Combining Simulation and Tabu Search for Oil-derivatives Pipeline Scheduling

García-Sánchez, Álvaro; Arreche, Luis Miguel; Ortega-Mier, Míguel

doi:10.1007/978-3-540-78985-7_12

Cited by 28 publications

(25 citation statements)

References 9 publications

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“…It permits to easily find alternative detailed schedules by using different operational criterion. Several approaches were proposed to study pipeline scheduling problems, including rigorous optimization models, knowledge-based techniques (Sasikumar et al 1997), discrete-event simulation (Mori et al 2007, García-Sánchez, Arreche, andOrtega-Mier 2008), and decomposition frameworks (Hane and Ratliff 1995, Neves et al 2007, Boschetto et al 2008. Rigorous optimization methods generally consist of a single MILP (Mixed Integer Linear Programming) or MINLP (Mixed Integer Nonlinear Programming) mathematical formulations and are usually grouped into two classes: discrete and continuous, depending on the way that volume and time domains are handled.…”

Section: Pipeline Schedulingmentioning

confidence: 99%

“…When all unsatisfied demands q k (i,j) are null, the output schedule for run k has been generated. Previous contributions (Mori et al 2007, García-Sánchez, Arreche, andOrtega-Mier 2008) assumed that the destination for each entity was already given by the optimization package. In our approach, some capabilities have been provided to the proposed simulation model for selecting the route to be followed by every entity based on three key elements: (i) the assignment matrix Q k o (i,j) for every run k, (ii) the batch to which each entity belongs, and (iii) a set of priority rules selecting both the leaving entity and the receiving terminal, if several cut operations are eligible for execution.…”

Section: Hierarchical Solution Approachmentioning

confidence: 99%

See 1 more Smart Citation

Oil-derivatives pipeline logistics using discrete-event simulation

Cafaro¹,

Méndez²,

Cerdá,³

2010

Proceedings of the 2010 Winter Simulation Conference

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The management of oil-product pipelines represents a critical task in the daily operation of petroleum supply chains. Efficient computational tools are needed to perform this activity in a reliable and costeffective manner. This work presents a novel discrete event simulation system developed on Arena® for the detailed scheduling of a multiproduct pipeline consisting of a sequence of pipes that connect a single input station to several receiving terminals. The pipeline is modeled as a non-traditional multi-server queuing system involving a number of servers at every pipe-end that perform their tasks in a synchronized manner. Based on priority rules, the model decides which server should dispatch the entity waiting for service to the associated depot. Each priority rule can lead to a different delivery schedule, which is evaluated by using several criteria. Combined with optimization tools, the proposed simulation technique permits to easily manage real-world pipelines operations with low computational effort.

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Section: Pipeline Schedulingmentioning

confidence: 99%

Section: Hierarchical Solution Approachmentioning

confidence: 99%

Oil-derivatives pipeline logistics using discrete-event simulation

Cafaro¹,

Méndez²,

Cerdá,³

2010

Proceedings of the 2010 Winter Simulation Conference

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“…An alternative to the application of mathematical programming methods in the pipeline scheduling represent for example heuristic modules [10], constraint programming [11] and also discrete-event simulation [12]. These approaches are suitable mainly in the scheduling of pipeline networks with more complex structures [13].…”

Section: Introductionmentioning

confidence: 99%

Support of Scheduling of Multiproduct Pipeline Systems Using Simulation in Witness

Strachotova¹,

Dyntar²

2021

Int. j. simul. model.

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In this paper, we describe the efficient discrete modelling of multiproduct pipeline systems in Witness simulation software environment. Our simulation model consists of 6 blocks called System structure, Pumping modes, Pumping schedule, Pumping execution, Collecting of outputs and Simulation run control and is supported by MS Excel. Algorithm for the assignment of a scheduled pumping requirement to execution is proposed too. Our model is innovative in that pipelines for the transport of products, tanks for storing products in warehouses as well as the products themselves and their material flow are represented neither by the continuous physical elements (Pipes, Tanks, Fluids) nor their discrete equivalent (Conveyors, Buffers, Parts) but by the logical elements called Variables. Avoiding physical elements enables efficient modelling of bidirectional flow of products in a pipeline and furthermore leads to the high speed of a simulation run. Based on the outputs of simulation of a simple pipeline system we show how our model can be used to support scheduling in complex multiple sources, multiple destinations pipeline networks.

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“…Some researchers proposed heuristic algorithms to optimize the scheduling of tree-structure pipeline networks with branching terminals. This method cannot derive an optimal solution but can attain satisfactory schedules quickly . Some other studies proposed mathematical formulation based on discrete or continuous time representation for a pipeline network with a branching structure. − Recent studies on scheduling of pipeline networks have focused on mesh-structure pipeline networks.…”

Section: Introductionmentioning

confidence: 99%

Operation Scheduling Optimization of a Transfer Hub with Multiple Tank Farms Based on a New Continuous Time Representation

Wang

Huang

et al. 2018

Ind. Eng. Chem. Res.

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This study addresses the operation-scheduling problem of a transfer hub with multiple tank farms (M-TH) connected to a multiproduct pipeline network. As part of the pipeline network, the THs serve as links that receive, store, and deliver oil products. Previous scheduling optimization works on pipeline networks with oil products largely focused on the batch plans of each pipeline, without considering the operation schedule of the THs. In this study, the main task is to optimize the loading and unloading scheduling operations of the tanks in the M-TH, which are subjected to given schedules of upstream/downstream pipelines. The schedules of pipelines that carry different types of products can be decomposed into several independent single product scenarios. A mixed-integer linear programming (MILP) formulation is presented. The proposed formulation takes into account the capacities of the tanks, operational rules of the tanks and tank farms, structural constraints, and settling time after the loading operation, while minimizing the switch operations between the tanks and switchovers between the tank farms. Moreover, the MILP formulation can be solved for one separate products at one time as there is no interference among the single-product scheduling problems within the transfer hub. The formulation is based on a new continuous time representation with static and dynamic time slots. The scheduling horizon is partitioned into several static time slots on the basis of the time points of batches arriving at and leaving from the M-TH or the flow rate changed. Each static time slot is divided into several dynamic time slots, whose duration and starting/ending points are determined by the optimization process. The proposed formulation is validated using the optimal results of a real-world case. A time horizon partitioning strategy is employed to deal with the long-term horizon scenario. The results help verify that we can substantially save computational time and obtain a satisfactory solution; however, the optimality is compromised.

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Combining Simulation and Tabu Search for Oil-derivatives Pipeline Scheduling

Cited by 28 publications

References 9 publications

Oil-derivatives pipeline logistics using discrete-event simulation

Oil-derivatives pipeline logistics using discrete-event simulation

Support of Scheduling of Multiproduct Pipeline Systems Using Simulation in Witness

Operation Scheduling Optimization of a Transfer Hub with Multiple Tank Farms Based on a New Continuous Time Representation

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