A Decision Support System for Managing Demand-Driven Collection Development in University Digital Libraries

Author(s):  
Mohamed Hemili ◽  
Mohamed Ridda Laouar ◽  
Sean B. Eom

In recent years, academic digital libraries have become a very important source of information. Academic digital libraries provide a rich collection in order to satisfy user need for information. The augmentation of user population and the volume of new publications causes many challenges to librarians in the collection development process and determining user needs of information is the fundamental challenge that librarians face. This article presents a demand-driven collection development decision support system based on the PROMETHEE II method. The DSS supports the librarians to make decisions in the collection development process to provide a rich collection that meets the users' needs. The DSS evaluates and determines a set of electronic resources for purchase, subscription, contract reviewing or cancelation. The decision support system extracts users' queries from log files to determine user preferences. Then, the revised Simos' procedure is used to derive the criteria weights. Finally, the authors applied the PROMETHEE II method to evaluate and rank the electronic resources.

Author(s):  
Mohamed Hemili ◽  
Mohamed Ridda Laouar ◽  
Sean B. Eom

In recent years, academic digital libraries have become a very important source of information. Academic digital libraries provide a rich collection in order to satisfy user need for information. The augmentation of user population and the volume of new publications causes many challenges to librarians in the collection development process and determining user needs of information is the fundamental challenge that librarians face. This article presents a demand-driven collection development decision support system based on the PROMETHEE II method. The DSS supports the librarians to make decisions in the collection development process to provide a rich collection that meets the users' needs. The DSS evaluates and determines a set of electronic resources for purchase, subscription, contract reviewing or cancelation. The decision support system extracts users' queries from log files to determine user preferences. Then, the revised Simos' procedure is used to derive the criteria weights. Finally, the authors applied the PROMETHEE II method to evaluate and rank the electronic resources.


Author(s):  
Dwika Assrani ◽  
Mesran Mesran ◽  
Ronda Deli Sianturi ◽  
Yuhandri Yuhandri ◽  
Akbar Iskandar

Vocational schools that have been licensed from BNSP to LSP P1 (first party professional certification institute) are schools that have been able to carry out their own competency certification exams for their students and later a competency assessor who will test and declare the eligibility of the students, competency assessors are productive teachers who have participated in and been given training by the government, in that training the schools choose from the number of productive teachers from each department to become competency assessor trainees in accordance with predetermined criteria so a decision support system is needed so there is no gap in the selection of productive teacher assessor training participants, a vocational school that has become a P1 LSP must have a competency assessor and is a requirement to be a P1 LSP. one of the solutions to the problem is the right one by using the Decision Support System (SPK). Decision Support System (DSS) can help the school in making the decision to choose the productive teacher of the appropriate assessor training and improve the efficiency of the decision. The Extended Promethee II (EXPROM II) is a development of the Promethee II method based on the ideal and anti-ideal solution. Promethee II itself is a method of making decisions on the function of preferences with problems through an outranking approach (ranking) or is a multicriteria analysis, comparing one alternative to another and calculating the alternative gap in pairs so as to produce an output that is alternative ranking based on the highest value.Keywords: Competitive Assessor LSP P1, SPK, The Extended Promethee II


2019 ◽  
Vol 12 (2) ◽  
pp. 85
Author(s):  
Ratih Kartika Dewi

This paper proposes the integration of AHP and TOPSIS to generate the ranking results of culinary recommendation for a group of users to provide better recommendation results. Formerly, Group Decision Support System (GDSS) for culinary recommendations has been developed with the TOPSIS method. TOPSIS has low algorithm complexity, so it is suitable to be applied in mobile devices. However, GDSS with TOPSIS has its disadvantages, TOPSIS have not been able to facilitate the preferences of each user inside a group so the recommendation result always consist only on dominant user. TOPSIS method produces unchanging rankings, because this method recommends a food menu based on the 1 dominant user so that the ranking is always consistent. Meanwhile, this study aims to integrate AHP for weighting criteria from each user and TOPSIS for ranking culinary recommendations. Based on rank consistency testing results that conducted in 6 different user groups, unlike the previous research, AHP-TOPSIS shows inconsistency ranking, which means that changes in user preferences affect the recommendation results that are generated by application. The AHP-TOPSIS method proved can be accommodated the computation of various preferences of each user in GDSS culinary recommendation


Author(s):  
Aapo Siljamäki

AbstractThis paper describes the decision support approach used in the development process of the S Group's Prisma hypermarket chain in Finland. The management was looking for a new and sustainable operating model for the rapidly growing chain, and contacted the author to consult in the process. Fierce competition forced the search for new business ideas, tools and methods that would provide a clear competitive advantage. To find new perspectives, we decided to use statistical approaches and various decision support system options, such as multi-criteria modelling. A database was available for research and analysis, including data on purchasing behavior and key performance indicators (KPI). The approach had to take into account the role and impact of customers. It was highly important to include customer behavior in the analysis using shopping basket data. Shopping basket data was central in the current paper. From these, an observation matrix was created combining shopping basket data, product data and customer background information. Using multivariate methods, customer groupings and profiles were created with the data from the observation matrix. Using the customer profile and KPI data, a multi-criteria decision support system was produced to support strategic planning. The decision support system (DSS) model was created together with a market chain operational expert and an external methodological expert. We used the VIG software package developed by Korhonen (Belg J Oper Res Stat Comput Sci 27(3):15, 1987) to solve the problem because it is easy to use and requires no prior knowledge of computers or multi-objective linear programming models. Pareto Race plays a central role in the VIG system. The chain expert easily learned how to use and work with the model. The results were immediately visible and could be used to examine alternatives and assess their appropriateness. It was decided to present five different scenarios to the hypermarket chain management. The main objective of the development process was to develop a strategy that would provide the Prisma hypermarket chain with a long-term competitive advantage. Various models were developed and used to support the strategy work by analysing and exploring the data collected, prioritising and selecting decision options. Two currently retired managers (Mönkkönen, S Group, the chain manager, Prisma chain, Interview 02.06.2021, 2021), who were involved in the development process, rated the strategy process as very successful and the modelling carried out during the process significantly supported decision-making. The immediate help of DSS modelling for decision making comes from being able to provide decision makers with reasonable, better solution options to support their decision making. The final impact of decisions could be evaluated after a longer period of time, which in the case of the Prisma development project results means several comparable financial years. Finland suffered exceptionally badly from the financial crisis and the global economic downturn in 2008–2009. The Prisma chain has survived the periods and crises described above without any loss-making years, and the whole chain has grown from 16 units in 1992 to 68 units in 2020.


2021 ◽  
Vol 5 (2) ◽  
pp. 352
Author(s):  
Sri Poedji Lestari ◽  
Bernadus Gunawan Sudarsono

Apprenticeship students are students who work and study directly at one of the companies they occupy according to the major they take, students who have done an apprenticeship and do a very good job at one of the companies will usually be proposed to be employees of the company, many students and from various universities participating in a company with the same department and in the selection must be done in a fair manner based on appropriate criteria and not based on who has family relations in a company, therefore it is necessary to make a selection involving a system, namely a decision support system where this system uses the extended promethee II method, this method is used to accumulate every value that each student candidate has that is supported by the company to become an employee, the results of the application of this method are king with the highest score who is the best candidate


2012 ◽  
Vol 13 (1) ◽  
pp. 91 ◽  
Author(s):  
Jacek Blazewicz ◽  
Marcin Borowski ◽  
Wahiba Chaara ◽  
Pawel Kedziora ◽  
David Klatzmann ◽  
...  

2020 ◽  
Vol 11 (1) ◽  
pp. 216
Author(s):  
Mohammed Jabreel ◽  
Najlaa Maaroof ◽  
Aida Valls ◽  
Antonio Moreno

Nowadays, most decision processes rely not only on the preferences of the decision maker but also on the public opinions about the possible alternatives. The user preferences have been heavily taken into account in the multi-criteria decision making field. On the other hand, sentiment analysis is the field of natural language processing devoted to the development of systems that are capable of analysing reviews to obtain their polarity. However, there have not been many works up to now that integrate the results of this process with the analysis of the alternatives in a decision support system. SentiRank is a novel system that takes into account both the preferences of the decision maker and the public online reviews about the alternatives to be ranked. A new mechanism to integrate both aspects into the ranking process is proposed in this paper. The sentiments of the reviews with respect to different aspects are added to the decision support system as a set of additional criteria, and the ELECTRE methodology is used to rank the alternatives. The system has been implemented and tested with a restaurant data set. The experimental results confirm the appeal of adding the sentiment information from the reviews to the ranking process.


Author(s):  
Ratih Kartika Dewi ◽  
Mahardeka Tri Ananta ◽  
Lutfi Fanani ◽  
Komang Candra Brata ◽  
Nurizal Dwi Priandani

Mobile based culinary recommendation system has received significant attention in recent mobile application research . Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has regained popularity in supporting multi-criteria decision making due to this method allowing inclusion of many factors and criteria into the decision making process. Previous works on mobile based scenario culinary recommendation system reveal that TOPSIS stand out from other recommendation approaches like AHP and Fuzzy by providing a lightweight computation algorithm that have promising performance in time complexity. However, computing a culinary recommendation using TOPSIS has own limitations especially in the menu judgment processes due to the alternatives priority only include personal preferences for recommendation. In such a culinary recommendation system scenario, users more likely search culinary menus in group instead of alone. This research aims to develop a culinary recommendation system based on group decision support system (GDSS) using TOPSIS that possible to calculate a recommendation by using group preferences instead of personal preferences. The experimental results show that the overall functional of proposed GDSS gives better recommendation result. GDSS using TOPSIS have 100% rank consistency for 6 group of users with 5 combination of menus. The accuracy testing shows that 83,33 % recommendation of GDSS TOPSIS are match with real user preferences. Furthermore, it can be run well in various type of Android smartphone.


2020 ◽  
Vol 1 (3) ◽  
pp. 200
Author(s):  
Nurlela Nurlela ◽  
Muhammad Syahrizal ◽  
Fadlina Fadlina ◽  
Abdul Karim

Decision Support System is a system that can help management in making the right decision, which is needed at a management level. Likewise at the Lubuk Pakam Sub-District Office in selecting the best village head. So far, the Camat Office has never determined the best village head in Lubuk Pakam Subdistrict, so that it encounters obstacles in choosing the village head election. SPK is able to provide alternative solutions to semi / unstructured problems for individuals or groups and in a variety of decision making processes and styles, SPK uses data, databases and analyzes of decision models. Seeing this, researchers are interested in conducting research by applying the Extended Promethee II (EXPROM II) method to elect the best village head in a decision support system. It is expected that the results of the research can help the Lubuk Pakam sub-district


Author(s):  
Rahma Dewi

PMI (Indonesian Red Cross) is a national association organization in Indonesia engaged in the field of social humanity. In carrying out its duties PMI does not make a difference but rather prioritizes victims who most need immediate help for the safety of their souls. The Indonesian Red Cross Office (PMI) Medan provides a reward or award with the aim of improving the performance of the blood distribution process in charge, and to determine the priority of blood distribution, alternatives and criteria are needed to become a reference in the selection process. Decision Support System is one way that can be used in the priority process of blood distribution. In this study, the authors used the Extended Promethee II (Exprome II) method to find the weight value of the criteria, and to find the final value or to find the priority value of blood distribution. Thus a Decision Support System is needed in order to help the Indonesian Red Cross (PMI) Medan to determine the priority of blood distribution.Keywords: Decision Support System, The Extended Promthee II, Priority Selection of Blood distribution


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