Computing the policy parameters for perishable inventory management and cold supply chains under stochastic demand

The objective of the work in this dissertation to introduce techniques to compute the near-optimal policy parameters for the perishable inventory problem and sustainable cold food supply chains with environmental concerns under stochastic demand, as well as tackling and dealing with the demand uncertainty and fluctuations arising from the nature of demand patterns. The solutions we present in this dissertation for the perishability problem hybridises the well-known Wagner-Whitin algorithm, the Silver-Meal heuristic on a network graph setting. Our solution for sustainable cold supply chains employs and combines the inventory routing problem and several formulations found in the literature to calculate environmental effects. We consider a perishable inventory system under a finite planning horizon, periodic review, non-stationary stochastic demand, zero lead time, first in first out issuing policy, and a fixed shelf life. The inventory system has a fixed setup cost and linear ordering, holding, and outdating costs per item. We consider the penalty cost and the service level cases separately and develop two different versions for each case. In addition, we propose a mixed-integer programming model for cold food supply chains to solve a multi-period inventory routing problem under non-stationary stochastic demand, route-dependent costs, and environmental concerns. The study aims to find the replenishment and vehicle routing plans to minimise the total expected cost while producing a minimum amount of CO₂ emissions. The first contribution presented herein is a mixed-integer linear programming model that computes near-optimal (R, S) policy parameters for the sustainable cold food supply chain problem with environmental concerns, while maintaining flexibility in ordering decisions under a pre-determined replenishment schedule. Our numerical experiments show that the (R, S) policy reduces inventory costs significantly, since it solves the excess inventory issue caused by higher excess ending inventory levels arising from the pre-determined and inflexible order quantities in the (R, Q) policy. However, the CO₂ emission levels and routing costs remain similar in both models. The reduced inventory costs make the (R, S) policy significantly reduce the total cost. In addition, our numerical experiments show that the difference between the cumulative ending inventory levels for the (R,Q) and (R,S) policies increasingly grows as the time horizon gets longer, and it results in increasingly larger differences in the total cost values for both policies. The second contribution is a heuristic for the penalty cost case of the perishable inventory problem. The problem can be solved to optimality by the stochastic dynamic programming technique; however, due to the increasing dimensionality of the inventory state which is defined by the inventory levels of each item age, it becomes impractical for longer shelf lives. Our heuristic represents the inventory model as a network graph, and then calculates the shortest path in the graph in a recursive way, based on the hybridisation of the Wagner-Whitin algorithm and the Silver-Meal heuristic. It firstly determines the replenishment periods and cycles using the deterministic-equivalent shortest path approach. Using the replenishment plan determined in the first step, it calculates the order quantities based on the demand observations as a second step. We hereby employ Bookbinder and Tan's static-dynamic uncertainty strategy to respond to demand fluctuations. Our extensive numerical studies show that the computation time is significantly reduced and the heuristic finds near-optimal policy parameters. The final contribution is the modification of our proposed heuristic for the service-level case. To our knowledge, there is no procedure in the literature that calculates the optimal solution. Our extensive computational experiments that use the near-optimal stochastic dynamic programming solution as a benchmark show that the heuristic has a similar performance in terms of near-optimality and significantly reduced computation times.

Grantor dc:publisher

University College Cork

Year dc:date.issued

2025

Author dc:creator

• Gulecyuz, Suheyl

Advisors dc:contributor.advisor

• O'Sullivan, Barry
• Visentin, Andrea

• Inventory control
• Perishability
• Supply chain management
• Sustainability
• Lot sizing
• Non-stationary stochastic demand
• Replenishment cycle policy
• Finite-horizon total cost
• Inventory routing
• CO₂ emission

Statement dc:rights

• © 2025, Suheyl Gulecyuz.

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10468/18799

OAI identifier oai:identifier

oai:cora.ucc.ie:10468/18799