Inventory and Pricing Management of Perishable Products with Fixed and Random Shelf life
Bibliographic record
Abstract
In this dissertation, we study inventory and revenue management problems for perishable products with customer choice considerations. This dissertation is composed of six chapters. In Chapter 1, we provide an overview and the motivation of problems. Subsequently, in Chapter 2, we propose a joint inventory and pricing problem for a perishable product with two freshness levels. After a stochastic time, a fresh item turns into a non-fresh item, which will expire after another random duration. Under an (r, Q) ordering policy and a markdown pricing strategy for non-fresh items, we formulate a model that maximizes the long-run average profit rate. We then reduce the model to a mixed-integer bilinear program (MIBLP), which can be solved efficiently by state-of-the-art commercial solvers. We also investigate the value of using a markdown strategy by establishing bounds on it under limiting regimes of some parameters such as large market demand. Further, we consider an Economic Order Quantity (EOQ)-type heuristic and bound the optimality gap asymptotically. Our results reveal that although the clearance strategy is always beneficial for the retailer, it may hurt customers who are willing to buy fresh products. In Chapter 3, we extend this model to the dynamic setting with multiple freshness levels of perishable products. Due to the complexity of the problem, we study the structural properties of value function and characterize the structure of the optimal policies by using the concept of anti-multimodularity. The structural analysis enables us to devise three novel and efficient heuristic policies. We further extend the model by considering donation policy and replenishment system. Our results imply that freshness-dependent pricing and dynamic pricing are two substitute strategies, while freshness-dependent pricing and donation strategy are two complement strategies for matching supply with demand. Also, high variability in product quality under dynamic pricing benefits the firm, but it may result in significant losses with a static pricing strategy. In Chapter 4, we study a joint inventory-pricing model for perishable items with fixed shelf lives to examine the effectiveness of different markdown policies, including single-stage, multiple-stage, and dynamic markdown policies both theoretically and numerically. We show that the value of multiple-stage markdown policies over single-stage ones asymptotically vanishes as the shelf life, market demand, or customers’ maximum willingness-to-pay increase. In chapter 5, with a focus on blood products, we optimize blood supply chain structure along with the operations optimization. Specifically, we study collection, production, replenishment, issuing, inventory, wastage, and substitution decisions under three different blood supply chain channel structures, i.e., the decentralized, centralized, and coordinated. We propose a bi-level optimization program to model the decentralized system and use the Karush–Kuhn–Tucker (KKT) optimality conditions to solve that. Although centralized systems result in a higher performance than decentralized systems, it is challenging to implement them. Thus, we design a novel coordination mechanism to motivate hospitals to operate in a centralized system. We also extend the model to the case with demand uncertainty and compare different issuing and replenishment policies. Analysis of a realistic case-study indicates that integration can significantly improve the performance of the system. Finally, Chapter 6 concludes this dissertation and proposes future research directions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".