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Short‐term crude oil scheduling with preventive maintenance operations: a fuzzy stochastic programming approach
Author(s) -
Evazabadian Farshid,
Arvan Meysam,
Ghodsi Reza
Publication year - 2019
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/itor.12408
Subject(s) - unavailability , preventive maintenance , mathematical optimization , computer science , scheduling (production processes) , fuzzy logic , stochastic programming , job shop scheduling , operations research , reliability engineering , mathematics , engineering , schedule , artificial intelligence , operating system
This paper presents a fuzzy stochastic mathematical model for a short‐term crude oil scheduling problem with preventive maintenance for charging tanks. The objective function of the proposed model minimizes the supply chain total cost. Lost sale is also reflected in the objective function acting as a criterion for the supply chain responsiveness. This study is among the first attempts to consider preventive maintenance operations, unavailability of the charging tanks, and minimizing lost sale for crude oil scheduling problem. The model encounters two types of uncertainty, namely fuzzy and stochastic uncertainty caused by demand, cost, and time parameters. Fuzzy programming and chance constraint method are employed to formulate the nondeterministic model. The model's applicability is illustrated with a case study. Results indicate that in cases where demand is sizable compared to storage volumes and maximum flow rate is sufficiently large, a preventive maintenance system can be scheduled regularly. Furthermore, the performed sensitivity analyses reveal that the problem is more sensitive to demand uncertainty than to cost and time uncertainty.