International Journal of Information Technology & Decision Making

Due to increasing environmental regulation and customers’ demand for environmentally friendly products, organizations have been required to adopt sustainable manufacturing practices by implementing clean technology (Cleantec) to manufacture green products. By adopting environmental practices, organizations can also achieve qualitative and quantitative benefits that help them remain competitive in the market while meeting governmental environmental policies, such as lowering energy and the cost of materials. The significant number of articles addressing sustainability in manufacturing published in the past few years attests to the importance of the topic. However, not many studies have been developed to understand the decision-making process in sustainable manufacturing. Therefore, the objective of this paper is to conduct a systematic literature review on the application of multi-attribute decision-making (MADM) methods in sustainable manufacturing. A total of 158 papers, published between 2009 and 2018, met the criteria set in the research methodology. The 158 papers were then analyzed and classified into seven categories: (i) SM domain, (ii) activity within the organization, (iii) decision goals, (iv) decision-makers involved (group or individual), (v) uncertain data, (vi) SM aspects (social, environmental, and economic), and (vii) MADM methods. Among the results, we identified that AHP is the most applied MADM method and, regarding the activities of the organization, MADM methods have been the most frequently applied to strategy management and supply chain. We also identified a tendency to consider uncertain and imprecise data in the decision-making process. Another result is that all the three domains — economic, environmental and social — were considered in most of the papers, followed by the combination of the economic and environmental perspectives. In the conclusion, some recent trends and future research directions are highlighted.

A water utility requires myriads of data for effective decision-making. As the sources and ranges of data are becoming increasingly complex, the use of a metadata framework can play a significant role in effective data management. Using case study method, this research analyzed data needs of a water supply system in a small town in South Australia and designed a demo portal of a metadata framework. As part of the case study, the project team undertook a broad investigative approach using focus group (interviews), observation, exploration of potential data sources, identification of knowledge leaders and information technology systems. The metadata framework comprised two separate but interconnected metadata groups, (1) metadata elements to describe the metadata source and (2) metadata elements to describe the datasets held in each data source. The metadata framework was populated to describe data sources and data held in each of the sources. The data catalogue created by this process showed that it was accomplishable and appropriate to describe data sources and datasets via a metadata framework.

Context. This paper deals with bilateral joint decision making in supply chains, and more specifically focuses on coordinating the decisions taken by the supplier and the producer in lot sizing. Research gap. Previous existing works in lot sizing have modeled the coordination task as a bi-level optimization problem. Unfortunately, the bi-level model causes a hierarchy between the two actors by making the leader imposing the decisions that suits his/her interests to the follower. This induces a significant conflict of interest between the two stakeholders because the leaders benefit is always greater than the follower’s one. Objective. The main goal of this work is to attenuate the conflict of interest issue between both actors by proposing a multi-objective model that alleviates the hierarchy and creates a win–win situation. Method. We propose an effective multi-objective lot sizing model, called Supplier-Producer Multi-Objective Lot Sizing (SP-MOLS); that alleviates the hierarchy between the actors’ objectives by assigning them the same importance degree and hence optimizing them simultaneously. The resolution of our SP-MOLS model using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), as an effective meta-heuristic search engine, provides a set of trade-off solutions, each expressing a compromise degree between the two actors: the supplier and the producer. Results. To validate our approach, we use five test problems each containing 100 instances with a planning horizon of 10 periods and we analyze the obtained trade-off solutions using the compromise degree and the gap between costs as main consensus metrics. The obtained results reveal that a small sacrifice in the leader’s benefit could produce a significant improvement in the follower’s one. For instance, a 10% increase of the producer’s cost may generate a 42% decrease in the supplier’s one. Reciprocally, a 0.4% increase of the supplier’s cost may generate a 49% decrease in the producer’s cost. Method algorithmic improvement. As solutions of interests for both stakeholders are usually located within the extreme regions of the Pareto front, we propose NSGA-II with Focus on Extreme Regions (NSGA-II-FER) as a new variant of NSGA-II that focuses the search in the extreme regions of the Pareto front thanks to a modified crowding measure that is adaptively managed during the evolution process. This variant has shown its ability to eliminate dominance-resistant solutions and thus to come up with better extreme regions. Based on the experimental results, NSGA-II-FER is shown to have the ability to provide the decision makers with more convergent and more diversified extreme non-dominated solutions, expressing better trade-off degrees between both actors’ costs. Managerial implications. The promising results obtained by our proposal encourage decision makers’ to adopt a multi-objective approach rather than a bi-level one. From our personal perspective, we recommend running the three models (the multi-objective model and the two bi-levels ones); then analyzing the solutions of all models in terms of compromise degrees and logistic costs. This would allow both actors to observe how the hierarchy incurred by the bi-level models increases conflicts, while the multi-objective one generates solutions with much improved consensus degrees. Such observations will convince the supply chain stakeholders to adopt our multi-objective approach, while keeping an eye on the bi-level models’ solutions and the consensus degrees. Finally, we also recommend focusing on the extreme regions of the Pareto front since they contain rich solutions in terms of consensus. Such solutions are more convincing in the negotiation process and thus could lead to better win–win situations.

Inventory cost control is an essential factor in supply chain management. If the supplier’s inventory is insufficient, then the chance to trade the product will be reduced. The manufacturer’s inadequate material inventory will have an effect in termination of production, delays, and a waste of resources and time. On the other hand, postponed transportation will certainly raise costs such as transportation costs and cancellation of orders. Therefore, the operation costs of enterprises will be more, which will lower profits. In conventional supply chains, inventory costs control is not feasible for the view of the entire supply chain. The main intent of this paper is to plan for intelligent inventory management using blockchain technology under the cloud sector. The inventory management of the supply chain includes “multiple suppliers, a manufacturer, and multiple distributors”. The proposed inventory management models consider some significant costs like “transaction cost, inventory holding cost, shortage cost, transportation cost, time cost, setup cost, backordering cost, and quality improvement cost”. This multi-objective cost function is minimized by a novel hybrid optimization algorithm; the concept of WOA is integrated to produce the new algorithm which is termed as Whale-based Multi Verse Optimization (W-MVO) algorithm. For securing the data of distributors, using blockchain technology in a cloud environment helps from the leakage of data to other unauthorized users. Once the cost is reduced in all aspects based on the proposed hybrid optimization algorithm, the distributer will store the concerning data in the blockchain under the cloud sector, where each distributer holds a hash function to store its data, which cannot be restored by the other distributers. The valuable performance analysis over the conventional optimization algorithms proves the effective and reliable performance of the proposed model over the conventional models.

Owing to vague concepts frequently represented in decision data, the crisp values are inadequate to model real-life situations. In this paper, the rating of each alternative and the weight of each criterion is described by linguistic terms which can be expressed in triangular fuzzy numbers. Next, we focus on fuzzy TOPSIS (FTOPSIS) method. We show that, however, the conventional FTOPSIS is interesting, but it suffers from some flaws. The shortcomings of classical FTOPSIS are shown and some solutions are given. Further, a new similarity index is proposed and then is illustrated using numerical examples. By treating the separations of an alternative from the fuzzy positive ideal solution (FPIS) and the fuzzy negative ideal solution (FNIS) as “cost” criterion and “benefit” criterion, respectively, we reduce the original fuzzy multiple criteria decision making (FMCDM) problem to a new one with two criteria. Illustrative examples are given to show the advantages of the proposed approach.

An algorithm is derived for the development of portfolio decision-support information service. The algorithm allows being automated evaluations for the definition and solution of portfolio problems. Small set of historical data of asset returns with limited set of assets are used for the portfolio, which is the case for no institutional portfolio manager. The algorithm applies analytical relations for decreasing the computational workload for the estimation of the market parameters due to the limited number of assets. The subjective expert views in the Black–Litterman (BL) model are defined from additional assessment of historical data of the asset returns. The algorithm makes comparisons of the results for active portfolio management from the mean variance (MV) model, the BL one and the equal-weighted investment strategy. Benefits of the algorithm are the usage of small set of historical data and limited number of assets, which are proved in investment rolling horizon.

Considering the decision maker’s psychological state will influence their evaluation result in the risky multi-attribute decision-making problem, and the uncertainty of evaluation information. In this paper, we will propose a behavioral risky multiple attribute decision making with interval type-2 fuzzy ranking method and TOPSIS method. The interval type-2 fuzzy set is used to express the uncertainty of evaluation information, the prospect theory is applied to describe people’s psychological state in the processing of risk decision making. First, we define a new ranking method for interval type-2 fuzzy set to compare the interval type-2 fuzzy evaluation information and the expectation. Second, we give a relative distance for interval type-2 fuzzy set to get the distance between the interval type-2 fuzzy evaluation information and expectation. Third, we use the prospect theory, the new defined ranking method and the new defined distance formula to obtain the comprehensive prospect value. Fourth, we use the improved TOPSIS method and the comprehensive prospect value to rank the alternatives. Based on the above-mentioned steps, we give the solution for risky interval type-2 fuzzy multiple attribute decision-making problem, which named as the behavioral risky multiple attribute decision making with interval type-2 fuzzy ranking method and TOPSIS method. Finally, we use an example to show the rationality of this method.

The vast quantity of customer data and its ubiquity, as well as the inabilities of conventional segmentation tools, have diverted researchers in search of powerful segmentation techniques for generating managerially meaningful information. Due to its noteworthy practical use, soft computing-based techniques, especially fuzzy clustering, can be considered one of those contemporary approaches. Although there have been various fuzzy-based clustering applications in segmentation, intuitionistic fuzzy sets that have the complimentary feature have appeared in limited studies, especially in a comparative context. Therefore, this study extends the current body of the pertaining literature by providing a comparative assessment of intuitionistic fuzzy clustering. The comparison was carried out with two other well-known segmentation techniques, $k$-means and fuzzy $c$-means, based on transaction data that belong to Turkey’s two major cities. Over 10,000 records of customers’ data were processed for segmentation purposes, and the comparative approaches were presented. According to the results, the intuitionistic fuzzy clustering approach outperformed the other methods in terms of the clustering efficiency index being utilized. The validity of the segmentation structure obtained by the superior approach was ensured via nonsegmentation variables. The comparative assessment and the potential managerial implications could be considered as a contribution to the corresponding literature. This study also compares the effects of the different parameter values used in the proposed model.

Websites of environmental content constitute an important tool for promoting environmental information, affect environmental attitudes and promote protected areas as touristic destinations. However, these websites have to be evaluated to ensure that they reach their final goal. The use of multi-criteria decision-making (MCDM) models in website evaluation is relatively new and not many models have been tested for this purpose. Comparisons of such models have been implemented in various domains but not for the purposes of environmental website evaluation. The main objective of this paper is on presenting the procedure of comparison of MCDM models spherical by providing in detail the steps that have to be followed. This process was implemented for website evaluation and investigated the comparative performance of the TOPSIS and VIKOR models. This comparison process involves reliability analysis of the questionnaire and the sample of decision makers, pairwise comparisons of the models by calculating the Pearson correlation coefficient and estimation of the Cohen’s Kappa for testing the inter-rater comparability, using the models as raters. Furthermore, a sensitivity and robustness analysis of those models is implemented, which also has not been implemented before in the application of those models in website evaluation. The tests implemented and presented in this paper reveal that the reasonable disagreement that was often observed among the methods did not affect their reliability. As a result, MCDM models proved very effective for evaluating websites of environmental content.

Nowadays, environmental problems have attracted the attention of many craftsmen and researchers. They have developed various policies and practices to protect the environment throughout the supply chain. Since suppliers are one of the supply chain partners, paying attention to these policies by them will be very effective in keeping the supply chain green. In today’s dynamic environment, there is uncertainty in all decisions of the organizations. The wood and paper industry also faces uncertainty in choosing green and environmentally friendly suppliers. These uncertainties must be taken into account when selecting suppliers. Decision-making techniques will cover uncertainty if developed in a gray environment. Many techniques have been developed in a gray environment. But using a combination of DANP and MABAC techniques in a gray environment will be very useful because of the benefits of each. Therefore, using the combination of DANP and MABAC in a gray environment in order to select a green supplier in the wood and paper industry is a gap that is felt and in this paper we try to overcome it. The research findings show that the identified criteria will be very effective in selecting the green supplier and Gray DANP–MABAC can be used for managers’ decisions in the wood and paper industry.

Hitherto, the presented models for measuring the efficiency score of multi-stage decision-making units (DMUs) either are nonlinear or require to specify the weights for combining their divisional efficiencies. The nonlinearity leads to high computational complexity for these models, especially when used for problems with enormous dimensions, and also assigning various weights to the divisional efficiencies causes to obtain different efficiency scores for the multi-stage network system. To tackle these problems, this study contributes to network DEA by introducing a novel enhanced Russell graph (ERG) efficiency measure for evaluating the general two-stage series network structures. Then, the proposed model is extended into the general multi-stage series network structures. This study also describes the managerial and economic implications of measuring the efficiency score of the multi-stage DMUs and provides two numerical and empirical examples for illustrating the use of our proposed model.

Scientific explorations and research in the Antarctic region have specific issues which need to be handled with special measures. Clothing of the scientists is one of the main problems. The clothes are expected to be enduring against compelling conditions and they must have certain features to ensure the safety and comfort of the scientists. Polar clothing is a field that is yet to be studied with different engineering approaches. To generate a better understanding, the polar clothing can be approached as a multiple criteria decision-making problem because many criteria such as layer number, material type, and waterproofness should be considered while evaluating the various alternatives. In this evaluation, expert judgments are used because no strict objective rules determine the conditions of the polar clothing. Also, possible influences among these criteria should be revealed and considered while reaching a decision. In order to deal with the uncertainty and vagueness of the expert judgments, this study proposes a Pythagorean fuzzy version of DEMATEL which is one of the well-known multiple criteria decision-making tools with the aim of evaluating the related selection attributes affecting the decision and searching for the potential influences among them. Since Pythagorean fuzzy sets provide a wider preference domain to the experts, this version was developed as a contribution to the literature. Also, the decision process is kept Pythagorean fuzzy until a conclusion is reached so that there is no early defuzzification problem. The method’s application on overcoat selection for the Antarctic region reveals the relations among attributes, such as “Water Vapor Permeability”, “All-Weather Protection” and “Performing Best in Dry/Wet State”. A sensitivity analysis is conducted to find the changes in influences.