NATURE- INSPIRED OPTIMIZATION FOR INVENTORY CONTROL OF NON-INSTANTANEOUSLY DETERIORATING ITEMS

Abstract

Deterioration is a natural process for many products such as food, chemicals, fruits, vegetables, flowers, etc. This study investigates some inventory control policies pertaining to non instantaneously degradable items (NDIs). The prime aim of our research is to model inventory control problems under realistic assumptions, such as preservation technology investments, mutual spoilage reduction through inspection, demand functions sensitive to selling price, stock and advertisement frequency, time-sensitive holding cost functions, trade credit, advance payment policies, carbon reduction measures, product freshness, etc. Memory-based inventory control models via fractional calculus approaches are also explored. Due to the complexity and nonlinearity of the profit maximization problems, various nature inspired optimization techniques, viz., real coded genetic algorithm (RCGA), particle swarm optimization (PSO), differential evolution (DE), etc., are implemented to obtain optimal inventory control policies. A dimensional learning-based metaheuristic framework is developed to improve the search performance of the existing metaheuristic algorithms. The applicability of the suggested inventory control models is examined through a number of numerical illustrations. The optimal design and variability of the different inventory control descriptors have been investigated through optimization and sensitivity analysis. The investigation done on inventory control policies for NDIs is arranged into ten chapters. Chapter 1 provides an overview of the basic concepts along with methodological aspects used in inventory control of NDIs. Nature-inspired optimization approaches, viz., GA, PSO, QPSO, DE, and GWO, are described to deal with complex optimization problems of inventory systems. A review of the literature pertaining to the research conducted in this thesis is also included. In Chapter 2, the product’s freshness-sensitive demand and shelf-life dependent deterioration rate are taken into account to develop an inventory policy. Promotional efforts and price sensitivity factors are also included in the demand function. In order to reduce carbon emissions and achieve sustainable objectives, a carbon cap and trade scheme is used. The concerned inventory optimization issues are handled by using PSO, QPSO and DE metaheuristics. Chapter 3 focuses on a two-level partial trade credit policy for a finite horizon inventory problem for NDIs by considering shelf-time sensitive degradation and inflation effect. A preservation investment strategy is developed to control the deterioration. The demand is assumed to vary with inflated selling price, advertisement frequency, and downstream credit period. Due to the non-linear characteristic of the proposed optimization problem, metaheuristics, viz., PSO, DE, and grey wolf optimizer (GWO) are employed.