scholarly journals Oil and Gas supply chain optimization using Agent-based modelling (ABM) integration with Big Data technology

2020 ◽  
Vol 4 (9) ◽  
pp. 155192
Author(s):  
Jamal Maktoubian ◽  
Mehran Ghasempour-Mouziraji ◽  
Mohebollah Noori
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Oil And Gas ◽  
Supply Chain Optimization ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Gas Supply ◽  
Big Data Technology

Blockchain and 6G-Based Supply Chain Management Framework for Oil and Gas Shipment

2021 ◽  
Author(s):  
Md Abdur Rahman ◽  
Syed M. Belal
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Oil And Gas ◽  
Current System ◽  
Oil And Gas Industry ◽  
Chain Process ◽  
Chain Management ◽  
It Systems ◽  
Supply Chain Management System ◽  
Gas Supply

Abstract Keeping track of the oil and gas supply chain is challenging task as the route and transportation requires sophisticated security environment - both physical systems’ and IT systems’ security. Thanks to the recent advancement in IoT, specialized sensors can keep track of the required supply chain environment. With the help of blockchain, the supply chain data can be immutably saved for further sharing with stakeholders. Due to the introduction of AI as an embedded element within 6G networks, the end-to-end supply chain process can now be automated for safety, security, and efficiency purposes. By leveraging 6G, AI, blockchain, and IoT, the supply chain data during the transportation or at rest can be monitored for any changed environment during the movement of the ship through national or international routes. In this paper, we study the requirements of such intelligent and secure supply chain management system conducive to the oil and gas industry. We also show our proof-of-concept implementation and initial test results. Our obtained results show promising prospect of the current system to be deployed to safeguard the oil and gas supply chain.

scholarly journals Assessment of methane emissions from the U.S. oil and gas supply chain

Science ◽  
2018 ◽  
pp. eaar7204 ◽  
Author(s):  
Ramón A. Alvarez ◽  
Daniel Zavala-Araiza ◽  
David R. Lyon ◽  
David T. Allen ◽  
Zachary R. Barkley ◽  
...  
Keyword(s):  
Supply Chain ◽  
Oil And Gas ◽  
Methane Emissions ◽  
Gas Supply ◽  
The U.S

An Integrated Machine-Learning Approach to Shale-Gas Supply-Chain Optimization and Refrac Candidate Identification

2019 ◽  
Vol 22 (04) ◽  
pp. 1201-1224 ◽  
Author(s):  
Hope I. Asala ◽  
Jorge A. Chebeir ◽  
Vidhyadhar Manee ◽  
Ipsita Gupta ◽  
Arash Dahi-Taleghani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Shale Gas ◽  
Learning Approach ◽  
Supply Chain Optimization ◽  
Machine Learning Approach ◽  
Candidate Identification ◽  
Gas Supply

scholarly journals A multi-agent based system with big data processing for enhanced supply chain agility

2016 ◽  
Vol 29 (5) ◽  
pp. 706-727 ◽  
Author(s):  
Mihalis Giannakis ◽  
Michalis Louis
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Semantic Web ◽  
Supply Chains ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Agent Systems ◽  
Supply Chain Agility ◽  
Multi Agent

Purpose Decision support systems are becoming an indispensable tool for managing complex supply chains. The purpose of this paper is to develop a multi-agent-based supply chain management system that incorporates big data analytics that can exert autonomous corrective control actions. The effects of the system on supply chain agility are explored. Design/methodology/approach For the development of the architecture of the system, a sequential approach is adopted. First three fundamental dimensions of supply chain agility are identified – responsiveness, flexibility and speed. Then the organisational design of the system is developed. The roles for each of the agents within the framework are defined and the interactions among these agents are modelled. Findings Applications of the model are discussed, to show how the proposed model can potentially provide enhanced levels in each of the dimensions of supply chain agility. Research limitations/implications The study shows how the multi-agent systems can assist to overcome the trade-off between supply chain agility and complexity of global supply chains. It also opens up a new research agenda for incorporation of big data and semantic web applications for the design of supply chain information systems. Practical implications The proposed information system provides integrated capabilities for production, supply chain event and disruption risk management under a collaborative basis. Originality/value A novel aspect in the design of multi-agent systems is introduced for inter-organisational processes, which incorporates semantic web information and a big data ontology in the agent society.

scholarly journals Multi-objective optimization model for a downstream oil and gas supply chain

2017 ◽  
Vol 52 ◽  
pp. 689-708 ◽  
Author(s):  
Ahmed M. Ghaithan ◽  
Ahmed Attia ◽  
Salih O. Duffuaa
Keyword(s):  
Supply Chain ◽  
Optimization Model ◽  
Oil And Gas ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Gas Supply

scholarly journals Pricing Strategies of a Three-Stage Supply Chain: A New Research in the Big Data Era

2017 ◽  
Vol 2017 ◽  
pp. 1-16
Author(s):  
Pan Liu
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Stackelberg Game ◽  
Chain Model ◽  
Pricing Strategies ◽  
Supply Chain Model ◽  
Investment Cost ◽  
New Research ◽  
New Situation ◽  
Big Data Technology

In the Big Data era, Data Company as the Big Data information (BDI) supplier should be included in a supply chain. In the new situation, to research the pricing strategies of supply chain, a three-stage supply chain with one manufacturer, one retailer, and one Data Company was chosen. Meanwhile, considering the manufacturer contained the internal and external BDI, four benefit models about BDI investment were proposed and analyzed in both decentralized and centralized supply chain using Stackelberg game. Meanwhile, the optimal retail price and benefits in the four models were compared. Findings are as follows. (1) The industry cost improvement coefficient, the internal BDI investment cost of the manufacturer, and the added cost of the Data Company on using Big Data technology have different relationships with the optimal prices of supply chain members in different models. (2) In the retailer-dominated supply chain model, the optimal benefits of the retailer and the manufacturer are the same, and the optimal benefits of the Data Company are biggest in all the members.

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