Industrial Engineering in the Covid-19 Era

Frontmatter

Algorithms for Multiple Autonomous Robotic Systems in Warehouse Order Picking Operations: A General Review

Abstract

Warehouses have always been indispensable components of supply chains for the smooth flow of materials from supplier to customer. Expansion of e-commerce, requiring faster delivery of smaller orders, promoted stock management and consequently warehouse operations. The search for increased efficiency in stock management and warehouse operations yielded the deployment of autonomous robotic systems. One example of such a system is Amazon’s Kiva system. It has been claimed that the Kiva system reduces the unnecessary time and cost of pickers close to zero. These recent developments are invaluable since order picking is the most labor-intensive and capital-intensive operation in all warehouse operations. An enhancement in the order picking process decreases warehouse expenses, increases the throughput of the warehouse and customer service level, and implicitly improves the supply chain performance. Hence, intelligent systems are essential to optimize the order fulfillment process. Increasing the throughput and the speed of the system requires the employment of more pickers. Operating autonomous robotic systems simultaneously is more sophisticated. The problem of batching and routing jointly is complex by itself. When it is required to embed congestion and collision prevention into the batching and routing of multiple pickers, the problem can get prohibitively complex. In this study, we review algorithms for the order picking problem both for single picker and multiple picker cases which form the basis for the development of intelligent batching and routing algorithms for multiple autonomous robotic systems.

Zehra Duzgit, Ayhan Ozgur Toy

E-Grocery Challenges and a Solution Approach from Multi-objective Perspectives

Abstract

This paper provides an overview of the complex structure of the e-grocery industry, highlighting recent trends and challenges including the increasing customers’ expectations. Customers’ satisfaction can be driven by multiple objectives, which can create significant trade-offs. We propose a new approach as a future work for e-grocery businesses to leverage multi-objective perspectives, maximizing product availability and sustainability and minimizing cost. Specifically, we propose an e-grocery store assignment policy while consumers are using apps, which is developed on a real-time data-driven approach from customer ordering behaviors. With the help of data availability and data analytic tools, data-based solutions can foster continuous improvement in businesses. In a simulation study, imitating different demand profiles and online ordering behaviors might help develop a good solution approach for a multi-objective perspective.

Laura Foresti, Sara Perotti, Banu Y. Ekren, Lorenzo B. Prataviera

Product Recovery Option Evaluation for Different Departmental Objectives via Fuzzy AHP

Abstract

Product recovery has an important role in providing a sustainable environment and economy. Reuse, refurbishment, repair, remanufacturing, and recycling are some of the recovery options considered within the decision process of the product recovery system. Since each recovery option has different monetary and non-monetary effects on the system, evaluation of the options is important to minimize the total cost. Rental products make up a special subset of recovery products because their lifecycle takes a long time, and they have many returns. Additionally, the effect of the same recovery option changes with respect to the type of department in a company as their own targets differs from each other. Hence, it becomes important to determine the recovery option evaluation factors and their importance weights concerning different departments such as purchasing, sales, marketing, customer relations, and so on. For this purpose, an application is performed in a telecommunication company. Modems that are used as rental products in the company are considered within the recovery process. The factors used in the evaluation of various recovery options are revealed and their importance weights are obtained via the Fuzzy Analytic Hierarchy Process (FAHP) method for the marketing and customer relations departments of the company. It is concluded that the importance weights of the evaluation factors for recovery options vary with respect to the type of department.

Sevan Katrancioglu, Huseyin Selcuk Kilic, Cigdem Alabas Uslu

A Novel Hesitant Fuzzy Association Rule Mining Model

Abstract

In this paper, a new Hesitant Fuzzy Association Rule model is proposed for the first time to mine the Frequent itemset and generate the rule by hesitant transaction matrix instead of the Boolean database. One fast FFI miner algorithm is used to find the frequent itemset without candidate generation. Also, pruning strategies are used to decrease the algorithm's run time and ignore the unnecessary itemsets. Another strength of our study is calculating the support and confidence metrics based on Hesitant Maximum Scalar Cardinality. A numerical example is conducted to show the performance of the proposed FFI-Miner algorithm compared to the Apriori-based approaches in terms of execution time and the number of evaluated itemsets.

Elmira Farrokhizadeh, Basar Oztaysi

Evaluating the Criteria in the Dimensions of the Kraljic Purchasing Portfolio Matrix

Abstract

The Kraljic purchasing portfolio model is an approach for categorizing a company’s purchased components into four quadrants based on the significance of the components (specified by the impact that they have on the profitability of the firm) and the complexity (or risk) of the supply market (such as the number of suppliers, lead time, logistical proximity). The Kraljic model enables the company to identify the most appropriate purchasing strategies for the four types of components. The neutrosophic concept can handle inconsistent and vague information with the membership functions of truth, indeterminacy, and falsity. Therefore, we integrated the neutrosophic concept the with AHP method to evaluate the criteria for defining the Kraljic matrix and compute their weights.

Ahmet Selcuk Yalcin, Emre Cevikcan

Predicting Sovereign Credit Ratings Using Machine Learning Algorithms

Abstract

Sovereign credit ratings are major indicators of a country’s financial structure as they provide an assessment of the creditworthiness of a country and its capability to meet its financial obligations. On the other side, how they are established by credit rating agencies (CRAs) is considered as not transparent and objective enough. This study aims to suggest a prediction framework for sovereign credit ratings based on machine learning (ML) algorithms using the following predictors: Credit Default Swap, Government Bond Yield, GDP/Capita, Consumer Price Index, Currency Volatility, and Political Risk.

Mehmet Ekmekcioglu, Tolga Kaya, Kaya Tokmakcioglu

Healthcare Waste Disposal Method Selection in the Era of COVID-19: A Novel Spherical Fuzzy CRITIC TOPSIS Approach

Abstract

Healthcare waste disposal method selection is among the most important decisions that must be made by healthcare organizations, particularly in light of the COVID-19 pandemic issue. Such a problem has a number of contradictory criteria and alternatives, and decision experts may have considerable uncertainty while evaluating the problem. In this paper, a new fuzzy multi-criteria decision-making (MCDM) methodology is provided for evaluating the healthcare waste disposal method selection problem. The CRiteria Importance Through Intercriteria Correlation (CRITIC) is used for obtaining criterion weights in an objective manner, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used for ranking the predetermined alternatives. For modeling the uncertainty in the nature of the problem, the proposed methodology is developed in a three-dimensional spherical fuzzy atmosphere. The step-by-step solution of the proposed methodology is followed by a sensitivity analysis. This study contributes to the work of both academics and practitioners in the healthcare industry, as well as other sectors facing similar types of problems.

Akin Menekse, Hatice Camgoz Akdag

Online Learning and Lab Courses vs. Traditional Ones in Chemistry During the COVID-19 Pandemic: The Comparative Perceptions of the Undergraduate Students

Abstract

This study aimed to reveal the comparative perceptions of the students of the online and physical chemistry lab courses, and their comparative perceptions of the contributions of online learning and in-class learning modes in increasing their knowledge, laboratory skills, and social skills during the COVID-19 pandemic. This online questionnaire-based study was conducted at Istanbul Technical University (ITU) with the participation of 58 students who did take the chemistry lab courses in-class and online using the Zoom platform. Our results show that online chemistry lab classes were perceived as too far away to be better than traditional chemistry lab classes. In addition, online learning was perceived as too far away to be better than in-class learning in increasing students’ knowledge, lab skills, and social skills. Since in-class lab learning is vital in chemistry education, traditional lab exposure has to be ensured in educating undergraduate students and should be used together with effective online teaching as much as possible in the future. However, effective online teaching necessitates unique knowledge about how technology and pedagogical practices could be best integrated and then used in teaching chemistry-specific content.

Ferah Calisir, Fethi Calisir

Digital Twins for Decision Making in Supply Chains

Abstract

This paper studies the utilization of digital twins (DTs) as a decision support tool in supply chains (SCs) by providing a framework. DT is an emerging technology-based modeling approach reflecting a virtual representation of an object or system that can help organizations monitor operations, perform predictive analytics, and improve their processes. For instance, it may provide a digital replica of operations in a factory, communications network, or the flow of goods through an SC system. In this paper, by focusing on SC systems, we explore the critical decisions in SCs and their related data to track, to make the right decisions within DTs. We introduce six main functions in SCs and define frequent decisions that can be taken under those functions. After defining the required decisions, we also identify which data/information would help to make correct decisions within those DTs.

Oray Kulac, Banu Y. Ekren, A. Ozgur Toy

Micro-fulfilment Centres in E-Grocery Deliveries

Abstract

This paper studies micro-fulfilment centres (MFCs) as a response to rising e-grocery sales and customer expectations from decreased delivery time and cost requests. MFC is a business solution that allows orders to be picked and packed in a hyper-local facility. The study’s aim is to provide an overview of this subject from two research questions: i) how MFCs affect the last-mile delivery challenges? and ii) what design decisions are critical in building MFCs? While we evaluate the advantages and disadvantages of centralised versus decentralised warehousing strategies in the first question, we discuss the critical decisions in designing MFCs in the second question. In that, we discuss location and technology selection decisions as well as other warehousing design criteria. Further, this study provides future research directions at the end of this study.

Ventola Alessandro, Tinor Mirko, Perotti Sara, Banu Y. Ekren, Hendrik Reefke

A Comparison Between Linear and Non-linear Combinations of Priority Rules for Solving Flexible Job Shop Scheduling Problem

Abstract

Priority rules (PRs) have gained importance in the literature since they are suitable for solving especially large-scale scheduling problems in the industry. There are different types of PRs, of which composite PRs (CPRs), i.e., combinations of multiple rules, are known to have better performance in general. In this study, a basis for the generation of CPRs from linear and non-linear combinations of different PRs is defined and a comparison is made between these two approaches. Genetic algorithm and particle swarm optimization are operated for linear combination, and gene expression programming is used for non-linear combination, whose details are given. The employed benchmarks are from the flexible job shop scheduling problem, but the algorithms can also be employed to solve different scheduling problems. Along with the rules obtained based on the two approaches, a comparison is made between the famous simple PRs (SPRs) in the literature in terms of solution quality and time. The results show that usually, the non-linear combination provides better results. Since there is no process for rule extraction for SPRs, their computation time is very low. Both the used benchmarks and source codes are made available to the readers.

Aydin Teymourifar, Jie Li, Dan Li, Taicheng Zheng

A Comparison Between Two Definitions of Idle Time in Offline Scheduling of Flexible Job Shop Problem

Abstract

Calculating idle time (IDT) based on different definitions for solving flexible job shop scheduling problems (FJSSPs), may lead to dissimilar results. This is valid for both offline and online scheduling. Therefore, it is important to clarify the description of IDT. In this study, the differences between offline and online scheduling concepts are first explained. The advantages and disadvantages of these two approaches are analyzed in detail. The details of an offline scheduling method are illustrated through a step-by-step example, which is solved manually to be elucidative. Two definitions for IDT are then given over the waiting of the operations and machines, and an FJSSP is solved offline with a priority rule defined based on them. The differences in the results of the two definitions are demonstrated through the illustrative example. In addition, a source code is written in MATLAB for offline scheduling, with which some benchmarks are solved. Details of benchmarks are presented and the results are discussed.

Aydin Teymourifar, Jie Li, Dan Li, Taicheng Zheng

A Comparative Analysis of Apriori and FP-Growth Algorithms for Market Basket Analysis Using Multi-level Association Rule Mining

Abstract

Nowadays, many companies have massive amounts of data. Data mining is essential to gain business insights from large amounts of data. Market basket analysis is a data mining technique that analyzes customer buying behavior by discovering relationships between pairs of products purchased together. This analysis helps companies to design a better strategy for business decisions such as marketing and campaign management, store layout optimization, and inventory control. FP-Growth and Apriori are two widely used algorithms for market basket analysis. In this study, Apriori and FP-Growth algorithms are applied for market basket analysis with real-life data from an FMCG retailer. Furthermore, the performance of these algorithms is compared using multilevel association rules mining. Numerical results showed that the FP-Growth has greater performance than Apriori in terms of run time and memory for all levels of product groups. On the other hand, compared to FP-Growth, Apriori is better in terms of generating good candidates. At this point, a hybrid approach can be developed that minimizes the disadvantages of both algorithms.

Dilara Alcan, Kubra Ozdemir, Berkay Ozkan, Ali Yigit Mucan, Tuncay Ozcan

Supplier Selection with Fuzzy TOPSIS- A Case Study on a Pharmacy in Izmir

Abstract

Multi-criteria supplier selection problems are widely studied problems in the OR literature. Various criteria are inspected during the evaluation process. Although, selecting the appropriate criteria is not cumbersome, deciding on the weights of each criterion and performance weights for the suppliers is not easy. The difficulty arises due to fuzziness in the evaluation process. This fuzziness can be handled by some fuzzy methods due to its fuzzy nature. In this study, the aim is to show an appropriate selection of suppliers for a pharmacy located in Turkey by using the fuzzy TOPSIS method. The motivation behind selecting a pharmacy to conduct a case study is the significance of medicine supply, especially after Covid-19. In the case study, the fuzzy TOPSIS method is implemented on 4 suppliers from the pharmaceutical industry based on 11 selection criteria. Evaluations of 6 decision-makers who work in the pharmaceutical industry are gathered. Then, results are reported and conclusions, as well as future directions, are mentioned.

Gamze Erdem, Gozde Ulutagay, Mert Paldrak

Supplier Selection with Fuzzy Analytical Hierarchy Process

Abstract

Today, competition is increasing rapidly due to developments in technology. Firms should follow new methods in the process of the product from the producer to the consumer (Supply Chain) to maintain their place in the competition. For the continuation of a successful Supply Chain, long-term cooperation based on mutual trust and cooperation should be established between the supplier and the customer. Purchasing timely and quality raw materials from suppliers is the first step of this chain. If one of the supplier links is broken, the entire chain will be broken. As a result, business decisions on supplier selection are critical. The difficulty of supplier selection is complicated by several competing considerations. Cost, quality, design capability, manufacturing competence, technical capability, technological capability, performance history, managerial capability, degree of collaboration, financial performance, and closeness are only a few of these considerations. In this instance, multi-purpose decision-making methodologies should be applied rather than traditional supplier selection analyses. One of the Multi-Criteria Decision Making strategies that aid the decision maker is the Fuzzy Analytical Hierarchy Process. This study started by examining the factors affecting supplier selection. The literature review focused on Fuzzy AHP applications. Criteria suitable for the examined company were determined and the process was started by creating matrixes. Finally, the best supplier was determined.

Gozde Ulutagay, Merve Yildiz Ozen

Determining Graduate Level University Selection Criteria Weights by Using Type-2 Fuzzy AHP

Abstract

University selection is a process where a person is affected by universities’ education, culture, and other perspectives during their student years. While university selection consists of many criteria, graduate-level university selection is another concern since the applicant has experience with at least one university and the reason for application is somewhat different. Therefore, it is important to construct an analysis for the graduate level and define the most important criteria in the selection process. In this study, graduate-level university selection is discussed to explore the criteria with the highest priority to understanding the selection process. To achieve this objective, Type-2 Fuzzy AHP is used to construct the decision model and determine the criteria weights along with students to compare the criteria. The results show that professor quality, education quality, and professor choice are the highest-ranking criteria for graduate-level applications.

Zeynep Burcu Kizilkan, Basar Oztaysi

A System Proposal for Monitoring Ergonomic Risks at Workstations in a Manufacturing Company

Abstract

In every industry, managing costs and increasing productivity and quality at the same time is one of the important goals. For this purpose, companies today pay attention to the workers’ health by focusing more on ergonomic risk assessments and improvements and trying to prevent work-related musculoskeletal disorders in every part of the workplace. In most industrial workplaces, there is a shortage of resources for ergonomic improvements and a lack of attention to ergonomic issues because the production concern is at the forefront and this causes a high rate of occupational diseases. This results in less productivity and higher costs in long term. There are different examples of expert systems or software around the world established to be used for ergonomic assessments and risk monitoring, and it is critical to develop and use the most appropriate system for risk assessment and operator assignment to reach the best results in terms of productivity and minimization of occupational diseases. It also saves time compared to manual tracking and assignment processes as it is faster through software and helps prevent wastage of paper in most cases.

Tugba Delice, Fethi Calisir

Inventory and Maintenance Optimization of Conditional Based Maintenance Using Fuzzy Inference System

Abstract

Complex and multifunctional systems are used in operations in industry and service sectors and companies may have to deal with huge losses in case of breakdown and failure in them. When the system should be maintained is very important because, in the case of early maintenance, there are losses due to the replacement of the unworn machines, while in the case of late maintenance, the wear of the component affects the whole machine and the machine must be completely replaced, therefore it is important when to perform maintenance on the system. To decide when to maintain, we need to get information on the components from the system and this information can be obtained from the system about whether there is a need for maintenance with the help of sensors, but it is not economically feasible to install the sensors on each component. I propose a new policy that receives partial information from the system regarding conditional-based maintenance. I compare this policy with its subset of the classical conditional-based maintenance policy by doing numerical experimentation. I will also analyze how components with different reliability affect the system. In this study, conditional-based maintenance policies were created for maintenance work based on motor machinery driven, which was previously maintained with the Fuzzy Inference system.

Berk Kaya, Gozde Ulutagay

The Impacts of COVID-19 on Turkish Real Estate Industry: Perception vs. Reality

Abstract

In the very early stages of COVID-19, a survey was conducted to find out the perception of the people living in Turkey about the effects of the pandemic on the Turkish real estate industry. 284 respondents were asked questions about their perception of how the pandemic would affect the real estate industry in terms of housing prices, mortgage interest rates, the gross sales of the shopping malls, the vacancy rates of the offices, and the occupancy rates of the hotels. This study analyzes the responses of the survey participants and compares the results with the current real estate market figures where two years left behind the declaration of the pandemic by the World Health Organization (WHO) in March 2020. According to the survey results, the participants perceived a decrease in housing prices, a decline in mortgage interest rates, and an increase in vacancy rates in offices due to COVID-19, but oppositely, the housing prices and mortgage interest rates increased and despite the remote working models, vacancy rates in the offices decreased. The decrease in gross sales in shopping malls and decline in occupancy rates in hotels during the COVID-19 period were predicted accurately by more than 70% of the respondents respectively. This research, being one of the most comprehensive studies that is covering the Turkish real estate industry during the COVID-19 period, aims to provide insight for the developers and investors while determining their future investment strategies.

Levent Sumer

Evaluation of Understandability of the Concept of Sustainability by Companies: Automotive Sector Spare Parts Industry

Abstract

In this study, it is aimed to show how the companies working in the spare parts industry in the automotive sector support the concept of sustainable development and to give an idea about the effect of sustainability as an added value activity that increases productivity. In the sustainability analysis of the TR62 (Adana-Mersin) region automotive spare parts sector in our country, a qualitative research method was used and certain questions were asked to the companies under the dimensions of economic, social, and environmental sustainability. The findings obtained in line with the answers received were examined in detail under the headings of sustainability. It can be qualified that the companies providing spare parts in the automotive sector have general sustainability perspectives and sensitivities about sustainability dimensions. A comprehensive analysis of sustainability in the automotive spare parts industry and the findings reveal the originality of the study by considering the contribution to the literature. With this study, spare parts companies can determine their position in terms of sustainability criteria, put the works to be implemented in the order of operations, and adapt the general sustainability framework to their company.

Emel Yontar, Fatma Ersoy Duran

Usability Evaluation of Maritime Websites with Different End User Groups

Abstract

Maritime-related websites should be properly developed to guarantee a good level of usability, especially when end users comprise a variety of people with varied wants and desires. This study’s primary goal was to examine how various end users with different use purposes—including members of the working population in the maritime sector, maritime students, and seafarers’ relatives—react to maritime websites in terms of usage efficiency, satisfaction, which collectively form websites’ usability. The assessments were obtained through formal usability testing including think-aloud protocol during task performing and questionnaires. The results of the statistical analysis show that different groups of end users achieve different levels of efficiency in the completion of tasks. Also, it has been found that only for students there is a satisfaction difference between evaluated maritime websites. Suggestions to enhance websites’ usability, particularly for inexperienced users, based on the identified weaknesses are also provided.

Muhittin Orhan, Ayse Elvan Bayraktaroglu

A Stochastic Multi-objective Model for Sustainable Soybean Supply Chain Network Configuration

Abstract

Over the years, economic, environmental, and social challenges in the food sector have led researchers to focus on research on incorporating sustainability concepts into the design of food supply chains. This paper presents a new multi-objective model, using stochastic mixed-integer linear programming to design a sustainable agri-food supply chain network under an uncertain environment. Moreover, the presented model is applied to a Canadian soybean company. Total profit, total job opportunities, and CO₂ emissions are three objective functions in this model. Furthermore, to facilitate the decision-making process for decision-makers, the multi-objective model is solved using the augmented ε-constraint method. The results show that the model can perform well in an uncertain environment. Finally, the obtained results indicate the model’s significance in incorporating all three sustainability dimensions for studying food supply chains.

Ebrahim Sharifi, Saman Hassanzadeh Amin, Liping Fang

A Novel Arc-Routing Optimization Model for Residential Waste Collection Problem with Time Windows, Vehicle-Arc Compatibility, and Fullness Information

Abstract

Waste collection is an important public health service provided by municipalities using public funds. Service must be provided within specific periods considering waste generation and the fullness of bins to be collected, otherwise uncollected waste can become hazardous to public health and the environment. Based on these concerns and owing to the complex nature of this problem, the decision to efficiently collect waste is an important and improvable study area. Recent years have witnessed several developments in the use of sensors for waste containers, enabling collection information regarding their current fullness. Consequently, implementing this information can enable the management of resources efficiently and effectively. We propose an extended novel arc-routing-based mathematical model for the residential waste collection problem by considering a heterogeneous vehicle fleet, fullness information of the bins, vehicle-arc compatibility, driving limits, and lunch-rest breaks planning in the Waste Collection Vehicle Routing Problem with time windows. The objective here is to minimize the total collection cost consisting of total traveling cost, as well as the expected penalty cost due to overfilling of the uncollected bins, utilizing information regarding current fullness via sensors. The proposed model is tested using IBM OPL CPLEX and analysis of results from test cases is provided.

Selen Burcak Akkaya, Mahmut Ali Gokce

Demand Forecasting Using Machine Learning and Deep Learning Approaches in the Retail Industry: A Comparative Study

Abstract

Demand forecasting is one of the crucial issues in the retail industry in terms of minimizing costs, setting correct inventory levels, optimal use of inventory space, and reducing the out-of-stock problem. Predicting future demand accurately is a challenging task for retailers and wholesalers because of sudden changes in demand levels, lack of historical data, new trends, and seasonal demand spikes. This paper presents a comparative analysis of Machine Learning (ML) and Deep Learning (DL) techniques (i.e., Random Forest, Gradient Boosting Regression, and Long Short-Term Memory) to forecast the product demand, using large amounts of time series historical data. The forecasting models’ performance and accuracy are evaluated by comparing Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results indicate that Random Forest is more efficient and more promising than the other considered techniques in this study due to its prediction accuracy.

Asma ul Husna, Saman Hassanzadeh Amin, Ahmad Ghasempoor

Demand Forecasting of Spare-Parts Using the Data Mining Techniques

Abstract

Nowadays, many businesses use traditional inventory management techniques. Proper inventory management techniques save businesses from additional costs. Make traditional and experience-based forecasting is not enough to manage inventory. Collecting, storing, and interpreting data is vital for the future of businesses. Vehicle maintenance is necessary for safe, comfortable, and accessible public transportation services. The quality of the vehicles is crucial for the satisfaction of passengers. Spare parts that are out of stock cause delays in the maintenance of the vehicles and the inability to provide service. On the other hand, spare parts purchased more than needed cause increasing holding costs and unnecessary expenses. The increase in spare part prices after the pandemic affects public transportation negatively, especially in our country. That is why demand forecasting is much more important today. The majority of public transport vehicles are buses. Filters are one of the most common spare parts affecting the operation of the buses among the spare parts. There are so many filter types in the business where the study was conducted. The aim of the study is to minimize the loss with the proposed inventory management practices and to ensure that the maintenance of the buses is done at the right time. In the study, the needs of filters were calculated using “Linear Regression”, “Support Vector Regression”, “Neural Network” and “Random Forest”. The accuracy of the techniques used was proven by using real data.

İlker Mutlu, Gözde Ulutagay

Evaluating the Quality of Water & Performance of WTPs in Iraq Using RII & TOPSIS Methods

Abstract

The quality of drinking water is always considered the highest significant field for researchers worldwide as it has a direct impact on the population’s lives. In Fallujah city - Iraq, this research was conducted to evaluate the water quality & the overall performance of three water treatment plants WTPs. A survey was designed with a specific question related to the Water Quality (WQ) generally and the WQ Parameters (WQPs) particularly to identify the significance or the impact of the number of fourteen parameters that have a direct impact on the quality of drinking water. The survey was carried out with 32 experts working in the same field. The assessment of the WQPs in the survey was conducted based on the Likert scale of 1–7. The RII calculation was implemented on the primary weights to extract the more reliable weights required for the TOPSIS method. Then, the TOPSIS was executed using Octave software where three variables were identified and created which represent the decision matrix taken from the WQ testing results of these WTPs, the final weights generated using RII values, and the beneficial & non-beneficial values that were identified using the water quality parameters scale prepared for this research. The results of the TOPSIS method reveal that the final ranking over the six months showed that WTP-2 got the highest ranking for January and February 2021, while for March, April, May, and June WTP-3 got the highest ranking than WTP-1 & WTP-2.

Ayad Al-Mohammedi, Gozde Ulutagay

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