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Design of a Supply Chain-Based Production and Distribution System Based on Multi-Stage Stochastic Programming

Iswanto A. Heri, Alazzawi Fouad Jameel Ibrahim, Guerrero John William Grimaldo, Ahmed Alim Al Ayub, Chetthamrongchai Paitoon, Vladimirovich Kabanov Oleg, Kadhim Mustafa M., Jawad Mohammed Abed, Surendar A.

Foundations of Computing and Decision Sciences

2023

Vol. 48, No. 3

357--370

Supply chains are one of the key tools in optimizing production and distribution simultaneously. However, information uncertainty is always a challenge in production and distribution management. The main purpose of this paper is to design a two-echelon supply chain in a multi-cycle state and in conditions of demand uncertainty. The task includes determining the number and location of distribution centers, planning capacity for active distribution centers, and determining the amount of shipments between different levels so that the total costs of the chain are minimized. Uncertainty is applied through discrete scenarios in the model and the problem is formulated by multi-stage stochastic programming method in the form of a mixed integer linear model. The results acquired using two indicators called VMS and VSS demonstrated that modeling the supply chain design problem with the multi-stage stochastic approach can result in significant costs reduction. Plus, utilizing mathematical expectation can generate misleading results, therefore resulting in the development of supply chain designs incapable of satisfying demand due to its overlooked limitations.

Oilfield development and operations planning under geophysical uncertainty

Redutskiy Y.

Engineering Management in Production and Services

2017

Vol. 9, no. 3

10--27

The oil and gas industry nowadays is challenged by dealing with nonconventional reserves and offshore environments. Decision-making associated with projects in the petroleum sector has to handle various technological issues, risks, and uncertainty. The Smart Fields approach was introduced to cope with complicated production conditions and make the production of hydrocarbons economically efficient. A significant part of this approach is proactive planning which implies taking into account the uncertainty, or lack of knowledge of the recoverable reserves, future hydrocarbon prices and various operational issues inherent in the projects. In this study, a multi-stage stochastic programming approach is employed to cover the relevant engineering issues of oilfield development and petroleum production while addressing the geophysical uncertainty related to the developed deposit. The proposed model covers such aspects as well drilling, gathering pipeline infrastructure planning, capacity selection for the infrastructure and the processing units, as well as planning the production operations with consideration of artificial lift efficiency. The model aims to optimise the entire field lifecycle, given the chosen planning criterion, that is an economic criterion of the project’s net present value. The contribution of the developed model to the area of planning in the petroleum industry is the detailed consideration of the technology: the flows and pressures in the planned infrastructure, reservoir behaviour, and the artificial lift performance. The goal of including these technological details is to apprehend the economic tradeoff between investments, operating costs and the prospective revenues, given the lack of knowledge of the geophysical properties of the developed deposit. The stochastic modelling implemented in this study is relevant to the development projects in nonconventional environments, where several deposits of various sizes are present; however, not each deposit's properties get to be studied in detail.