Purpose: This paper aims to model and optimize the closed loop supply chain for maximizing the profit by considering the fixed order quantity inventory policy in various sites at multiple periods.Design/methodology/approach: In forward supply chain, a standard inventory policy can be followed when the product moves from manufacturer, distributer, retailer and customer but the inventory in the reverse supply chain of the product with the similar standard policy is very difficult to manage. This model investigates the standard policy of fixed order quantity by considering the three major types of return-recovery pair such as commercial returns, end- of- use returns, end –of- life returns and their inventory positioning at multiple periods. The model is configured as mixed integer linear programming and solved by IBM ILOG CPLEX OPL studio.Findings: To find the performance of the model a numerical example is considered for a product with three Parts (A which of 2nos, B and C) for 12 multiple periods. The results of the analysis show that the manufacturer can know how much should to be manufacture in multiple periods based on Variations of the demand by adopting the FOQ inventory policy at different sites considering its capacity constraints. In addition, it is important how much of parts should be purchased from the supplier at the given 12 periods.Originality/value: A sensitivity analysis is performed to validate the proposed model two parts. First part of the analysis will focus on the inventory of product and parts and second part of analysis focus on profit of the company. The analysis which provides some insights in to the structure of the model.

Port resilience is a critical operational and strategic imperative in the face of disruptions affecting global supply chains. Response to natural disasters, geopolitical events, and cyber threats significantly impair port operations by disrupting cargo movement and compromising port functionality. During such times, operational (immediate), tactical (short- to medium-term), and strategic (long-term) measures are required. This chapter explores the resilience measurement frameworks and strategic responses adopted by Mundra Port in India and Port of Tanjung Pelepas in Malaysia. The authors analyze quantitative data such as traffic volume, TEUs handled, tonnage, vessel calls, quay crane performance, and operational downtime; and qualitative data from annual reports, investor presentations, ESG disclosures, and risk assessments to better understand the resilience landscape. The comparative analysis underscores that port resilience is the result of deliberate strategic planning, technological integration, sustainability commitments, and data-driven decision-making.