Environmentally Conscious Design of Upstream Crude Oil Supply Chain

In the past few decades, very rapid growth in environmental legislation has resulted in an increasing will of companies to address environmental thinking through their supply chain. In this context, great strides have been made to incorporate the environmental concerns, such as “green” and “sustainable” supply chains, along with the traditional economic indicators. To assess the environmental impacts, there is no agreement on a universal environmental metric. Hence, a plethora of indicators has been developed to measure environmental impacts. The environmental indicators (e.g., Eco-indicator 99) that are founded on the Life Cycle Assessment (LCA) framework and nowadays are becoming among the popular environmental assessment methodologies. The crude oil tankers and utilities consumptions are the main origins of emissions through the crude oil supply chain. The energy required for operating upstream facilities in the crude oil supply chain represents enormous energy consumption. Whereas, ships and oil tankers are of the highest polluting combustion origins, per unit of fuel used. As a result, environmentally conscious design of upstream crude oil supply chain has created intriguing new challenges for practitioners and the managers. In this work, we introduce an environmentally conscious mathematical model to design the Upstream Oil Supply Chain (i.e., oil field development, and crude oil transportation). The model configure the supply network, select the technologies, establish the pipeline network, and plan the oil tankers with optimizing the economic objective value (i.e., the net present value) and the environmental metric value (i.e., the standard Eco-indicator 99 value)

A multiagent simulator for supporting logistic decisions of unloading petroleum ships in habors

This work presents and evaluates the performance of a simulation model based on multiagent system technology in order to support logistic decisions in a harbor from oil supply chain. The main decisions are concerned to pier allocation, oil discharge, storage tanks management and refinery supply by a pipeline. The real elements as ships, piers, pipelines, and refineries are modeled as agents, and they negotiate by auctions to move oil in this system. The simulation results are compared with results obtained with an optimization mathematical model based on mixed integer linear programming (MILP). Both models are able to find optimal solutions or close to the optimal solution depending on the problem size. In problems with several elements, the multiagent model can find solutions in seconds, while the MILP model presents very high computational time to find the optimal solution. In some situations, the MILP model results in out of memory error. Test scenarios demonstrate the usefulness of the multiagent based simulator in supporting decision taken concerning the logistic in harbors