An Improved Hybrid Heuristic Algorithm for Pickup and Delivery Problem with ThreeDimensional Loading Constraints In the logistics industry, the integration of the vehicle routing problem and the container loading problem which has been generalised as pickup and delivery problem with threedimensional loading constraints (3LPDP) is very challenging. It involves not only ensuring the shortest route of travel but also minimising the effort of reloading goods. Traditional optimisation methods such as exhaustive search and greedy search are hard to achieve an optimal solution for such kinds of complex problems. In this paper, a hybrid heuristic algorithm for the 3LPDP problem is extended by two key improvements: the usage of a tabu strategy for enlarging the local search space of the large neighbourhood search (LNS) algorithm, which is proposed in the framework of the simulated annealing process, and the employment of complex block generation and depthfirst heuristic for incrementally finding one proper box at a time in the packing phase. The experimental results show that the improved hybrid heuristic algorithm outperforms its origin regarding total travel distance on benchmark instances proposed by Li and Lim or Dirk and Andreas.

Conditional Markov Chain Search for the Generalised Travelling Salesman Problem for Warehouse Order Picking The Generalised Travelling Salesman Problem (GTSP) is a wellknown problem that, among other applications, arises in warehouse order picking, where each stock is distributed between several locations xe2x80x93 a typical approach in large modern warehouses. However, the instances commonly used in the literature have a completely different structure, and the methods are designed with those instances in mind. In this paper, we give a new pseudorandom instance generator that reflects the warehouse order picking and publish new benchmark testbeds. We also use the Conditional Markov Chain Search framework to automatically generate new GTSP metaheuristics trained specifically for warehouse order picking. Finally, we report the computational results of our metaheuristics to enable further competition between solvers.