Metaheuristic Optimization for Multi Item Supply Chains with Price Indices and Advance Payment

Abstract Business organizations might gain competitive advantages by employing optimal strategies on various aspects of their operations such as pricing, sales effort, product quality, price index and backlogging. In the present article, collaborative and non-collaborative policies for a multi-item two-echelon supply chain (SC) with advance payment are proposed. The Wholesale Price Index (WPI) and Consumer Price Index (CPI) have a substantial impact on commodity prices. In order to maintain the customer purchasing power and profitability, a SC inventory model is developed to study pricing strategies by considering WPI at the manufacturer level and CPI at the retailer level. The centralized and decentralized policies have been suggested to account for the changes in the general price level to maintain consumer buying power. The price and sales team effort dependent demand is considered to evaluate the optimal gross profit, production rate, manufacturer’s cycle duration, shortage time, selling price and initiatives of sales team. The prediction of backlog periods is also helpful for uninterrupted operations and customer satisfaction. For optimization and sensitivity analysis, numerical results are computed and compared using two different metaheuristic approaches namely differential evolution algorithm and particle swarm optimization. The analysis shows that the WPI and CPI have a substantial impact on commodity prices, which makes the profit function vulnerable to variations in the inflation rate. For SC participants, the provision of advance payment arrangements may enhance the cash flow and financial predictability.