In the supply chain modeling context, the agent-based model aims to represent not only each node, but also the information sharing process among these nodes. Despite the complexity of the configuration, the agent-based model can be applied straightforwardly to support the collaborative planning process. This allows the parties to achieve common goals effectively. Thus by sharing accurate, actio...

Problem, research strategy, and findings A growing consensus has formed among planners, especially planning scholars, that promoting accessibility is a major policy goal. However, efforts to promote accessibility-based face conceptual impediment: common assumption equates the benefits of travel cost savings (TCS). Starting from this assumption, many researchers have interpreted absence TCS (e.g...

In AI planning, macro learning is the task of finding subsequences of operators that can be added to the planning domain to improve planner performance. Typically, a single set is added to the domain for all problem instances. A number of techniques have been developed to generate such a macro set based on offline analysis of problem instances. We build on recent work on instance-specific and f...

We give an overview of a new set of learningbased algorithms for fast and robust motion planning with noisy sensor data. These include improved search in configuration spaces using instance-based learning, and robust proximity queries in configuration spaces with noisy data. We demonstrate the performance of these new proximity and planning algorithms on the PR2 robot with noisy point-cloud data.

Configuration design for mass customized vehicles necessitates the coordination of customer requirements, product characteristics, production processes, and logistics networks, in order to achieve rapid response to customer orders. Existing product configurators are mainly used as sales tools, and fail to account for the requirements of the entire customer order fulfillment process. In this reg...

We consider a dynamic facility location model in which the objective is to find a planning horizon, x*, and a first period decision, XI*, such that XI* is a first period decision for at least one optimal policy for all problems with planning horizons equal to or longer than x*. In other words, we seek a planning horizon, "r*, such that conditions after x* do not influence the choice of the opti...

In this paper, we present a dynamic simulation model which projects the future population of the city of Leeds as a basis for policy analysis and scenario planning. We argue that microsimulation modelling is not entirely effective in the representation of student populations. Alternative approaches using both spatial interaction models and student agents are presented and evaluated. The results...