Best-worst Multi-criteria Decision-making Method Research Articles

An MCDM optimization based dynamic workflow scheduling used to handle priority tasks for fault tolerance in IIOT

Cloud computing's prominence can be mainly attributed to its convenient on-demand service delivery via the internet. The difficulty in managing these requests and allocating them optimally is exacerbated by the growing number of users making them and the variety of criteria used to apply cloud resources. Some of these difficulties include scheduling work while considering the user's Quality of Service (QoS) preferences, ensuring adequate load balancing, and adapting to the ever-changing conditions of the cloud environment. As server capabilities evolve, static load balancers, which distribute requests using fixed ratios of available resources, fail to provide optimum throughput. It has been shown that no other scheduling algorithms offer an adaptive method that considers both load balancing and maximizing the QoS criteria. The authors of this study present a novel adaptive strategy for dealing with this issue by fusing the best-worst multi-criteria decision-making method (BWM) with the compromise ranking method (VIKOR). Task priorities are determined by using the VIKOR method as a decision-maker. Numerical experiments are used to verify the validity of the suggested method, and the methodology is compared to other scheduling methods in terms of a variety of performance criteria. The simulation findings show that the presented method outperforms its rivals regarding throughput, makespan, waiting time, virtual machine (VM) utilization, and VM usage cost across the board of simulated experimental settings.

A model for green-resilient supplier selection: fuzzy best-worst multi-criteria decision-making method and its applications.

Supplier selection is regarded as the primary goal of supply chain management (SCM) because it affects its performance, productivity, pleasure, flexibility, and system speed in lockdown. A new method is proposed based on a multi-stage fuzzy sustainable supplier index (FSSI). Experts can use the triple bottom line (TBL) criteria to select the best supplier. In addition, the worst method is proposed based on trapezoidal membership and fuzzy membership functions, which can cover uncertainties and ambiguous environments. Because it collects the related criteria and sub-criteria and uses a direct fuzzy methodology, this research has impacted the SCM literature because it helps solve previous expert methods' computational difficulties. In addition, an ordered mean integration representation method has been implemented to prioritize the selection of the best supplier (SS) based on the sustainability performance of the best supplier, which improves the selection accuracy compared to the previous ranking method. This study can be used as a benchmark to determine which supplier is the best in sustainability. To provide the superiority and broad applicability of the proposed model, a practical case study was completed. On the other hand, the COVID-19 pandemic harms productivity, company performance, and selecting the best suppliers based on sustainability performance. The lockdown situation caused by the COVID-19 pandemic hurts company performance and management.

An interval type-2 fuzzy enhanced best–worst method for the evaluation of ship diesel generator failures

The environmental destruction in our century necessitates the implementation of more safety-based rules and the strict implementation of failure analysis, especially in the maritime sector. The generator system, considered the ship's powerhouse, is expected to be ready and well-maintained for efficient and safe operations. This study proposes a new methodology to analyze ship generator failures. This method includes a version of the best-worst multi-criteria decision-making method enriched with interval type-2 fuzzy logic (IT2FE-BWM). Enrichment with interval type-2 fuzzy numbers shows that a parameter about how much the decision-maker trusts the pairwise comparisons are added to the model. Thus, a more sensitive methodology has been proposed for the confidence of the maritime sector evaluators. A solid decision mechanism has been developed to prioritize generator failures. In addition, a comparison study with some other BWM derivatives and a creative sensitivity analysis were added to this study and its robustness was tested.

Multi-criteria decision-making methods for the evaluating of a real green supply chain in companies with fast-moving consumer goods

ABSTRACT This research aims at determining the main criteria of the green supply chain management (GSCM) and providing a framework to assess and compare the greenness of dairy industries. There has been a research gap in evaluating the greenness of this industry in Iran to be bridged. In the first phase, through studying literature and acquiring academics and experts’ opinions, the framework is classified into 8 criteria and 32 sub-criteria as enablers and relevant to the GSCM, considered as base subjects in preparing questionnaires. The second phase uses experts' opinions and through the Fuzzy Delphi method, 26 sub-criteria are screened, confirmed, and employed. Also, the cause-and-effect criteria and sub-criteria (C&SC) are identified by the Fuzzy DEMATEL method. In this context, the fuzzy best-worst multi-criteria decision-making method (FBWM) indicates the most and least important C&SC. Then, by using a Weighted Aggregated Sum Product Assessment Method (WASPAS), the considered companies are evaluated and ranked in terms of greenness, followed by some analyses over the results of real case studies. Ultimately, some managerial insights are extracted, which are fundamental for being regarded green and play the central role in making the companies much more functional with lots of amazing and needful results for them..

An integrated decision support system for multi-target forecasting: A case study of energy load prediction for a solar-powered residential house

This paper proposes a data-driven framework for multi-target forecasting. We apply the proposed framework to forecast energy load in solar-powered residential houses using meteorological and temporal inputs. We adopt five predictive models of gradient boosting, least angle regression, multi-layer perceptron, random forest, and partial least square as well as three information fusion techniques of simple average, proportionate mean squared error, and learned weights, and compare their forecasting accuracy. We also perform a comprehensive sensitivity analysis and extensive tuning of the parameters of these models. The results of the energy load application reveal that the learned weights information fusion technique generates the most accurate predictive model. The results unfold that energy consumer can reliably plan for their forecasted energy loads considering expected meteorological conditions. Finally, we apply the best-worst multi-criteria decision-making method to find the weight of importance of target features using energy experts’ opinions and the percentage overall load of each output. The proposed framework can be applied to any multi-target forecasting problem.

Safety Assessment of Transport Aircraft Heavy Equipment Airdrop: An Improved STPA-BN Mechanism

Aviation safety has been an eternal theme, and safety assessment is taken as the important method to assess the aircraft safety, but it is difficult to assess the safety of a heavy equipment airdrop from a transport aircraft due to mission complexity and strong coupling. This study uses an improved STPA-BN methodology to assess the safety of transport aircraft heavy equipment airdrop. To this purpose, qualitative safety analysis is performed by STPA, and then quantitative safety assessment is performed by BN. In contrast to the-state-of-the-art, the distinguishing feature of the proposed design lies in the introduction of DS evidence theory, which makes the probability of nodes in BN a <i>priori</i>. More precisely, best-worst multi-criteria decision-making method (BWM) is skillfully incorporated into the DS evidence theory so as to tackle the conflict issue that exists in most existing designs. Importance analysis is utilized to determine the importance of the different risk nodes. A case of transport aircraft heavy equipment airdrop mission is used to test the effectiveness of the proposed method.

A novel modified fuzzy best-worst multi-criteria decision-making method

One of the latest multi-criteria decision-making methods is best-worst method (BWM). In the procedure of BWM, decision maker (DM) identifies the most and the least important criteria namely, best and worst. Thereafter, DM identifies the degrees which he believes the best criterion is better than the other criteria, and identifies the degrees which he believes the other criteria are better than the worst criterion. Because of uncertainties in comparisons due to using linguistic variables for pairwise comparisons and also the lack of complete information, the crisp values of pairwise comparisons cannot appropriately model the problems. This paper develops the BWM for considering fuzzy pairwise comparisons (FBWM) by proposing a new fuzzy mathematical model which yields crisp weights from a fuzzy pairwise comparison matrix. Unlike to some previous papers that obtains fuzzy weights from fuzzy pairwise comparison matrix, the crisp weights of the proposed method of this paper eliminates the supplementary aggregation of fuzzy weights and ranking procedures. Moreover, the proposed method avoids obtaining the different ranking results due to the different ranking procedures of fuzzy numbers.Another outstanding advantage of this paper is that the obtained weights of the proposed method better satisfy the initial judgments compared to previous methods, while as we know, the satisfaction of the initial judgments is essential for pairwise comparison judgments. This paper presents several numerical examples to prove the good performance and the merit of the proposed method. According to the provided numerical examples, the proposed method of this paper absolutely outperforms the two well-known previous methods.

An approach to using the best-worst method for supporting sustainability reporting decision-making in SMEs

This paper proposes a novel methodological approach to help decision-makers in SMEs evaluate and prioritize sustainability reporting (SR) standards. It can thus help SMEs to overcome some of the problems they encounter when initiating SR. In order to explore this decision in more depth, a set of decision criteria are identified, reflecting the costs and benefits that could be derived from SR adoption. The methodological approach proposed is based on the best-worst multi-criteria decision-making method and is tested on a sample of Spanish SMEs. The results highlight the relevance of SR for the legitimacy and reputation of the firm, with GRI ranking first among the SR alternatives. This research helps to strengthen the link between academia and business, by developing a tool with which firms can select a reporting standard. This is especially relevant for SMEs given their relevant role in national economies and their general failure to disclose such information.

Sustainable waste disposal technology selection: The stratified best-worst multi-criteria decision-making method

Waste disposal technology selection is a key problem in the field of municipal solid waste (MSW). This decision may have long-term impacts on environmental development and economic growth. The literature suggests using multi-criteria decision-making (MCDM) methods to address this problem. MCDM techniques commonly require decision makers to assign weightings of importance to the decision criteria based on which, the available technologies are ranked. However, this technology selection problem is concerned with selecting a technology to be used for a relatively long period of time. It is important to take into consideration any uncertainty the decision maker may have with regard to the weightings of the criteria in the future. To take this uncertainty into consideration, this study suggests utilizing a recently developed MCDM technique, namely the stratified MCDM. This technique is designed to help decision makers structure the uncertain future through the consideration of a set of states, which are placed in different strata. The paper shows how the stratified MCDM technique in combination with the best-worst method (labelled stratified BWM) can be employed to compute the ranking of the available technologies. This research is expected to stimulate future applications of the stratified BWM to facilitate long-term decision making.

Robust Optimization of Best-worst Multi-criteria Decision-making Method

The Best-worst multi-criteria decision-making method can determine optimal weight value of each criteria. It uses two vectors for pairwise comparisons in multi-criteria decision-making problem. This paper improves the original method from the perspective of robust optimization. Four robust counterpart constraints instead of two linear constraints in original optimization model are proposed. The decision-making problem can divide into full consistent and non-full consistent problems by classifying parameter value. We can achieve a unique set of interval solution in full consistent decision-making problem. Non-full consistent problem can result in multiple sets of optimal interval solution. The result which we get from this method is more effective than the original method. Each criterion can achieve optimal weight interval value. Then we take quantile value of each interval as optimal weight. This is effectively illustrated in the numerical test at the end of the paper.

Selecting the best stabilization/solidification method for the treatment of oil-contaminated soils using simple and applied best-worst multi-criteria decision-making method

Oil-contaminated soils resulted from drilling activities can cause significant damages to the environment, especially for living organisms. Treatment and management of these soils are the necessity for environmental protection. The present study investigates the field study of seven oil-contaminated soils treated by different stabilization/solidification (S/S) methods, and the selection of the best treated site and treatment method. In this study, first, the ratios of consumed binders to the contaminated soils (w/w) and the treatment times for each unit of treated soils were evaluated. The ratios of consumed binders to the contaminated soils were between 6 and 10% and the treatment times for each unit of treated soils were between 4.1 and 18.5 min/m3. Physicochemical characteristics of treated soils were also determined. Although S/S methods didn’t change the water content of treated soils, they increased the porosity of soils. Unexpectedly, the cement-based S/S methods didn’t increase the pH of the treated soils. The highest and the lowest leaching of petroleum hydrocarbons was belonging to S/S using diatomaceous earth (DE) and the combination of Portland cement, sodium silicate and DE (CS-DE), respectively. The best acid neutralization capacity was obtained for soils treated using the combination of Portland cement and sodium silicate (CS). Based on the best-worst multi-criteria decision-making method (BWM-MCDM), the soils treated using CS-DE was select as the best. The BWM-MCDM can be used as an effective tool for the selection of the best alternative in all areas of environmental decontamination.

An extended best-worst multi-criteria decision-making method by belief functions and its applications in hospital service evaluation

In the healthcare industry, hospital service quality assessment is an important work, which can not only promote medical service providers to improve the status quo of the industry, but also ensure patients to enjoy better service. Since hospital service evaluation involves a great many qualitative and quantitative factors, it is a complex multi-criteria decision-making (MCDM) issue. In the real-life MCDM problems, the ambiguity of information expression often occurs because of the lack of complete knowledge, and the vagueness of decision makers in qualitative judgment is quite common, so the crisp values of criteria may be inadequate to deal with such complex situations. To achieve the evaluation of hospital service quality under uncertain environment, in this paper, the traditional best-worst method (BWM) is extended based on belief function theory (BFT) that has powerful ability of uncertain expression and reasoning. This paper describes in detail the implementation process of evidential BWM and the proposed hospital service quality evaluation framework based on it. A case study is presented to evaluate hospital service quality based on the constructed model. The results manifest the effectiveness of the proposed method and offer an insightful reference for the evaluation of healthcare industry.

An adaptive scheduling approach based on integrated best-worst and VIKOR for cloud computing

Cloud computing is a new distributed environment to provide on-demand services through the internet and has become popular due to this specific feature. By increasing the count of user requests and different criteria in applying the cloud resources, there exist challenges for managing these requests and their optimal allocation. Some of these challenges consist of considering the dynamic nature of the cloud environment, finding the values of the different criteria and the scheduling tasks by considering the user’s Quality of Service (QoS) preferences, load balancing, etc. Static load balancers distribute requests based on pre-known server capability ratios and these models do not produce optimal throughput when the server capabilities change overtime. None of the other scheduling algorithms is able to provide an adaptive approach to consider both of the load balancing and optimizing the QoS requirements. To solve this problem, this paper proposes a new adaptive approach based on a combination of the concept of the best-worst multi criteria decision-making method (BWM), and compromise ranking method (VIKOR). The VIKOR method is implemented as a decision maker to specify the task priorities. The proposed approach is validated with numerical experiments and is compared with the existing scheduling algorithms over different performance metrics. The simulation results indicate that the proposed approach improves the performance metrics like throughput, makespan, waiting time, virtual machine (VM) utilization and VM usage cost for all considered experimental scenarios in comparison with its counterparts.

Bioethanol supply chain network design considering land characteristics

Biomass is becoming an increasingly widespread source of energy. Yet land, as one of the most important resources in biomass production, is surprisingly understudied in the literature of biomass supply chain planning. This study proposes a novel framework that combines the literature of bioethanol supply chain design with agricultural land planning to simultaneously address optimal supply chain planning and sustainable land use in a bioethanol supply chain. A bi-objective mixed-integer linear programming (MILP) model is proposed to formulate the optimal design and planning of a bioethanol supply chain network considering competition of food and biomass feedstock over the available croplands. The proposed model is capable of making strategic decisions (i.e. locations and capacities of facilities, sourcing and allocation of biomass feedstocks to biorefineries), along with some tactical decisions (i.e. land planning, inventory and production of both biomass feedstock and bioethanol). The model incorporates the two objectives of minimum cost and maximum suitability of crops with their assigned croplands. A novel integration of the FAO framework, the best-worst multi-criteria decision-making method, PROMETHEE II and GIS is used to determine the suitability of available croplands according to the croplands’ soil and topographical characteristics. The performance of the proposed model is demonstrated through a multi-feedstock bioethanol supply chain in Fars province, Iran. It is concluded that the proposed integrated land planning-network design framework outperforms hierarchical approaches in which network design and land planning problems are solved separately in a sequential manner. Also, the case study shows that conditional on implementing second generation bioethanol production, Fars province has the potential to satisfy three percent of the fuel demand for transportation in the country.

Presenting an integrated BWM-VIKOR-based approach for selecting suppliers of raw materials in the supply chain with emphasis on agility and flexibility criteria (Case study: Saipa Corporation)

In this study, to select suppliers of raw materials in Saipa Automotive Corporation as one of the largest factories in Iran, environmental criteria, flexibility and agility criteria are considered and some sub-criteria are also considered for each criterion. The sub-criteria include green design, clean technology, environmental performance, agility in operational systems, market agility, logistics agility, product flexibility, flexibility in transportation, resource flexibility. It should be mentioned that the said criteria may need modification and revision due to opinions of experts during the research implementation. Therefore, main variables for the issue of identification of criteria affecting selection of suppliers are studied with regard to environmental factors within the organization. In order to rank suppliers and to select the best option based on a best-worst multi-criteria decision-making method (BWM) and VIKOR-based approach was used. According to the calculations based on the proposed process in this study and the information about the desired criteria, 7 suppliers were selected as the best options. As the approach presented in this study has combined two worst-best method to determine weights and VIKOR method for final ranking of options, this approach can be also used in other studies.

The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next?

After the first paper regarding the Best Worst Method (BWM) was published in Omega in 2015 (J. Rezaei, Best-worst multi-criteria decision-making method, Omega 53 (2015) 49–57), it has attracted many scholars’ attention due to the efficiency of this method in reducing the times of pairwise comparisons and the good performance in maintaining consistency between judgments. Lots of researches related to this method have been published over the past several years. This paper concentrates on the state-of-the-art survey of the BWM based on the in-depth analysis over the publications concerning this method published from 2015 to 26th, January 2019. This paper intends to answer five questions about the BWM: (1) How does this method perform in bibliometric analysis? (2) Why to propose this method and what is it? (3) Which integrations that the BWM were focused on and which areas did they apply to? (4) What extensions of this method were investigated? (5) What are the challenges and future research directions regarding this method? In view of the fact that the research on this method is still in infancy, this paper has guiding significance for the later research related to the BWM. From the theoretical point of view, the reasonable value of consistency ratio, the inconsistency improving methods, the uncertain extensions of the BWM and the techniques for solving multi-optimality model in the BWM are good research issues that need to be further investigated in the future. From the perspective of application, the software packages for this method, the various integrations of this method, the wider application areas, and the international cooperation on this method are good topics to consider.

Supply Chain Partner Selection under Cloud Computing Environment: An Improved Approach Based on BWM and VIKOR

The wide application of cloud computing technology makes supply chain partner share information effectively and increase corporate profits. However, in cloud computing environment, the evaluation process of supply chain partner selection is dynamic and fuzzy, and its evaluation index is uncertain. To solve these problems, this paper proposes a new method combed with rough number, best-worst multicriteria decision-making method (BWM), and compromise ranking method (VIKOR). Firstly, Rough number is used to integrate data from different evaluation matrix. Secondly, rough number improves a new method, BWM, to get RBWM criteria weight methods. Thirdly, the rough numbers are introduced into VIKOR and combined with RBWM to evaluate and sort the supply chain partners. Finally, an example is given to demonstrate the rationality and feasibility of the method.

A Hybrid Fuzzy BWM-COPRAS Method for Analyzing Key Factors of Sustainable Architecture

Sustainable development by emphasizing on satisfying the current needs of the general public without threating their futures, alongside with taking the environment and future generations under consideration, has become one of the prominent issues in different societies. Therefore, identifying and prioritizing the key factors of sustainable architecture according to regional and cultural features could be the first step in sustaining the architecture as a process and an outcome. In this paper, the key indicators of the environmental sustainability in contemporary architecture of Iran has been identified and prioritized. This study has been performed in three phases. First, identifying key factors of environmental sustainability according to the experts&rsquo; point of view and transforming the collected data to triangular fuzzy numbers. Subsequently, the best-worst multi-criteria decision-making method (henceforth BWM) under grey system circumstances has determined the weights and priority of the identified criteria. Eventually, identified key factors were prioritized by the complex proportional assessment method (hereafter COPRAS) under the condition of fuzzy sets. The results indicate that the key factors of creating engagement between buildings and other urban systems has the highest priority in the built environment sustainability in contemporary architecture and proving building management systems has the lowest.

Fuzzy best-worst multi-criteria decision-making method and its applications

Considering the vagueness frequently representing in decision data due to the lack of complete information and the ambiguity arising from the qualitative judgment of decision-makers, the crisp values of criteria may be inadequate to model the real-life multi-criteria decision-making (MCDM) issues. In this paper, the latest MCDM method, namely best-worst method (BWM) was extended to the fuzzy environment. The reference comparisons for the best criterion and for the worst criterion were described by linguistic terms of decision-makers, which can be expressed in triangular fuzzy numbers. Then, the graded mean integration representation (GMIR) method was employed to calculate the weights of criteria and alternatives with respect to different criteria under fuzzy environment. According to the concept of BWM, the nonlinearly constrained optimization problem was built for determining the fuzzy weights of criteria and alternatives with respect to different criteria. The fuzzy ranking scores of alternatives can be derived from the fuzzy weights of alternatives with respect to different criteria multiplied by fuzzy weights of the corresponding criteria, and then the crisp ranking score of alternatives can be calculated by employing GMIR method for optimal alternative selection. Meanwhile, the consistency ratio was proposed for fuzzy BWM to check the reliability of fuzzy preference comparisons. Three case studies were performed to illustrate the effectiveness and feasibility of the proposed fuzzy BWM. The results indicate the proposed fuzzy BWM can not only obtain reasonable preference ranking for alternatives but also has higher comparison consistency than the BWM.

Best-worst multi-criteria decision-making method: Some properties and a linear model

The Best Worst Method (BWM) is a multi-criteria decision-making method that uses two vectors of pairwise comparisons to determine the weights of criteria. First, the best (e.g. most desirable, most important), and the worst (e.g. least desirable, least important) criteria are identified by the decision-maker, after which the best criterion is compared to the other criteria, and the other criteria to the worst criterion. A non-linear minmax model is then used to identify the weights such that the maximum absolute difference between the weight ratios and their corresponding comparisons is minimized. The minmax model may result in multiple optimal solutions. Although, in some cases, decision-makers prefer to have multiple optimal solutions, in other cases they prefer to have a unique solution. The aim of this paper is twofold: firstly, we propose using interval analysis for the case of multiple optimal solutions, in which we show how the criteria can be weighed and ranked. Secondly, we propose a linear model for BWM, which is based on the same philosophy, but yields a unique solution.