Strategic and Tactical Crude Oil Supply Chain: Mathematical Programming Models

Crude oil industry very fast became a strategic industry. Then, optimization of the Crude Oil Supply Chain (COSC) models has created new challenges. This fact motivated me to study the COSC mathematical programming models. We start with a systematic literature review to identify promising avenues. Afterwards, we elaborate three concert models to fill identified gaps in the COSC context, which are (i) joint venture formation, (ii) integrated upstream, and (iii) environmentally conscious design

Analysis of Disruptions in the Gulf of Mexico Oil and Gas Industry Supply Chain and Related Economic Impacts

Catastrophic events are human and economic tragedies in collaboration. Oil spills have enormous impacts on the local economy of the area and for the local labor markets. The Deepwater Horizon oil spill was caused by an explosion on semisubmersible drilling rig (Macondo) on April 20, 2010. Another regional disaster, Hurricane Katrina as it ripped over the core of the Gulf of Mexico producing zone, one of the most important oil and gas production region. With Geological complexities, continued of drilling and production in GoM increases the risk of having leak/spill. Therefore, the Econometrics methods, and Modeling to forecast impacts of potential disasters are utilized and conduct optimization modeling to capture key components for building reasonable supply chain models of actual situations for petroleum industry in order to make the best possible choices consequences of disaster in this dissertation,. The dynamic response of a different of industrial sectors in Louisiana to oil and gas disasters is considered. The likely magnitude of the net economic impact of a major oil spill (Macondo) will be determined in terms of jobs and wages with Vector Autoregressive method. Forecast the potential impacts of future changes in employment after disaster on economy will be studied. In the second part, the offsetting economic injection due to BP expenditures in the economy, will estimate by economic impact analysis method, which is Input-output models. Then the gross economic damage, which is created by BP oil spill will be calculated. The final results provide beneficial knowledge on determining the potential economic impact of future large-scale catastrophes and helpful for companies to react better to the economic impact of events. At the end, a mathematical framework will be presented for optimal network design of oil and gas supply chain with application for Louisiana Offshore Oil Port (LOOP); due to determine the optimal oil flow through the mid-stream/ downstream networks and its profit even if it is experiencing natural/ man-made damages. The outcome of this work is a new distributed decision support framework which is intended to help optimize the profit for critical energy zone and to boost economy under unpredictable situations

Strategic analysis and optimization of bioethanol supply chains

In modern times, the interest in renewable energy has been increasing considerably in response to the growing energy demand and to the simultaneous concern about global warming effects. The urgency of this issue is related to dissociation between the perspective of a steady growth in demand for fuel and its supply, which is projected to become ever more uncertain and expensive. The phenomenon of climate change is widely recognized as a consequence of the increased concentration of greenhouse gases (GHG) in the atmosphere caused by anthropogenic activity, and to which the transport sector is a significant contributor. Among biofuels, biomass-based ethanol has been in a leading position for substituting petroleum-based road-fuels. Even if its actual carbon footprint is still debated, it is generally acknowledged a reduction in net GHG emissions with respect to oil. The complexity of the context discussed previously, guides us to the transition towards a more sustainable transport system which requires the adoption of effective quantitative tools able to encompassing the problem to the whole production chain (supply chain), that may help defining a more comprehensive view of biofuels. In dealing with such problems involving high decisional level, the analytical modelling is recognized as the best optimization option, particularly in the initial phase of design of unknown infrastructures in order to cope with a comprehensive management of production systems taking into account all supply chain stages. Mixed Integer Linear Programming (MILP) in particular, emerges as one of the most suitable tools in determining the optimal solutions of complex supply chain design problems where multiple alternatives are to be taken into account. In this sense, the multi-objective MILP (moMILP) enables simultaneous consideration of conflicting criteria (i.e., financial, environmental) to assist the decisions of interested parties on biofuels industry at strategic and tactical levels. Moreover, this complex analysis is addressed effectively by incorporating the principles of Life Cycle Analysis (LCA) within supply chain analysis techniques aiming at a quantitative assessment of the environmental burdens of each supply chain stage. Accordingly, the main purpose of the research presented in this Thesis is to cover this gap of knowledge in the literature. In the context of the development and adoption of bioenergy systems, the overall objective of this work is to provide quantitative and deterministic tools to analyze and optimize the supply chain as whole, to thereby identify the most suitable and feasible strategies for the development of future road transport systems. In this sense, the research design for this Thesis begins with the development and analysis of a multi-period moMILP modelling framework for the design and the optimization of bioethanol supply chain where economics and environmental sustainability (GHG emissions reductions potential) for first generation ethanol is addressed, considering possibilities of several technologies integration (including biogas production). Then, the analysis is focused on the general interactions of market policies under the European Emission Trading System in order to enhance the bioethanol market development trends to boost sustainable production of bioethanol. Next, a comprehensive modelling analysis to predict commodity price evolution dynamics and to extend the price forecasts to other goods related to bioethanol production is addressed. An assessment of the impact on the supply chain design of the recent proposed by the European Commission to amend the existing Directive in terms of accountability technique for biofuels is analyzed and discussed. Besides, multi-criteria decision making tools to support strategic design and planning on biofuel supply chains including several Game Theory features are evaluated. Finally to close up, the main achievements of the Thesis are exposed as well as the main shortfalls and possible future research lines are outlined. Models capabilities in steering decisions on investments for bioenergy systems are evaluated in addressing real world case studies referring to the emerging bioethanol production in Northern Italy

Integrating bio-hubs in biomass supply chains: Insights from a systematic literature review

Biomass sources are geographically scattered, and seasonal changes influence their availability. Variations in location, type, and feedstock quality impose logistical and storage challenges. Such a dispersion and variety of biomass sources, as well as the dispersion of demand points, may undermine the economies of scale and increase the risk of supply shortage. By consolidating biomass preprocessing and distribution activities in bio-hub facilities, they can contribute to the overall resilience of biomass supply chains (BSCs) and ensure a more sustainable and cost-efficient approach to bioenergy production. As such, investigating the advantages and challenges associated with bio-hub implementation can offer invaluable insights on the efficiency and sustainability of BSCs. Despite its critical role, a major part of the literature on BSCs is confined to the decision-making processes related to biomass suppliers and bioconversion facilities. To bridge this research gap, the current study conducts a systematic literature review on bio-hub implementation within BSCs in the period of the last ten years. Shortlisted papers are classified and analyzed meticulously to extract possible improvements from BSC and modeling perspectives. From the BSC viewpoint, one notable gap is the little attention to mid-term and short-term decisions of bio-hub operations such as inventory control, resource management and production planning. Furthermore, the results revealed that environmental and social aspects of bio-hub implementation require considerable attention. From the modeling perspective, findings illustrate the underutilization of integrated approaches to incorporate micro-level and macro-level information in decision-making. In this regard, a number of areas are suggested for further exploration

Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

[EN] Lack of homogeneity in the product (LHP) appears in some production processes that confer heterogeneity in the characteristics of the products obtained. Supply chains with this issue have to classify the product in different homogeneous subsets, whose quantity is uncertain during the production planning process. This paper proposes a generic framework for reviewing in a unified way the literature about production planning models dealing with LHP uncertainty. This analysis allows the identification of similarities among sectors to transfer solutions between them and gaps existing in the literature for further research. The results of the review show: (1) sectors affected by LHP inherent uncertainty, (2) the inherent LHP uncertainty types modelled, and (3) the approaches for modelling LHP uncertainty most widely employed. Finally, we suggest a conceptual model reflecting the aspects to be considered when modelling the production planning in sectors with LHP in an uncertain environment.This research was initiated within the framework of the project funded by the Ministerio de Economía y Competitividad [Ref. DPI2011-23597] entitled ‘Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity’ (PLANGES-FHP) already finished. After, the project leading to this application has received funding from the European Union’s research and innovation programme under the H2020 Marie Skłodowska-Curie Actions with the grant agreement No 691249, Project entitled ’Enhancing and implementing Knowledge based ICT solutions within high Riskand Uncertain Conditions for Agriculture Production Systems’ (RUC-APS).Mundi, I.; Alemany Díaz, MDM.; Poler, R.; Fuertes-Miquel, VS. (2019). 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A mixed integer programming model to evaluate the impact of business factors on global manufacturing relocation decisions

In recent years, dramatic changes in business factors have triggered a trend of manufacturing relocation out of 'The World's Factory', which is the Pearl River Delta (PRO), China. Global manufacturers in PRO have been facing unprecedented operating cost pressure, due to RI1B currency appreciation, rising labor cost, highly volatile oil price, tax rebate adjustment and industry policy changes. This paper presents a Mixed Integer Programming (MIP) model, to evaluate the impact of business factors on global manufacturing relocation decisions. Objective function of the MIP model is to minimize Total Landed Cost (TLC) for international markets. Application of the MIP model is illustrated through a case study with a hypothetical footwear manufacturer. Managerial implications on supply chain dynamics and regional economy are derived from modeling results and analysis.published_or_final_versio

A framework for the near-real-time optimization of integrated oil & gas midstream processing networks

The oil and gas industry plays a key role in the world’s economy. Vast quantities of crude oil, their by-products and derivatives are produced, processed and distributed every day. Indeed, producing and processing significant volumes of crude oil requires connecting to wells in different fields that are usually spread across large geographical areas. This crude oil is then processed by Gas Oil Separation Plants (GOSPs). These facilities are often grouped into clusters that are within approximate distance from each other and then connected laterally via swing lines which allow shifting part or all of the production from one GOSP to another. Transfer lines also exist to allow processing intermediate products in neighbouring GOSPs, thereby increasing complexity and possible interactions. In return, this provides an opportunity to leverage mathematical optimization to improve network planning and load allocation. Similarly, in major oil producing countries, vast gas processing networks exist to process associated and non-associated gases. These gas plants are often located near major feed sources. Similar to GOSPs, they are also often connected through swing lines, which allow shifting feedstock from some plants to others. GOSPs and gas plants are often grouped as oil and gas midstream plants. These plants are operated on varied time horizons and plant boundaries. While plant operators are concerned with the day-to-day operation of their facility, network operators must ensure that the entire network is operated optimally and that product supply is balanced with demand. They are therefore in charge of allocating load to individual plants, while knowing each plants constraints and processing capabilities. Network planners are also in charge of producing production plans at varied time-scales, which vary from yearly to monthly and near-real time. This work aims to establish a novel framework for optimizing Oil and Gas Midstream plants for near-real time network operation. This topic has not been specifically addressed in the existing literature. It examines problems which involve operating networks of GOSPs and gas plants towards an optimal solution. It examines various modelling approaches which are suited for this specific application. It then focuses at this stage of the research on the GOSP optimization problem where it addresses optimizing the operation of a complex network of GOSPs. The goal is to operate this network such that oil production targets are met at minimum energy consumption, and therefore minimizing OpEx and Greenhouse Gas Emissions. Similarly, it is often required to operate the network such that production is maximized. This thesis proposes a novel methodology to formulate and solve this problem. It describes the level of fidelity used to represent physical process units. A Mixed Integer Non-Linear Programming (MINLP) problem is then formulated and solved to optimize load allocation, swing line flowrates and equipment utilization. The model demonstrates advanced capabilities to systematically prescribe optimal operating points. This was then applied to an existing integrated network of GOSPs and tested at varying crude oil demand levels. The results demonstrate the ability to minimize energy consumption by up to 51% in the 50% throughput case while meeting oil production targets without added capital investment.Open Acces