Operational Research in Engineering Sciences: Theory and Applications Vol. 4, Issue 2, 2021, pp. 140-150 ISSN: 2620-1607 eISSN: 2620-1747 DOI: https://doi.org/10.31181/oresta20402140z * Corresponding author. sa.hashemkhani@gmail.com (S.Hashemkhani Zolfani), ebaditorkayesh@sabanciuniv.edu (A. Ebadi Torkayesh.), Ramin.bazrafshan88@gmail.com (R. Bazrafshan) VISION-BASED WEIGHTING SYSTEM (VIWES) IN PROSPECTIVE MADM Sarfaraz Hashemkhani Zolfani 1, Ali Ebadi Torkayesh 2, Ramin Bazrafshan 3* 1 School of Engineering, Catholic University of the North, Coquimbo, Chile 2 Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey 3 Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran Received: 14 June 2021 Accepted: 06 July 2021 First online: 08 July 2021 Original scientific paper Abstract: Policy-making is an undeniable decision-making process in every company where different kinds of decisions are taken based on different goals and preferences in each vision. “Prospective Multiple Attribute Decision Making (PMADM)” is one of the well-known decision-making frameworks that have been used as a flexible decision- making tool for developing policies and making future decisions over different periods. This study presents a multi attribute problem with three different visions where a decision-making process is required for each vision in order to prioritize the potential set of alternatives. Evaluation Based on the Distance from the Average Solution (EDAS) is used as a MADM model to show the applicability and feasibility of the PMADM framework. A vision-based weighting system (ViWeS) prepares a new opportunity to take proper decisions in different visions and time requirements. This research is analyzed three-time vision (Current, 2025, and 2030) and showed by changing the time, the rank of the alternatives also is changed. In numerical example is indicated in the current vision, Alternative 5 gets rank one and alternative six get rank 2, for 2025 vision, the rank one and two don’t change, and in vision 2030, the rank of one does not change, but the rank of second change from Alternative 6 to 3. Key words: Prospective Multiple Attribute Decision Making (PMADM), Vision-based weighting system (ViWeS), Evaluation Based on the Distance from the Average Solution (EDAS), Policy-Making, Weighting system 1. Introduction Multiple Attribute Decision Making (MADM) models are considered reliable decision-making models that can help decision-makers and policymakers address Vision-based Weighting System (ViWeS) in Prospective MADM 141 complex evaluation problems such as supplier selection problems. Logistics provide problem, waste management, location selection problem considering multiple attributes (Ignatius et al. 2016; Yazdani et al. 2017; Ebadi Torkayesh et al. 2019; Hashemkhani Zolfani et al. 2020). MADM models such as BWM (Fazlollahtabar et al. 2021; Pamučar and Savin, 2020), SWARA (Radović and Stević, 2018), AHP (Alosta et al. 2021), FUCOM (Durmić et al. 2020) are applied to determine the importance of decision criteria, while models such as EDAS (Stević et al 2016), CoCoSo (Biswas et al. 2019), CODAS (Badi et al. 2018), TOPSIS, MARCOS (Đalić et al. 2021) are applied to evaluate alternatives of a multi attribute problem (Mardani et al. 2016; Kumar et al. 2017). MADM models are able to address a complex problem with n criteria and m alternatives for a specific time. However, the decision-making process can be due to several changes in weight of criteria and then an evaluation framework, considering a decision maker’s or a company’s visions and goals for different periods. Therefore, the decision-making process and obtained results from traditional MADM models may not be reliable in the following years. So, nonexistent a MADM model which considers the future makes more sense than previously. Prospective MADM (PMADM) is a new framework that can be used to process different companies' visions. The MADM models make decisions in a steady and stable state (fix situation), but PMADM expands this decision environment and considers the time that hasn’t happened. The PMADM uses two items for studying the future, limiters and boosters. These items in the different situations given different values to alternatives in evaluation. Companies can facilitate their policy-making process using PMADM, where several visions can be defined based on companies' goals. By this method, managers can survey and evaluate their future outcomes and modify their decisions and plan. There are many MADM methods like PROMETHEE (Brans 1982) and VIKOR (Opricovic, 1988), and TOPSIS (Hwang and Yoon in 1981), but they don’t consider the future, and this shortage of them causes managers less willing to use them. In contrast, these methods could help them make better policy decisions. For developing and make more efficient methods, this idea formed in our minds that using the PMADM framework can promote their performance and activities. The PMADM approach considers future vision and changes the value of criteria. This conversion affects the rank of alternatives. The researchers who study in the MADM context usually consider the current time in their studies - considering the future in decision making expressed by the PMADM method. But, researchers don’t use this method in their studies. This paper considers the future in decision-making by the use of the PMADM method. In this paper, we develop a PMADM framework and define three visions for a numerical decision-making example where weights of criteria are different in each vision based on goals and preferences and possible events that may happen in the future. For the evaluation part, the EDAS model, as a reliable and frequently used tool, is applied to prioritize the alternatives for each vision. 2. Literature review As said before, most of the time, Managers concentrate on future actions and goals and make plans to reach them. Due to a lot of factors, managers are confused Hashemkhani Zolfani et al. /Oper. Res. Eng. Sci. Theor. Appl. 4 (2) (2021) 140-150 142 about set or ranking company priorities. Therefore head manager or an administrator needs to see the future more clearly and make an appropriate decision. To determine the direction and guide policymakers or officers to make a better decision. This section first reviewed the PMADM model and its uses in various contexts and then described a MADM model. Because according to the subject of the article and for futuristic decisions, researchers want to use the PMADM framework for a MADM model. Hashemkhani Zolfani et al. (2016) developed a new framework for MADM problem, called Prospective MADM, which not only facilitate the decision-making process at the moment but also enables decision-makers to consider future visions and extend the decision making process using different sets of inputs based on possible events or goals that are planned for each vision. Later, Zolfani et al. (2018) studied the prospective MADM framework for sustainability assessment problems, focusing on a multi-aspect set of criteria that can be used for multi-attribute problems. They consider futures sustainability an umbrella for sustainability, which consists of the future economy, environment, and social position. As to the importance of development in sustainability, they introduced a trend for Exergy, which consists of energy, environment, and sustainable development. In this trend, energy is presented as a core item. Zolfani and Masaeli (2020) presented a comprehensive framework for the prospective MADM approach and its application for the health device industry of Iran, considering several visions during sanctions. By the PMADM, they achieved their goal to increase the medical device market share ten times more like a sustainable market for the country. In this research, Max capacity, ideal directed scenario, and supportive backup criteria are used in the PMADM framework. Hashemkhani Zolfani et al. (2020) used a text-mining tool, latent semantic analysis, as a criteria selection and weighting system in prospective MADM. They use this for machine tool selection and introduce five criteria as (1) Cost and Serviceability; (2) Technical Features and Safety; (3) Size and Precision; (4) Flexibility; and (5) Productivity. After calculating and ranking them, they report that Cost and Serviceability have the highest priority among these criteria. After explaining the uses of PMADM in various contexts, it talks about a MADM method and its uses in articles or case studies. Evaluation Based on the Distance from the Average Solution (EDAS) is one of the recently developed MADM models that is used to prioritize a set of alternatives concerning multiple factors (Keshavarz Ghorabee et al. 2015). Kahraman et al. (2017) proposed a new extension of the EDAS model under fuzzy set theory to evaluate the waste disposal location selection process. In this research, they determined three alternatives and three criteria. The criteria uses are water pollution (w), distance to residential areas (D), and slope (S). For solving, they decided to use the interval-valued intuitionistic fuzzy EDAS (IVIF EDAS) method. In the IVIF method, membership and non-membership function and unknown degree (hesitancy degree) are calculated. Ecer (2018) integrated AHP and EDAS models under fuzzy set theory to address third-party logistics (3PLs) provider selection problems. First, fuzzy AHP was used to determine the importance of decision criteria, and then fuzzy EDAS was used to prioritize alternatives. He determined that cost, quality, and professionalism are the most critical factors for 3PLs provider selection. Vision-based Weighting System (ViWeS) in Prospective MADM 143 Li et al. (2019) developed another extension of the EDAS method using a neutrosophic set to consider the uncertainty that may happen in the decision-making process. They proposed a convex weighted average operator of multivalued neutrosophic numbers (MVNNs) to calculate the average solution of criteria. Torkayesh et al. (2020) proposed an integrated MADM model using the Shanon Entropy and EDAS methods. The proposed decision-making model has applied a neighborhood selection problem for a new international student who wants to be located in Istanbul, Turkey. The usability and capacity of five renewable resources: solar PV, Solar thermal, wind power, geothermal, and biomass concerning economic, technical, social, and environmental aspects are measured. By EDAS method are ranked these resources and showed wind power is the most suitable energy for their case study. Behzad et al. (2020) used a hybrid decision-making model by using BWM and EDAS models to make an evaluation framework in order to assess waste management status in Nordic countries. They use seven criteria as waste generation, composting waste, recycling waste, landfilling waste, recycling rate, waste to the energy rate, and greenhouse gas emissions from waste. Comparing these criteria concludes that Sweden has the best waste management profile. 2.1. Main contribution In this article, researchers are trying to develop a decision-making policy to consider the future in decision-making furthermore to the current time. In most conventional decision-making methods, only the present time is considered. This paper attribute to this issue attempt to introduce a vision-based weighting system that facilitates the decision processes. This system is a combination of the PMADM framework with the MADM method. The ViWeS helps administers or managers decide by considering time vision. Finding or verdict in current time is different from the future because the weight of criteria to time vision changes. For example, suppose someone has a plan for reaching a specific goal in two years and wants to determine his alternative priorities; if he doesn’t consider the future vision, he may gain the wrong rank of alternatives and doesn’t reach his aim. In the numerical example section, this rank changing by time vision changing is shown by an example. 3. Methodology This section describes the EDAS model that can be applied for PMADM problems that consider different types of weighting visions based on events that may happen in the future and affect the decision-making process. One of the reasons which opt EDAS method is it needs fewer computations concerning most of the other multi- attribute decision-making methods. At the same time, it can produce the same ranking of alternatives (Kahraman et al. (2017)). 3.1. Evaluation Based on the Distance from the Average Solution (EDAS) Keshavarz Ghorabaee et al. 2017 proposed a new brand Multiple Attribute Decision Making (MADM) method, called EDAS, to address multi-attribute problems such as supply chain management, transportation problem, waste management, etc. By measuring the distance from ideal and nadir solutions, is determined the best Hashemkhani Zolfani et al. /Oper. Res. Eng. Sci. Theor. Appl. 4 (2) (2021) 140-150 144 alternative. After calculating these distances, the one that has a lower distance from the ideal solution and a higher distance from the nadir solution is our perfect answer. The EDAS method calculating these distances from the average solution (AV). This method defines the positive distance from average (PDA) and negative distance from average (NDA) and specified the best alternative after comparing these distances. For more detail of this method in continuing to explain the steps of this. The steps of the EDAS method are explained below. Step 1. In this step, the decision-maker constructs the initial decision matrix. 11 12 1 21 22 2 * 1 2 ... ... . . . . . . . . . . . . ... m m ij n m n n nm X X X X X X X X X X X          = =             (1) Step 2. The average solution for each criterion is calculated based on equations. 1* j m AV AV =   (2) 1AV n ij i j X n ==  (3) Step3. Positive distance from average (PDA) and negative distance from average (NDA) are calculated. * ij n m PDA PDA =   (4) * NDA= ij n m NDA   (5) if j th criterion is beneficial, max(0, ( )) PDA , ij j ij j X AV AV − = (6) max(0, ( )) NDA , j ij ij j AV X AV − = (7) if j th criterion is non-beneficial, max(0, ( )) PDA , j ij ij j AV X AV − = (8) max(0, ( )) NDA , ij j ij j X AV AV − = (9) Step 4. We calculate the weighted sum of PDA and NDA for all alternatives which are denoted as SP and SN. Vision-based Weighting System (ViWeS) in Prospective MADM 145 1 * ; m i j ij j SP w PDA = =  (10) 1 * ; m i j ij j SN w NDA = =  (11) Step 5. We normalize the obtained values in step 4. These values are then added and construct a new vector, called NSP (normalized weighted sum of PDA) and NSN (normalized weighted sum of NDA). ; max ( ) i i i i SP NSP SP = (12) NSN 1 ; max ( ) i i i i SN SN = − (13) Step 6. Finally, appraisal score (AS) for each alternative is calculated. 1 ( ), 2 i i i AS NSP NSN= + (14) 4. Numerical example In this part, define a numerical example in order to show the applicability and feasibility of the EDAS based PMADM framework. A multiple attribute problem is considered in the numerical example, which includes five decision criteria and six alternatives that should be evaluated accordingly. Weight of decision criteria is proposed for three different visions as current vision, vision 2025, and vision 2030. The importance of decision criteria varies in each vision due to the possible changes that may happen and affect the decision-making process. For each set of weights, the EDAS model is used to solve the decision-making problem and identify the ranking order of alternatives for each time vision. In Table 1, the initial decision matrix including scores of alternatives concerning each criterion is reported. The weight of criteria for each time vision is also reported in Table 1. Table 1. Initial decision matrix Criteria C1 C2 C3 C4 C5 Max/Min Max Min Max Max Max Weights Current vision 0.2 0.25 0.2 0.15 0.2 Vision 2025 0.22 0.21 0.18 0.2 0.19 Vision 2030 0.25 0.18 0.18 0.24 0.15 A1 7 6 8 6 7 A2 6 7 8 7 8 A3 8 6 7 6 7 A4 7 7 7 7 8 A5 8 7 8 7 7 A6 6 5 8 6 7 Hashemkhani Zolfani et al. /Oper. Res. Eng. Sci. Theor. Appl. 4 (2) (2021) 140-150 146 In the next step, the EDAS model is used based on the steps explained in the previous section to solve the decision-making process. For this purpose, the SP, SN, NSP, NSN, AS, and the final ranking of each alternative for each set of weights for each time vision is calculated. In Table 2, the results for EDAS parameters and the corresponding ranking order of each alternative are reported. Alternatives A5 and A6 are selected as the most preferred alternatives with respect to the current vision. In table 3, the results of the EDAS model for vision 2025 are reported. As same as the current vision, alternatives A5 and A6 are selected as the most preferred alternatives. For vision 2030, the results of the EDAS model are reported in table 4. Alternatives A5 and A3 are selected as the most preferred alternatives. Table 2. EDAS values for current vision SP SN NSP NSN AS Ranking A1 0.022 0.021 0.356 0.781 0.569 3 A2 0.038 0.094 0.626 0.000 0.313 5 A3 0.042 0.064 0.680 0.318 0.499 4 A4 0.030 0.083 0.485 0.118 0.302 6 A5 0.049 0.009 0.796 0.904 0.850 1 A6 0.061 0.049 1.000 0.479 0.739 2 Table 3. EDAS values for vision 2025 SP SN NSP NSN AS Ranking A1 0.019 0.024 0.346 0.723 0.534 3 A2 0.040 0.087 0.741 0.000 0.370 6 A3 0.042 0.062 0.777 0.287 0.532 4 A4 0.033 0.071 0.598 0.182 0.390 5 A5 0.055 0.009 1.000 0.900 0.950 1 A6 0.052 0.055 0.952 0.360 0.656 2 Table 4. EDAS values for vision 2030 SP SN NSP NSN AS Ranking A1 0.017 0.025 0.279 0.696 0.487 4 A2 0.040 0.083 0.644 0.000 0.322 6 A3 0.045 0.060 0.729 0.279 0.504 2 A4 0.032 0.063 0.518 0.241 0.380 5 A5 0.062 0.007 1.000 0.918 0.959 1 A6 0.046 0.061 0.737 0.266 0.502 3 Figure 1 shows the ranking order of each alternative with respect to each time vision defined in this study. Alternative A5 is selected as the top alternatives in all visions. Although alternative A6 is selected as the second important alternative, it is ranked as the third one in vision 2030. The ranking order of other alternatives is also slightly changed concerning each time vision. Vision-based Weighting System (ViWeS) in Prospective MADM 147 Figure 1. Ranking order of alternatives in different time visions The main idea for this part is to show that essence could be changed in the current vision to future vision in a particular case. This numerical example is showing this issue of what a proper decision when considering the future is. This example shows in vision 2030, our alternative could be changed, and to reach the company's aim, this issue should be considered. The priorities in time vision could be changed. This issue helps decision-makers to set their decision by this long-term vision. 5. Managerial tips Managers usually write the company’s goals and attempt to reach them. For reaching goals and make a decision, a suitable plan should be set. This plan is consists of a set of alternatives and criteria, and managers should rank these alternatives and consider them according to priorities. Since in the real situation, various criteria involve in decision making, managers should use multiple criteria decision making for finding the best alternative. As previously mentioned, the future vision is one of the crucial issues managers should consider in their decision-making. If a multi-criteria decision-making method exists that considers the future vision, it helps stakeholders take a proper decision in the current time. The current decision that considers the future facilitates the way for achieving the company’s goals. 6. Conclusions Decisions need to be taken according to the current needs and strategic plans and situations. When a policymaker wants to decide by classic MADM form of study, everything must be considered fix in an acceptable primary evaluation. PMADM outline changes the previous games as a game-changer. New items have been developing the class MADM structure since 2016 by introducing PMADM. In this study, a new flexible weighting system, as Vision-based Weighting System (ViWes), Hashemkhani Zolfani et al. /Oper. Res. Eng. Sci. Theor. Appl. 4 (2) (2021) 140-150 148 presented shows how a decision can be made now but with proper preparation for all possible changes in the decisions. Moreover, it can enhance this ability to show when we need to consider all the new changes in the priorities and alternatives due to the importance of the criteria in different periods and future visions. It is illustrated in the numerical example to see how the importance of alternatives can vary due to different criteria’s expectations. As a suggestion for future studies, something can be mentioned as to how policymakers and decision-makers can make a flexible vision-based decision making when alternatives can be different when they agree to change according to the essential needs and rules. When decision alternatives want to be adopted by necessary changes, a dynamic situation will happen in the classic way of decision making, and that would be a new challenge in the field of MCDM and Prospective MADM. This research is used the PMADM structure for considering the future in the EDAS MADM model. Comparing table 1 to 4 found that in the current vision, Alternative 5 gets rank one and alternative six get rank 2, for 2025 vision, the rank one and two don’t change, and in vision 2030, the rank of one does not change, but the rank of second change from Alternative 6 to 3. These conclusions show that makes the decision is changed over time and should consider the vision of time in decision making. The PMADM is a method that helps managers or stakeholders to consider time vision in their decision policy. By this, managers could reach their goals and plan. The limitation of this study was there isn’t an actual case study for this method, and future research, using the real case study is beneficial, and the combination of other MADM methods with PMADM could be actionable. 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Yazdani, M., Chatterjee, P., Zavadskas, E.K. and Zolfani, S.H., (2017). Integrated QFD- MCDM framework for green supplier selection. Journal of Cleaner Production, 142, pp.3728-3740 Zolfani, S.H., Zavadskas, E.K., Khazaelpour, P. and Cavallaro, F., (2018). The multi- aspect criterion in the PMADM outline and its possible application to sustainability assessment. Sustainability, 10(12), 4451. © 2021 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Vision-based Weighting System (ViWeS) in Prospective MADM Sarfaraz Hashemkhani Zolfani 1, Ali Ebadi Torkayesh 2, Ramin Bazrafshan 3* 1. Introduction 2. Literature review 2.1. Main contribution 3. Methodology 3.1. Evaluation Based on the Distance from the Average Solution (EDAS) 4. Numerical example 5. Managerial tips 6. Conclusions Reference