Optimizing Fresh Agricultural Product Distribution Paths Under Demand Uncertainty

Consumers' demand for fresh agricultural products (FAPs) and their quality requirements are increasing in the current agricultural-product consumption market. FAPs' unique perishability and short shelf-life features mean a high level of delivery efficiency is required to ensure their freshness and quality. However, consumers' demand for FAPs is contingent and geographically dispersed. Therefore, the conflicting relationship between the costs associated with the logistics distribution and the level of delivery quality is important to consider. In this paper, the authors consider a fresh agricultural-product distribution path planning problem with time windows (FAPDPPPTW). To address the FAPDPPPTW under demand uncertainty, a mixed-integer linear programming model based on robust optimization is proposed. Moreover, a particle swarm optimization algorithm combined with a variable neighborhood search is designed to solve the proposed mathematical model. The numerical experiment results show the robustness and fast convergence of the algorithm.</p

Supply chain business modelling

The developed work is motivated by the hypothesis that the presented Supply Chain Business Model is a practical and comprehensive approach to support not only operational day-to-day business decisions, but most importantly strategic and long term decisions that may define the success and the longevity of a business. Conceptually, the Business Supply Chain Model developed in this thesis replicates the behaviour and decision making of the different agents in a supply chain, and an Optimisation Module determines the optimised parameters that maximise the overall business profit, whatever scenario it may be. In the optimisation module, a Genetic Algorithm was used to determine the best equation parameters for each individual agent that optimise the overall supply chain profit. Furthermore, several business case-scenarios are presented and the findings highlighted. These case-scenarios prove that: the HC model is robust when subjected to predictable or unpredictable causes of variability; the bullwhip effect can be reduced significantly by applying GA as the optimisation tool; the improvement of profits needs to be evaluated at a global scale, independently of the individual agents’ profit; impact of supply shortages in the SC ; retail expansion analysis; delivery patterns change impact in profitability; impact of sourcing decisions in the SC profitability; model suitability for seasonal vs. non-seasonal products. The SC Modelling framework generic and globalising approach means that is easily applied and transposed to any other business realities and it can be easily changed to reflect other SC scenarios. The costing model associated means that, at any point in the network, all costs and profits can be easily measured. For the first time the shelf-life of a product captured and losses of product due to BBE dates, quantified. In this model the optimisation methodology runs parallel to the developed simulation tool, so the optimisation should be only run for new scenarios