Constraint programming based column generation heuristics for a ship routing and berthing time assignment problem

Author name used in this publication: King-Wah PangRefereed conference paper2010-2011 > Academic research: refereed > Refereed conference paperOther VersionPublishe

Efficient People Movement through Optimal Facility Configuration and Operation

There are a variety of circumstances in which large numbers of people gather and must disperse. These include, for example, carnivals, parades, and other situations involving entrance to or exit from complex buildings, sport stadiums, commercial malls, and other type of facilities. Under these situations, people move on foot, commonly, in groups. Other circumstances related to large crowds involve high volumes of people waiting at transportation stations, airports, and other types of high traffic generation points. In these cases, a myriad of people need to be transported by bus, train, or other vehicles. The phenomenon of moving in groups also arises in these vehicular traffic scenarios. For example, groups may travel together by carpooling or ridesharing as a cost-saving measure. The movement of significant numbers of people by automobile also occurs in emergency situations, such as transporting large numbers of carless and mobility-impaired persons from the impacted area to shelters during evacuation of an urban area. This dissertation addresses four optimization problems on the design of facilities and/or operations to support efficient movement of large numbers of people who travel in groups. A variety of modeling approaches, including bi-level and nonlinear programming are applied to formulate the identified problems. These formulations capture the complexity and diverse characteristics that arise from, for example, grouping behavior, interactions in decisions by the system and its users, inconvenience constraints for passengers, and interdependence of strategic and operational decisions. These models aim to provide: (1) estimates of how individuals and groups distribute themselves over the network in crowd situations; (2) an optimal configuration of the physical layout to support large crowd movement; (3) an efficient fleet resource management tool for ridesharing services; and (4) tools for effective regional disaster planning. A variety of solution algorithms, including a meta-heuristic scheme seeking a pure-strategy Nash equilibrium, a multi-start tabu search with sequential quadratic programming procedure, and constraint programming based column generation are developed to solve the formulated problems. All developed models and solution methodologies were employed on real-world or carefully created fictitious examples to demonstrate their effectiveness

A domain transformation approach for addressing staff scheduling problems

Staff scheduling is a complex combinatorial optimisation problem concerning allocation of staff to duty rosters in a wide range of industries and settings. This thesis presents a novel approach to solving staff scheduling problems, and in particular nurse scheduling, by simplifying the problem space through information granulation. The complexity of the problem is due to a large solution space and the many constraints that need to be satisfied. Published research indicates that methods based on random searches of the solution space did not produce good-quality results consistently. In this study, we have avoided random searching and proposed a systematic hierarchical method of granulation of the problem domain through pre-processing of constraints. The approach is general and can be applied to a wide range of staff scheduling problems. The novel approach proposed here involves a simplification of the original problem by a judicious grouping of shift types and a grouping of individual shifts into weekly sequences. The schedule construction is done systematically, while assuring its feasibility and minimising the cost of the solution in the reduced problem space of weekly sequences. Subsequently, the schedules from the reduced problem space are translated into the original problem space by taking into account the constraints that could not be represented in the reduced space. This two-stage approach to solving the scheduling problem is referred to here as a domain-transformation approach. The thesis reports computational results on both standard benchmark problems and a specific scheduling problem from Kajang Hospital in Malaysia. The results confirm that the proposed method delivers high-quality results consistently and is computationally efficient

A domain transformation approach for addressing staff scheduling problems

Staff scheduling is a complex combinatorial optimisation problem concerning allocation of staff to duty rosters in a wide range of industries and settings. This thesis presents a novel approach to solving staff scheduling problems, and in particular nurse scheduling, by simplifying the problem space through information granulation. The complexity of the problem is due to a large solution space and the many constraints that need to be satisfied. Published research indicates that methods based on random searches of the solution space did not produce good-quality results consistently. In this study, we have avoided random searching and proposed a systematic hierarchical method of granulation of the problem domain through pre-processing of constraints. The approach is general and can be applied to a wide range of staff scheduling problems. The novel approach proposed here involves a simplification of the original problem by a judicious grouping of shift types and a grouping of individual shifts into weekly sequences. The schedule construction is done systematically, while assuring its feasibility and minimising the cost of the solution in the reduced problem space of weekly sequences. Subsequently, the schedules from the reduced problem space are translated into the original problem space by taking into account the constraints that could not be represented in the reduced space. This two-stage approach to solving the scheduling problem is referred to here as a domain-transformation approach. The thesis reports computational results on both standard benchmark problems and a specific scheduling problem from Kajang Hospital in Malaysia. The results confirm that the proposed method delivers high-quality results consistently and is computationally efficient

Constraint Programming-Based Heuristics for the Multi-Depot Vehicle Routing Problem with a Rolling Planning Horizon

Der Transportmarkt ist sowohl durch einem intensiven Kostenwettbewerb als auch durch hohe Erwartungen der Kunden an den Service geprägt. Die vorliegende Dissertation stellt zwei auf Constraint Programming basierende heuristische Frameworks vor, die eine Reoptimierung bereits geplanter Touren zu festgelegten Zeitpunkten erlauben und so eine Reaktion auf die gesteigerte Wettbewerbsdynamik und den Kostendruck ermöglichen.Actors on the transportation market currently face two contrary trends: Cost pressure caused by intense competition and a need for prompt service. We introduce two heuristic solution frameworks to enable freight carriers to deal with this situation by reoptimizing tours at predefined points in time. Both heuristics are based on Constraint Programming techniques

A Framework for Constraint Programming Based Column Generation

Column generation is a state-of-the-art method for optimally solving difficult large-scale optimization problems such as airline crew assignment. We show ho