The interval TOPSIS method for group decision making

Cel – Celem pracy jest przedstawienie nowego podejścia do rankingu wariantów decyzyjnych z danymi przedziałowymi dla grupowego podejmowania decyzji, wykorzystującego metodę TOPSIS. Metodologia badania – W proponowanym podejściu, wszystkie pojedyncze oceny decydentów są brane pod uwagę w wyznaczaniu końcowych ocen wariantów decyzyjnych oraz ich rankingu. Kluczowym jego elementem jest przekształcenie macierzy decyzyjnych dostarczonych przez decydentów, w macierze wariantów decyzyjnych. Wynik – Nowe podejście do grupowego podejmowania decyzji wykorzystujące metodę TOPSIS. Oryginalność/wartość – Proponowane podejście jest nowatorskie oraz łatwe w użyciu.Goal – The purpose of the paper is to present a new approach to the ranking of alternatives with interval data for group decision making using the TOPSIS method. Research methodology – In the proposed approach, all individual assessments of decision makers are taken into account in determining the final assessments of alternatives and their ranking. The key stage of the proposed approach is the transformation of the decision matrices provided by the decision makers into a matrices of alternatives. Score – A new approach for group decision making using the TOPSIS method. Originality/value – The proposed approach is innovative and easy to use.Badania zostały zrealizowane w ramach pracy nr S/WI/1/2016 i sfinansowane ze środków na naukę [email protected]ł Informatyki, Politechnika BiałostockaAbdullah L., Adawiyah C.W.R., 2014, Simple Additive Weighting Methods of Multicriteria Decision Making and Applications: A Decade Review, “International Journal of Information Processing and Management”, vol. 5(1), pp. 39-49.Behzadian M., Otaghsara S.K., Yazdani M., Ignatius J., 2012, A state-of the art survey of TOPSIS applications, “Expert Systems with Applications”, vol. 39, pp. 13051-13069.Boran F.E., Genc S., Kurt M., Akay D., 2009, A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method, “Expert Systems with Applications”, vol. 36, pp. 11363-11368.Chen C.T., 2000, Extensions of the TOPSIS for group decision-making under fuzzy environment, “Fuzzy Sets and Systems”, vol. 114, pp. 1-9.Cloud M. J., Kearfott R.B., Moore R.E., 2009, Introduction to Interval Analysis, SIAM, Philadelphia.Dymova L., Sevastjanova P., Tikhonenko A., 2013, A direct interval extension of TOPSIS method, “Expert Systems with Applications”, vol. 40, pp. 4841-4847.Hu B.Q., Wang S., 2006, A Novel Approach in Uncertain Programming Part I: New Arithmetic and Order Relation for Interval Numbers, “Journal of Industrial and Management Optimization”, vol. 2(4), pp. 351-371.Hwang C.L., Yoon K. 1981 Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag, Berlin.Jahanshahloo G.R., Hosseinzadeh Lotfi F., Izadikhah M., 2006, An Algorithmic Method to Extend TOPSIS for Decision Making Problems with Interval Data, “Applied Mathematics and Computation”, vol. 175, pp. 1375-1384.Kacprzak D., 2017, Objective Weights Based on Ordered Fuzzy Numbers for Fuzzy Multiple Criteria Decision Making Methods, “Entropy”, vol. 19(7), pp. 373.Kacprzak D., 2018, Metoda SAW z przedziałowymi danymi i wagami uzyskanymi za pomocą przedziałowej entropii Shannona, „Studia Ekonomiczne. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach”, vol. 348, pp. 144-155.Kacprzak D., 2019, A doubly extended TOPSIS method for group decision making based on ordered fuzzy numbers, “Expert Systems with Applications”, vol. 116, pp. 243-254.Roszkowska E., 2009, Application TOPSIS methods for ordering offers in buyer-seller transaction, “OPTIMUM, Studia Ekonomiczne”, vol. 3(43), pp. 117-133.Roszkowska E., 2011, Multi-Criteria Decision Making Models by Applying the TOPSIS Method to Crisp and Interval Data, “Multiple Criteria Decision Making”, vol. 6, pp. 200-230.Roszkowska E., Kacprzak D., 2016, The fuzzy SAW and fuzzy TOPSIS procedures based on ordered fuzzy numbers, “Information Sciences”, vol. 369, pp. 564-584.Rudnik K., Kacprzak D., 2017, Fuzzy TOPSIS method with ordered fuzzy numbers for flow control in a manufacturing system, “Applied Soft Computing”, vol. 52, pp. 1020-1041.Senvar O., Otay İ., Boltürk E., 2016, Hospital site selection via hesitant fuzzy TOPSIS. “IFAC-PapersOnLine”, vol. 49, pp. 1140-1145.Shih H.S., Shyur H.J., Lee E.S., 2007, An extension of TOPSIS for group decision making, “Mathematical and Computer Modelling”, vol. 45, pp. 801-813.Wang T.C., Chang T.H., 2007, Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment, “Expert Systems with Applications”, vol. 33, pp. 870-880.Ye F., Li Y.N., 2009, Group multi-attribute decision model to partner selection in the formation of virtual enterprise under incomplete information, “Expert Systems with Applications”, vol. 36, pp. 9350-9357.Yue Z., 2011, An extended TOPSIS for determining weights of decision makers with interval numbers, “Knowledge-Based Systems”, vol. 24, pp. 146-153.Yue Z., 2012, Developing a straightforward approach for group decision making based on determining weights of decision makers, “Applied Mathematical Modelling”, vol. 36, pp. 4106-4117.4(94)25627

A systematic review on multi-criteria group decision-making methods based on weights: analysis and classification scheme

Interest in group decision-making (GDM) has been increasing prominently over the last decade. Access to global databases, sophisticated sensors which can obtain multiple inputs or complex problems requiring opinions from several experts have driven interest in data aggregation. Consequently, the field has been widely studied from several viewpoints and multiple approaches have been proposed. Nevertheless, there is a lack of general framework. Moreover, this problem is exacerbated in the case of experts’ weighting methods, one of the most widely-used techniques to deal with multiple source aggregation. This lack of general classification scheme, or a guide to assist expert knowledge, leads to ambiguity or misreading for readers, who may be overwhelmed by the large amount of unclassified information currently available. To invert this situation, a general GDM framework is presented which divides and classifies all data aggregation techniques, focusing on and expanding the classification of experts’ weighting methods in terms of analysis type by carrying out an in-depth literature review. Results are not only classified but analysed and discussed regarding multiple characteristics, such as MCDMs in which they are applied, type of data used, ideal solutions considered or when they are applied. Furthermore, general requirements supplement this analysis such as initial influence, or component division considerations. As a result, this paper provides not only a general classification scheme and a detailed analysis of experts’ weighting methods but also a road map for researchers working on GDM topics or a guide for experts who use these methods. Furthermore, six significant contributions for future research pathways are provided in the conclusions.The first author acknowledges support from the Spanish Ministry of Universities [grant number FPU18/01471]. The second and third author wish to recognize their support from the Serra Hunter program. Finally, this work was supported by the Catalan agency AGAUR through its research group support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/ 501100011033.Peer ReviewedPostprint (published version

Collected Papers (on Neutrosophic Theory and Applications), Volume VIII

This eighth volume of Collected Papers includes 75 papers comprising 973 pages on (theoretic and applied) neutrosophics, written between 2010-2022 by the author alone or in collaboration with the following 102 co-authors (alphabetically ordered) from 24 countries: Mohamed Abdel-Basset, Abduallah Gamal, Firoz Ahmad, Ahmad Yusuf Adhami, Ahmed B. Al-Nafee, Ali Hassan, Mumtaz Ali, Akbar Rezaei, Assia Bakali, Ayoub Bahnasse, Azeddine Elhassouny, Durga Banerjee, Romualdas Bausys, Mircea Boșcoianu, Traian Alexandru Buda, Bui Cong Cuong, Emilia Calefariu, Ahmet Çevik, Chang Su Kim, Victor Christianto, Dae Wan Kim, Daud Ahmad, Arindam Dey, Partha Pratim Dey, Mamouni Dhar, H. A. Elagamy, Ahmed K. Essa, Sudipta Gayen, Bibhas C. Giri, Daniela Gîfu, Noel Batista Hernández, Hojjatollah Farahani, Huda E. Khalid, Irfan Deli, Saeid Jafari, Tèmítópé Gbóláhàn Jaíyéolá, Sripati Jha, Sudan Jha, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Darjan Karabašević, M. Karthika, Kawther F. Alhasan, Giruta Kazakeviciute-Januskeviciene, Qaisar Khan, Kishore Kumar P K, Prem Kumar Singh, Ranjan Kumar, Maikel Leyva-Vázquez, Mahmoud Ismail, Tahir Mahmood, Hafsa Masood Malik, Mohammad Abobala, Mai Mohamed, Gunasekaran Manogaran, Seema Mehra, Kalyan Mondal, Mohamed Talea, Mullai Murugappan, Muhammad Akram, Muhammad Aslam Malik, Muhammad Khalid Mahmood, Nivetha Martin, Durga Nagarajan, Nguyen Van Dinh, Nguyen Xuan Thao, Lewis Nkenyereya, Jagan M. Obbineni, M. Parimala, S. K. Patro, Peide Liu, Pham Hong Phong, Surapati Pramanik, Gyanendra Prasad Joshi, Quek Shio Gai, R. Radha, A.A. Salama, S. Satham Hussain, Mehmet Șahin, Said Broumi, Ganeshsree Selvachandran, Selvaraj Ganesan, Shahbaz Ali, Shouzhen Zeng, Manjeet Singh, A. Stanis Arul Mary, Dragiša Stanujkić, Yusuf Șubaș, Rui-Pu Tan, Mirela Teodorescu, Selçuk Topal, Zenonas Turskis, Vakkas Uluçay, Norberto Valcárcel Izquierdo, V. Venkateswara Rao, Volkan Duran, Ying Li, Young Bae Jun, Wadei F. Al-Omeri, Jian-qiang Wang, Lihshing Leigh Wang, Edmundas Kazimieras Zavadskas

An investigation into Supplier Selections and Contingency Freight Consolidation for Less-Than-Truckload Logisitics

This study deals with the supplier selection problem in which truck com panies are considered as supplier for the transportation service and freight consolidation scheduling problems for Third Party Logistic (3PL) companies. We present two novel investigations for the supplier selection problem. In the first one, we make some analyses on the commonly used methods for supplier selection problems which are the Multi-Criteria Decision Making (MCDM) methods, namely Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VIekriter ijumsko KOmpromisno Rangiranje (VIKOR). Then we evaluate the results of each method based on the other two methods and conduct some tests for relying on a single method by the regret based measure approaches that we developed. In this way, we offer two effective approaches for combining the results of the individual MCDM approaches. Note that we do not propose an integration of the approaches, but a combination of the results of different MCDM methods in a systematic way instead of relying on a result of an indi vidual method. In the second study of supplier selection, we handle the issue of missing expert knowledge. When data is not available, researchers rely on expert knowledge. Therefore, there is a tendency to use MCDM methods for supplier selection problem due to working ability of MCDM methods with expert knowledge. However, experts do not always have full knowledge of all evaluation criteria. We offer a reliable solution for this problem. We integrate MCDM methods and Bayesian Network (BN) in a novel way that they can compensate each others' limitations with their strengths. We mainly rank the alternative suppliers with TOPSIS which has two inputs: weights of the deci sion criteria and initial decision matrix. We obtain the weights of the criteria from AHP and elicit the initial decision matrix from BN. Causal graphical structure and parameterization of BN is done by Decision Making Trial and Evaluation Laboratory (DEMATEL). Here, experts submit their knowledge about the decision criteria linguistically. Ranked Nodes tool of BN provides the experts to submit their knowledge with verbal expressions in an ordinal scale as low, medium, high. If the experts do not have full knowledge about some of the criteria BN estimates the missing value of criteria based on the available knowledge of the experts and causal relationship between the cri teria probabilistically. According to the obtained new knowledge(evidence) BN updates the values of the network and provides updated information to decision makers dynamically. Finally, we conducted sensitivity analyses for the value of knowledge followed by a case study. In the second part of this research, we investigate the freight consolidation scheduling problem. We address the problem in a particular way due to the preference of a 3PL company that operates in the UK. We consolidate the orders up to 3. First we investigate the possible consolidation configurations of orders as singleton(one), pair and triplets. We compute all the savings obtained by consolidation among non-consolidation case. Then we use these configurations and their saving values as input in our exact approaches like the 0-1 Integer Linear Programming (ILP) and the set partitioning formula tion which we developed. We also presented some tightening constraints into the set partitioning formulation and tested them for different size of the data sets. On the other hand we also tackled the problem using metaheuristics to overcome the computational time for larger instances. We offered Variable Neighbourhood Search (VNS) algorithm using six neighbourhood structures and two local searches: one performs within the route and the other one performs between the routes. The proposed neighbourhood structures are compatible with the purpose of the improvement of the consolidated ship ment configurations up to three requests. On the other hand to perturb the solution and improve it with the repair mechanism we offered Large Neigh bourhood Search (LNS) algorithm. In LNS algorithm, one of the removal operators performs effectively in a guided way by destroying the consolida tion configurations which have negative effect on savings. We also propose to hybridize the VNS/LNS algorithms. Lastly we discuss about the com putational results in terms of deviation from the optimal results and com putational time effectiveness. We finalize the study with a summary of the research, limitations and suggestions for further work. The thesis is made up of eight chapters. In the first chapter, the problem definition, a brief of the study and contributions are presented. In chapter 2, the literature review for supplier selection and order consolidation scheduling problems are given. Chapter 3 propose a deterministic rule for the combina tion of the results of different methods for supplier selection problem while Chapter 4 deals with the case of lack of complete expert knowledge for the supplier selection problem and proposes a novel MCDM-BN integration for this purpose. Chapter 5 discusses the order consolidation scheduling problem and defines all the possible configurations with their respective savings. In chapter 6, exact approaches for the order consolidation scheduling problem are provided, namely, a 0-1 ILP and a set partitioning formulation enhanced by valid inequalities. Chapter 7 treats the same scheduling problem by de signing and implementing two metaheuristics approaches, namely, variable neighbourhood search (VNS) and large neighbourhood search (LNS) as well as their hybridisation. Chapter 8 is the final chapter, it covers a summary of our findings and present limitations of the study and outlines suggestions as future work