Rune Larsen

Efficient algorithms for real-life instances of the variable size bin packing problem

In this paper we present a local search heuristic for real-life instances of the variable size bin packing problem, and an exact algorithm for small instances. One important issue our heuristic is able to satisfy is that solutions must be delivered within (milli)seconds and that the solution methods should be robust to last minute changes in the data. Furthermore we show that we are able to incorporate the concept of usable leftovers on a single bin, and the implementation of many additional constraints should be supported by the straightforward solution representation. The heuristic is compared to others from the literature, and comes out ahead on a large subset of the instances.

Full-shipload tramp ship routing and scheduling with variable speeds

Abstract This paper investigates the simultaneous optimization problem of routing and sailing speed in the context of full-shipload tramp shipping. In this problem, a set of cargoes can be transported from their load to discharge ports by a fleet of heterogeneous ships of different speed ranges and load-dependent fuel consumption. The objective is to determine which orders to serve and to find the optimal route for each ship and the optimal sailing speed on each leg of the route so that the total profit is maximized. The problem originated from a real-life challenge faced by a Danish tramp shipping company in the tanker business. To solve the problem, a three-index mixed integer linear programming formulation as well as a set packing formulation is presented. A novel Branch-and-Price algorithm with efficient data preprocessing and heuristic column generation is proposed. The computational results on the test instances generated from real-life data show that the heuristic provides optimal solutions for small test instances and near-optimal solutions for larger test instances in a short running time. The effects of speed optimization and the sensitivity of the solutions to the fuel price change are analyzed. It is shown that speed optimization can improve the total profit by 16% on average and the fuel price has a significant effect on the average sailing speed and total profit.

Solving a real-life, large-scale energy management problem

This paper introduces a three-phase hybrid heuristic for a large-scale energy management and maintenance scheduling problem. The problem is to schedule maintenance periods and refueling amounts for nuclear power plants with a time horizon of up to five years, and handling a number of scenarios for future demand and prices. The goal is to minimize the expected total production cost. The first phase of the heuristic solves a constraint programming model of a simplified version of the problem, the second performs a local search, and the third handles overproduction in a greedy fashion.This work was initiated in the context of the ROADEF/EURO Challenge 2010. In the concluding phase of the competition, our team ranked second in the junior category and sixth overall.After correcting a small implementation bug in the program that was submitted for final evaluation, our solver ranks first in the overall results from the competition.

Centralised horizontal cooperation and profit sharing in a shipping pool

Horizontal cooperation in logistics has attracted an increasing amount of attention in both industry and the research community. The most common form of cooperation in the tramp shipping market is the shipping pool, formed by a fleet of ships from different ownerships operated by a centralised administration. This paper studies such a centralised horizontal cooperation, a product tanker pool in Denmark, and addresses the operational challenges, including how to maximise the pool profit and how to allocate it fairly. We apply discrete event simulation and dynamic ship routing and speed optimisation in order to maximise the pool profit in a highly dynamic environment and apply methods derived from cooperative game theory when allocating the total profit. Through a large number of experiments on realistic data, we evaluate the benefit of cooperation under different scenarios, present the results from the profit allocation and analyse the effect of pool size on the total profit and ship utilisation rate.

A framework for dynamic rescheduling problems

Abstract Academic scheduling problems usually assume deterministic and known in advance data. However, this situation is not often met in practice, since data may be subject to uncertainty and it may change over time. In this paper, we introduce a general rescheduling framework to address such dynamic scheduling problems. The framework consists mainly of a controller that makes use of a solver. The solver can assume deterministic and static data, whereas the controller deals with the uncertain and dynamic aspects of the problem and it is in charge of triggering the solver when needed and when possible. Extensive tests are carried out for the job shop problem, and we demonstrate that the framework can be used to ascertain the benefit of using rescheduling over static methods, decide between rescheduling policies, and finally we show that it can be applied in real-life applications due to a low time overhead. The framework is general enough to be applied to any scheduling environment where a fast enough deterministic solver exists.