Group Spatial Preferences of Residential Locations—Simplified Method Based on Crowdsourced Spatial Data and MCDA

Residential location preferences illustrate how the attractiveness of particular neighbourhoods is perceived and indicate what improves or lowers the comfort of life in a city according to its residents. This research analyses the residential preferences of students who were asked to indicate their most preferred residential locations and to define their selection criteria. The study was conducted in two phases: in 2019, before the outbreak of the pandemic, and in 2020 during the second wave of the COVID-19 outbreak. The methodology of spatial multi-criteria analyses and the developed simplified approach to determining collective preferences from crowdsourced data FCPR (first criteria partial ranking) were used to analyse the preferences. The following research questions were asked: (1) whether the developed simplified FCPR methodology would provide results similar to the methods currently used to determine group weightings of criteria; (2) what spatial aspects were important for the students when choosing where to live, and (3) whether these aspects change in the face of the pandemic. The results obtained confirmed the effectiveness of the simplified approach. They indicated a significant relationship between an efficient public transport system and residence preferences, even with prolonged distance learning. They also showed the increased importance of location close to family or friends in the face of the pandemic. Only a combined analysis of the preferences expressed both in the form of a ranking of criteria and directly indicated locations provides complete information.

Review of PROMETHEE method in transportation

In recent decades, decision support system has been constantly growing in the field of transportation planning. PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) method is an efficient decision-making support deployed in case of a finite number of criteria. It provides a partial ranking through PROMETHEE I and a complete ranking with PROMETHEE II. This outranking methodology is characterized by the elimination of scale effects between criteria and managing incomparability with the comprehensive ranking. However, PROMETHEE does not provide guidance to assign weights to criteria and assumes that decision makers are able to allocate weights. This review presents an overview of PROMETHEE models applied in transportation and points out the found gaps in literature.

Evaluation of Online Teaching Quality: An Extended Linguistic MAGDM Framework Based on Risk Preferences and Unknown Weight Information

Evaluation of online teaching quality has become an important issue because many universities are turning to online classes due to the Corona Virus Disease 2019 (COVID-19) pandemic. In this paper, online teaching quality evaluation is considered as a linguistic multi-attribute group decision-making (MAGDM) problem. Generally, the evaluation sematic information can be symmetrically or asymmetrically distributed in linguistic term sets. Thus, an extended linguistic MAGDM framework is proposed for evaluating online teaching quality. As the main contribution, the proposed method takes into account the risk preferences of assessment experts (AEs) and unknown weight information of attributes and sub-attributes. To be specific, the Delphi method is employed to establish a multi-level evaluation indicator system (EIS) of online teaching quality. Then, by introducing the group generalized linguistic term set (GLTS) with two risk preference parameters, a two-stage optimization model is developed to calculate the weights of attributes and sub-attributes objectively. Subsequently, the linguistic MAGDM framework was divided into two stages. The first stage maximizes the group comprehensive rating values of teachers on different attributes to obtain partial ranking results for teachers on each attribute. The latter stage maximizes the group comprehensive rating values of teachers to evaluate the overall quality. Finally, a case study is provided to illustrate how to apply the framework to evaluate online teaching quality.

Evaluating one-shot tournament predictions

We introduce the Tournament Rank Probability Score (TRPS) as a measure to evaluate and compare pre-tournament predictions, where predictions of the full tournament results are required to be available before the tournament begins. The TRPS handles partial ranking of teams, gives credit to predictions that are only slightly wrong, and can be modified with weights to stress the importance of particular features of the tournament prediction. Thus, the Tournament Rank Prediction Score is more flexible than the commonly preferred log loss score for such tasks. In addition, we show how predictions from historic tournaments can be optimally combined into ensemble predictions in order to maximize the TRPS for a new tournament.

A Partially Ranked Choice Model for Large-Scale Data-Driven Assortment Optimization

The assortment of products carried by a store has a crucial impact on its success. However, finding the right mix of products to attract a large portion of the customers is a challenging task. Several mathematical models have been proposed to optimize assortments. Most of them are based on discrete choice models that represent the buying behavior of the customers. Among them, rank-based choice models have been acknowledged for representing well high-dimensional product substitution effects and, therefore, reflect customer preferences in a reasonably realistic manner. In this work, we extend the concept of (strictly) fully ranked choice models to models with partial ranking that additionally allow for indifference among subsets of products, that is, on which the customer does not have a strict preference. We show that partially ranked choice models are theoretically equivalent to fully ranked choice models. We then propose an embedded column-generation procedure to efficiently estimate partially ranked choice models from historical transaction and assortment data. The subproblems involved can be efficiently solved by using a growing preference tree that represents partially ranked preferences, enabling us to learn preferences and optimize assortments for thousands of products. Computational experiments on artificially generated data and a case study on real industrial retail data suggest that our proposed methods outperform existing algorithms in terms of scalability, prediction accuracy, and quality of the obtained assortments.

Sustainability assessment of healthcare waste treatment systems using surrogate weights and PROMETHEE method

This study aims to assess the sustainability of healthcare waste treatment systems using surrogate weights and the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE). For this purpose, ten treatment systems, including land disposal, incineration and non-incineration systems, were evaluated in terms of environmental, financial, social, and technical criteria. Firstly, fifteen reputed experts assigned their preferred rankings for the groups of criteria and the sub-criteria. The conversion of these rankings into numerical weights was performed using the SR function, which is an additive combination of Sum and Reciprocal weight functions. Secondly, the alternatives’ performance with regards to each criterion allowed PROMETHEE to generate the outranking flows for each alternative. The complete ranking revealed that the rotary kiln (A4) is the most sustainable system followed by steam disinfection (A8), dry heat disinfection and microwave disinfection. However, the municipal landfill is the least sustainable system, while chemical disinfection is ranked in the penultimate position of sustainability. The partial ranking indicated that A4 and A8 are incomparable and both were ranked as most sustainable. Therefore, the sustainability of a system cannot be assessed properly without the exact specification of the system itself. In addition, it is preferable to act on the criteria that affect negatively the system to improve its performance.

Competitive advantage from marketing the SDGs: a luxury perspective

PurposeShall luxury firms promote their sustainable development goals (SDGs)? What are the risks and the competitive advantages? Some answers from sustainability-oriented luxury buyers are provided.Design/methodology/approachQuantitative and qualitative analysis from an online survey of 315 luxury buyers in 28 countries.FindingsSustainability-oriented luxury buyers want branding strategies aligned with the SDGs and rank SDGs in order of importance for the luxury industry. However, they are unable to rank consistently most brands based on their sustainability efforts. The Stella McCartney brand is a clear exception to the general findings: sustainability-oriented luxury buyers rank Stella the most sustainable luxury brand by a vast margin, show willingness to purchase more from this brand, recommend it and are ready to pay a premium.Research limitations/implicationsThe paper uses partial ranking of 20 luxury brands because in pretests, luxury buyers found it difficult to provide a complete ranking of the sustainability efforts of all the brands. Further research in more cultural or geographical contexts is needed.Originality/valueThe research empirically provides an example of a successful marketing strategy leveraging the SDGs to meet sustainability-oriented luxury buyers with targeted actions and messaging to gain competitive advantage.

Outranking Methods: Promethee I and Promethee II

This article highlights the application of the Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) I and II in selecting the best laptop model among six different available models in the market. Seven important criteria, that is, processor, hard disk capacity, operating system, RAM, screen size, brand, and color, are selected, based on which the selection process have been made. Analytic hierarchy process (AHP) is adopted for calculating the weightages of the seven criteria and PROMETHEE is applied to select the best alternative. PROMETHEE I provides the partial ranking and preferences of one model over another, whereas PROMETHEE II provides the complete ranking of the alternatives. From this analysis, Model 4 is coming out to be the best laptop model occupying the first position and Model 1 occupies the last position, thus indicating it as the worst model among the group. The objectives of this article are to select the best laptop model among six available alternatives and to understood the steps of both multiple criteria decision-making (MCDM) methodologies, that is, PROMETHEE and AHP, in details.