Detailed Scheduling of Operations in Single-Source Refined Products Pipelines
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- 作者：Vanina G. Cafaro ; Diego C. Cafaro ; Carlos A. Me虂ndez ; Jaime Cerda虂
- 刊名：Industrial & Engineering Chemistry Research
- 出版年：2011
- 出版时间：May 18, 2011
- 年：2011
- 卷：50
- 期：10
- 页码：6240-6259
- 全文大小：2035K
- 年卷期：v.50,no.10(May 18, 2011)
- ISSN：1520-5045
文摘
The short-term scheduling of refined products pipelines is a very complex problem with many operational constraints to be considered. Available nondiscrete planning approaches just provide the input schedule and the set of aggregate product deliveries to be performed from in-transit lots to depots at every batch injection. To determine the sequence of individual cuts on each batch to be accomplished by the pipeline operator, it is still necessary to refine such an aggregate plan. To do so, new computational tools for efficiently generating the detailed schedule of single-source pipelines with multiple distribution terminals are presented. Two types of methodologies are proposed. On one hand, it is developed a continuous-time mixed-integer linear programming (MILP) formulation that seeks to minimize both the total flow restart volume and the number of single-delivery pumping runs over the planning horizon. In this way, substantial savings in energy consumption and pump maintenance costs are achieved. Effective solution strategies for the MILP model are also designed to deal with large pipeline scheduling problems. On the other hand, three different heuristic rules for selecting the receiving terminal are introduced. By applying those rules in combination with a discrete-event simulation model, not only alternative detailed schedules can be generated in a very short CPU time but also some of them are near-optimal solutions. Three instances of a case study aimed at finding the detailed schedule of a real-world single-source pipeline system are solved through the proposed optimization and discrete-event simulation methods. Results are analyzed to assess the quality of the generated solutions and the required computational costs.