Efficient decomposition-based algorithm to solve long-term pipeline scheduling problem

Abstract

This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers. By increasing the number of batches and time periods, maintaining the model resolution by using linear programming-based methods and commercial solvers would be very time-consuming. In this paper, we make an attempt to utilize the problem structure and develop a decomposition-based algorithm capable of finding near-optimal solutions for large instances in a reasonable time. The algorithm starts with a relaxed version of the model and adds a family of cuts on the fly, so that a near-optimal solution is obtained within a few iterations. The idea behind the cut generation is based on the knowledge of the underlying problem structure. Computational experiments on a real-world data case and some randomly generated instances confirm the efficiency of the proposed algorithm in terms of the solution quality and time.

Optimization of petroleum products distribution via pipeline systems: Modeling and computational challenges

“…Although this kind of contribution may be of questionable relevance from a practical perspective, it is also of tremendous value from a theoretical point of view. In line with these undertakings, other works have been more explicit by clearly stating that although the volume is constant, it also depends on the pair of adjacent products [121] , [87] , [71] , [138] , [93] .…”

“…Interface costs are used as part of the optimization criteria in a significant portion of the included literature [135] , [102] , [113] , [101] , [111] , [38] , [121] , [92] , [37] , [39] , [58] , [73] , [44] , [114] , [50] , [120] , [75] , [78] , [83] , [82] , [22] , [81] , [67] , [88] , [106] , [91] , [59] , [16] , [105] , [63] , [71] , [46] , [138] , [93] , [86] . According to [37] , [50] , the product contamination at the interface of two consecutive batches of different products requires additional operations to separate them.…”

“…The third baseline hard assumption that has explicitly been made quite often is that the batches of products inside the line are moved by the injection of new batches of products without any separation device among them [111] , [43] , [117] , [114] , [65] , which results in product contamination; also known as interfaces or transmix [117] , [87] , [50] , [75] , [83] , [72] , [69] , [68] , [115] , [88] , [129] , [106] , [91] , [77] , [21] , [80] , [60] , [28] , [64] , [104] , [71] , [138] , [65] , [93] . Due to the high reprocessing costs of certain interfaces, some sequences of products have been considered to be forbidden [121] , [114] , [120] , [75] , [72] , [82] , [88] , [106] , [129] , [91] , [80] , [71] , [93] . Nonetheless, for some of the research work, these volumes of contaminated products have been considered to be either minimized in the constraints of the sequence [110] , or not having relevance to the operation of the pipeline [64] .…”

Mathematical formulation and hybrid meta‐heuristic algorithms for multiproduct oil pipeline scheduling problem with tardiness penalties

“…BeheshtiAsl and MirHassani 19 introduced a new scenario‐based MILP two‐stage stochastic model for scheduling and managing pipeline with flow rate uncertainty situation. Moradi et al 20 applied an efficient decomposition‐based algorithm for long‐term pipeline scheduling problems. Their algorithm starts with a relaxed version of the model and adds a family of cuts on the fly.…”

MILP formulations for highway petrol station replenishment in initiative distribution mode

“…Researchers often focus on the formulation of scheduling plans (Zhang et al 2016) and transmission plans (Fernandes et al 2013). Zhang et al (2017) established a MILP model to solve problems, and some researchers (Moradi et al 2019) had also proposed efficient algorithm to solve scheduling studies. Liang et al (2012) considered regional differences in electricity prices along the pipeline, established an optimization model for a multi-product pipeline which has a known delivery demand and operation plan for each off-take station.…”