This paper presents an aircraft load allocation optimisation model, which uses a hybrid of simulated annealing and genetic algorithm methods to solve a multi-objective optimisation problem associated with allocating a set of cargo items across a heterogeneous fleet of available airlift assets. It represents candidate solutions using macrochromosomes comprised of an ordered list of available transport assets followed by an ordered list of cargo items. A bin packing heuristic is used to map each individual to a point in asset-utilization space where a novel convex hull based fitness function is used to evaluate the relative quality of each individual and drive an elitist application of genetic operators on the population - including a novel extinction operation that infrequently culls solutions comprising of aircraft chalks that cannot be load balanced. Proof of concept computational results are presented.

Using deformed products of arrangements, we construct a family of linearprograms with n inequalities in Rd on which, in the worst-case, the least-index criss-cross methodrequires Ω(nd) (for fixed d) pivots to reach optimality.