production target into consideration. The multistage system usually divides the dispatching problem into
two sub-problems including setting the production target in an upper stage and truck assignment for
shovels in a lower stage with the objective of achieving the production targets set by the upper stage. The
focus of this study is to address the lower stage of this problem.
Various heuristic methods are used to solve the lower stage problem [3-5]. However, they tend to give
suboptimal solution of the dispatching problem because the criteria used are either to maximize the
tonnage production or to minimize equipment inactivity (truck waiting and/or shovel idle time). While it
is intuitive to do so, the optimality of the solution is not guaranteed because they are rules-based
approaches. It is also noted that the most successful mine truck dispatching system is claimed to be the
one using dynamic programming method [6]. But little detail of that approach is known since it is
developed for the commercial software DISPATCH™.
In this study, the truck dispatching problem is modeled as an integer programming problem with the
goal to meet production target with minimum operating cost. It is noticed that, instead of solving a realtime
vehicle routing problem, this study focuses on solving the truck dispatching problem at a higher level
that determines the numbers of trips to and from a dump site (shovel) in a complete shift. Result of this
study can be used as input for a vehicle routing algorithm that solves the timetabling problem for each
truck in real-time environment. A formula to analytically determine the optimal fleet size making use of
the dispatching result is also presented. In comparison to the fixed truck assignment method, the proposed
approach can achieve 15.65% reduction of operating cost savings.
The integer programming model of the truck dispatching problem is described in Section 2 followed by
the optimal fleet size determination criterion presented in Section 3. Experiments are given in Section 4 to
validate the efficiency and effectiveness of the approach. Lastly, conclusion is drawn in Section 5.