| dc.description.abstract | Costs related to transportation and the time it takes for deliveries are crucial factors in
the functioning of supply chain operations. Companies encounter the difficulty of managing
cost reduction while meeting customer demands for timely shipments. It is essential to employ
successful strategies to achieve savings in expenses while maintaining swift and effective
deliveries.
In this thesis, the growing efficacy of the cluster first-route second approach in the
delivery flow of the ABC corporation is carried out on multiple products, multiple
compartments and multiple trips VRP of an esteemed dairy firm in Australia. In alignment with
the business objective, the aim is to identify the quantity, locations, and capacities of new
Regional Distribution Centers (RDCs) to be established. This is done to cater to the demands
of the existing Hub while minimizing the overall transportation expenses. The research
methodology involves implementing Greedy Heuristics to solve the problem derived from the
Set Covering Problem, and the execution is carried out using Python software. The outcomes
obtained assist in determining the optimal location for the new Regional Distribution Center
(RDC), deciding whether to place an order, and specifying the order quantity to achieve overall
cost reduction. The outcomes of the preliminary phase, utilizing Greedy heuristics for Set
covering, are then used in the subsequent stage, inputted in Guided Local Search using Python
to minimize transportation costs by optimizing the initial routes, vehicles, and distances to get
the best route while ensuring vehicle capacity limitations. A mathematical model result by
Cplex demonstrates which compartments containing which product type per trip by which
vehicles.
The validation analysis is performed by comparing the outcomes of this thesis with the
methodology of the main reference on the data presented in this paper to demonstrate
optimization. | en_US |
