(372d) Measures and Approaches for A Priori Analysis of Schedule Robustness

Crude oil scheduling is a complex process involving the allocation of arriving crude parcels to charging tanks and flow allotment of tanks to crude distillation units (CDU's) typically over a 1-4 week horizon. Many scheduling algorithms have been proposed in the literature which take as input the problem data such as number of charging tanks, CDUs and the amount of crude arriving over the horizon and give optimized allocation and flow decisions.

However, often, a schedule is never completely followed as proposed due to numerous disruptions that occur like parcel arrival delay (due to ship delay), CDU unavailability at full capacity, tank unavailability to name a few. Typically, in a month, there are about 4-5 occasions on average when crude oil transportation by sea to the refinery is delayed. Similarly use of crude oil from storage is constrained four to five times each month due to entrained rainwater [1]. In general, the schedule that is obtained from an optimisation program is aimed at maximising profit or some other performance measure and hence is expected to be operating at the limits of flows/throughput etc. Consequently there is reduced flexibility to adapt to disturbances. If we could quantify the flexibility of a given schedule, it could give an insight into the robustness of a schedule. This paper presents ways in which we could obtain quantitative measures of robustness for any given schedule.

Rescheduling approaches to the crude oil scheduling problem have been proposed in literature [2, 3]. However these studies take a reactive scheduling approach, i.e proposing changes after a disruption occurs. In this paper, we present an approach to evaluate and quantify the effects of various disturbances a priori which would enable the planner/scheduler to understand the criticality of any disruption. A flexible problem representation which enables different problem data to be analyzed has been implemented. Different refinery configurations which differ in the numbers of tanks, CDU's etc. can be easily handled by this flexible problem representation. The initial schedule whose robustness is to be evaluated is obtained from the scheduling algorithm of Reddy et al. [4]. Three different measures which can give insight into the robustness of a schedule and how they give an insight into the ability of a schedule to adapt to disruptions are proposed. These can help identify the more critical of the disruptions/equipment to take precautionary measures for a given schedule. It can also be used to set limits on when it would be better to reschedule. In other words, when does the ability of the current schedule to cope has deteriorated beyond profitable/feasible limits and necessitates rescheduling.

*To whom correspondence should be addressed: Tel: +65-6516-8041 Fax: +65-6779-1936 Email: chergs@nus.edu.sg

References

[1] Singapore Refining Company, Personal communications. 2003

[2] Adhitya A., Srinivasan R., Karimi I.A., A model based rescheduling framework for managing abnormal supply chain events. Computers and Chemical Engineering, Vol. 31, Nos 5-6 2007, 496-518

[3] Adhitya, A., Srinivasan, R., and Karimi, I. A. (2007). Heuristic rescheduling of crude oil operations to manage abnormal supply chain events. AIChE Journal, Vol 52, No 2, pp 397-422.

[4] C.P. Reddy, I.A. Karimi and R. Srinivasan, Novel solution approach for optimizing crude oil operations, AIChE Journal 50 (2004) (6), pp. 1177?1197.