CP Methods for Scheduling and Routing with Time-Dependent Task Costs

Kelareva E, Tierney K, Kilby P. CP Methods for Scheduling and Routing with Time-Dependent Task Costs. In: Gomes C, Sellmann M, eds. Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. 10th International Conference, CPAIOR 2013, Yorktown Heights, NY, USA, May 18-22, 2013. Proceedings. Lecture Notes in Computer Science. Vol 7874. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013: 111-127.A particularly difficult class of scheduling and routing problems involves an objective that is a sum of time-varying action costs, which increases the size and complexity of the problem. Solve-and-improve approaches, which find an initial solution for a simplified model and improve it using a cost function, and Mixed Integer Programming (MIP) are often used for solving such problems. However, Constraint Programming (CP), particularly with Lazy Clause Generation (LCG), has been found to be faster than MIP for some scheduling problems with time-varying action costs. In this paper, we compare CP and LCG against a solve-and-improve approach for two recently introduced problems in maritime logistics with time-varying action costs: the Liner Shipping Fleet Repositioning Problem (LSFRP) and the Bulk Port Cargo Throughput Optimisation Problem (BPCTOP). We present a novel CP model for the LSFRP, which is faster than all previous methods and outperforms a simplified automated planning model without time-varying costs. We show that a LCG solver is faster for solving the BPCTOP than a standard finite domain CP solver with a simplified model. We find that CP and LCG are effective methods for solving scheduling problems, and are worth investigating for other scheduling and routing problems that are currently being solved using MIP or solve-and-improve approaches

CP methods for scheduling and routing with time-dependent task costs

Kelareva E, Tierney K, Kilby P. CP methods for scheduling and routing with time-dependent task costs. EURO Journal on Computational Optimization. 2014;2(3):147-194.A particularly difficult class of scheduling and routing problems involves an objective that is a sum of time-varying action costs, which increases the size and complexity of such problems. Solve-and-improve approaches, which find an initial solution for a simplified model and improve it using a cost function, and mixed integer programming (MIP) are often used for solving such problems. However, constraint programming (CP), particularly with lazy clause generation (LCG), has been found to be faster than MIP for some scheduling problems with time-varying action costs. In this paper, we compare CP and LCG against a solve-and-improve approach for two recently introduced problems in the area of maritime logistics with time-varying action costs: the liner shipping fleet repositioning problem (LSFRP) and the bulk port cargo throughput optimisation problem (BPCTOP). We present a novel CP model for the LSFRP, which is faster than all previous methods and outperforms a simplified automated planning model without time-varying costs. We show that a LCG solver is faster for solving the BPCTOP than a standard finite domain CP solver with a simplified model. We find that CP and LCG are effective methods for solving problems with time-dependent task costs and are worth investigating for other scheduling and routing problems that are currently being solved using MIP or solve-and-improve approaches, even when customized global constraints are not available. We also investigate a novel approach to solving the BPCTOP—converting the problem into a vehicle routing problem (VRP) and solving using an existing VRP solver

Ship scheduling with time-varying draft restrictions: a case study in optimisation with time-varying costs

In the last few decades, optimisation problems in maritime transportation have received increased interest from researchers, since the huge size of the maritime transportation industry means that even small improvements in efficiency carry a high potential benefit. One area of maritime transportation that has remained under-researched is the impact of draft restrictions at ports. Many ports have restrictions on ship draft (distance between the waterline and the keel) which vary over time due to variation in environmental conditions. However, existing optimisation problems in maritime transportation ignore time variation in draft restrictions, thus potentially missing out on opportunities to load more cargo at high tide when there is more water available for the ship to sail in, and more cargo can be loaded safely. This thesis introduces time-varying restrictions on ship draft into several optimisation problems in the maritime industry. First, the Bulk Port Cargo Throughput Optimisation Problem is introduced. This is a novel problem that maximises the amount of cargo carried on a set of ships sailing from a draft-restricted bulk export port. A number of approaches to solving this problem are investigated, and a commercial system - DUKC Optimiser - based on this research is discussed. The DUKC Optimiser system won the Australia-wide NASSCOM Innovation Student Award for IT-Enabled Business Innovation in 2013. The system is now in use at Port Hedland, the world's largest bulk export port, after an investigation showed that it had the potential to increase export revenue at the port by $275 million per year. The second major contribution of this thesis is to introduce time-varying restrictions on ship draft into several larger problems involving ship routing and scheduling with speed optimisation, starting from a problem involving optimising speeds for a single ship travelling along a fixed route, and extending this approach to a cargo routing and scheduling problem with time-varying draft restrictions and speed optimisation. Both the Bulk Port Cargo Throughput Optimisation Problem and the speed optimisation research shows that incorporating time-varying draft restrictions into maritime transportation problems can significantly improve schedule quality, allowing more cargo to be carried on the same set of ships and reducing shipping costs. Finally, this thesis also considers issues beyond time-varying draft restrictions in the maritime industry, and investigates approaches in the literature for solving optimisation problems with time-varying action costs. Several approaches are investigated for their potential to be generalisable between different applications, and faster, more efficient approaches are found for both the Bulk Port Cargo Throughput Optimisation problem, and another problem in maritime transportation - the Liner Shipping Fleet Repositioning Problem