@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 = {<p>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.</p>
},
	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}
}
@article {Ghosh2014667,
	title = {Stochastic model driven capacity planning for an infrastructure-as-a-service cloud},
	journal = {IEEE Transactions on Services Computing - IEEE Computer Society},
	volume = {7},
	number = {4},
	year = {2014},
	note = {cited By 1},
	pages = {667-680},
	publisher = {Institute of Electrical and Electronics Engineers},
	abstract = {<p>From an enterprise perspective, one key motivation to transform the traditional IT management into Cloud is the cost reduction of the hosted services. In an Infrastructure-as-a-Service (IaaS) Cloud, virtual machine (VM) instances share the physical machines (PMs) in the provider{\textquoteright}s data center. With large number of PMs, providers can maintain low cost of service downtime at the expense of higher infrastructure and other operational costs (e.g., power consumption and cooling costs). Hence, determining the optimal PM capacity requirements that minimize the overall cost is of interest. In this paper, we show how a cost analysis and optimization framework can be developed using stochastic availability and performance models of an IaaS Cloud. Specifically, we study two cost minimization problems to address the capacity planning in an IaaS Cloud: 1) what is the optimal number of PMs that minimizes the total cost of ownership for a given downtime requirement set by service level agreements? and, 2) is it more economical to use cheaper but less reliable PMs or to use costlier but more reliable PMs for insuring the same availability characteristics? We use simulated annealing, a well-known stochastic search algorithm, to solve these optimization problems. Results from our analysis show that the optimal solutions are found within reasonable time. {\textcopyright} 2013 IEEE.</p>
},
	keywords = {Capacity planning, Capacity requirement, Clouds, Cost benefit analysis, Cost reduction, Downtime, Infrastructure as a service (IaaS), Maintenance, Optimization, Optimization framework, Optimization problems, Service Level Agreements, Simulated annealing, Stochastic models, Stochastic search algorithms, Stochastic systems, Total cost of ownership},
	issn = {19391374},
	doi = {10.1109/TSC.2013.44},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84919717849\&partnerID=40\&md5=34c70f14a9a3f838562335050a628985},
	author = {Rahul Ghosh and Francesco Longo and Ruofan Xia and Vijay K. Naik and Kishor S. Trivedi}
}
