Decision Support Systems XI: Decision Support Systems, Analytics and Technologies in Response to Global Crisis Management
Description
The EWG-DSS series of International Conference on Decision Support System Technology (ICDSST) is planned to consolidate the tradition of annual events organized by the EWG-DSS in offering a platform for European and international DSS communities, comprising the academic and industrial sectors, to present state-of-the-art DSS research and developments, to discuss current challenges that surround decision-making processes, to exchange ideas about realistic and innovative solutions, and to co-develop potential business opportunities.
The main aim of this year's conference is to investigate the role DSS and related technologies can play in mitigating the impact of pandemics and post-crisis recovery.
The 10 papers presented in this volume were carefully reviewed and selected from 44 submissions. They were organized in two topical sections named: multiple criteria approaches and advances in decision support systems' technologies and methods.
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Content
Multiple Criteria Approaches.- The Multi-Actor Multi-Criteria Analysis (MAMCA) for Mass-Participation Decision Making.- Using FITradeoff Method for supply selection with decomposition and holistic evaluations for Preference Modelling.- DEX2Web - A Web-Based Software Implementing the Multiple-Criteria Decision-Making Method DEX.- Comparison of AHP, PAPRICA, PROMETHEE, DEX and TOPSIS on an application for employee selection.- A Survey on Criteria for Smart Home Systems with Integration into the Analytic Hierarchy Process.- Advances in Decision Support Systems' technologies and methods.- Modelling the Effects of Lockdown and Social Distancing in the Management of the Global Coronavirus Crisis - Why the UK Tier System Failed.- A case study initiating discrete event simulation as a tool for decision making in I4.0 manufacturing.- Methodology for Multi-Aspect Ontologies Development: Use Case of DSS Based on Human-Machine Collective Intelligence.- Investigating oversampling techniques for fair machine learning models.- Using AI to Advance Factory Planning: A Case Study to Identify Success Factors of Implementing an AI-Based Demand Planning Solution.
