Abstract
Relying on multiobjective programming techniques, we have developed an optimization software to improve the services provided by the social co-operative OMNIA. OMNIA’s mission is to supply home-care assistance to children and people with disabilities. Our method is intended to optimize the social workers’ shift planning aiming at, on the one hand, maximizing the overall quality of the social care services, on the other hand, minimizing costs associated with OMNIA’s activity. In particular, our software provides Pareto optima of the resulting (difficult) bi-objective model by resorting to both a standard a priori weighted-sum approach and a new MINLP no-preference (hypervolume maximization-type) method. The product of this research is successfully employed by OMNIA.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Theory of the hypervolume indicator: optimal distributions and the choice of the reference point. In: Proceedings of the 10th ACM SIGEVO Workshop on Foundations of Genetic Algorithms, pp. 87–102. ACM (2009)
Avriel, M., Diewert, W., Schaible, S., Zang, I.: Generalized Concavity. SIAM, Philadelphia (2010)
Bazaraa, M., Sherali, H., Shetty, C.: Nonlinear Programming, Theory and Algorithms, vol. 2. Wiley, Hoboken (1993)
Belotti, P., Kirches, C., Leyffer, S., Linderoth, J., Luedtke, J., Mahajan, A.: Mixed-integer nonlinear optimization. Acta Numer. 22, 1–131 (2013)
Burkard, R., Dell’Amico, M., Martello, S.: Assignment problems, revised reprint. Siam 106 (2012)
Burkard, R.E.: Quadratic assignment problems. Eur. J. Oper. Res. 15(3), 283–289 (1984)
Burkard, R.E.: Quadratic assignment problems. Handbook of Combinatorial Optimization, pp. 2741–2814. Springer, Berlin (2013)
Carraresi, P., Malucelli, F.: A new lower bound for the quadratic assignment problem. Oper. Res. 40(1–supplement–1), S22–S27 (1992)
Cesarone, F., Lampariello, L., Sagratella, S.: A risk-gain dominance maximization approach to enhanced index tracking. Financ. Res. Lett. 29, 231–238 (2018)
Fleischer, M.: The measure of Pareto optima applications to multi-objective metaheuristics. Evolutionary Multi-criterion Optimization, pp. 519–533. Springer, Berlin (2003)
Lootsma, F., Athan, T., Papalambros, P.: Controlling the search for a compromise solution in multi-objective optimization. Eng. Optim.+ A35 25(1), 65–81 (1995)
Miettinen, K.: Nonlinear Multiobjective Optimization, vol. 12. Springer Science & Business Media, Berlin (2012)
Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms: a comparative case study. Parallel Problem Solving from Nature-PPSN V, pp. 292–301. Springer, Berlin (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Lampariello, L., Manno, A., Sagratella, S. (2019). Improving Social Assistance Services for Minors and Disabled People by Using Multiobjective Programming. In: Dell'Amico, M., Gaudioso, M., Stecca, G. (eds) A View of Operations Research Applications in Italy, 2018. AIRO Springer Series, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-25842-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-25842-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-25841-2
Online ISBN: 978-3-030-25842-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)