Decisions of allocating berth segments to incoming vessels, in maritime container terminals, has been extensively modeled in the scientific literature by resorting to formulations of mathematical programming with integer variables. Both vessel arrival times and processing times are usually considered as a deterministic input to the mathematical model despite of the uncertainty affecting berth decisions at the operational level, when several unpredictable events and operation delays occur and require to be managed. In this paper, we propose to apply the methodology of simulation based optimization to cope with uncertainty: a constructive algorithm is used to obtain a weekly plan at the tactical level; the allocation decisions are then adjusted at the operational level. Randomness in events and operations is taken into account by Monte Carlo simulation, while moving-average sample mean estimators are used to reduce the number of simulation runs required. Preliminary numerical results are also given.

Addressing robust berth planning under uncertainty via simulation based optimization

LEGATO Pasquale;MAZZA Rina Mary
2013

Abstract

Decisions of allocating berth segments to incoming vessels, in maritime container terminals, has been extensively modeled in the scientific literature by resorting to formulations of mathematical programming with integer variables. Both vessel arrival times and processing times are usually considered as a deterministic input to the mathematical model despite of the uncertainty affecting berth decisions at the operational level, when several unpredictable events and operation delays occur and require to be managed. In this paper, we propose to apply the methodology of simulation based optimization to cope with uncertainty: a constructive algorithm is used to obtain a weekly plan at the tactical level; the allocation decisions are then adjusted at the operational level. Randomness in events and operations is taken into account by Monte Carlo simulation, while moving-average sample mean estimators are used to reduce the number of simulation runs required. Preliminary numerical results are also given.
978-88-97999-23-2
Simulation Optimization; Statistical Selection; Port Logistics; Berth Planning
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/184452
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact