Optimal trade-offs in decision-making for sustainability and resilience in manufacturing supply chains

2021 ◽  
pp. 127596
Author(s):  
R. Rajesh
Keyword(s):  
Decision Making ◽  
Supply Chains ◽  
Trade Offs

Post COVID-19 Potentials and Innovation: The Future Supply Chain

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 1054-1057
Author(s):  
Bindu Swetha Pasuluri ◽  
Anuradha S G ◽  
Manga J ◽  
Deepak Karanam
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Supply Chain ◽  
Supply Chains ◽  
World Health Organization ◽  
World Wide ◽  
World Health ◽  
Future Supply ◽  
Entire World ◽  
Health Organization

An unanticipated outburst of pneumonia of inexperienced in Wuhan, , China stated in December 2019. World health organization has recognized pathogen and termed it COVID-19. COVID-19 turned out to be a severe urgency in the entire world. The influence of this viral syndrome is now an intensifying concern. Covid-19 has changed our mutual calculus of ambiguity. It is more world-wide in possibility, more deeply , and much more difficult than any catastrophe that countries and organizations have ever faced. The next normal requires challenging ambiguity head-on and building it into decision-making. It is examined that every entity involved in running supply chains would require through major as employee, product, facility protocols, and transport would have to be in place. It is an urgent need of structuring to apply the lessons well-read for our supply chain setup. With higher managers now being aware of the intrinsic hazards in their supply chain, key and suggestions-recommendations will help to guide leader to commit to a newly planned, more consistent supply chain setup. Besides, the employees’ mental health is also a great concern.

Renewable energy selection based on a new entropy and dissimilarity measure on an interval-valued neutrosophic set

2021 ◽  
pp. 1-18
Author(s):  
ShuoYan Chou ◽  
Truong ThiThuy Duong ◽  
Nguyen Xuan Thao
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Membership Function ◽  
Fossil Fuels ◽  
Multiple Criteria Decision Making ◽  
Clean Energy ◽  
Dissimilarity Measure ◽  
Neutrosophic Set ◽  
Trade Offs ◽  
And Performance

Energy plays a central part in economic development, yet alongside fossil fuels bring vast environmental impact. In recent years, renewable energy has gradually become a viable source for clean energy to alleviate and decouple with a negative connotation. Different types of renewable energy are not without trade-offs beyond costs and performance. Multiple-criteria decision-making (MCDM) has become one of the most prominent tools in making decisions with multiple conflicting criteria existing in many complex real-world problems. Information obtained for decision making may be ambiguous or uncertain. Neutrosophic is an extension of fuzzy set types with three membership functions: truth membership function, falsity membership function and indeterminacy membership function. It is a useful tool when dealing with uncertainty issues. Entropy measures the uncertainty of information under neutrosophic circumstances which can be used to identify the weights of criteria in MCDM model. Meanwhile, the dissimilarity measure is useful in dealing with the ranking of alternatives in term of distance. This article proposes to build a new entropy and dissimilarity measure as well as to construct a novel MCDM model based on them to improve the inclusiveness of the perspectives for decision making. In this paper, we also give out a case study of using this model through the process of a renewable energy selection scenario in Taiwan performed and assessed.

scholarly journals Sustainability Assessment and Agricultural Supply Chains Evidence-Based Multidimensional Analyses as Tools for Strategic Decision-Making—The Case of the Pineapple Supply Chain in Benin

2021 ◽  
Vol 13 (4) ◽  
pp. 2060
Author(s):  
Doriane Desclee ◽  
David Sohinto ◽  
Freddy Padonou
Keyword(s):  
Decision Making ◽  
Sustainable Development ◽  
Developing Countries ◽  
Supply Chains ◽  
Agricultural Sector ◽  
Sustainability Assessment ◽  
Assessment Tools ◽  
Strategic Decision ◽  
Evidence Based ◽  
Agricultural Supply

Contributing to Sustainable Development Goals and Agenda 2030 is a shared objective of all institutions and people. The challenges differ according to the characteristics of every context. In developing countries, strongly dependent on the agricultural sector, agricultural supply chains are recognized as crucial for economic growth and enablers for livelihood improvement. Moreover, sustainable development issues are correlated and can meet in agricultural supply chains. For several decades, parallel to decision-makers, the research community has elaborated sustainability assessment tools. Such tools evolved to fit with actuality, but it is challenging to find decision-making support tools for sustainable development adequate in agricultural supply chains and developing countries contexts. There is a necessity to define evidence-based tools and exhaustive analytical frameworks according to sustainability multidimensionality and strategical tradeoffs necessity. The VCA4D method aims to go beyond the limits of previous methods. It proposes a combination of multidisciplinary analytical tools applied empirically to analyze agricultural supply chains in their context. It provides evidence-based analytical results allowing to identify enablers for strategic sustainable and inclusive interventions. However, to even better meet contextual exhaustiveness’s expectations and indicators’ robustness to lead to relevant interventions, we should insist on a stricter framing of contextual data collection processes.

scholarly journals Using Deliberative Democracy for Better Urban Decision-Making through Integrative Thinking

Urban Science ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 3
Author(s):  
Janette Hartz-Karp ◽  
Dora Marinova
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Deliberative Democracy ◽  
Decision Makers ◽  
Capital City ◽  
City Region ◽  
Multiple Sources ◽  
Trade Offs ◽  
Urban Issues ◽  
Sustainability Challenges

This article expands the evidence about integrative thinking by analyzing two case studies that applied the collaborative decision-making method of deliberative democracy which encourages representative, deliberative and influential public participation. The four-year case studies took place in Western Australia, (1) in the capital city Perth and surrounds, and (2) in the city-region of Greater Geraldton. Both aimed at resolving complex and wicked urban sustainability challenges as they arose. The analysis suggests that a new way of thinking, namely integrative thinking, emerged during the deliberations to produce operative outcomes for decision-makers. Building on theory and research demonstrating that deliberative designs lead to improved reasoning about complex issues, the two case studies show that through discourse based on deliberative norms, participants developed different mindsets, remaining open-minded, intuitive and representative of ordinary people’s basic common sense. This spontaneous appearance of integrative thinking enabled sound decision-making about complex and wicked sustainability-related urban issues. In both case studies, the participants exhibited all characteristics of integrative thinking to produce outcomes for decision-makers: salience—grasping the problems’ multiple aspects; causality—identifying multiple sources of impacts; sequencing—keeping the whole in view while focusing on specific aspects; and resolution—discovering novel ways that avoided bad choice trade-offs.

Novel Application of Artificial Intelligence with Potential to Transform Well Planning Workflows on the Norwegian Continental Shelf

2021 ◽  
Author(s):  
Jon Gustav Vabø ◽  
Evan Thomas Delaney ◽  
Tom Savel ◽  
Norbert Dolle
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Continental Shelf ◽  
Data Science ◽  
Search Algorithm ◽  
Solution Space ◽  
Good Decision ◽  
Trade Offs ◽  
Well Trajectory ◽  
Norwegian Continental Shelf

Abstract This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process. Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard. In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while taking into account complex issues such as anti-collision with existing wells, drilling hazards and trade-offs between cost, value and risk. Designing drillable well trajectories involves a sequence of decisions, which makes the process very suitable for AI algorithms. Different solver architectures, or algorithms, can be used to play this game. This is similar to how companies such as Google-owned DeepMind develop customized solvers for games such as Go and StarCraft. The chosen method is a Tree Search algorithm with an evolutionary layer on top, providing a good balance in terms of performance (i.e., speed) vs. exploration capability (i.e., it looks "wide" in the option space). The algorithm has been deployed in a full stack web-based application that allows users to follow an end-2-end workflow: from defining well trajectory design rules and constraints to running the AI engine and evaluating results to the optimization of multi-well drilling campaigns based on risk, value and cost objectives. The full-size paper describes different Norwegian Continental Shelf (NCS) use cases of this AI assisted well trajectory planning. Results to-date indicate significant CAPEX savings potential and step-change improvements in decision speed (months to days) compared to routine manual workflows. There are very limited real transformative examples of Artificial Intelligence in multi- disciplinary workflows. This paper therefore gives a unique insight how a combination of data science, domain expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization.

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