Distribution of bulk resources in the South Island
This project was completed as a requirement of the University of Canterbury MSCI Honours course in 2009. We were approached by Contact Energy to investigate potential changes to their distribution strategy of a specific physical commodity which, due to confidentiality agreements will be referred to as “resource”. We used optimisation to solve the Vehicle Scheduling Problem (VSP) and then simulation to analyse possible changes to Contacts’ current distribution strategy.
· The problem is restricted to the South Island of New Zealand.
· There are two depots, which can receive and store an assumed unlimited supply of resource.
· Due to the geographical layout of the depots and customers, the problem can be divided into two sub-problems, North and South, with a depot in each.
· There are about 60-100 customers in each sub-problem, approximately 10-15 of which will require a delivery on a given day.
· There are a limited number of trucks available to service these customers, with limited capacities.
· Stock-outs occur when a customer runs out of resource. These should be avoided if at all possible.
There are a number of issues which complicate this problem:
· Demand is highly seasonal.
· Demand can vary significantly in the short term.
· Some locations can only accept deliveries during certain ‘time-windows’, if a truck turns up outside these windows, then they will have to wait, or come back later.
· Some locations take priority over others.
· The storage capacity is different at each customer location.
We have been given the task of investigating potential ways to improve distribution, with the goal of reducing long term delivery costs while maintaining a high level of customer service. Delivery cost reductions can occur in two main ways:
1) By increasing storage capacity at a number of the customer locations.
2) By lowering the inventory re-order point for specific customers, allowing larger deliveries less frequently.
Implementing either of these two changes at any location will come at a cost (whether it be the cost of a new warehouse, or the cost of having more stock-outs) and so we must conduct a cost-benefit analysis regarding each change. This involves finding the costs associated with each change, and weighing them up against the potential long term savings that could result from that particular change.
The following is a diagrammatic representation of the simulation process. The same demand scenarios are fed into two identical copies of the model, with one using the current parameters while the other uses hypothetical parameters. Only one parameter is changed at a time in order to isolate the resulting changes in delivery costs associated with that parameter. Capital Expenditure (CAPEX) is taken into account when determining the long term feasibility of each of the parameter changes.
Example: Output graph from VSP model showing which trucks to send to which locations on a given day.
We would like to acknowledge the continued support of our supervisors, Dr John F. Raffensperger and Dr John George and all members of the department for their feedback, as well as our classmates.
Thanks also to Mark Armstrong, Bob Kooge and Trisha Upton from Contact for their help and for sponsoring the project.