Multi-criteria decision making with linguistic labels: a comparison of two methodologies applied to energy planning

This paper compares two multi-criteria decision making (MCDM) approaches based on linguistic label assessment. The first approach consists of a modified fuzzy TOPSIS methodology introduced by Kaya and Kahraman in 2011. The second approach, introduced by Agell et al. in 2012, is based on qualitative reasoning techniques for ranking multi-attribute alternatives in group decision-making with linguistic labels. Both approaches are applied to a case of assessment and selection of the most suitable types of energy in a geographical area.Peer ReviewedPostprint (published version

Economic and environmental strategies for process design

This paper first addresses the definition of various objectives involved in eco-efficient processes, taking simultaneously into account ecological and economic considerations. The environmental aspect at the preliminary design phase of chemical processes is quantified by using a set of metrics or indicators following the guidelines of sustainability concepts proposed by . The resulting multiobjective problem is solved by a genetic algorithm following an improved variant of the so-called NSGA II algorithm. A key point for evaluating environmental burdens is the use of the package ARIANE™, a decision support tool dedicated to the management of plants utilities (steam, electricity, hot water, etc.) and pollutants (CO2, SO2, NO, etc.), implemented here both to compute the primary energy requirements of the process and to quantify its pollutant emissions. The well-known benchmark process for hydrodealkylation (HDA) of toluene to produce benzene, revisited here in a multiobjective optimization way, is used to illustrate the approach for finding eco-friendly and cost-effective designs. Preliminary biobjective studies are carried out for eliminating redundant environmental objectives. The trade-off between economic and environmental objectives is illustrated through Pareto curves. In order to aid decision making among the various alternatives that can be generated after this step, a synthetic evaluation method, based on the so-called Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) (), has been first used. Another simple procedure named FUCA has also been implemented and shown its efficiency vs. TOPSIS. Two scenarios are studied; in the former, the goal is to find the best trade-off between economic and ecological aspects while the latter case aims at defining the best compromise between economic and more strict environmental impact

Group decision-making based on heterogeneous preference relations with self-confidence

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Preference relations are very useful to express decision makers’ preferences over alternatives in the process of group decision-making. However, the multiple self-confidence levels are not considered in existing preference relations. In this study, we define the preference relation with self-confidence by taking multiple self-confidence levels into consideration, and we call it the preference relation with self-confidence. Furthermore, we present a two-stage linear programming model for estimating the collective preference vector for the group decision-making based on heterogeneous preference relations with self-confidence. Finally, numerical examples are used to illustrate the two-stage linear programming model, and a comparative analysis is carried out to show how self-confidence levels influence on the group decision-making results

An integrated MCDA software application for forest planning : a case study in southwestern Sweden

Forest planning in Sweden today translates not only into planning of timber production, but also for the provision of other functions and services. Multi-criteria decision analysis (MCDA) methods provide a way to take also non-monetary values into account in planning. The purpose of this study was to gain experience on how to use a forest decision support system combined with an MCDA tool in practical forestry. We used a new forest planning tool, PlanWise, which includes an integrated MCDA module, PlanEval. Using the software, the decision maker can compare different forest plans and evaluate them against his/her objectives in a structured and analytical manner. The analysis thus provides a ranking of the alternatives based on the individual preferences of the decision maker. PlanEval and the MCDA planning process are described in a case study, where the manager of a forest estate in southwestern Sweden used the program to compare different forest plans made for the estate. In the paper, we analyze possibilities and challenges of this approach and identify problems such as the adherence to formal requirements of MCDA techniques and the difficulty of comparing maps. Possibilities to expedite an MCDA planning process further are also discussed. The findings confirm that integration of an MCDA tool with a forest decision support system is valuable, but requires expert assistance to be successful

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI

Integration of preference elicitation and the development of alternative forest plans : focusing on the requirements of the decision maker

Modern forest management frequently revolves around the concepts of developing strategic, tactical and operational level plans. These plans are developed through the use of simulation and optimization software, based on scientific models and mathematical programming. The optimal management schedule depends upon the decision maker(s) (DM) preferences. When developing forest plans for the DM(s) the method of acquiring preference information should be as value free as possible. To facilitate a DM-orientated approach, a selection of alternatives based on the acquired preferences should be made available to the DM so that a true choice can be made. The development of the forest plans should represent the desires and wishes of the forest owner at the time the plan is created. In order to balance the costs with the quality of the service provided, tools are required which develop client specific forest plans. The first objective of this thesis is to analyse different preference elicitation methods and study the impacts of information content on the selection of a plan. In papers I and II, plans were selected using an a posteriori method of preference elicitation. For paper III, preference elicitation was done in an interactive fashion, to develop an acceptable forest plan using both a priori and a posteriori preference elicitation methods. The second objective is to develop techniques for incorporating preference information into optimization methods. In paper IV, a series of goal programming models were used to incorporate the preference information from several DMs to generate a number of potentially desirable forest plans. Paper V develops a goal programming formulation which separates the treatment of different goals into two partitions; one strives to maintain the difference from the target for the goals in balance, the other strives to obtain the most efficient aggregate solution.Nykyaikainen metsäsuunnittelu keskittyy usein sellaisille käsitteellisille tasoille kuin strateginen, taktinen ja operatiivinen suunnittelu. Suunnitelmat on toteutettu käyttämällä simulointi- ja optimointiohjelmistoja, jotka perustuvat tieteellisiin malleihin ja matemaattiseen ohjelmointiin. Kuitenkin päätöksentekijän /jien (PT) preferenssit määrittelevät optimaalisen aikataulun metsänhoidolle. Metsäsuunnitelmia tuotettaessa menetelmän tulisi olla mahdollisimman vapaa suunnittelijan omista arvoista ja mielipiteistä. Jotta lähestymistapa olisi mahdollisimman PT-ystävällinen, pitäisi päätöksentekijälle esittää useita metsänsuunnittelun vaihtoehtoja, joiden perusteella PT voi tehdä aidosti henkilökohtaisen valintansa. Tuotettujen metsäsuunnitelmien tulisi vastata metsänomistajan sen hetkisiä toiveita ja mieltymyksiä. Jotta suunnitelmien kustannusten ja laadun välille saadaan tasapaino, tarvitsemme työkaluja joilla muokata metsäsuunnittelua paremmin asiakaslähtöiseksi. Tämän tutkimuksen ensimmäinen tavoite oli analysoida eri preferenssien hankintamenetelmiä, sekä selvittää saadun tiedon määrän vaikutus suunnitelman valintaan. Artikkeleissa I ja II suunnitelma valittiin a posteriori menetelmän avulla. Artikkelissa III preferenssien hankinta toteutettiin interaktiivisesti, siten, että hyväksyttävä metsäsuunnitelma saatiin aikaiseksi hyödyntämällä sekä a priori, että a posteriori preferenssien valintamenetelmiä. Tutkimuksen toinen tavoite oli kehittää tekniikoita, joilla sisällytetään preferenssitietoa osaksi optimointimenetelmiä. Artikkelissa IV on käytetty sarjaa tavoiteohjelmointimalleja, joiden tavoitteena oli sisällyttää preferenssitietoja useilta eri päätöksentekijöiltä, joiden pohjalta sitten tuotettiin useita PT:itä potentiaalisesti kiinnostavia metsäsuunnitelmia. Artikkeli V kehitti uuden tavan formuloida tavoiteohjelmoinnin tehtävä, , joka erottaa tavoitteiden käsittelyn kahteen osaan; toinen pyrkii löytämään mahdollisimman tasapainoisen ratkaisun ja toinen pyrkii löytämään kaikista tehokkaimman ratkaisuyhdistelmän

A new and efficient intelligent collaboration scheme for fashion design

Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time–cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance