Corporate Sustainable Resource Management in Artificial Intelligent Era
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".
Deadline for manuscript submissions: closed (31 March 2019) | Viewed by 39085
Special Issue Editors
Interests: industrial management; sustainable supply chain management; multi-criteria decision-making
Special Issues, Collections and Topics in MDPI journals
Interests: corporate sustainability; sustainable supply chain management; supply chain management; operations management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As natural resources are becoming exhausted, researchers and practitioners are striving to identify an optimal way to manage corporate resources to achieve sustainability. Corporate sustainable resource management seeks to address such sustainability issues. Each corporate economic system rests upon utilizing natural resources in a balanced way to maximize human well-being without obstructing the support of living society (Bringezu & Bleischwitz, 2017). However, there are few prior studies on sustainable resource management. Corporate sustainable resource management must take into account the fields of marketing, human resource management, research and development, products and finance to discover the optimal way to achieve sustainability. It also relies on establishing qualitative and quantitative data that deals with corporate sustainability issues (Lee et al., 2018; Lei et al, 2017; Cui et al., 2017).
Recently, artificial intelligence (AI) research has aimed to include reasoning, knowledge representation, marketing planning, self-learning, natural language processing, human perception and the ability to move and manipulate objects. There are multiple approaches to AI, including statistical methods, computational intelligence, and traditional symbolic approaches. Many tools are used in AI, including mathematical optimization, artificial neural networks, statistics, probability and economics. Corporate sustainable resource Management combines these qualitative and quantitative features, and recent studies attempted to adopt AI to increase the accuracy and speed of decision-making, including as artificial neural networks based on the prediction model and fuzzy logic (Aibinu et al., 2017; Hwang et al., 2010). AI is a device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals (Legg & Hutter, 2007; Poole et al., 1998). It refers to applications where a machine imitates human cognitive function, such as learning and problem solving (Russell & Norving, 2016). Thus, this Special Issue seeks to present research that enhances or guides corporations in managing sustainable resources in this era of artificial intelligence.
Interested topics include, but are not limited to:
- Systematic frameworks for corporate sustainable resource management and its influential attributes;
- AI approaches to promoting corporate sustainable resource management;
- Innovative approaches for the sustainable assessment and improvement of AI tools;
- Novel theories and methods for corporate sustainable resource management in the AI era.
Contributors are encouraged to communicate with the editors by e-mail: [email protected]. Accepted papers will be published online immediately. Please follow the Sustainability “Instructions for Authors” when preparing your manuscripts. Please submit your manuscripts via https://www.mdpi.com/journal/sustainability/instructions.
References:
- Aibinu, A.M.; Onumanyi, A.J.; Adedigba, A.P.; Ipinyomi, M.; Folorunso, T.A.; Salami, M.J.E. Development of hybrid artificial intelligent based handover decision algorithm. Sci. Tech. Inter. J. 2017, 20, 381–390.
- Bringezu, S.; Bleischwitz, R. (Eds.) Sustainable Resource Management: Global Trends, Visions and Policies; Routledge: London, UK, 2017.
- Cui, L. Fuzzy approach to eco-innovation for enhancing business functions: A case study in China. Manage. Data Syst. 2017, 117, 967–987.
- Hwang, R.C.; Chen, Y.J.; Huang, H.C. Artificial intelligent analyzer for mechanical properties of rolled steel bar by using neural networks. Expert Syst. Appl. 2010, 37, 3136–3139.
- Lee, C.H.; Wu, K.J.; Tseng, M.L. Resource management practice through eco-innovation toward sustainable development using qualitative information and quantitative data. Clean. Prod. 2018, 202, 120–129.
- Legg, S.; Hutter, M. A collection of definitions of intelligence. Frontiers Artificial Intelligence Appl. 2007, 157, 17.
- Poole, D.L.; Mackworth, A.K.; Goebel, R. Computational Intelligence: A Logical Approach (Vol. 1); Oxford University Press: New York, NY, USA, 1998.
- Russell, S.J.; Norvig, P. Artificial Intelligence: A Modern Approach; Pearson Education Limited: Petaling Jaya, Malaysia, 2016.
- Shi, L.; Wu, K.J.; Tseng, M.L. Improving corporate sustainable development by using an interdependent closed-loop hierarchical structure. Conserv. Recy. 2017, 119, 24–35.
Prof. Dr. Kuo-Jui Wu
Prof. Dr. Ming-Lang Tseng
Prof. Dr. Fuyume Sai
Guest Editors
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