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2022 | Buch

Decision Support Systems XII: Decision Support Addressing Modern Industry, Business, and Societal Needs

8th International Conference on Decision Support System Technology, ICDSST 2022, Thessaloniki, Greece, May 23–25, 2022, Proceedings

herausgegeben von: Ana Paula Cabral Seixas Costa, Jason Papathanasiou, Uchitha Jayawickrama, Daouda Kamissoko

Verlag: Springer International Publishing

Buchreihe: Lecture Notes in Business Information Processing


Über dieses Buch

This book constitutes the proceedings of the 8th International Conference on Decision Support Systems Technologies, ICDSST 2022, held during May 23-25, 2022.

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 15 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections as follows: decision support addressing modern industry; decision support addressing business and societal needs, and multiple criteria approaches.



Decision Support Addressing Modern Industry

Blockchain Technology Potential to Transform Global Value Chains
Technological changes in transport and communications have shown to be one of the most important determinants of geographic fragmentation of production process which led to shaping of global value chains (GVCs). Governance of these complex production systems is becoming increasingly challenging, especially due to the lack of information about the processes and actors in the chain, which leads to sustainability issues and higher costs of assuring resilience and robustness. Blockchain technology (BT) provides opportunities to address these challenges. This paper provides a systematic overview of BT potential to transform GVCs. It points out transparency as one of the most important features of BT, which provides traceability and efficiency gains potential in a range of areas, as it enables real-time information-based decision making of leading firms and other members of GVCs. It also points out that BT can provide decision-support to consumers through information that was not previously available to them, thus facilitating their choices. Furthermore, it can facilitate government’s international trade regulation enforcement. These changes have the potential to contribute to upgrading opportunities and bring another global production reorganization. Decision-support is needed to foster and facilitate BT adoption process.
Zoran Wittine, Sanja Franc, Antea Barišić
Predicting the Rating of an App Beyond Its Functionalities: Introducing the App Publication Strategy
Mobile applications (or apps) are present on every portable device and have become the center of tremendous attention from developers and software vendors. Some apps meet significant success with high profitability, but most of them tend to remain anonymous, with weak returns on investment. The risk incurred when launching a new app is therefore significant. In this article, we introduce the concept of Publication Strategy, resulting from the numerous decisions made by an app designer on all the variables which are publicly visible on the stores (screenshots, description, title, etc.). This paper studies the extent to which the success of an app may be predicted using such Publication Strategy. To do this, we use metadata about more than 40,000 apps from both the Google Play Store and the Apple App Store and adopt a machine learning research strategy by training and testing a number of classification models. We observe that in about 50% of the cases, it is possible to predict the rating of an app based solely on its Publication Strategy. The results are very similar between the 2 stores. These results bring us to the definition of a number of research avenues to further explore the notion of App Publication Strategy which can be used to support apps designers in their decisions.
Mathieu Lega, Corentin Burnay, Stéphane Faulkner
Improving Machine Self-Diagnosis with an Instance-Based Selector for Real-Time Anomaly Detection Algorithms
The diffusion of smart sensor technology in production enables real-time monitoring of production conditions. Machine self-diagnosis shall serve the analysis of these conditions by differentiating expected data from anomalies. Several algorithms have been developed in practice and academia to detect anomalies in real-time and to support machine self-diagnosis, so that counteractions can be taken. However, due to the algorithms’ different functionalities, they yield different results when applied to the same data. Our research aims to leverage complementary potentials among these algorithms. To this end, we use a design science research approach to design and prototypically implement a real-time anomaly detection algorithm selector to support decision making regarding machine self-diagnosis. The selector decides in real-time for each sensor-emitted data point, which algorithm yields the most reliable result in terms of anomaly detection. We evaluate functionality and feasibility with two real-world case studies. The evaluation shows that the algorithm selector may outperform single algorithms and that it is applicable in practice.
Philip Stahmann, Jon Oodes, Bodo Rieger
Blockchain and Artificial Intelligence in Real Estate
Since their development, blockchain and artificial intelligence (AI) technologies have gained substantial momentum and immense adoption in different industries worldwide. The innovations of cryptocurrencies and machine learning algorithms have had significant implications for the growth and advancement of these technologies. The combination of the two presents incredible benefits to organizations in various sectors in terms of harnessing existing data for pattern recognition and insight identification. The technologies have impacted how industries do their businesses. This study includes a systematic review that explores how blockchain and AI, have changed the real estate industry, as well as the way the related businesses can take advantage of the technologies’ capabilities to stay afloat within this new technological development. This research adopts the Prisma methodology to explore how the application of blockchain and AI has impacted the real estate sector. The main finding is that in real estate, the combination of blockchain and AI has great potential, especially in modeling data and valuation, storing information in digital formats and securing transactions.
Christos Ziakis
Modelling the Development and Deployment of Decentralized Applications in Ethereum Blockchain: A BPMN-Based Approach
Decentralized Applications (DApps) have emerged as a new model for building massively scalable and profitable applications. A DApp is a software application that runs on a peer-to-peer blockchain network offering censorship resistance, resilience, and transparency that overcome the challenges of typical centralized architectures. Developing and deploying a DApp in a blockchain network is highly challenging. Developers need to initially decide if developing a DApp is justified, before considering different aspects of blockchain technology (e.g., cryptography, transactions, addresses, etc.). This adversity along with the plethora of previously published works highlight the need for new tools and methods for the development of blockchain-based applications. Throughout literature there is a lack of procedures that can guide practitioners on how to develop and deploy their own applications. This paper aims to address this research gap, by standardizing and modelling such processes, through the employment of the BPMN modelling technique. Initially, a DMN decision model is presented that can facilitate developers to determine whether developing a DApp is justified. Consequently, two BPMN models are introduced, namely the DApp development and the DApp deployment process models. The models can orchestrate new DApp initiatives and facilitate the developers’ communication and implementation transparency. We expect that they can serve as a roadmap for enhancing the decision-making in the act of developing a DApp and reducing the implementation time and cost. Finally, we further discuss how the models implement a DApp for the registration and verification of academic qualifications and how BPMN can constitute a powerful tool for the development of DApps.
Nikolaos Nousias, George Tsakalidis, Sophia Petridou, Kostas Vergidis

Decision Support Addressing Business and Societal Needs

Strengthening EU Resilience: Labor Market Integration as a Criterion for Refugee Relocation
Migrant labor market integration is one of the key areas mentioned in the new EU Action Plan on Integration and Inclusion. Enhanced resilience of the European economy is envisaged through migrant labor market integration which is considered to generate large economic gains, fiscal profits and contributions to national pension schemes and welfare. In view of the new EU Pact on Migration and Asylum, this paper employs the multiple criteria decision making analysis method PROMETHEE to formulate a relocation model for refugees in the EU28, for a period between 2015 and 2019, based on the labor market integration outcomes of the resident migrant population in the EU. The purpose of this paper is to indicate a relocation plan based on migrant labor market integration prospects that could favor the newcomers’ sustainable independent living and social inclusion. Under this lens, the legal commitments, and the actual contributions of the Member States to the EU emergency relocation scheme are observed. The suggested decision making approach to relocation allows policy makers to define the preferences and weights of the criteria so as to assure fair sharing of responsibility among the EU countries. The paper provides evidence that countries opposed to the relocation scheme could have been more favorable destinations for the relocation of migrants since 2015.
Anastasia Blouchoutzi, Georgios Tsaples, Dimitra Manou, Christos Nikas
Towards an Inclusive Europe: Ranking European Countries Based on Social Sustainability Indicators
The aim of the paper is to assess the state of social sustainability throughout European countries, based on the inclusion of various indicators that reflect the social dimension of sustainable growth. Using the methods for creating composite indexes combined with official social statistics, the ranking of European countries based on poverty and social exclusion indicators is provided. The terms poverty and social exclusion refer to various types of social disadvantages, related to the problems such as unemployment, income inequality, material deprivation and the inability to participate in social and political activities. Our analysis enables the evaluation of social sustainability, at the country level, through formation of a composite index that includes all observed indicators. The indicators included in the analysis are classified into three groups: (1) income distribution and monetary poverty, (2) living conditions (health, labour, and housing) and (3) material deprivation. Research is based on the data provided by European Union (EU) statistics on income and living conditions, a comparative statistic on income distribution and social inclusion for EU countries as well as accession candidate countries. The results are based on analysis that includes 33 countries. For the assessment of social sustainability, a multi-criteria analysis model is developed, combining the CRITIC method (CRiteria Importance Through Intercriteria Correlation) for determining the relative importance of criteria and PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) method for ranking countries. The results clearly indicate that candidate countries have a lower level of social sustainability compared to EU countries.
Jelena J. Stanković, Marija Džunić, Ivana Marjanović
Decision Support Ecosystems: Definition and Platform Architecture
Decision support systems are crucial in helping decision makers to quickly identify optimal business decisions in increasingly volatile and complex business environments. However, the ideal DSS for one decision maker may not optimally address the requirements for decision support of another decision maker. This is due to differences between decision makers in business goals, regulatory restrictions or availability of resources such as data. By using a suboptimal DSS, decision makers risk implementing suboptimal decision recommendations which endanger the success of their business. This presents DSS developers with the challenge to implement a customizable DSS which can be tailored to the individual requirements for decision support of a single decision maker. In order to address this challenge, we suggest a decision support ecosystem in which DSS developers, decision makers and other domain experts collaborate using a shared platform to provide and combine reusable decision support services into a tailored DSS. The contribution of our paper is twofold: First, we define the concept of a decision support ecosystem with respect to existing digital business ecosystems and discuss expected benefits and challenges. Second, we present a reference architecture for a shared platform supporting the realization of a decision support ecosystem. We demonstrate our contributions in the example application domain of regional energy distribution network planning.
Jonas Kirchhoff, Christoph Weskamp, Gregor Engels
A Systematic Research Methodology for Business Model Decision Making in Commercialising Innovative Healthcare Diagnostic Technologies
Business models and decision making play a vital role in the delivery and implementation of technological innovations in the healthcare industry, especially for entrepreneurs, healthcare providers, managers, researchers and policy makers. However, despite its significance, current conceptualisations of business models do not adequately guide in designing business models particular to the complex and dynamic healthcare environment. With the exploratory nature and lack of research in this area, this paper aims to design a new methodology to develop business models incorporating multidimensional implications of various stakeholder perspectives. A systematic literature review of existing methods in business models and decision making has been done and a new methodology has been proposed, entitled Thematic + TISM + MICMAC = TTM methodology. An application of this method has been tested with empirical findings from the healthcare diagnostics value chain to establish the key factors of innovative business model development in healthcare decision making. Limitations, future directions and challenges in the proposed methodology are also discussed. It is hoped that this study will guide practitioners in future work towards advancement of these techniques and will help the managers to select better decisions by making use of these methodologies.
Aira Patrice R. Ong, Shaofeng Liu, Genhua Pan, Xinzhong Li
A DSS Based on a Control Tower for Supply Chain Risks Management
Propose: This paper presents a supply chain control tower deployed with a decision support system for supply chain risk management in a multi-source data and risk environment. The study provides a digital risk management process and a group decision making approach for companies to improve their supply chain resilience in a supply chain risk environment. We have designed the system from two perspectives. Supply Chain Control Tower and Supply Chain Risk Management. Supply chain risks are mainly all the risks faced in the process from product design to delivery to customers. Supply chain risk management is a very complex activity that requires assessing the vulnerability of all participants in the supply chain. It is a multi-step process. The Supply Chain Control Tower is a dashboard that integrates information from across the supply chain. The supply chain control tower integrates multiple data sources, key performance indicators and activity sources in the supply chain. The control tower should include an intelligent decision support system that uses decision support models and technologies, such as machine learning, to provide decision support and ranking of alternative strategies for supply chain managers.
Results. In this paper, a decision support system-based supply chain control tower is designed to support supply chain decision makers in selecting the most appropriate alternative strategies to reduce the risk impact and enhance the resilience of the supply chain.
Chenhui Ye, Pascale Zaraté, Daouda Kamissoko

Multiple Criteria Approaches

Using the FITradeoff Method for Solving a Truck Acquisition Problem at a Midsize Carrier
The study demonstrates the flexible functioning of the FITradeoff method that integrates the Holistic Evaluation with the Elicitation by Decomposition. For that purpose, the new features of the FITradeoff method in which integrates the two paradigms of preference modeling have been explored to solve a real multi-criteria decision problem. In this paper, a truck acquisition problem, at a midsize carrier faced with an uncertain and turbulent scenario due to the Coronavirus pandemic, was solved using the FITradeoff method. In this problem, seven criteria were considered to represent the Decision-Maker objectives. Also, six trucks (alternatives) have been examined by the Decision-Maker (Financial Director). The FITradeoff DSS supported the company as to obtain, through the combination of Holistic Evaluation and Elicitation by Decomposition, a ranking of all the trucks based on the preferences expressed during the decision process to ensure lower costs and higher profits in the long run, also guaranteeing a quicker (more efficient) resolution of the problem.
Mariana Wanderley Cyreno, Lucia Reis Peixoto Roselli, Adiel Teixeira de Almeida
Maturity Assessment in the Context of Industry 4.0 - an Application Using FITradeoff Method in a Textile Industry
The Brazilian textile industry scenario has been unfavorable in relation to competition, mainly due to the entry of foreign products in the region. Thus, an opportunity to improve Brazil´s competitive potential in this sector can not only be perceived but also achieved by applying the technological tools of industry 4.0. Hence, this paper seeks to contribute to the organizational management of developing and applying a maturity model to evaluate companies in the textile sector in terms of technological maturity for industry 4.0, by taking a multicriteria approach based on the FITradeoff method for sorting problems. As a demonstration of the applicability of the model, three Brazilian companies were evaluated. As to results, it was possible to access the maturity levels of these three companies, and the main aspects associated with the attributes evaluated were also analyzed. Finally, it is believed that this study can serve as a support tool in the process of strategic planning for managers and others involved in companies in the textile and clothing market, who are seeking to develop strategies in relation to the aforementioned theme. In addition, this study contributes to mitigating the gap found in the literature.
Duan Vilela Ferreira, Ana Paula Henriques de Gusmão
Sustainable Mobility Engagement and Co-planning; a Multicriteria Analysis Based Transferability Guide
Engagement in sustainable mobility planning seems to act as a starting point to unlock a new era of responsible and sustainable behaviors. After almost a two-years experience of a global crisis (COVID-19) revealing that the only way out is through jointly walking on the way into sustainability and resilience, engaging people in shifting to sustainable mobility options has become an imperative need. The current paper exploits Multi-Criteria Decision Analysis (MCDA) in building a methodological 5-step framework for evaluating the transferability potentials of good practices (GPs) in citizens’ sensibilization and engagement in sustainable mobility. 10 good practices were selected in order to cover the whole cycle of sustainable mobility planning (SUMP cycle) while representatives from different EU Regions were involved in the assessment procedure resulting in this way in a general transferability guide. The guide, tailored to each case, can be a very useful tool in the hands of single authorities while making their mobility engagement plan.
Glykeria Myrovali, Maria Morfoulaki
A DSS for the Multi-criteria Vehicle Routing Problem with Pickup and Delivery and 3d Constraints
The Vehicle Routing Problem (VRP) with three-dimension (3D) constraints (3L-CVRP) is a variant of the VRP with many possible applications in real-world scenarios. The purpose of this paper is to propose a Decision Support System for the VRP with pickup and delivery and 3D constraints (3L-CVRP-PD), considering multiple criteria. A Graphical User Interface (GUI) is developed, aiming to provide a better interaction experience with the software. The GUI includes a 3D representation of the loading bay for each vehicle, a feature that makes solutions easier to comprehend. The routing segment of the 3L-CVRP-PD is solved using a heuristic method, while item packing takes place in an exhaustive manner and multiple solutions are generated for each problem. The solutions are ranked by a Multi-Criteria Decision Method (MCDM). Two MCDMs are tested in this paper, the Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) and the UTility Additives* (UTASTAR), on modified instances from the literature.
Themistoklis Stamadianos, Magdalene Marinaki, Nikolaos Matsatsinis, Yannis Marinakis
A Multicriteria Tool to Support Decision-Making in the Early Stages of Energy Efficiency Investments
The energy demand of modern communities contributes significantly to climate change, increasing the release of greenhouse gases into the atmosphere. Energy efficiency is recognised as the key pathway to reducing energy usage while sustaining an equivalent, contemporary economic activity. In other words, to avoid climate change, mainstreaming energy efficiency finance is considered a top priority. This study focuses on introducing a rating system based on a Multi-Criteria Decision Analysis method that aims to promote the implementation and financing of energy efficiency investments. To this end, a benchmarking Tool is being deployed in order to materialise the proposed methodology and introduce a standardised procedure for benchmarking energy efficiency potential projects during the preliminary stages of investment conceptualisation. The proposed Tool exploits the Multi-Criteria Decision Analysis method ELECTRE Tri, taking into account major key performance indicators that are broadly used by investors and financing institutions to identify bankable energy efficiency investments and promote green transition. The methodology has been applied to benchmark 114 energy efficiency investments from eight different European countries. It should be mentioned that for the successful and effective development of the proposed Tool, input and feedback has been received by a variety of stakeholders from the energy sector and financing community, who also tested the Tool and confirmed that the approach proved to be extremely helpful to those seeking for sustainable investments in energy efficiency. The analysis resulted in the conclusion that the Tool covers the necessity for a standardised benchmark, providing added value to the energy efficiency market.
Aikaterini Papapostolou, Filippos Dimitrios Mexis, Charikleia Karakosta, John Psarras
Decision Support Systems XII: Decision Support Addressing Modern Industry, Business, and Societal Needs
herausgegeben von
Ana Paula Cabral Seixas Costa
Jason Papathanasiou
Uchitha Jayawickrama
Daouda Kamissoko
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