International Journal of Information Technology & Decision Making

Web services are globally utilized by clients to accomplish the required functionality over the web. As a result of its popularity and flexibility in usage, thousands of functionally similar web services are available over the network. Hence, it becomes necessary to select the optimal web service to satisfy the clients’ need. Various methodologies like machine learning, genetic algorithm, bio-inspired techniques, multi-criteria decision making (MCDM) methods and many others aid in the process of selecting the best web service from thousands of alternatives. This paper aims in proposing a relatively new MCDM approach to solve the selection issue and thereby proposes a novel framework incorporating the proposed MCDM method to aid in the process of service selection. Reference ideal method (RIM) is a state-of-the-art MCDM technique to select the optimal web service based on user inputs. In spite of its popularity, this method is found to have multiple pitfalls which make the selection process less effective. This paper proposes a novel MCDM methodology named improved RIM (I-RIM) to overcome the existing pitfalls in RIM. The paper also proposes a novel framework which combines the power of graphics processing unit (GPU) and I-RIM to enhance the efficiency of the selection process. The proposed I-RIM when parallelized using GPU is found to outperform the parallelized MCDM techniques taken for study. The results also imply that the I-RIM is more consistent and stable towards the ranking process. It is also evident that the proposed framework which incorporates I-RIM outperforms RIM in terms of execution time, mean reciprocal rank and Spearman’s correlation coefficient which makes the framework more stable and reliable, thus, making it suitable for real-time web service selection.

Point-of-Sale (POS) data analysis is usually used to explore sales performance in business commence. This manuscript aims to combine unsupervised clustering and supervised classification methods in an integrated data analysis framework to analyze the real-world POS data. Clustering method, which is performed on sales dataset, is used to cluster the stores into several groups. The clustering results, data labels, are then combined with other information in store features dataset as the inputs of the classification model which classifies the clustering labels by using store features dataset. Non-dominated sorting generic algorithm-II (NSGA-II) is applied in the framework to employ the multi-objective of clustering and classification. The experimental case study shows clustering results can reveal the hidden structure of sales performance of retail stores while classification can reveal the major factors that effect to the sales performance under different group of retail stores. The correlations between sales clusters and the store information can be obtained sequentially under a series of data analysis with the proposed framework.

A fundamental issue about installation of photovoltaic solar power stations is the optimization of the energy generation and the fault detection, for which different techniques and methodologies have already been developed considering meteorological conditions. This fact implies the use of unstable and difficult predictable variables which may give rise to a possible problem for the plausibility of the proposed techniques and methodologies in particular conditions. In this line, our goal is to provide a decision support system for photovoltaic fault detection avoiding meteorological conditions. This paper has developed a mathematical mechanism based on fuzzy sets in order to optimize the energy production in the photovoltaic facilities, detecting anomalous behaviors in the energy generated by the facilities over time. Specifically, the incorrect and correct behaviors of the photovoltaic facilities have been modeled through the use of different membership mappings. From these mappings, a decision support system based on ordered weighted averaging operators informs of the performances of the facilities per day, by using natural language. Moreover, a state machine is also designed to determine the stage of each facility based on the stages and the performances from previous days. The main advantage of the designed system is that it solves the problem of “constant loss of energy production”, without the consideration of meteorological conditions and being able to be more profitable. Moreover, the system is also scalable and portable, and complements previous works in energy production optimization. Finally, the proposed mechanism has been tested with real data, provided by Grupo Energético de Puerto Real S.A. which is an enterprise in charge of the management of six photovoltaic facilities in Puerto Real, Cádiz, Spain, and good results have been obtained for faulting detection.

The threats like increasing pollution level and scarcity of natural resources are showing that Sustainable Consumption and Production (SCP) is one of the key solutions and the main contributor driver of Sustainable Development Goals (SDGs). Eventually, a mysterious disease named as COVID-19 started spreading very fast around the globe and it was declared a pandemic. To protect citizens from this pandemic, the government announced complete lockdown in the country. This lockdown due to COVID-19 has put its impact on every aspect of life. In this research study, the efforts have been made to identify various impacts of COVID-19 on SCP practices and propose various solutions to overcome these impacts of COVID-19. In the Indian perspective, a total fifteen impacts of COVID-19 under five different heads along with eighteen solutions to overcome these impacts have been identified with the help of literature, various reports and experts’ inputs. All these impacts and solutions were analyzed using a hybrid framework. Results revealed that COVID-19 majorly put its impact on nation, business and behavior followed by the impact on society and environment. All these impacts can be overcome by adopting solutions like strong and clear policies formation and its implementation, financial packages to industries to boost up the economy, firm determination of top management, safety of the workforce and tax reduction on sustainable products. The final results will be very helpful for all the actors focused on SCP.

Even though that there has been increasing focus on the energy-efficient operation of vessels and that the knowledge of cost-effective improvements is widespread in the industry, energy-efficient operation is only a minor topic on board many working vessels. A significant reduction in fuel can be achieved through changes in the operational practices, but to establish a successful system for best practices within energy-management the installation of a decision support system is essential. This article presents a decision support system for working vessels to determine best practice for the reduction of fuel consumption. Requirements for the system are defined through interviews with crew and observations on board vessels. Case studies are used for illustrating the usefulness. The use of generators onboard is analyzed using the software. It is found that the generators are not running optimally, but the crew can use the software to re-organize and find the most fuel-efficient loading range for the generators on board.

Worldwide demand for fish products is increasing continuously. Literature evidence indicates that there is a persistent decrease in ocean fisheries’ supply. Aquaculture bridges the gap between the reduced ocean fish supply and increased world fish food demand. Sustainable and profitable aquaculture is firmly facilitated by selective fish species. Species selection has been achieved through deeply analyzing the manifold and complex interrelationships between the numerous subjective risk categories intricate in aquaculture. Apparently an analytical system able to analyze massive subjective stakes in terms of its quantifiable equivalent that aids selecting an optimal fish species is nontrivial. This research provides quantifiable metrics that eases the analytical struggle towards the subjective aspect of species selection. The novelty involves providing hybrid multi-criteria-based viable decision support methodology that analyses extensive aquaculture domain knowledge and inference and emulates the logic and reasoning process so as to choose an optimal fish species for aqua farming. The methodology is based on the assessment of several species in accordance with analyzing numerous associated criteria and sub-criteria of risk factors. This research consists of nineteen sub-criteria which were classified under five comprehensive heads of evaluation criteria such as environmental, nutritional, disease outbreaks, biotic and physiological risk categories. The weight scores for each criteria and sub-criteria were determined using the Fuzzy Analytical Hierarchy Processing (FAHP) method. Consequently using the derived priority weights, the best fish species to choose from several alternative species is identified based on the relative closeness values and ranks assigned to them by applying the fuzzy Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method. A case study is performed on five varieties of most favored fish species for consumption to exemplify the effectiveness of the proposed model. The sturdiness of the suggested model is validated against the two standing multiple criteria decision-making approaches such as fuzzy ordered weighted average and fuzzy extent analysis–fuzzy weighted average methods. The research outcome strongly aids aqua farmers to identify an optimal fish species.

The control plane plays an essential role in the implementation of Software Defined Network (SDN) architecture. Basically, the control plane is an isolated process and operates on control layer. The control layer encompasses controllers which provide a global view of the entire SDN. The Controller selection is more crucial for the network administrator to meet the specific use case. This research work mainly focuses on obtaining a better SDN controller. Initially, the SDN controllers are selected using integrated Analytic Hierarchy Process and Technique for Order Preference Similarity to Ideal Solution (AHP and TOPSIS) method. It facilitates to select minimal number of controllers based on their features in the SDN application. Finally, the performance evaluation is carried out using the CBENCH tool considering the best four ranked controllers obtained from the previous step. In addition, it is validated with the real-time internet topology such as Abilene and ERNET considering the delay factor. The result shows that the “Floodlight” controller responds better for latency and throughput. The selection of an optimum controller-Floodlight, using the real-world Internet topologies, outperforms in obtaining the path with a 28.57% decrease in delay in Abilene and 16.94% in ERNET. The proposed work can be applied in high traffic SDN applications.

Decision-making is one of the significant and inevitable issues in the most real-world problems. Decision criteria interact in many of these problems, and traditional aggregation techniques, which are usually linear methods, cannot be exploited to consider these interactions and exert correlations between criteria. In such cases, nonadditive aggregation methods have attracted the attention of many researchers. This study presents a novel model based on the best-worst method (BWM) and the multi-criteria fuzzy Choquet integral technique to apply the interaction between the criteria. In the proposed model, we have reduced the effect of the inconsistency rate detected on the fuzzy measure (or Choquet capacity) by taking into account the positive or negative interaction between the criteria.

Multi-criteria decision making (MCDM) is a sub-discipline of operations research aimed at evaluating alternatives in consideration of various criteria. It is used by practically everyone in their daily lives and professional settings. Seeing the great value of MCDM, we conduct a comprehensive review to propel its innovation and development forward. Compared with any other reviews, we do not focus on introducing its methods, but on tracing its evolution and characteristics. As a general rule, every discipline has its own developing laws. They can be understood on various levels and can point out the direction the discipline is heading for. We firmly believe that our work can bring insight into MCDM’s frontiers and trend, which can eventually provide guidance on how to conduct the future research. In the first half of the paper, we investigate MCDM’s paradigm through literature review and colloquially divide its story into four stages: the Stone Age, the Iron Age, the Industrial Age and the New Stage. The first three stages symbolise its debut, growth and prosperity. Each one has distinctive thoughts, techniques and application, and to some extent hints the major works of the next stage. Since the turn of the 21st century, the paradigm has been once again experiencing dramatic changes, suggesting that it is on the threshold of a new era. Thus, in the second half of the work, a bibliometric analysis of the present stage ensues. Put simply, we take a global view of the stage by visualizing the stage’s publication quantity, publication distribution, and research categories. Then we design a snowballing co-citation method to explore its movements. The results demonstrate that MCDM is a dynamic, worldwide study for which China is the most productive country and the USA plays a pivot role in scholar communication. Method reviews, straightforward methods and MCDM-oriented fuzzy sets are predominant frontiers. The application, however, always changes with the requirements of the times. Now, it mainly refers to energy, environment, and supplier selection, while issues like sociology, tourism, education, etc. also emerge at a fast speed. Apart from this, experts have gradually shaken off the fetters of the traditional research style and are increasingly willing to structure methods and select application areas with a more personal touch. In the future, how to improve reviews, methods and fuzzy sets, how to understand and draw inspirations from society needs, and how experts can tailor MCDM to accommodate specific problems might be the pressing concerns.