COMPREHENSIVE SYSTEM ARCHITECTURE FOR HOUSEHOLD REPLENISHMENT SYSTEM: SIMULATION OPTIMIZATION FOR INVENTORY REPLENISHMENT POLICY CONSIDERING QUALITY DEGRADATION & STOCHASTIC DEMAND

Abstract

Food wastage because of the lack or incompletion of a Household Replenishment System is an essential topic to be addressed. Appropriately using the Internet of Things (IoT) and Artificial Intelligence (AI) technologies with particular components is needed to design a smart Household Replenishment System to reduce food waste. This dissertation develops a unified framework and conceptual system architecture for the implementation of a Household Replenishment System, presents an object recognition framework to identify labeled and unlabeled items inside a smart refrigerator, and showcases a low-cost installation model for a smart refrigerator. It develops and validate a simulation optimization model of perishable items inside a smart refrigerator for an optimal replenishment policy. To accomplish those goals, this dissertation initially provides comprehensive analyses and a summary of the literature using IoT and AI tools for perishable items storage compartments, as they are always full of items that need to be monitored. This comprehensive research followed the PRISMA search strategy, which was conducted to point out the approaches, contributions, components used, and limitations of the reviewed papers in developing a unified framework for a household replenishment system. More specifically, 70 papers were examined in chronological order starting from 2000 when LG Electronics invented the first smart refrigerator, and research on technology involvement in food storage compartments increased. The analysis found 43 approaches using IoT technology, 27 using AI, and in the past couple of years, the use of AIoT has been trending. The future directions for researchers were acquired from the limitations of the reviewed papers, and they could enhance the household replenishment system by adding the features to smart food storage compartments v The comprehensive research helps fulfill an objective of this dissertation, system architecture framework The system architecture acts as a road map for developers to implement a Household Replenishment System. It sheds more light on one of the most important techniques of AI, object recognition. A framework of object recognition is developed. The developed object recognition provides insight into the type of information about the stored items that could be obtained by the Household Replenishment System. A practical example of a cost model is presented. The developed cost model minimizes the total installation cost of the smart refrigerator based on household preferences using linear programming, adoption of capital budgeting, and multidimensional knapsack problems. The object recognition framework presented is conceptual. Therefore, several assumptions were used to develop a simulation optimization model of the Household Replenishment System. The simulation optimization model uses discrete-event simulations and a periodic policy considering the review period, minimum stock level, and maximum stock level (T, s, S). The simulation optimization model finds the optimal replenishment policy to minimize the total Household Replenishment Systems’ inventory cost. The simulation optimization model considers holding, shortage, wastage, and order costs as components in the objective function, which accounts for stochastic demand, variation in life span, and quality degradation rates of stored items. The simulation optimization model was tested on single and multiple items, with different scenarios for the multipleitem cases. Experimental runs of the simulation optimization model were completed, validated, and analyzed. The design of the experiment and sensitivity analyses were applied. The simulation optimization model successfully generated a set of top five optimal replenishment policies for the household to choose from. Further investigation into smart home appliances would lead to extensive approaches like smart shops, vi industries, and eventually smart cities. Future work for this dissertation could be achieved by enlarging the scope of research to involve patents, dissertations, and theses that used Artificial Intelligence of Things (AIoT) technologies to improve the Household Replenishment System.