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Abstract. Eco-Efficient stowage plans that are both competitive and sustainable have become a priority for the shipping industry. Stowage planning is NP-hard and is a challenging optimization problem in practice. We propose a new 2-phase approach that generates near-optimal stowage plans and fulfills industrial time and quality requirements. Our approach combines an integer programming model for assigning groups of containers to storage areas of the vessel over multiple ports, and a constraint programming and local search procedure for stowing individual containers.

In Ubiquitous Computing (Ubicomp) research, substantial work has been directed towards sensor-based detection and recognition of human activity. This research has, however, mainly been focused on activities of daily living of a single person. This paper presents a sensor platform and a machine learning approach to sense and detect phases of a surgical operation. Automatic detection of the progress of work inside an operating room has several important applications, including coordination, patient safety, and context-aware information retrieval. We verify the platform during a surgical simulation. Recognition of the main phases of an operation was done with a high degree of accuracy. Through further analysis, we were able to reveal which sensors provide the most significant input. This can be used in subsequent design of systems for use during real surgeries.

W e solve a central problem in the liner shipping industry called the liner shipping fleet repositioning problem (LSFRP). The LSFRP poses a large financial burden on liner shipping firms. During repositioning, vessels are moved between routes in a liner shipping network. Liner carriers wish to reposition vessels as cheaply as possible without disrupting cargo flows. The LSFRP is characterized by chains of interacting activities with a multicommodity flow over paths defined by the activities chosen. Despite its industrial importance, the LSFRP has received little attention in the literature. We introduce a novel mathematical model and a simulated annealing algorithm for the LSFRP with cargo flows that makes use of a carefully constructed graph; we evaluate these approaches using real-world data from our industrial collaborator. Additionally, we compare the performance of our approach against an actual repositioning scenario, one of many undertaken by our industrial collaborator in 2011. Our simulated annealing algorithm is able to increase the profit from $18.1 to $31.8 million using only a few minutes of CPU time. This shows that our algorithm could be used in a decision support system to solve the LSFRP.

Recently model checking representation and search techniques were shown to be efciently applicable to planning, in particular to non-deterministic planning. Such planning approaches use Ordered Binary Decision Diagrams (obdds) to encode a planning domain as a non-deterministic nite automaton and then apply fast algorithms from model checking to search for a solution. obdds can e ectively scale and can provide universal plans for complex planning domains. We are particularly interested in addressing the complexities arising in non-deterministic, multi-agent domains. In this article, we present umop, a new universal obdd-based planning framework for non-deterministic, multi-agent domains. We introduce a new planning domain description language, NADL, to specify non-deterministic, multi-agent domains. The language contributes the explicit de nition of controllable agents and uncontrollable environment agents. We describe the syntax and semantics of NADL and show how to build an e cient obdd-based representation of an NADL description. The umop planning system uses NADL and di erent obdd-based universal planning algorithms. It includes the previously developed strong and strong cyclic planning algorithms. In addition, we introduce our new optimistic planning algorithm that relaxes optimality guarantees and generates plausible universal plans in some domains where no strong nor strong cyclic solution exists. We present empirical results applying umop to domains ranging from deterministic and single-agent with no environment actions to non-deterministic and multi-agent with complex environment actions. Umop is shown to be a rich and e cient planning system. 1. The term precursor originates in Dean et al. (1995) in contrast to recurrent approaches that replan to recover from execution failures.

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