Intelligent Decision Making Using Evolutionary System for Optimizing Product-Mix Model

The development and deployment of managerial decision support system represents an emerging trend in the business and organizational field in which the increased application of Decision Support Systems (DSS) can be compiling by Intelligent Systems (IS). Decision Support Systems (DSS) are a specific class of computerized information system that supports business and organizational decision-making activities. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions. Competitive business pressures and a desire to leverage existing information technology investments have led many firms to explore the benefits of evolutionary system data management solutions such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). This study proposes a new model for product mix problem based on evolutionary system for optimizing constraint values as well as objective function. The formulations of the objective function for the minimization problem. This technology is designed to help businesses to finding multi objective functions

Intelligent Decision Making Using Particle Swarm Optimization for Optimizing Product-Mix Model

The development and deployment of managerial decision support system represents an emerging trend in the business and organizational field in which the increased application of Decision Support Systems (DSS) can be compiling by Intelligent Systems (IS). Decision Support Systems (DSS) are a specific class of computerized information system that supports business and organizational decision-making activities. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions. Competitive business pressures and a desire to leverage existing information technology investments have led many firms to explore the benefits of intelligent data management solutions such as Particle Swarm Optimization (PSO). This study proposes a new PSO (SPSO)-model based on product mix model for optimizing Constraint values as well as objective function. The formulations of the objective function for the minimization problem. This technology is designed to help businesses to finding multi objective functions, which can help to understand the purchasing behavior of their key customers, detect likely credit card or insurance claim fraud, predict probable changes in financial markets, etc

Product selection by consumers utilizing the membership values

Assessment and assortment of the most favorable, competent, and trustworthy products is getting to be more and more important for businesses in the current scenario, so the majority of the production industries do their utmost to achieve the requirements, standards, and expenditure patterns of the consumer to attain their expectations. Approaches are made to fulfill the needs of the consumer, as shown by the creation of diverse technologies for improving the capability to manage complex optimizations regarding decision attributes that involve uncertainty. The procedure of detecting the best alternative from all the feasible options is the basis of the decision-making problem. This article recommended two approaches: (a) multi-objective decision making by utilizing fuzzy technique and (b) fuzzy TOPSIS (Technique for Ordering Preferences by Similarity to Ideal Solution) method for choosing the best product while many products with many attributes are present in the marketplace. As per the need of the customer priority and their satisfaction level, this paper used linguistic quantifiers to evaluate the weight of the objective that is represented in triangular or trapezoidal fuzzy numbers. To demonstrate the technique used for the suggested approaches a case study was conducted. The simulation result shows a specific path that fuzzy logic can evolve in the problem of decision making as well as in the planning process in the product selection practice. Solutions obtained by employing both techniques were compared, which urges that the fuzzy TOPSIS approach provides more accurate information than the fuzzy MODM (multi-objective decision making) approach to find the best product from among many available products. This work helps to assist the managers to adopt suitable techniques while planning to design the product according to the customer requirements so that the consumer can get the best product as per their objectives