Search

Rough neutrosophic multisets (RNM) is an uncertainty set theory generalized from the rough neutrosophic set. In the same equivalence relation, the universal set is a neutrosophic multisets with boundary regions involving lower and upper approximation. To date, to handle the multiplicity of information collected, the rough neutrosophic multisets geometric aggregation operator (RNMGAO) is introduced. The algebraic operations of RNM used in the derivation of RNMGAO are defined. The entropy measure of RNM is also discussed as a weighted assign for each criterion simultaneously with the geometric aggregation operator. The roughness Dice similarity measure of RNM is combined in methodology for ranking purposed. The application in medical diagnosis of three epidemic diseases Coronavirus, Influenza, and Pneumonia is implemented as a case study.

In addressing asset-liability management (ALM) problems, goal programming (GP) has been widely applied to integrate multiple objectives. However, it is inadequate in handling data changes in ALM caused by interest rate fluctuations. Therefore, a more robust and improved ALM optimization method is needed to manage fluctuations in financial ratios in ALM. This study introduces a novel approach by combining a systematic literature review (SLR) with the preference reporting items for systematic reviews and meta-analysis (PRISMA) method and bibliometric analysis to investigate the application of robust goal programming (RGP) models in ALM. The methodology involved planning, search and selection, analysis, and result interpretation as part of the SLR process. Using PRISMA, seven relevant publications were identified. The results of this SLR present a new strategy to combine goal programming and robust optimization to enhance ALM. Model development steps include constructing weighted goal programming (WGP) or lexicographic goal programming (LGP) models, using factor analysis for financial ratios, applying the best-worst method or simple additive weighting (SAW) for prioritization, and modeling financial ratio uncertainty with robust counterparts. This research provides a foundation for further studies and offers guidance to non-financial companies on adopting RGP for strategic ALM decisions and optimizing ALM under uncertainty. [ABSTRACT FROM AUTHOR]

In recent years, the integration of sustainable principles into the Vehicle Routing Problem (VRP) for efficient waste management has gained significant attention. This paper provides an overview of advancements in multi-objective mathematical programming (MP) models and the optimization techniques for sustainable waste management practices within the VRP framework. Sustainable waste management practices in the context of the VRP involve optimizing routes and schedules aim to reduce the environmental impact of waste collection. It investigates the integration of multi-objectives with sustainability criteria, including social, economic, and environmental considerations. Furthermore, it surveys various optimization methodologies applied in waste management to enhance efficiency and sustainability practices. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was employed as a formal systematic review guideline for data gathering. Data was obtained from studies conducted between 2017 and 2023. The analysis included a total of 18 papers from online sources and databases. Based on the literature review, it has been observed that the majority of the research focused on employing Mixed-Integer Linear Programming (MILP) models to tackle the optimization problem for waste management. The literature survey demonstrated significant contributions in computational performance, cost-effectiveness, operational efficiency, and utilization of real-world data by improving the solution approaches, aiming to prioritize economic, social, and environmentally friendly solutions. The paper aims to serve as a convenient reference for researchers and practitioners seeking information on using the MP model and solution approaches to optimize VRP for waste management. [ABSTRACT FROM AUTHOR]

Objective - This study addresses the escalating challenges in aircraft component procurement within Indonesia's aviation industry. Methodology/Technique - To achieve this objective, an in-depth literature review was conducted, exploring diverse facets such as geographical challenges, parts availability intricacies, supplier preferences, quality assurance, time and pricing dynamics, customs clearance challenges, and cost management strategies within the realm of Indonesian Aviation Maintenance, Repair, and Overhaul (MRO). The research methodologically involved 30 participants from various MRO companies affiliated with the Indonesia Aircraft Maintenance Services Association (IAMSA). Subsequently, PLS-SEM analysis was employed to establish the robust reliability and validity of the study, revealing significant relationships that underscored the crucial impact of lead time on Preferred Suppliers and Procurement Process Performance. Finding - The Analysis revealed significant relationships, emphasizing the crucial impact of lead time on Preferred Suppliers and Procurement Process Performance within the Indonesian Aviation MRO sector. Novelty - This study provides nuanced insights for refining procurement strategies and enhancing operational efficiency within the dynamic Indonesian Aviation MRO sector, contributing to the ongoing discourse and offering a holistic understanding of key factors influencing the procurement landscape. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is evolving rapidly and has made many theoretical breakthroughs while widely applied in various fields. ML allows systems the ability to access data and use it to enable computers to execute cognitive processes such as learning and improving from previous experiences and solving complicated issues. Many first-order stochastic optimization methods have been used to solve the optimization model of ML. These algorithms adopt Barzilai-Borwein (BB) step size instead of fixed or diminishing step size to improve performance. However, the BB step size format involves fractional calculation, which inevitably leads to a zero denominator, especially when the objective function is non-convex. The BB technique will be violated if the denominator is near 0 or even negative. To improve the computation of the step size, a Positive Defined Stabilized Barzilai-Borwein (PDSBB) approach is introduced in this paper. Integrating PDSBB with the stochastic variance reduced gradient (SVRG) approach, a new method SVRG-PDSBB is proposed. Numerical experiments have shown that the new algorithm has stabilized the performance of the new step size, which successfully avoiding zero denominators and effectively solving the common problems in machine learning. The convergence of SVRG-PDSBB is theoretically and numerically proven, and the effectiveness of the new algorithm is shown by comparison with other algorithms. [ABSTRACT FROM AUTHOR]

Handling uncertainty is important in decision making, especially for SDGs problems. Robust Optimization (RO) is an applied optimization method that can be employed to handle optimization under uncertain data. With SDGs problems, many uncertain data have been considered in decision making. With RO, the data uncertainties are assumed to lay within a compact, convex continuous set. There are three special sets that can be used to represent the data, i.e., box, ellipsoidal, or polyhedral uncertainty sets. These special sets lead the SDGs problems to a computationally tractable optimization model, such that the global optimal solution is attained. However, literature reviews on the application of RO in SDGs decision-making is sparse, especially during the COVID-19 pandemic period. This paper examines the following topics: (1) the purposes of studies of RO and SDGs during the COVID-19 pandemic, (2) the state-of-the-art in RO-SDGs to determine the research objectives, and (3) the SDGs type of problems that have been modeled using RO. A systematic literature review is conducted in this paper, wherein discussion is based on a PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) flowchart. To this end, the database reference searching conducted on the Scopus, Science Direct, and SAGE databases, is completed using the help RStudio software. The analysis was carried out on two datasets, assisted by the output visualization using RStudio software with the "bibliometrix" package, and using the 'biblioshiny()' command to create a link to the "shiny web interface". In this paper, the research gap on application of RO to SDGs problems is analyzed in order to identify the research objectives, methods, and specific RO-SDGs problems. As a result, the application of RO to SDGs problems is rare; this finding provides a motivation to conduct a further study of RO and SDGs during the COVID-19 pandemic. An expansion is presented using the key phrase "Operations Research and Optimization Modeling", or "OROM". SDGs in Indonesia may be referenced as an example of the capacity building available through RO/OROM. [ABSTRACT FROM AUTHOR]

The rolling stock might function at an optimum level in reliability, availability, maintainability, and safety with comprehensive maintenance. The past decade has seen rapid development in the management of maintenance costs in many sectors such as the automotive and aviation industry. However, there is a lack in a number of studies focusing on rolling stock maintenance costs. This article provides comprehensive knowledge on the rolling stock maintenance cost. Recently, the research found no specific literature reviews that focus on typical rolling stock maintenance costs. This paper attempts to review, identify and discuss the influential costs involved in rolling stock maintenance. This research systematically reviews and classifies a substantial number of published papers and suggests a classification of specific cost categories according to rolling stock needs. The results revealed that 27 variables have contributed to the rolling stock maintenance costs. The highest among the influential costs are 13.8% spare part cost, 11% life cycle cost, 6.4% preventive maintenance cost, and 4.6% for the workforce, corrective maintenance, and cost of ownership, respectively. The interrelationship between influential costs and their effects on rolling stock costs is further discussed. More importantly, the paper is intended to provide a comprehensive view of influential costs affecting rolling stock maintenance and give useful references for personnel working in the industry as well as researchers. This research has highlighted the possibility of future major studies to minimize the identified maintenance cost and industry to optimize its operational cost. [ABSTRACT FROM AUTHOR]

This research aims to analyse, evaluate and rank the maintenance strategy practised by the train operating companies, specifically by the rolling stock maintenance team. A quantitative method was adopted for data collection. A total of five train operating companies were chosen to participate in a survey that has been carefully designed. The research first identified the maintenance strategy associated with the rolling stock maintenance through systematic literature reviews. Afterwards, six maintenance strategies adopted by the companies were identified. The type of maintenance strategies identified was used to structure the survey questionnaire. Judgemental sampling was utilised for sampling purposes. Finally, the data collected from the survey were analysed using an importance index to complete the ranking analysis. The research discovered that corrective and preventive maintenance strategies are the most commonly adopted among the five Malaysian train operating companies. This study also highlighted the factors that future studies should consider to establish predictive cost models for rolling stock maintenance. [ABSTRACT FROM AUTHOR]

A rough neutrosophic set theory is a generalization of uncertainty set theory with a combination of upper and lower approximation and a pair of neutrosophic sets which are characterized by truth membership degree (T), indeterminacy membership degree (I), and falsity membership degree (F). This set theory is suitable for representing each criterion’s relation in medical diagnoses, such as the relation of disease and symptom. This paper aims to propose a model of medical diagnosis via a distance-based similarity measure of a rough neutrosophic set. The first phase for the development model involves the roughness measure between information collected and a lower and upper approximation of rough neutrosophic set theory. Then, it is simultaneously used with extended Hausdorff distance measure to get the proper medical diagnosis. The result shows that each patient has a chest problem that contradicts the prior diagnosis. The finding shows that the roughness approximation is important to get the best result in a close distance-based similarity measure, especially for uncertainty information. [ABSTRACT FROM AUTHOR]

The concepts of rough neutrosophic multisets can be easily extended to a relation, mainly since a relation is also a set, i.e. a subset of a Cartesian product. Therefore, the objective of this paper is to define the definition of rough neutrosophic multisets relation of Cartesian product over a universal set. Some of the relation properties of rough neutrosophic multisets such as max, min, the composition of two rough neutrosophic multisets relation, inverse rough neutrosophic multisets relation, and reflexive, symmetric and transitive rough neutrosophic multisets relation over the universe are defined. Subsequently, their properties are successfully proven. Finally, the application of rough neutrosophic multisets relation for decision making in marketing strategy is presented. [ABSTRACT FROM AUTHOR]

Main sources of airport revenues are from aeronautical revenue and nonaeronautical revenues. Many airports put more emphasis on improving nonaeronautical revenues despite the fact that aeronautical revenue is still the main income contributor to most airports in sustaining their business. Airport's aeronautical revenues depend on the number of air traffic where relatively high number of traffic is assumed to generate greater aeronautical revenues. However, seldom airports analyse the impact of various factors related to air traffic such as flight types, aircraft types, flight time and day, and types of passengers arriving or departing, which can indicate the trend in passenger's preferences and potential aeronautical revenues. This research took into consideration these factors when developing the mathematical optimisation model, known as Aeronautical Revenue Optimisation Model (AROM), for generating maximum aeronautical revenues of regional airports. Based on the model and solutions, a Graphic User Interface (GUI) dashboard system has been developed for assessing the aeronautical revenue generated by different variables of the air traffic movements. The dashboard allows user to extract existing data, run the model and view results, including the potential revenue generated by each aircraft. Thus, it provides managers with in-depth information of each flight and its impact on potential revenue generation, which guides them in decision making, operations planning and improving the billing efficiency. [ABSTRACT FROM AUTHOR]

Many past studies largely described the concept of neutrosophic sets, neutrosophic multisets, rough sets, and rough neutrosophic sets in many areas. However, no paper has discussed about rough neutrosophic multisets. In this paper, we present some definition of rough neutrosophic multisets such as complement, union and intersection. We also have examined some desired properties of rough neutrosophic multisets based on these definitions. We use the hybrid structure of rough set and neutrosophic multisets since these theories are powerful tool for managing uncertainty, indeterminate, incomplete and imprecise information. [ABSTRACT FROM AUTHOR]

The purpose of this preliminary research is to identify corporate talents' positioning of the UiTM graduates especially the Generation X, within the industries. At the same time, there is also the need to ascertain the factors that contributed to the success of the UiTM Generation X graduates. Majority of the research which have been conducted on the graduates involved the employability rate. However, none has been carried out in identifying the graduates' career accomplishment. The sample of this research consisted of 191 UiTM graduates who are born between 1965 until 1985 (known as the Generation X). The responses were obtained via an online survey sent to random groups in UiTM emails. In addition, Facebook was also used as a medium in dissemininating the survey forms to random individuals whom has connections with the Generation X alumni. Rasch Measurement Model was employed by using the software WINSTEP 3.72.3, to measure the reliability and validity of the instrument. SPSS version 21 was used to answer the research objective of the study. The result has produced a fair person reliability at +0.66 while the Cronbach Alpha reliability value was at 0.72 and an excellent item reliability of 0.99. In terms of career positioning, 60% of the Generation-falls within the middle management position. This reflects the success of the Gen-X where most of them were able to obtain posts like lecturers, engineers and managers. As for the factors which contributed to the success of the alumni, it was found that involvement in clubs, soft skills and orientation towards religion play important roles in determining the career position of GenX. These findings validate past research and demonstrate that the pattern continues into the present. Surprisingly, it was found that academic achievement, hard skills and personality traits hardly had any influence on the career positions of the Gen-X. It was also discovered that the success of the Gen-X could be due to other factors which is beyond the horizon of this research. Therefore, it was recommended that students should be encouraged to be actively involved in clubs. This must be balanced with religious values exposure so that they know the limit of their involvement. [ABSTRACT FROM AUTHOR]

This paper address the problem at the inbound phase at the cross docking warehouse. The fundamental issues in cross docking facility is to assign the incoming truck to the door and to coordinate the sequences of the trucks in order to minimize the completion time at the inbound phase. Using the theories and methodologies of assignment and scheduling, paper on hand proposed a mixed integer programming model for solving the truck-to-door assignment and scheduling problem with the objective to minimize the total service time of trucks. Meanwhile, reduce the waiting time of trucks before being served at the designated door. A preliminary computation is conducted to verify the logic of the mathematical model proposed. [ABSTRACT FROM AUTHOR]