چكيده به لاتين
One of the essential activities of economic enterprises to survive in today's competitive markets is the control of the company's inventory, including raw materials, final products, materials in the process of manufacturing, in-hand products, and all materials and products in the production process. In recent years, inventory management and control have been very different from the past because the definition of products has changed a lot. In the past, it was thought that the properties, quality, demand, and efficiency of goods were constant over time. However, with the introduction of new definitions of products, nowadays, there are a few items whose properties are constant over time. The items whose properties change over time and their efficiency decreases over time are called deteriorating items. Today, almost all the goods in the market can be classified as deteriorating items, so it is necessary to pay attention to the inventory control of these products. Since the models presented in this literature are nonlinear and the methods of solving these models are limited, many real assumptions, such as the types of limitations in the inventory control literature, have been ignored. The primary purpose of this dissertation is to provide a suitable solution method to add realistic assumptions, including conventional constraints in inventory control problems to deteriorating items inventory problems. The primary solution method presented here is dynamic programming. The results show that this method can solve all kinds of problems related to this subject literature. In this dissertation, different models for controlling the inventory of deteriorating items with different constraints and real assumptions are presented, considering that all these models are in the category of nonlinear mixed integer models and the usual solution methods cannot solve them. These models have been solved using dynamic programming. Three different classifications have been presented to solve different types of these models, and the validation of the presented method and models has been done using the greedy search method. The results show that the presented method can find the global optimal solution. Several numerical examples are presented, and a small case study has been conducted to evaluate the presented models and the solution method to solve real-world problems. Several metaheuristic solution methods have been used to evaluate the efficiency of the proposed method. Sensitivity analysis on different problem parameters has been done, and the time and space complexity of the methods for different dimensions of the problem have been presented. The results show that the proposed method can solve medium-sized problems that include most of the problems in the literature, as well as real-world problems for medium-sized enterprises. Finally, for each model, an analysis of the parameters for each model is provided, and theoretical and management insights are provided for the users of this dissertation. Finally, future scopes for developing the present study have been provided.
