- Title
- Energy-aware loss-based warehousing and inventory optimization models for agri-fresh food supply chains
- Creator
- Paam, Parichehr
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2019
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- Customer awareness of food safety, limited lifetime of agri-fresh foods, food loss, environmental concerns, increasing cost of energy and sharp growth in the world’s population have all motivated agricultural fresh food supply chain businesses to rethink their supply chain operations and manage them in a more optimized and sustainable manner. Unfortunately, roughly one-third of food produced for human consumption is lost or wasted globally. In the agricultural fresh food supply chain, food loss is a crucial issue as agri-fresh food losses are between 14% and 70% depending on the product. Another big challenge in the agricultural fresh food supply chain is energy consumption, as it is one of the most energy-intensive industries. This study focuses on warehousing and inventory optimization in the agricultural fresh food supply chain due to the significant occurrence of food loss in the inventory stage, in addition to a great deal of energy consumption by warehouse operations. Three mathematical programming models of linear programming (LP), mixed-integer linear programming (MILP) and mixed-integer quadratic programming (MIQP) are developed, where each one extends the previous one. The MILP model extends the LP model by adding some special features to warehouses, such as determining different numbers of warehouses of each mode to be active per time period and determining the periods in which the warehouses should operate. Next, the MILP model is extended to a MIQP model for deteriorating items containing multiple products with different deterioration rates during storage. It also considers mode-switching and on/off switching options for warehouses. The objective function of LP and MILP models is to minimize total storage and processing costs, and the objective function of the MIQP model is to minimize total inventory and warehousing costs. Food loss is investigated based on perishability (a fixed lifetime) in the LP and MILP models and based on constant deterioration rates in the MIQP model. The performance of the models is tested and validated using a case study of an Australian apple supply chain company. The results demonstrate that the MILP model outperforms the LP model, as it decreases food loss by 7% and decreases total costs of storage and processing by 1.2%. In addition, a comprehensive sensitivity analysis on some of the parameters of the MILP model provides some noteworthy recommendations for stakeholders of the apple industry to improve their warehouse and inventory performance. Comparing the MIQP model results with the warehousing and inventory configuration of the case study for the year 2016, the model is validated. It shows the capability to minimize total warehousing and inventory costs by 8% and food loss by 20%. To test the final extended model (MIQP), an appropriate number of instances are randomly generated based on an instance collected from the case study; accordingly, different analyses, comprising scenario-based analyses of demand and supply, and analyses of different sizes of instances are carried out. Finally, a number of noteworthy insights are extracted. Decision makers and stakeholders of the agri-fresh food supply chain can benefit from applying the proposed mathematical models to their warehouse and inventory operations to move toward a more efficient and sustainable operation by minimizing their inventory-related costs and agri-food losses.
- Subject
- agricultural fresh food supply chain; apple supply chain; food loss; mixed integer programming; multi-product inventory optimization; sustainability
- Identifier
- http://hdl.handle.net/1959.13/1408842
- Identifier
- uon:35892
- Rights
- Copyright 2019 Parichehr Paam
- Language
- eng
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT01 | Thesis | 2 MB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Abstract | 481 KB | Adobe Acrobat PDF | View Details Download |