In this paper we analyze the effect of two modelling approaches for supplyplanningproblemsunderuncertainty: two-stage stochastic programming(SP) and robust optimization (RO). The comparison between the two approaches is performed through a scenario-based framework methodology, which can be applied to any optimization problem affected by uncertainty. For SP we compute the minimum expected cost based on the specific probability distribution of the uncertain parameters related to a set of scenarios. For RO we consider static approaches where random parameters belong to box or ellipsoidal uncertainty sets in compliance with the data used to generate SP scenarios. Dynamic approaches for RO, via the concept of adjustable robust counterpart, are also considered. The efficiency of the methodology has been illustrated for a supply planning problem to optimize vehicle-renting and procurement transportation activities involving uncertainty on demands and on buying costs for extra-vehicles. Numerical experiments through the scenario-based framework allow a fair comparison in real case instances. Advantages and disadvantages of RO and SP are discussed.

(2017). A scenario-based framework for supply planning under uncertainty: stochastic programming versus robust optimization approaches [journal article - articolo]. In COMPUTATIONAL MANAGEMENT SCIENCE. Retrieved from http://hdl.handle.net/10446/78035

A scenario-based framework for supply planning under uncertainty: stochastic programming versus robust optimization approaches

Maggioni, Francesca;Bertocchi, Maria
2017-01-01

Abstract

In this paper we analyze the effect of two modelling approaches for supplyplanningproblemsunderuncertainty: two-stage stochastic programming(SP) and robust optimization (RO). The comparison between the two approaches is performed through a scenario-based framework methodology, which can be applied to any optimization problem affected by uncertainty. For SP we compute the minimum expected cost based on the specific probability distribution of the uncertain parameters related to a set of scenarios. For RO we consider static approaches where random parameters belong to box or ellipsoidal uncertainty sets in compliance with the data used to generate SP scenarios. Dynamic approaches for RO, via the concept of adjustable robust counterpart, are also considered. The efficiency of the methodology has been illustrated for a supply planning problem to optimize vehicle-renting and procurement transportation activities involving uncertainty on demands and on buying costs for extra-vehicles. Numerical experiments through the scenario-based framework allow a fair comparison in real case instances. Advantages and disadvantages of RO and SP are discussed.
articolo
2017
Maggioni, Francesca; Potra, Florian A.; Bertocchi, Maria
(2017). A scenario-based framework for supply planning under uncertainty: stochastic programming versus robust optimization approaches [journal article - articolo]. In COMPUTATIONAL MANAGEMENT SCIENCE. Retrieved from http://hdl.handle.net/10446/78035
File allegato/i alla scheda:
File Dimensione del file Formato  
Maggioni - Scenario based framework.pdf

Solo gestori di archivio

Versione: publisher's version - versione editoriale
Licenza: Licenza default Aisberg
Dimensione del file 643.43 kB
Formato Adobe PDF
643.43 kB Adobe PDF   Visualizza/Apri
CMS2016_revisiondecember.pdf

Open Access dal 05/01/2018

Descrizione: This is a post-peer-review, pre-copyedit version of an article published in Computational Management Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10287-016-0272-3
Versione: postprint - versione referata/accettata senza referaggio
Licenza: Licenza default Aisberg
Dimensione del file 334.24 kB
Formato Adobe PDF
334.24 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/78035
Citazioni
  • Scopus 29
  • ???jsp.display-item.citation.isi??? 21
social impact