Solution to a Pipeline Scheduling Problem by Using a Mixed Integer Linear Programming Model

Abstract

ABSTRACT:Pipelines are efficient ways of conveying huge amounts of refined petroleum products to distant points. Different products are pumped successively, in the pipelines without a need of a separator between them. Pipelines should be chosen very carefully based on the pumping sequences, volumes to be conveyed, covering the constraints involved by cutting operational costs and focusing on market demands. The real life problem considered in this study consists of a unidirectional pipe distribution system used for pumping petroleum products between the sources and distribution centers. Problem was stated as a Mixed Integer Linear Programming (MILP) model and solved by using GAMS software thorough actual data. As a result of the study, an optimal pumping schedule for pipeline operations at a certain period of time is achieved. Key Words: Pipeline scheduling, GAMS, Mixed integer linear programming, Optimization Karma Tamsayılı Doğrusal Programlama Modeli İle Bir Boru Hattı Çizelgeleme Probleminin ÇözümüÖZ: Boru hatları büyük miktarlardaki rafine edilmiş petrol ürünlerinin uzak mesafelere taşınmasında ekonomik bir yoldur. Boru hatlarında farklı ürünler aralarında herhangi bir ayırıcı olmadan arka arkaya pompalanmaktadır. Pompaj hareketinin sırası ve uzunluğu, boru hattı operasyonel maliyetlerini azaltarak kısıtları karşılamak ve pazar taleplerine cevap vermek için dikkatli bir şekilde seçilmek zorundadır. Bu çalışmada ele alınan gerçek hayat problemi, kaynak ve dağıtım merkezleri arasında yakıt türevlerinin pompalanmasında kullanılan ve tek yönlü boru hattından oluşan bir dağıtım sistemini içermektedir. Problem mevcut veriler doğrultusunda Karma Tamsayılı Doğrusal Programlama modeli olarak ifade edilmiş ve GAMS yazılımı ile çözülmüştür. Çalışma neticesinde belirli bir zaman dilimindeki boru hattı operasyonlarını içeren optimal bir pompaj çizelgesi elde edilmiştir.

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

“…There has been a significant variety of goals when addressing this problem with a fairly diverse set of levels of complexity from the optimization point of view. For example, some authors state their goal as to determine a feasible solution for the problem [109] , [6] , [44] , [58] , [76] , [45] , [68] , [67] , [60] while other references pursue the optimization of an operational measure of effectiveness related with time [30] , [20] , customer demands [127] , [114] , [57] , [77] , [104] , [28] ; or an economic criterion resulting from the optimization of pumping schedules, which is an operational decision [70] , [16] , [79] , [64] or a combination of this with customer demands [112] , [114] , [74] , [133] , [72] , [61] , [21] . Nonetheless, some other works seek the determination of not just a feasible schedule, but optimal as well, considering a set of economic, operational, or both, criteria [111] , [124] .…”

“…Without loss of generality, in either static or dynamic applications, the goal when addressing the problem has been fivefold: to minimize a cost function, which has a fairly diverse set of criteria [130] , [27] , [111] , [38] , [43] , [117] , [92] , [112] , [37] , [73] , [19] , [89] , [50] , [75] , [103] , [78] , [132] , [74] , [72] , [82] , [133] , [129] , [106] , [91] , [21] , [80] , [61] , [16] , [105] , [64] , [65] , [93] , [86] , to meet every product demand at every terminal in a timely fashion [27] , [5] , [111] , [38] , [92] , [37] , [127] , [73] , [114] , [50] , [103] , [74] , [83] , [72] , [82] , [133] , [125] , [57] , [106] , [77] , [61] , [105] , [26] , [93] , [86] , to maintain the inventory level in the refinery and terminal tanks within the permissible ranges [27] , [38] , …”