Advances in Best-Worst Method

Best-Worst Method (BWM) is a pairwise comparison-based multi-criteria decision-making method to elicit the relative importance of the criteria (and alternatives) from a decision-maker. While most elicitation methods are based on only one reference point, BWM is based on two reference points (the most important: best; and the least important: worst) which are chosen by the decision-maker and used to conduct the pairwise comparisons. This mechanism is a salient feature of BWM that is rooted from behavioral decision-making theories and leads to more reliable results. In this study, experimental analysis is used to show how the use of these two reference points could mitigate possible anchoring bias which is inherent in judgments provided by decision-makers. First, three hypotheses are formulated to compare the weights coming from BWM with the weights calculated based on only one reference point. Two experimental analyses are used to test the hypotheses. The results clearly show that using one reference point leads to anchoring bias. The biases created by the two reference points are in opposite direction. This implies that using the two reference points in BWM optimization model could cancel out the impact of anchoring bias.

In the Best-Worst Method (BWM), the criteria weights are typically characterized by intervals, each value in the interval representing an optimal weight for the associated criterion according to the preferences of the decision-maker. While the intervals can potentially provide the DM with more information, it makes it challenging to process the weights of a group of DMs, e.g., for computing the aggregated weights or evaluating a set of alternatives with respect to the interval weights. The problem compounds when the performance matrix of the alternatives is also acquired by the BWM. This paper presents a Monte Carlo approach to address these shortcomings. First, a Monte Carlo approach is developed to compute the aggregated interval weights for group decision-making problems, as well as the extent to which a criterion is more important than another based on the preferences of the group. Second, another Monte Carlo method is developed to evaluate and compare the alternatives based on the interval weights. The experiments validate the applicability of the proposed approach for the BWM.

Finding a suitable item among thousands or even millions of items on e-commerce websites is a cumbersome task. Recommender systems are designed as a solution to this challenge. A decent recommender system helps the customers to find items matching their taste and preferences. This paper suggested that the clusters of multi-criteria decision-making (MCDM) weights can be used as a representation for the diversity of priorities in society. The weights are computed using the Best-Worst Method (BWM). A customer is assigned to a cluster of weights based on his/her former orders. During the next step, the probability of buying an item is computed. It has been discussed why the proposed model is suitable for real-world recommender systems. A general guidance on practical implementation is also provided. A case study of fifty-nine students on their preferred criteria of mobile and laptop will be analyzed to investigate the validation of the framework.

Nowadays, multi-criteria decision making (MCDM) problems are usually defined under changing contexts in which the emergence of uncertain information is common. Under these circumstances, linguistic information and computing with words (CW) processes have been successfully applied for modelling such uncertainty and computing the decision results. Particularly, the linguistic 2-tuple model presents important benefits both readability and precision points of view. In the MCDM resolution process, the experts’ preferences elicitation task plays a key role. The Best-Worst method (BWM) was proposed to solve some behavioral errors in similar MCDM methods and, consequently, reduces the number of pairwise comparisons and inconsistency in such a task. In the classical BWM, the experts use a numerical scale to provide their assessments. Lately, several BWM extensions under uncertain contexts have been proposed, but they still present drawbacks regarding the readability of the results. Hence, this contribution aims to introduce an BWM extension under a CW approach based on the linguistic 2-tuple model to model uncertainty, accomplish accurate computations and obtain understandable results. Furthermore, a novel consistency ratio to measure the experts’ preferences consistency is proposed. Finally, the proposal is applied to an illustrative MCDM problem.

The entrepreneurship ecosystem is a fundamental approach for policy makers to improve the performance of start-ups. The main focus of this paper is on how the entrepreneurship ecosystem contributes to the performance of start-ups and how the entrepreneurship ecosystem can be evaluated. Using Bayesian Best-Worst Method, we examine the importance of different entrepreneurship ecosystem attributes on entrepreneurial success (contains four measurements: profitability, sales growth, employment growth, and market share) in the Netherlands in the agri-food industry. Our findings highlight that, of the attributes, Talent has a significant importance for profitability, sales growth and employment growth. In other words, our results show the impact of Talent on effectiveness of ecosystem. Moreover, evaluating the current performance of entrepreneurship ecosystem and using Importance-Performance analysis showed that the Dutch agri-food entrepreneurship ecosystem is very successful and effective.

The increase in urbanization and developments in the production industry has led to rapid progress in the construction sector. Many new strategies are developed in the industry to reduce costs and improve building quality. In recent years, the necessity of sustainable construction practices comes to the fore to renew the building stock damaged as a result of natural disasters and reduce the cost concerns that arise in this situation to a reasonable level. Due to limited resources and environmental concerns, researchers and practitioners have begun to develop sustainable building materials. The problem of selecting these materials when constructing a new building is vital. In particular, depending on the sector's rapid growth in Turkey, it is becoming more and more important to select the best sustainable construction material. Therefore, this paper proposes a model to evaluate the most appropriate sustainable construction material via two multi-attribute decision-making (MADM) methods called “Bayesian Best-Worst Method (BWM) and Simple Additive Weighting (SAW)”. Initially, the criteria derived from existing literature were evaluated with the aid of construction sector-based respondents and extra information about the interrelationship between the criteria were determined by credal ranking in Bayesian BWM. Then, via SAW, the most appropriate material was selected among a set of alternatives. Two cases regarding sustainable insulating material selection are considered for the demonstration of the proposed MADM model.

Risks due to passenger flow have become an important factor affecting the operational safety of urban rail transit (URT) systems in China. Although several studies have focused on the problems raised by the volume of passenger flow, few have studied potential risks caused by the imbalance of passenger flow in time and space. This paper designs a multidimensional, multilevel risk assessment framework of passenger flow in a URT system, including 3 levels (stations, lines and the network) and 27 indicators. Additionally, the BWM (best and worst method) is selected as the evaluation method, and then the optimal weights of each index are calculated through pairwise comparison with the best and worst indicators. Finally, with the Chongqing URT system as a case study, 1920 sets of data from April 2018 are obtained for four scenarios (weekends, holidays, workdays, and the days before a holiday). The evaluation results show that the passenger flow risks on workdays and the days before a holiday present obvious morning and evening peaks. The extreme value of the passenger flow risk at the station level appears during the evening peak on days before a holiday, and the extreme values of passenger flow risk on the line and network level appear during the morning peak of workdays. Further comparison shows that the trends of the passenger flow risk on weekends, holidays, workdays and the days before a holiday are consistent with the result from a single indicator (inbound passenger flow).

Resilient bridge infrastructure is a prerequisite for sustainable and uninterrupted transportation systems. Even though the transportation agencies are struggling and encountering numerous challenges to ratify resilient bridge infrastructure against natural disasters like earthquakes, floods, hurricanes, identifying and prioritizing the resilient bridge infrastructure factors against the seismic hazard is obligatory enhancing the bridge's endurance and effective recovery. At first, 15 resilience attributes under two major resiliency factors, i.e., reliability and recovery, were chosen based on the literature review for bridge infrastructure sustainability against earthquake hazard. An expert opinion has been taken to integrate the nonlinear, complex relationship between seismic resilience parameters for bridge infrastructure. Based on the expert’s opinion, the weightage of the seismic resiliency factors has been computed using the Best-Worst method. The estimated weightage of resilience parameters provides the vulnerable scenarios of bridge infrastructure against seismic hazard. Moreover, prioritizing the sensitive parameters will guide the policymakers in making the right decision for resilient bridge infrastructures against future seismic hazard.

Landside operations in the air cargo industry are subject to strongly fluctuating demand with daily peak- and off-peak hours, which result in delays, unreliability and high costs for the entire chain. The purpose of this study is to develop and evaluate (unassisted) off-peak hour pickup and delivery (OHP&D) schemes that are supported by decision-makers and improve the performance. The methodology combines value-focused thinking (VFT) and the Bayesian Best-Worst Method (BWM). VFT ensures that the actor-specific objectives of decision-makers are central throughout the entire decision process. BWM determines the objective weights based on actor-specific sets and evaluations. We find that the risk level is an important evaluation criterion for all actors in this industry and that weights vary significantly across actors. Nine possible OHP&D schemes were generated with the value-based approach. When aggregating the utilities of the individual DMs, the results show that an unassisted OHP&D scheme with dedicated transport, information sharing, a priority lane and Carriage paid to liability agreements is the preferred concept. This concept has the potential to decrease costs up to 65%, driven by a reduction in truck waiting hours of 63%.

Many cities around the world have started to make investments in smart city projects to provide solutions to these challenges. In terms of city management applications, it is an essential requirement to benefit from a subsidiary tool that helps cities demonstrate their ability to adapt to Smart City principles and maturity competencies with a systematic procedure. In this article, first, a literature search is used to determine the qualitative and quantitative criteria sets needed to ensure smart city maturity. Then the importance of these criteria set is determined by interviewing experts and conducting questionnaires. It is found that technology management is the best main criterion. Finally, a digital maturity model, prepared using the Best-Worst Method, is proposed for municipalities to measure how “smart” their cities are, and according to the value obtained as a result of this measurement, to meet the conditions of the concept of “smart city” in their cities and to increase their scores. To show how it is used in practice, a real case study is conducted in the city of Istanbul.

The Internet of Things (IoT), as one of the enablers of the Fourth Industrial Revolution, has inspired many innovative logistics and supply chain applications and will affect supply chain management. In the future, IoT will provide us with an infrastructure for the vast global network of physical objects that will give significant transparency to supply chain management. Despite its numerous benefits, various industries have not yet been able to take full advantage of IoT instruments in logistics and supply chain; because IoT implementation has many barriers. Therefore, this study aims to review the literature and identify the multiple barriers to the implementation of the Internet of Things in the food industry and then uses the Bayesian Best-Worst Method (BWM) to investigate their priority. Findings show that lack of internet infrastructure is the most vital barrier to the implementation of IoT in the food industry. This research can be helpful for managers who want to install IoT platforms in their business.

The current consumption of clothing and textile causes great environmental concerns in the society, especially textile waste. With increasing environmental awareness, textile industries have begun to consider environmental issues in their production. This study evaluated and prioritized the important environmental performance criteria related to textile industry. In this paper, a recently developed multi-criteria decision making (MCDM) method, namely Best-Worst Method (BWM) which can solve decision making problems based on limited pairwise comparisons, was used to evaluate the environmental performance criteria in textile industry in order to derive the weights of the criteria and priorities of the sub-criteria. This study used five environmental performance criteria and twenty sub-criteria selected from the literature and validated by experts. After that, the BWM calculated the importance of each criterion. The findings showed that raw material, chemical oxygen demand and fresh water are the most important criteria while fabric waste, methane and non-biodegradable material are the least crucial criteria. This research provided useful insights for stakeholders in textile industry so that they can benefit from the results to optimize their operations.

Nowadays steel industry is one of the industries that plays an essential role in countries’ growth. Today, the integration of sustainability in the steel industry’s supply chain has become a significant concern of industry owners and researchers. Therefore, this study aims to identify and evaluate supply chain sustainability attributes in the steel industry. The experts’ panel in this study consisted of 7 senior and middle managers selected by the snowball sampling method. In the first step, the literature reviewed to identify supply chain sustainability attributes that 16 attributes extracted. In the second step, by using the Fuzzy Delphi method and using experts’ opinions, the extracted attributes were screened and customized. Five attributes in the economic dimension, four attributes in the environmental dimension, and five attributes in the social dimension were identified. In the third step, by using the Bayesian Best Worst Method (BWM), the customized attributes were weighted and prioritized. The results showed that the economical dimension was determined as the most important sustainability dimension. Also, among all attributes of the problem, market share, profitability, and waste recycling were recognized as the most important ones, respectively.

In this era of technological upsurge, electrical and electronics waste management has become a noteworthy dedication for all developed and evolving economies. In this research, eight potential barriers for electronic waste (E-waste) management for an evolving economy namely, Bangladesh were identified by a review of literature and from feedback of face-to-face interviews with five E-waste management experts’ of Bangladesh. The best worst method was used to evaluate these eight barriers. The findings disclosed that “Absence of legal framework for E-waste management” has been the key barrier in the context of Bangldesh. The result of the research is expected to contribute to address the key barriers to E-waste management policy through identification and priortization of barriers by government policymakers.

In the era of digitalization, the biggest challenge is to eliminate barriers of Industry 4.0 (I4.0) adoption, which maybe even difficult to predict. Successful I4.0 technology implementation can boost productivity, achieve optimum resource management, and meet dynamic customer needs through customized mass production. Studies indicate that the drawbacks of I4.0 adoption vary significantly in different circumstances. To elaborate, new technology implementation may pass a smoother process in developed countries. Although, there might be some different facilitation in developing countries and emerging economies. This research aims to identify the barriers and challenges of I4.0 adoption and prioritize them. For this purpose, at first, a list of potential risks is recognized by a comprehensive literature review. Then, the Fuzzy Delphi method (FDM) is used to assess and finalize the related barriers. Subsequently, the barriers are weighted to the experts’ opinion. With the intention to increase the consistency of the study results in this state-of-the-art inquiry and consider the uncertainty and reliability of decision makers’ perspectives, Z numbers are incorporated into BWM. Finally, improved group Z-BWM is conducted to find the most relevant barriers in Goldiran (LG-Gplus) company, as a case study in Iran, an emerging economy. The results indicated that lack of financial resources has the highest importance. The result may help DMs and industrial managers in the home appliance industry in Iran to find out the actual barriers in implementing I4.0 technologies and focus their attention on how to obtain the significant advantages of the fourth industrial revolution by a successful I4.0 adoption.

The aim of this study is to discover the supreme and other most important criteria that count in decision making considering vital uncertainties associated with certain parameters, risks, and costs for individuals in order to select the right Covid-19 vaccine based on a set of remarkable criteria. A survey study for assessment according to the given most important criteria based on expert opinion is conducted through the Best-Worst Method (BWM). A form including pairwise comparison vectors was sent to the participants in order to reveal priorities against their subjective decision-making criteria for vaccine selection. The essence of the study addresses that the efficacy criterion has the highest score and it is followed by the other given criteria such as storage requirements, incorporated vaccine technology, and international acceptance criterion. Participants tend to prioritize the origin and price of the vaccine behind all other criteria. Long-sought Covid-19 vaccine and its alternatives with different disclosed criteria of them have led to increasing indecision of people who have an opportunity to choose individually and the government officials who are responsible for country-wide procurement and policymakers; as a result, criteria evaluation is a challenging task. To solve the mentioned multi-criteria decision-making (MCDM) problem, BWM is newly employed in vaccine selection problems and its robust approach reveals the subjective priority of the criteria.

COVID-19’s Infection and the mortality rates pushed the governments to impose lockdowns that caused huge economical losses. This pandemic also changed our social and personal life and causes severe psychological problems. Continuous mutations of the COVID-19 virus and unabated transmission rate made it unsustainable to continue the lockdowns. Discovery of suitable vaccine brings a glimmer of hope to the race against this virus. However, the real task is to manufacture, distribute and vaccinate the world’s entire population within a reasonable time. Considering the state of healthcare infrastructure and vaccine cold storage facilities, this is going to be a challenge. This paper collected the opinion of 17 decision makers representing the various levels of Covid-19 vaccination programme such as vaccine manufacturer and vaccine administrator. Then, Best Worst Method, a Multi-Criteria Decision Making method was applied to understand the critical factors for the success of this vaccination programme in India. This method elicited consistent pairwise comparisons from the decision makers. Results signify the immediate need to scale up the investment in the vaccine cold storage and the need to reduce vaccine wastage for the success of this vaccination programme.

A variety of factors, including qualitative opinions of stakeholders, choosing complexities, and the long-term consequences of each decision may significantly challenge the system development process. The present study attempted to give a clarification of the role played by Best-Worst Method (BWM, a Multi-Attribute Decision-Making (MADM)) within the framework (SYRCS) of problem structuring methods with a specific focus on payment systems in Iran. To this end, the problem was analysed step by step, and then an innovative problem-structuring methodology was proposed. The framework includes different methods from different paradigms: Critical Systems Heuristics (CSH) from Emancipatory Operations Research, Robust Analysis (RA) and Strategic Choice Approach (SCA) from Soft Operational Research, and BWM from quantitative and Multi-Attribute Decision-Making methods. Through the final stages of survey, the long-term consequences of each decision were quantified and evaluated using BWM to help researchers find the suitable options. Moreover, the representatives of each stakeholder were asked to assess each option using some specific and relevant criteria used to reach out the best options through the lens of shared value creation.

In research on soft and emancipatory operations research, the lack of attention to the measurement and analyzing the differences stands as one of the significant weaknesses of qualitative approaches. Tackling this weakness may be very effective, and clearly, the more we know about measures and differences, the easier we will make decisions at later stages. This study mainly aims to design a hybrid method to find the gaps in various systems that can be criticized and investigated by critical approaches. Options and critiques are explored in the Critical Systems Heuristics (CSH) method as one of the methods for investigating the current and ideal condition of the system. Nevertheless, many options and critiques obtained from CSH remain without further quantitative assessments and sometimes might not be comprehensively assessed. Even in some cases, the options presented in the ideal conditions are more unfavourable than the current situation, but they may be regarded as an option for improvement due to their critical aspect because of the mental bias of decision-makers. The proposed hybrid method weighs and assesses the options and critiques raised in each question (Boundary Group) achieved from CSH using the capabilities of the best-worst method (BWM), providing a tool for quantitative assessment and avoiding decision-makers bias.