (VI)2 and the averagenumber of rejected requests (R) for the pALNS, while the fourth and fifth contains thesestatistics for the regret-2 heuristic. Running times are of 50s on average for the calculationof the initial solution and 11s for subsequent reoptimizations.These results show that the proposed approach leads to better solutions both in termsof quality of the routing (measured by VI) and ability to cope with new requests (measuredby R). In addition, we can note that the regret-2 approach rejects an average of 4.4 requests1We used the following subset of 100-customers instances: {[R,C,RC][101,102,201,202]}2The value of information for instance I is defined as the ratio z(I)−z(Ioff )z(Ioff )where z(I) is the value of thesolution found by the algorithm for the dynamic instance, and z(Ioff) is the value of the solution for staticinstance Ioff in which all requests are known beforehand, obtained running pALNS with 100k iterations.in 9 of the 36 instances, while pALNS rejects an average of 3.0 requests on only 5 instances.This illustrates the robustness of pALNS that is less likely to take bad decisions that willthen cause either the rejection of a higher number of requests, or result in a poor routing.4 Conclusions and research perspectivesIn this study we introduced a new challenging routing problem with numerous applica-tions, namely a variant of the Technician Routing and Scheduling Problem. We proposeda metaheuristic based on a parallel ALNS algorithm fast enough to be used to solve theproblem in a dynamic context. We validated and measured the performance of the pro-posed approach on randomly generated instances of the D-TRSP.Future work will focus on considering additional strategies such as waiting or requestbuffering to improve the quality of the dynamic routing, and using stochastic knowledgegathered from historical data to better anticipate its execution.Acknowledgements Financial support for this work was provided by the CPER (Contrat de ProjetEtat Region) Valle´e du Libre (France); and the Centro de Estudios Interdisciplinarios Ba´sicos y Aplicadosen Complejidad (CEIBA, Colombia). This support is gratefully acknowledged.References[1] J. Y. Xu and S. Y. Chiu. Effective heuristic procedures for a field technician scheduling problem.Journal of Heuristics, 7(5):495–509, 2001.[2] E. Tang, H.and Miller-Hooks and R. Tomastik. Scheduling technicians for planned maintenance ofgeographically distributed equipment. Transportation Research Part E: Logistics and TransportationReview, 43(5):591–609, 2007.[3] E. Tsang and C. Voudouris. Fast local search and guided local search and their application to britishtelecom’s workforce scheduling problem. Operations Research Letters, 20(3):119 – 127, 1997.[4] V. Pillac, M. Gendreau, C. Gue´ret, and A. L. Medaglia. A review of dynamic vehicle routing problems.Technical report, CIRRELT, 2011. CIRRELT-2011-62.[5] Jean-Yves Potvin and Jean-Marc Rousseau. A parallel route building algorithm for the vehicle routingand scheduling problem with time windows. European Journal of Operational Research, 66(3):331 –340, 1993.[6] D. Pisinger and S. Ropke. A general heuristic for vehicle routing problems. Computers & OperationsResearch, 34(8):2403–2435, 2007.[7] M. M. Solomon. Algorithms for the vehicle-routing and scheduling problems with time window con-straints. Operations Research, 35(2):254–265, 1987.[8] K. Lund, O. B. G. Madsen, and J. M. Rygaard. Vehicle routing problems with varying degrees ofdynamism. Technical report, IMM Institute of Mathematical Modelling, 1996.

On the dynamic technician routing and scheduling problem

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