Approximation Algorithms for Perishable Inventory Systems

Abstract

We develop the first approximation algorithms with worst-case performance guarantees for periodic-review perishable inventory systems with general product lifetime, for both backlogging and lost-sales models. The demand process can be nonstationary and correlated over time, capturing such features as demand seasonality and forecast updates. The optimal control policy for such systems is notoriously complicated, thus finding effective heuristic policies is of practical importance. In this paper, we construct a computationally efficient inventory control policy, called the proportional-balancing policy, for systems with an arbitrarily correlated demand process and show that it has a worst-case performance guarantee less than 3. In addition, when the demands are independent and stochastically nondecreasing over time, we propose another policy, called the dual-balancing policy, which admits a worst-case performance guarantee of 2. We demonstrate through an extensive numerical study that both policies perform consistently close to optimal.

Carbon-Constrained Perishable Inventory Management with Freshness-Dependent Demand

“…Multi-objective mixed-integer linear programming models for perishable food supply chains are developed in [18]. In [19] are presented approximation algorithms for perishable inventory models with backlogging and lost-sales.…”

Stochastic Inventory Routing for Perishable Products

“…In many real-life IRP settings, product shelf life is not unlimited. Perishable products constitute over 52% of sales revenue of grocery retail chains (Chao et al 2015), and roughly 10% of product is wasted before being sold , while the food retail profit margin hardly exceeds 2%; see EBRD-FAO (2009), NAICS (2012, FMI (2014). Therefore, the profitability highly depends on efficient and effective inventory routing policies.…”

“…Thus, many researchers turned to effective heuristic policies to handle these problems, mostly for the case of independent and identically distributed demands (Chao et al 2015). The most widely used periodic-review ordering policies are (R, S) (Chiu 1995, Cooper 2001, Deniz et al 2010) and (R, s, S) (Broekmeulen andVan Donselaar 2009, Lian andLiu 1999), where R refers to the number of periods between two consecutive reviews of the inventory system, s denotes the inventory level below which an order is triggered, and S is the order-up-to level value.…”

Economic Order Quality Model for Determining the Sales Prices of Fresh Goods at Various Points in Time

“…The assumption of the traditional EOQ model has been changed from "goods are nondegenerative" to "goods are degenerative goods (e.g., petroleum and other volatile products) that decrease in volume over time even when unsold" [33][34][35][36]. Since 2010, an increasing number of studies on deteriorating goods inventory have focused on fresh (perishable) goods whose quality deteriorates over time [25,[37][38][39][40][41][42][43][44][45][46][47][48].…”