Optimizing System Availability: Integrating Quantity-Based Discounts and Delivery Lead Times for Component Procurement using Markov process

Abstract

Purpose: The model allocates the system components orders to the suppliers to minimize the parts purchase costs, the system construction delay penalties, and maximize the system availability during its operations. It considers the quantity-based discount and variation of delivery lead time by ordering similar components which means providing components from the same supplier. The model also reflects the prerequisite relationships between construction activities and calculates the delay penalty resulting from parts delivery lead time.Methodology: This research presents a mathematical model for selecting suppliers of components of an industrial series-parallel multi-state system. A nonlinear binary mathematical program selects system components using the Markov process results. It minimizes the total system construction phase costs, including the components' price, the system construction delay penalty, and the system exploitation phase costs, including the system shutdown and working at half capacity. Findings: The model allocates optimal orders for the components of a typical industrial system, consisting of four elements. The main industrial system analyzed is the feedwater system (FWS) of heat-recovery steam-generator boilers used in combined cycle power plants in Iran. This system includes 22 components; however, due to the complexities of the Markov model, they were grouped into four subsystems. The proposed approach combines nonlinear binary programming with Markov process results to optimize life cycle parameters of the system, including construction costs and operational availability.Originality: By integrating Markov chain results into binary nonlinear mathematical programming, this study aims to balance the objectives of the construction and operational phases of an industrial unit.