@proceedings {Longo2015535, title = {Optimizing routine maintenance team routes}, journal = {Proceedings of the 17th International Conference on Enterprise Information Systems, Proceedings (ICEIS)}, volume = {1}, year = {2015}, note = {cited By 0; Conference of 17th International Conference on Enterprise Information Systems, ICEIS 2015 ; Conference Date: 27 April 2015 Through 30 April 2015; Conference Code:112657}, pages = {535-546}, publisher = {SciTePress}, address = {Barcelona, Spain, 27-30 April 2015}, abstract = {

Simulated annealing is a metaheuristic approach for the solution of optimization problems inspired to the controlled cooling of a material from a high temperature to a state in which internal defects of the crystals are minimized. In this paper, we apply a simulated annealing approach to the scheduling of geographically distributed routine maintenance interventions. Each intervention has to be assigned to a maintenance team and the choice among the available teams and the order in which interventions are performed by each team are based on team skills, cost of overtime work, and cost of transportation. We compare our solution algorithm versus an exhaustive approach considering a real industrial use case and show several numerical results to analyze the effect of the parameters of the simulated annealing on the accuracy of the solution and on the execution time of the algorithm.

}, keywords = {Controlled cooling, Cost of transportation, Industrial use case, Information systems, Maintenance, Meta-heuristic approach, Optimization, Optimization problems, Routine maintenance, Scheduling, Scheduling problem, Simulated annealing, Solution algorithms}, isbn = {9789897580970}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84939555682\&partnerID=40\&md5=4b6abf15640b1475952a6bfa5de0117c}, author = {Longo, F. and Andrea R. Lotronto and Marco Scarpa and Antonio Puliafito} } @inbook {Longo2015256, title = {A simulated annealing-based approach for the optimization of routine maintenance interventions}, booktitle = {Lecture Notes in Business Information Processing}, volume = {241}, year = {2015}, note = {cited By 0; Conference of 17th International Conference on Enterprise Information Systems, ICEIS 2015 ; Conference Date: 27 April 2015 Through 30 April 2015; Conference Code:164419}, pages = {256-279}, publisher = {Springer Verlag}, organization = {Springer Verlag}, abstract = {

Metaheuristics are often adopted to solve optimization problems where some requests need to be scheduled among a finite number of resources, i. e., the so called scheduling problems. Such techniques approach the optimization problems by taking inspiration from a certain physical phenomenon. Simulated annealing is a metaheuristic approach inspired to the controlled cooling of a material from a high temperature to a state in which internal defects of the crystals are minimized. In this paper, we use a simulated annealing-based approach to solve the problem of the scheduling of geographically distributed routine maintenance interventions. Each intervention has to be assigned to a maintenance team and the choice among the available teams and the order in which interventions are performed by each team are based on team skills, cost of overtime work, and cost of transportation. We compare our solution algorithm versus an exhaustive approach. First, we consider a real industrial use case and show several numerical results to analyze the effect of the parameters of the simulated annealing on the accuracy of the solution and on the execution time of the algorithm. Then, we provide results varying the parameters and dimension of the considered problem highlighting how they affect reliability and efficiency of our algorithm. {\textcopyright} Springer International Publishing Switzerland 2015.

}, keywords = {Cost of transportation, Industrial use case, Information systems, Maintenance, Meta-heuristic approach, Optimization, Optimization problems, parameter estimation, Physical phenomena, Problem solving, Routine maintenance, Scheduling, Scheduling problem, Simulated annealing, Solution algorithms}, isbn = {9783319291321}, issn = {18651348}, doi = {10.1007/978-3-319-29133-8_13}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958970833\&doi=10.1007\%2f978-3-319-29133-8_13\&partnerID=40\&md5=38ef9117d84652f8dfd7526db0dba19c}, author = {Francesco Longo and Andrea Lotronto and Marco Scarpa and Antonio Puliafito} }