English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Climate change and demand for greener energy alternatives are putting increased pressure on the use of agricultural land for not just for food and fibre production but also biofuels, carbon sequestration, biodiversity and other non-traditional uses. A key question is how this competition might impact on not only future land use but also on the composition of the supply chains that process the products of the land. In this paper we address a major part of this question by considering the location of processing centres alongside land use change in an integrated optimisation model. CSIRO has previously developed a model of landuse trade-offs that considers the possible evolution of agricultural land areas in Australia over the next 40 years. This can be modelled as a large scale multi-stage linear programming problem. Here we consider in addition the construction of some processing centres for bio-fuel, bio-energy, livestock facilities and so forth, which introduces a new combinatorial aspect to the model. The decisions of land use and the location of processing centres are interlinked, because transport costs based on distances are often instrumental in determining the economic viability of some of the land uses and conversely economies of scale are necessary to justify investment in processing plants. In this paper we introduce a model containing both problems of a land allocation and a facility location simultaneously which results in a large scale mixed integer linear programming (MILP) problem and therefore is computationally difficult to solve. We suggest an algorithm to solve the problem which utilises some decomposition techniques including aggregation and disaggregation, column generation and a concept of clustering. Furthermore, some numerical results are provide to empirically show the computational feasibility of the suggested solution methodology.

An integrated model of land-use trade-offs and expanding agricultural processing centres