This paper aims to improve the ABC analysis method used for inventory management by applying the Pythagorean Fuzzy TODIM approach. ABC analysis is one the well-known and widely used inventory classification techniques which divides inventory items into three categories according to their importance and value. However, the traditional ABC analysis does not consider the imprecision and vagueness of real-world inventory data, which can lead to inaccurate results and poor inventory management decisions. The proposed approach enhances the traditional ABC analysis by incorporating fuzzy numbers to be considered in real-world inventory data. The improved ABC analysis helps companies to optimize inventory levels, reduce costs, improve customer service, and increase overall operational efficiency. To check for the reliability and effectiveness of the developed model under different scenarios sensitivity analysis is conducted. Additionally, the comparative analysis among other existing models further demonstrates the model's accuracy. The model prepared shows that the Pythagorean Fuzzy TODIM approach is superior to the conventional ABC analysis in terms of reliability and dealing with the uncertain inventory data. Overall, this paper provides a novel and effective approach to inventory management and offers valuable insights for practitioners and researchers in the field.

In disease mapping, it is preferable to estimate the risk rather than the significance in general, but the variation in estimation precision across the geographical map of the study region must also be taken into consideration. In such a situation the conventional methods would not yield the best estimates. Heterogeneity is an important aspect to be considered as significant in Disease Mapping and relative risk estimation. The simple regression models often do not capture the extent of the variation exhibited in the spatial count data. This is the case when the spatial data is over-dispersed or there is spatial correlation due to unobserved confounders. In such situations, it is appropriate to include some additional terms, which may be in the form of the prior distribution. In this paper, a Poisson model with Gamma prior is used to model and map the dengue incidences in Tamil Nadu to explain the patterns of variations.

This paper presents a unified multi items general inventory model for integrated production of new items and remanufacturing of returned and defected items for a finite planning horizon. In this paper, a production model that takes into account learning, instantaneous deterioration rate and inflation is proposed. In addition, we also consider that the holding cost is a non-negative, non-decreasing and continuous function of time. In this model, the preservation technology is used to reduce the rate of product deterioration. A theory is developed to find the optimal solution of the proposed model; it is then exemplified with the help of several numerical examples. An efficient solution procedure is also provided to find the optimal strategy. Finally, sensitivity of the optimal solution to changes in the values of different parameters of the system and the convexities of the cost functions are also studied and represented through the graphs.

In this paper a hybrid algorithm for solving bound constrained optimization problems having continuously differentiable objective functions using Fletcher Reeves method and advanced Genetic Algorithm (GA) have been proposed. In this approach, GA with advanced operators has been applied for computing the step length in the feasible direction in each iteration of Fletcher Reeves method. Then this idea has been extended to a set of multi-point approximations instead of single point approximation to avoid the convergence of the existing method at local optimum and a new method, called population based Fletcher Reeves method, has been proposed to find the global or nearer to global optimum. Finally to study the performance of the proposed method, several multi-dimensional standard test functions having continuous partial derivatives have been solved. The results have been compared with the same of recently developed hybrid algorithm with respect to different comparative factors.

Manufacturing industries are consistently working on improving their operational performance to remain competitive in the market. LM is a well-recognized approach for improving the overall performance. It contains several elements covered under a few lean attributes. This paper presents the application of Graph Theory and Matrix Approach (GTMA) for the identification of relative importance of different lean attributes in a lean environment using qualitative and quantitative factors. The Lean Manufacturing Attributes (LMA’s), affecting the overall LM environment, of a manufacturing industry were identified and analyzed for the implications on the managerial decisions. .In this proposed study, The GTMA approach is applied to prioritize the LMA’s based on their relative importance.

Bullwhip effect is one of the serious problems caused by uncertainty in supply chain management. Graph theoretical approach has been utilized by some of the authors towards evaluation of various phenomena performance. This paper has been an attempt to develop bullwhip effect mitigation index (BWEMI), which is a single numerical benchmarking index utilizing graph theory and matrix (GTM) method. Review of literature and subsequent discussions with experts to obtain their valuable opinions regarding managing supply chains have been utilized to complement and supplement the variables. Digraph theory has been used to develop various matrices showing interactions among bullwhip effect mitigation factors. The tool so developed may help supply chain managers to analyze and quantify efforts towards bullwhip effect mitigation.

Nowadays, industrial robots are being pervasively used in almost every manufacturing organization for improving operational quality, safety and productivity. Depending on the nature of task to be performed, many varieties of robots are now commercially available from different manufacturers. For efficiently carrying out the designed task, a number of functional attributes of an industrial robot are also simultaneously responsible. Therefore, selection of an appropriate and competitive robot alternative becomes a complicated and equally challenging task for the decision makers. A quite strong model of multi-criteria decision-making is needed to deal with this problem of industrial robot evaluation and selection. In this paper, the applicability of fuzzy axiomatic design (FAD) principles is explored for solving a real time robot selection problem. Seven candidate robots which are commercially available for light assembly operations are evaluated with respect to a mix of nine criteria. All these criteria are either qualitative in nature or expressed as a range of numerical values. Suitability rankings of all the feasible alternatives are derived using FAD methodology, thus establishing it as a systematic and dependable tool for solving industrial robot selection problems in fuzzy environment.

With ever-increasing demands for high surface finish and complex shape geometries on various difficult-to-machine materials, conventional metal removal methods are now being replaced by non-traditional machining (NTM) processes. These NTM processes use energy in its direct form to remove material from the workpiece surface. They are also cost effective for a wide range of micro- and nano-level applications. For effective utilization of different NTM processes, it is quite important to study their characteristics and material removal mechanisms in order to identify the most significant control parameters affecting the process responses. In this paper, a data mining approach using classification and regression tree algorithm is employed to identify the most important input parameters of three NTM processes, i.e. micro electro discharge milling process, wire electrical discharge machining process and laser beam machining process. The derived observations are also validated using the analysis of variance results.

With the inception of high-speed internet data services and ever-growing technical advancement in manufacturing technology, the integration of production systems and the internet of things to produce different types of jobs via cloud computing has become possible. The internet-enabled advanced automatic production systems can be referred to as the cyber-physical production systems (CPPS). The use of CPPS via cloud computing and the internet of things (IoT) can offer high productivity and high flexibility for the production of jobs in a dynamic production environment with varying specifications. The aim of this paper is to present a generalized synergic framework between different production facilities locating at different geographical locations to realize an energy-saving and efficient cyber-physical production system for the production of different types of jobs in the context of the industry 4.0 and beyond. In addition to the above, the study also identifies a need to address large scale multi-objective optimization issues to make the best decisions for different combinatorial production scenarios by using CPPS that are functioning in smart production facilities at different geographical locations.