Application of F-WASPAS in the Ranking of Crops for Agro-Processing: The Case of Ikondo Ward in Njombe, Tanzania

The purpose of this work was to develop and apply a Fuzzy Weighted Aggregated Sum Product Assessment (F-WASPAS) method in ranking selected crops for agro-processing at Ikondo Ward in Njombe Region, Tanzania. The fuzzy technique for order preferences by similarity to ideal solution (TOPSIS) was applied in determining the fuzzy importance weights of criteria, while the fuzzy WASPAS successfully ranked the crops, and maize was ranked the highest. Keywords:    F-WASPAS, Linguistic variables, Fuzzy aggregation, Agro-processing, Decision making, Multi-Criteria Decision Making

INTUITIONISTIC FUZZY MACONT METHOD FOR LOGISTICS 4.0 BASED CIRCULAR ECONOMY INTERESTED REGIONS ASSESSMENT IN THE AGRI-FOOD SECTOR

This study aims to evaluate and prioritize the key interested regions of Circular Economy (CE) in terms of implementing the industry 4.0 technologies for the performance of logistics activities in the agri-food sector. For this purpose, we introduce a hybrid ranking framework based on Relative Closeness Coefficient (RCC)-based objective weighting model, the RANking COMparison (RANCOM) subjective weighting procedure and the Mixed Aggregation by Comprehensive Normalization Technique (MACONT) with Intuitionistic Fuzzy Information (IFI). In this framework, new IF-score function and an improved distance measure are proposed in the context of IFI to evade the limitations of existing ones. A hybrid IF-RCC-RANCOM-MACONT framework is introduced to prioritize the options over defined criteria. To prove the applicability of introduced approach, it is employed on a case study of circular economy interested regions assessment in the agri-food sector, consisting of five alternatives and nine criteria under the dimensions of sustainability. Sensitivity analysis is shown to highlight the impact of used parameters on the final outcomes. At last, a comparison with extant approaches is made to demonstrate the robustness of obtained results

Menentukan Topik Skripsi Mahasiswa Dengan Menggunakan Relasi Fuzzy Intuisionistik

One of the problems that often occurs is  the students cannot complete the thesis as expected. One of the factors that always occurs is that the students choose or determine the topic of their thesis that is not in accordance with their competence. He is more likely to choose supervisor.  This article is the result of research that aims to determine the suitability of a student's thesis topic according to their academic competence. The research was conducted on Unesa Mathematics students who are at the fourth year. The number of subjects are 35 students who were at the beginning of semester 7 and at that time were taking a mathematics seminar course. Seminar courses are the beginning of the preparation of their thesis. The method used is a modification of the "Medical Diagnostic" method in the health, which uses the application of the intuitionistic fuzzy relation concept. The results showed that there were 13 students or only 43.3% whose choice of thesis topic was in accordance with their competence, there were 17 students or 56.7% whose choice of thesis topic was not in accordance with their competencies

Computational Intelligence in Modeling Complex Systems and Solving Complex Problems

Editoria

Modeling multi-criteria decision-making problems with applications in last mile delivery and school safety assessment

The last-mile delivery option has become a focal point of academic research and industrial development in recent years. Multiple factors such as increased demands on delivery flexibility, customer requirements, delivery urgency, and many others are enforcing to adopt this option. For fulfilling this paradigm shift in delivery and providing additional flexibility, drones can be considered as a viable option to use for last-mile delivery cases. Numerous drones are available in the market with varying capacities and functionalities, posing a significant challenge for decision-makers to select the most appropriate drone type for a specific application. For this purpose, this study proposes a comprehensive list of criteria that can be used to compare a set of available last-mile delivery drones. Additionally, we introduced a systematic multi-criterion, multi-personnel decision-making approach, referred to as the Interval Valued Inferential Fuzzy TOPSIS method. This method is robust and can handle the fuzziness in decision-making, thereby providing quality drone selection decisions under different applications. We then apply this method to a real-life test setting. Results suggest that smaller drones or quadcopters are considered viable to use in urban environments, while long-range drones are preferred for the last mile delivery needs in rural settings

Assessment of the agriculture supply chain risks for investments of agricultural small and mediumsized enterprises (SMEs) using the decision support model

A key challenge in responding to the emerging challenges in agri-food supply chains is encouraging continued new investment. This is related to the recognition that agricultural production is often a lengthy process requiring ongoing investments that may not produce expected returns for a prolonged period, thereby being highly sensitive tomarket risks. Agricultural productions are generally susceptible to different serious risks such as crop diseases, weather conditions, and pest infections. Many practitioners in this domain, particularly small and medium-sized enterprises (SMEs), have shifted toward digitalization to address such problems. To help with this situation, the current paper develops an integrated decision-making framework, with the Pythagorean fuzzy sets (PFSs), the method for removal effects of criteria (MEREC), the ranksum (RS) and the gained and Lost dominance score (GLDS) termed as PF-MEREC-RS-GLDS approach. In this approach, the PF-MEREC-RS method is applied to compute the subjective and objective weights of the main risks to assess the agriculture supply chain for investments of SMEs, and the PF-GLDS model is used to assess the preferences of enterprises over different the main risks to assess of the agriculture supply chain for investments of SMEs. An empirical case study is taken to evaluate the main risks to assess the agriculture supply chain for SME investments. Also, comparison and sensitivity investigation are made to show the superiority of the developed framework

EVALUATION OF GREEN ENERGY SOURCES: AN EXTENDED FUZZY-TODIM APPROACH BASED ON SCHWEIZER-SKLAR AND POWER AVERAGING OPERATORS

To address the problem of green energy source selection, this paper proposes a novel decision-making framework using fuzzy-TOmada de Decisao Interativa Multicriterio (TODIM) method in an interval-valued intuitionistic environment. The proposed framework integrates the prospect theory approach with Schweizer-Sklar and power averaging operators to evaluate the green energy sources including solid waste, solar, tidal, carbon capture storage, hydrogen, marine, hydel, biogas, wind, concentrating solar, geothermal and biomass under the influence of nine conflicting criteria such as annual generation, capacity factor, mitigation potential, useful life, installation period,  energy requirement, CO2 emission, generating cost and operations and maintenance cost. The vagueness associated with the evaluations as well as biased evaluations is taken care of by Schweizer-Sklar and power averaging operators while TODIM method provides due consideration to the psychological behavior of the decision maker. Solar photovoltaic emerges as the best energy source. Sensitivity analysis has also been performed to assess the robustness of the proposed decision-making framework.

Investment decision analysis of international megaprojects based on cognitive linguistic cloud models

The investment decision analysis of international megaprojects is a major area of interest. The choice of interna tional megaprojects usually depends on the multi-discipline knowledge from experts. Besides, experts may not be able to provide accurate or crisp evaluations such as deterministic numbers on each criterion because of the complexity of the decision problem. In this case, natural evaluation language, either single linguistic variable or multiple linguistic variables, is a good expression tool for experts to sharing their opinions freely and flexibly. To this end, this paper introduces a cognitive linguistic cloud model for the investment decision analysis of international megaprojects as a decision support system and provides a survey of the cloud model. Afterwards, the technique to tackle multi-granularity of cognitive linguistic information is proposed to capture personalized semantics. In addition, operators of the cognitive linguistic model are proposed to aggregate natural language. The proposed approach has the advantages of more accurate utilization of experts’ knowledge, reducing uncertainties, and more effective operations of cognitive clouds for decision analysis in comparing with the state of the art. Finally, a case study about the investment of international megaprojects is given to show the flexibility and understandability of the cognitive linguistic model