Clearance Pricing and Inventory Policies for Retail Chains
Management Science
Congestion-dependent pricing of network services
IEEE/ACM Transactions on Networking (TON)
Swarm intelligence
Interfaces - Special issue: marketing engineering
Pricing and the News Vendor Problem: a Review with Extensions
Operations Research
Combined Pricing and Inventory Control Under Uncertainty
Operations Research
Note: The Newsvendor Model with Endogenous Demand
Management Science
Commissioned Paper: An Overview of Pricing Models for Revenue Management
Manufacturing & Service Operations Management
Manufacturing & Service Operations Management
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
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The retail market is governed by customer behavior, demand pattern and inventory replenishment policies. It is also observed that any decision would prove to be full of errors, and objective of enhancing the market share could not be achieved, without inclusion of these factors and policies. While an extensive set of literature exists on single and multi-product dynamic pricing, the issue of liquidation of leftover inventory has so far received scant attention from the researchers of Operations Management community. The current work primarily tries to bridge this research gap by addressing dual objectives of revenue maximization and reduction of salvaging losses. In this paper an inter-temporal dynamic pricing model for multiple products is developed under a market setup with price-sensitive demand. Ideas proposed by [1] and [2] have been taken into account for constructing a revenue structure. The formulated objective function is found to be tractable for deriving prices and procurement quantities of large product portfolios. A multi-objective problem has been devised to handle the optimization of normal and clearance revenue by satisfying several pragmatic constraints. Subsequently, an effective algorithm deriving its traits from Particle Swarm Optimization has been proposed to address this problem. An illustrative example from retail apparel industry has been simulated and solved by the afore-mentioned approach. To validate the model statistical analysis has been carried out and the managerial insights portrayed to reveal the practical complexities involved.