Multi-product multi-region supply chain optimisation for seasonal crops

Abstract

There is a large demand for seasonal crops year-round within Australia, even when they are considered out of season. The demand is satisfied by continually moving production throughout the year to climates where the crops are in season. Management of the supply chain for a major national grower is challenging for several reasons. Due to the large number of planting decisions, it is standard practice for a team of production planners to create the annual production plan. For fresh food production, the supply chain does not contain intermediate storage requiring that the production plan is carefully timed not to waste resources with overproduction. In this paper, we develop a supply chain model that simultaneously manages the production of multiple crops across many growing regions. Production is set to satisfy the demand of multiple end-products while considering the packing plants' throughput capacity, each growing region's harvest capacity, and farm capacity. A time delay may be applied when moving between stages of the supply chain due to the geographic scale being modelled. A deterministic Mixed Integer Program is used to find the optimal planting plan, which minimises the deviation from demand for all products year-round at a minimal cost. Due to the excessive runtime for solving the model, a heuristic solution method is introduced. Numerical experiments demonstrate the advantage of the proposed model over the current manual planning process, which can solve the problem faster and with less deviation across the planning horizon.

Keywords:

• Fresh produce supply chain optimisation
• Supply chain management
• Agri-food supply chain
• Seasonal crops
• Modelling and optimisation
• Linear programming

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

Additional information

Funding

This project was supported by funding from Food Agility CRC Ltd., funded under the Commonwealth Government CRC Program. The CRC Program supports industry-led collaborations between industry, researchers and the community.

Notes on contributors

Harry Sisley

Harry Sisley is a PhD Candidate at the Queensland University of Technology (QUT), within the School of Mathematical Sciences. The focus of his doctoral thesis has been the optimisation of seasonal crop supply chains at the tactical level. In particular developing techniques to overcome the difficulties with optimising daily production planning over long time horizons has been of great interest.

Guvenc Dik

Guvenc Dik Dr. is a Research Associate in Operations Research (OR) in the School of Mathematical Sciences at Queensland University of Technology (QUT). He teaches undergraduate level OR units to students at QUT. His research interests include development of advanced OR techniques for complex, real-life planning, routing and scheduling problems. He has so far focused on various application areas including agriculture, freight logistics, health care and environmental management. He has translated his research into decision-support for end-users and stakeholders in industry.

James McGree

James McGree is a Professor of Statistics in the School of Mathematical Sciences at the Queensland University of Technology (Australia). He received his PhD in Statistics from the University of Queensland (Australia) in August 2008 for research conducted in design of experiments. The main focus of his current research is the development of new statistical methods in design of experiments, Bayesian statistics and big data analytics. His methods have been applied to collect and analyse data across science including in areas of medical, ecological and biological research.

Paul Corry

Paul Corry is an Associate Professor in Operations Research (OR) in the School of Mathematical Sciences at Queensland University of Technology (QUT). His research interests include development of OR techniques for new applications in scheduling, routing, logistics and capacity planning problems. His research has spanned many different application areas including agriculture, manufacturing, mining, transport, freight logistics, health care and environmental management. He is most motivated to tackle research problems that will have impact in the real-world, through translation of research into decision support for end-users and stakeholders in industry. Associate Professor Corry teaches OR units into the Bachelor of Mathematics and Bachelor of Data Science at QUT, and also at post-graduate level. He is passionate about teaching students in a real-world context through authentic case-studies and assessments, and connecting students directly with industry stakeholders and industry's problems. On behalf of the project team, Associate Professor Corry acknowledges the Turrbal and Yugara, the First Nations owners of the lands where QUT stands (where this research was conducted), and we pay respect to the Elders past and present. The lands on which QUT stands have always been places of teaching, research and learning.