Search results

Chapter 1. Introduction: Decision-making and sustainability (Jafar Rezaei) -- Chapter2. Sustainability-based framework for MCDM problems in renewable energies(Siamak Kheibari, Jafar Rezaei) -- Chapter 3. Evaluation of manufacturing organizations ability to overcome internal barriers to green innovations (Himanshu Gupta, Mukesh Kumar Barua) -- Chapter 4. Green Hospital Attributes' Prioritization Under Hesitant Fuzzy Environment Using a VIKOR-Based Method (Hatice Camgöz Akdağ, Aziz Kemal Konyalıoğlu, Tuğçe Beldek) -- Chapter 5. Logistics in the Circular Economy: Challenges and Opportunities (Alistair Beames, Renzo Akkerman, Jacqueline Bloemhof) -- Chapter 6.The impact of internalization policies on freight transport decisions: an exploration at different geographical scales (Lori Tavasszy) -- Chapter 7. Multi-Criteria Decision Analyses to assess territorial transformations: reflections of a research group. (Lami I.M., Bottero M., Abastante F.) -- Chapter8. Sustainable supplier segmentation (Hamidreza Fallah Lajimi) -- Chapter 9. Greenwashing' greenhouse emission cuts? (Behnam Taebi, Azar Safari and Ibo van de Poel) -- Chapter 10. The Impact of Collaborative Scheduling and Routing for Interconnected Logistics: A European Case Study (Yousef Maknoon, Shadi Sharif Azadeh, Michel Bierlaire Yousef Maknoon) -- Conclusion(Jafar Rezaei).

AbstractThis paper presents a new multi‐criteria decision‐making (MCDM) method, namely the ratio product model (RPM). We first overview two popular aggregating models: the weighted sum model (WSM) and the weighted product model (WPM). Then, we argue that the two models suffer from some fundamental issues mainly due to ignoring the ratio nature of the alternatives' scores with respect to the criteria and the importance weights of the criteria. Building on the notion of compositional data analysis, the developed RPM regards performance scores and criteria weights as compositions, which solves the issues around the WSM and WPM. Using several examples, we show that the WSM and WPM could lead to erroneous conclusions, whereas the RPM could lead to fully reliable conclusions. Since many MCDM methods rely on some aggregation approaches, the proposed method is a significant contribution to the field and puts forward the correct way to analyze decision problems while respecting the nature and constraints of the input data.

This book presents recent advances in the theory and application of the Best-Worst Method (BWM). It includes selected papers from the Third International Workshop on Best-Worst Method (BWM2022), held in Delft, the Netherlands, from 9 to 10 June 2022. The book provides valuable insights on why and how to use BWM in a diverse range of applications including health, energy, supply chain management, and engineering. Moreover, it highlights the use of BWM in different settings including individual decision-making vs group decision-making, and with complete information vs incomplete and uncertain information. Academics and practitioners whose work involves multi-criteria decision-making and decision analysis will particularly benefit from the papers gathered here.

AbstractPriorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in fallacious statistical analysis. This article studies three prevalent fallacies in group MCDM along with solutions based on compositional data analysis to avoid misusing statistical operations. First, we use a compositional approach to aggregate the priorities of a group of DMs and show that the outcome of the compositional analysis is identical to the normalized geometric mean, meaning that the arithmetic mean should be avoided. Furthermore, a new aggregation method is developed, which is a robust surrogate for the geometric mean. We also discuss the errors in computing measures of dispersion, including standard deviation and distance functions. Discussing the fallacies in computing the standard deviation, we provide a probabilistic criteria ranking by developing proper Bayesian tests, where we calculate the extent to which a criterion is more important than another. Finally, we explain the errors in computing the distance between priorities, and a clustering algorithm is specially tailored based on proper distance metrics.

Eco-efficiency and resource optimization for business strategy and the environment can be achieved by the circular economy (CE) practices in supply chains (SCs). The leather industry is a significant industrial contributor to the economic growth of some countries, but at the same time, it leads to tremendous environmental pollution. This research focuses on the identification and evaluation of critical success factors (CSFs) needed in the business strategy development of CE practices as well as to minimize environmental pollution in leather industry SCs. The CSFs are identified via a comprehensive literature review and are validated by experts' opinions. The validated CSFs are further analyzed using the best–worst method (BWM) and the decisionmaking trial and evaluation laboratory (DEMATEL). The BWM is used to identify the weights of the CSFs, and DEMATEL is used to determine the cause–effect relationship between the CSFs. The findings show that "leadership and top management commitment" is the most important CSF. Six CSFs are classified as causal towards CE practices: "leadership and top management commitment," "strong legislation towards CE practices," "ecological scarcity of resources," "knowledge of CE practices," "funding support for R&D from the government," and "competitor pressure on CE practices." The findings of this study can help managers in the leather industry implement CE practices in their existing SCs to minimize waste.