Integrated Approach to Model Liquid Fuels Supply Chain Interactions Due to Disasters and Accidents

2021 ◽  
Author(s):  
Hitesh Mohan ◽  
Grace Kuczma ◽  
Marshall Carolus
Keyword(s):  
Supply Chain ◽  
Crude Oil ◽  
Supply And Demand ◽  
Value Added ◽  
The United States ◽  
Analytical Tool ◽  
Supply Chain Network ◽  
Secondary Markets ◽  
Area Of Interest ◽  
Geographical Regions

Abstract Objectives/Scope Historically, facility outages, cyber-attacks, natural disasters, supply interruptions and other disruptions have caused significant impacts to the flow of crude oil and petroleum products. The impacts on assets, primary and secondary markets, and economic indices need to be quantified to address the effects of disruptions efficiently and effectively. Methods, Procedures, Process This paper details the design process that was used to develop an analytical tool to predict the impacts from disruptions, determine the cascading impacts on downstream markets and dependent assets and provide estimates for recovery potential. The entire petroleum supply chain, upstream, midstream, and downstream facilities, were included in the development of the modeling capability. To best capture the supply chain network and geographic dependencies, the area of interest was divided into finite regional markets based on supply and demand market dynamics and connectivity. Extensive review was conducted of historical disruptions to understand the impacts on infrastructure and the petroleum supply chain network interdependencies to support a holistic approach to disruption simulation. Algorithms were developed to determine the probability and severity of damage of disruptions at varying intensities. The modeling capability was designed to utilize a comprehensive up-to-date database and geospatial system. The database was designed considering public, private and proprietary data, the frequency of updates, validity of source, and value added. Results, Observations, Conclusions The petroleum disruption analytical modeling tool that was developed assesses disruptions to the supply chain network and predicts the duration and severity of impacts on facilities and the cascading effect on primary and secondary markets. The tool provides disruption results in tabular, graphical, and geospatial forms for individual assets, regions, and the nation as a whole. For the United States, the tool models 26 geographical regions delineated by refining sectors, mainly along the coastlines, and dependent demand markets. The crude oil and petroleum product supply and demand is balanced for each region using local supply, net imports, interregional connectivity, and the multi-modal transportation network. This paper demonstrates how the analysis tool provides business intelligence insights for the days of stocks available throughout the disruption duration and the total loss in products to the markets. An economic submodule was integrated with the tool that determines the impacts on the crude oil and gasoline price and the resulting impact on Gross Domestic Product (GDP). The model was benchmarked during Hurricane Laura 2020 and the predicted reduction in refining production was 85% of realized losses. Novel/Additive Information The innovative analytical tool simulates disruptions and provides predicted forecast of facility, market, and economic impacts for an extended period of time after the event occurs supporting response, recovery, and planning efforts. The model can be assimilated for any geographical or geopolitical region with consideration given to region specific disruptions. This paper provides case studies exemplifying use cases and model simulated results for petroleum supply disruptions.

scholarly journals Determining nodal capacities for military distribution problems

2019 ◽  
Vol 3 (2) ◽  
pp. 110-130 ◽  
Author(s):  
Dave C. Longhorn ◽  
Joshua R. Muckensturm
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
The United States ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Multiple Time ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Content Type ◽  
Distribution Problems

Purpose This paper aims to introduce a new mixed integer programming formulation and associated heuristic algorithm to solve the Military Nodal Capacity Problem, which is a type of supply chain network design problem that involves determining the amount of capacity expansion required at theater nodes to ensure the on-time delivery of military cargo. Design/methodology/approach Supply chain network design, mixed integer programs, heuristics and regression are used in this paper. Findings This work helps analysts at the United States Transportation Command identify what levels of throughput capacities, such as daily processing rates of trucks and railcars, are needed at theater distribution nodes to meet warfighter cargo delivery requirements. Research limitations/implications This research assumes all problem data are deterministic, and so it does not capture the variations in cargo requirements, transit times or asset payloads. Practical implications This work gives military analysts and decision makers prescriptive details about nodal capacities needed to meet demands. Prior to this work, insights for this type of problem were generated using multiple time-consuming simulations often involving trial-and-error to explore the trade space. Originality/value This work merges research of supply chain network design with military theater distribution problems to prescribe the optimal, or near-optimal, throughput capacities at theater nodes. The capacity levels must meet delivery requirements while adhering to constraints on the proportion of cargo transported by mode and the expected payloads for assets.

scholarly journals An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model

2018 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
Ayda Emdadian ◽  
S. G. Ponnambalam ◽  
G. Kanagaraj
Keyword(s):  
Supply Chain ◽  
Differential Evolution ◽  
Network Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Value Added ◽  
Raw Material ◽  
Supply Chain Network ◽  
De Algorithm ◽  
Chain Problem

In this paper, five variants of Differential Evolution (DE) algorithms are proposed to solve the multi-echelon supply chain network optimization problem. Supply chain network composed of different stages which involves products, services and information flow between suppliers and customers, is a value-added chain that provides customers products with the quickest delivery and the most competitive price. Hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between several objectives. The supply chain problem utilized in this study is taken from literature which incorporates demand, capacity, raw-material availability, and sequencing constraints in order to maximize total profitability. The performance of DE variants has been investigated by solving three stage multi-echelon supply chain network optimization problems for twenty demand scenarios with each supply chain settings. The objective is to find the optimal alignment of procurement, production, and distribution while aiming towards maximizing profit. The results show that the proposed DE algorithm is able to achieve better performance on a set of supply chain problem with different scenarios those obtained by well-known classical GA and PSO.

scholarly journals Machine Learning for Supply Chain’s Big Data: State of the art and application to Social Networks’ data

2018 ◽  
Vol 200 ◽  
pp. 00015
Author(s):  
Radouane El-Khchine ◽  
Amine Amar ◽  
Zine Elabidine Guennoun ◽  
Charaf Bensouda ◽  
Youness Laaroussi
Keyword(s):  
Supply Chain ◽  
Operations Research ◽  
State Of The Art ◽  
Supply And Demand ◽  
Supply Chain Network ◽  
Huge Amount ◽  
Strategic Sourcing ◽  
Quantum Neural Network ◽  
Twitter Data ◽  
Insight Into

In the context of today ’s pattern of globalization and a huge amount of information, a smart supply management chain is required. Naturally, statistics and operations research are used for optimizing supply and demand objectives. However, the new context brings out new opportunities at descriptive, predictive and prescriptive levels for supply chain network design, logistics and distribution and strategic sourcing. The key question is still how to capture and to use information. One striking example can be taken from social media, where their use allow to gain insight into the perception of consumers and to capture a real time overview of consumer reactions, regarding one or more specific events. In this regard, different modern approaches, such as IoT or Quantum neural network, are developed. In the same line of thought, we propose an analytic approach, based on KNN, Logistic Regression and SVM with the use of Twitter data in chicken supply chain management. Results identify the main concerns related to chicken products and allow to the development of a consumer-centric supply chain. The proposed approach can be extended to other topics such as anomaly detection and codification of customer intelligence.

Design of Simulation Tools for Training Programs in Lean Manufacturing

2005 ◽  
Author(s):  
Alok K. Verma ◽  
Harsh Hirkannawar ◽  
Jyotsna Devulapalli
Keyword(s):  
Supply Chain ◽  
Lean Manufacturing ◽  
Training Programs ◽  
Value Added ◽  
The United States ◽  
Value Stream Mapping ◽  
Design Development ◽  
Simulation Tools ◽  
Newport News ◽  
Value Stream

Lean Manufacturing is a powerful philosophy, which advocates minimization of waste throughout the value stream both within the organization and enterprise which includes the supply chain. A concept, which was first used by automobile manufacturers to enhance their operational efficiencies, Lean focuses on driving out non-value added activities from a company’s operation, while streamlining its value added activities. A number of companies in the United States have adopted the Lean Manufacturing philosophy to reduce cost and increase efficiency. To augment the existing training programs five new simulation tools in Ship Design Processes, Supply Chain Integration, Ship Repair Processes, Value Stream Mapping and Scheduling were developed under a grant from National Shipbuilding Research Program (NSRP) by Old Dominion University, Northrop Grumman Newport News and South Tidewater Association of Ship Repairers. The paper will discuss the design, development and implementation of two of these new simulation tools.

scholarly journals Designing a clothing supply chain network considering pricing and demand sensitivity to discounts and advertisement

2020 ◽  
Author(s):  
Mohammad Mahdi Paydar ◽  
Marjan Olfati ◽  
chefi Triki
Keyword(s):  
Supply Chain ◽  
Programming Model ◽  
Rapid Development ◽  
Supply And Demand ◽  
Clothing Industry ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Supply Chain Network ◽  
Total Profit ◽  
Demand Sensitivity

These days, clothing companies are becoming more and more developed around the world. Due to the rapid development of these companies, designing an efficient clothing supply chain network can be highly beneficial, especially with the remarkable increase in demand and uncertainties in both supply and demand. In this study, a bi-objective stochastic mixed-integer linear programming model is proposed for designing the supply chain of the clothing industry. The first objective function maximizes total profit and the second one minimizes downside risk. In the presented network, the initial demand and price are uncertain and are incorporated into the model through a set of scenarios. To solve the bi-objective model, weighted normalized goal programming is applied. Besides, a real case study for the clothing industry in Iran is proposed to validate the presented model and developed method. The obtained results showed the validity and efficiency of the current study. Also, sensitivity analyses are conducted to evaluate the effect of several important parameters, such as discount and advertisement, on the supply chain .  The results indicate that considering the optimal amount for discount parameter can conceivably enhance total profit by about 20% compared to the time without this discount scheme. When we take the optimized parameter into account for advertisement, 12% is obtained for the total profit. Based on our findings, the more the expected profit value, the higher the total amount of total profit and risk.  The results of this research also provide some interesting managerial insights for managers.

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