Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems

Dounias, Georgios; Vassiliadis, Vassilios S.

doi:10.4018/978-1-4666-7456-1.ch012

Cited by 3 publications

(1 citation statement)

References 211 publications

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“…AI can be used to optimize supply chains by predicting demand, optimizing delivery routes, and managing inventory [39]. This can help businesses reduce costs and improve efficiency.…”

Section: Ai Impact On Economic Modelingmentioning

confidence: 99%

Progress, Evolving Paradigms and Recent Trends in Economic Analysis

Damasevicius

2023

FEL

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This paper provides a thorough review of the shifting landscape of economic analysis, spotlighting recent trends and predicting future paths. While traditional economic models remain key for interpreting economic activity, they are being supplemented by fresh methods and cross-disciplinary viewpoints. The increased attention to inequality studies, using advanced statistical techniques and unique data sources, underscores the growing emphasis on fairness and distribution within economic analysis. The incorporation of behavioral elements into economic models also expands our comprehension of economic decision-making and market results. Notably, the emergence of computational economics-integrating artificial intelligence (AI), big data, and machine learning into economic scrutiny-represents a major development. Often referred to as ’smart economics,’ this field employs technology to formulate, address complex economic dilemmas, and perceive economic activity in unconventional ways. Yet, the application of AI and machine learning in economics introduces new hurdles around data privacy, algorithmic bias, and the transparency of model outcomes. The impact of the digital revolution on economic analysis is significant, as the advent of computational economics and the surge of big data are transforming research techniques and policy implications. Concurrently, the advent of the circular economy indicates a radical shift in our perspective on economic sustainability, carrying considerable implications for environmental policy and business tactics.  In the future, it’s anticipated that these trends will further modify the realm of economic analysis, with AI and machine learning integration, emphasis on sustainability and fairness, and the influence of big data becoming more pronounced. As these changes take place, it’s imperative for researchers, policymakers, and practitioners to remain adaptable and flexible, prepared to capitalize on the opportunities and tackle the challenges these trends present.

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“…AI can be used to optimize supply chains by predicting demand, optimizing delivery routes, and managing inventory [39]. This can help businesses reduce costs and improve efficiency.…”

Section: Ai Impact On Economic Modelingmentioning

confidence: 99%

Progress, Evolving Paradigms and Recent Trends in Economic Analysis

Damasevicius

2023

FEL

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The Applications of Nature-Inspired Algorithms in Logistic Domains: A Comprehensive and Systematic Review

2021

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The self-thinking supply chain

Calatayud

Mangan

Christopher

2019

SCM

156

150

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Purpose An emerging theme in the practitioner literature suggests that the supply chain of the future – enabled especially by developments in ICT – will be autonomous and have predictive capabilities, bringing significant efficiency gains in an increasingly complex and uncertain environment. This paper aims to both bridge the gap between the practitioner and academic literature on these topics and contribute to both practice and theory by seeking to understand how such developments will help to address key supply chain challenges and opportunities. Design/methodology/approach A multi-disciplinary, systematic literature review was conducted on relevant concepts and capabilities. A total of 126 articles were reviewed covering the time period 1950-2018. Findings The results show that both IoT and AI are the technologies most frequently associated with the anticipated autonomous and predictive capabilities of future supply chains. In addition, the review highlights a lacuna in how such technologies and capabilities help address key supply chain challenges and opportunities. A new supply chain model is, thus, proposed, one with autonomous and predictive capabilities: the self-thinking supply chain. Originality/value It is our hope that this novel concept, presented here for the first time in the academic literature, will help both practitioners to craft appropriate future-proofed supply chain strategies and provide the research community with a model (built upon multidisciplinary insights) for elucidating the application of new digital technologies in the supply chain of the future. The self-thinking supply chain has the potential in particular to help address some of today’s key supply chain challenges and opportunities.

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Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems

Cited by 3 publications

References 211 publications

Progress, Evolving Paradigms and Recent Trends in Economic Analysis

Progress, Evolving Paradigms and Recent Trends in Economic Analysis

The Applications of Nature-Inspired Algorithms in Logistic Domains: A Comprehensive and Systematic Review

The self-thinking supply chain

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