Discrete Event Simulation of Driver's Routing Behavior Rule at a Road Intersection

Benzaman, Ben; Pakdamanian, Erfan

doi:10.48550/arxiv.1907.00450

Cited by 1 publication

(1 citation statement)

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“…The decision variables in this model including location, new facilities and pipelines installation, capacity expansion in the existing facilities and pipelines, close down of facilities, inventory, production, imports, exports, routing and selection of transportation modes in strategic and tactical levels. In the model presented at the next sections the routing part has been defined as the shortest path between defined centers, while to have a more detailed model shortest traveling time and also the congestion of the routes can be added to it as well [27].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Sustainable Competitive Petroleum Supply Chain Model for Various Stakeholders with Shared Facilities

Moradinasab,

Jafarzadeh,

Amin-Naseri

et al. 2019

Preprint

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Petroleum industry is the world's biggest energy source, and its associated industries such as production, distribution, refining and retail are considered as the largest ones in the world. Having the increasing price and governments job creation and international environmental policies, the petroleum companies try to maximize the number of created job, and their profit and minimize the air pollution simultaneously. To meet these objectives, an effective detailed and precise planning is needed. On the other hand, the dynamic environment and the presence of various stakeholders add to the complexity of planning and design of petroleum supply chain. Therefore, the multi-period, multi-objective, multi-level and multi-product dynamic sustainable competitive petroleum supply chain (DSCPSC) model taking into consideration the various stakeholders have been proposed in this paper. The proposed model is an MILP model and GAMS 24.1.2 software has been used to run it for a part of real petroleum supply chain data. Sensitivity analysis was then performed to determine the sensitivity of the results to the variation of the coefficients in objective function. Sensitivity analysis reveals that the highest variations of the objective function were observed with respect to the variable costs, facility installation costs and pipeline transportation costs.

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Section: Introductionmentioning

confidence: 99%