RUC-APS was present at the International Conference on Decision Support System Technology (ICDSST2019), in Madeira, Portugal, May 27-29.
Three articles were presented, that disseminate some of the findings of RUC-APS. They were are published in proceedings published by Springer, Lecture Notes in Business Information Process (https://link.springer.com/book/10.1007/978-3-030-18819-1).
Pascale Zaraté, Guy Camilleri IRIT, MME Alemany, Ana Esteso Alvarez, and Mariana del Pino. How to support group decision making in horticulture: An approach based on the combination of a centralized mathematical model and a Group Decision Support System.
Abstract: Decision making for farms is a complex task. Farmers have to fix the price of their production but several parameters have to be taken into account: harvesting, seeds, ground, season etc… This task is even more difficult when a group of farmers must make the decision. Generally, optimization models support the farmers to find no dominated solutions, but the problem remains difficult if they have to agree on one solution. In order to support the farmers for this complex decision we combine two approaches. We firstly generate a set of no dominated solutions thanks to a centralized optimization model. Based on this set of solution we then used a Group Decision Support System called GRUS for choosing the best solution for the group of farmers. The combined approach allows us to determine the best solution for the group in a consensual way. This combination of approaches is very innovative for the Agriculture domain.
Alejandro Fernandez, Gabriela Bosetti, Sergio Firmenich, and Pascale Zarate. Logikós: Augmenting the web with multi-criteria decision support
Abstract: There are activities that on-line customers daily perform, which involve a multi-criteria decision challenge. Choosing a destination for traveling, buying a book to read, or buying a mobile phone are some examples. Customers analyze and compare alternatives considering a set of shared characteristics, and under certain criteria. E-commerce websites frequently present the information of products without special support to compare them by one or many properties. Moreover support for decision making is limited to sorting, filtering, and side-by-side comparison tables. Consequently, customers may have the feeling that the merchants interests influence their choices, which are no longer grounded on the rational arguments they would like to put in practice. Moreover, the alternatives of interest for the customer are frequently scattered across various shops, with no support to collect and compare them in a consistent and customized manner. In this article, we propose empowering users with multi-criteria decision making support on any website, and across different websites. We also present Logikós, a toolbox supporting multi-criteria decision making depending on the presentation layer of any Web page.
Guoqing Zhao, Shaofeng Liu, Huilan Chen, Carmen Lopez, Jorge Hernandez, Cécile Guyon , Rina Iannacone, Nicola Calabrese, Hervé Panetto, Janusz Kacprzyk, MME Alemany. Value-Chain Wide Food Waste Management: A Systematic Literature Review
Abstract: The agriculture value chain, from farm to fork, has received enormous attention because of its key role in achieving United Nations Global Challenges Goals. Food waste occurs in many different forms and at all stages of the food value chain, it has become a worldwide issue that requires urgent actions. However, the management of food waste has been traditionally segmented and in an isolated manner. This paper reviews existing work that has been done on food waste management in literature by taking a holistic approach, in order to identify the causes of food waste, food waste prevention strategies, and elicit recommendations for future work. A five step systematic literature review has been adopted for a thorough examination of the existing research on the topic and new insights have been obtained. The findings suggest that the main sources of food waste include food overproduction and surplus, food waste caused by processing, logistical inconsistencies, and households. Main food waste prevention strategies have been revealed in this paper include policy solutions, packaging solutions, date-labelling solutions, logistics solutions, changing consumers’ behaviours, and reuse and redistribution solutions. Future research directions such as using value chain models to reduce food waste and forecasting food waste have been identified in this paper. This study makes a contribution to the extant literature in the field of food waste management by discovering main causes of food waste in the value chain and eliciting prevention strategies that can be used to reduce/eliminate relevant food waste.