Scheduling and Platforming Trains at Busy Complex Stations

We consider the problem of train planning or scheduling for large, busy, complex train stations, which are common in Europe and elsewhere, though not in North America. We develop the constraints and objectives for this problem, but these are too computationally complex to solve by standard combinatorial search or integer programming methods. Also, the problem is somewhat political in nature, that is, it does not have a clear objective function because it involves multiple train operators with conflicting interests. We therefore develop scheduling heuristics analogous to those successfully adopted by train planners using ''manual'' methods. We tested the model and algorithms by applying to a typical large station that exhibits most of the complexities found in practice. The results compare well with those found by traditional methods, and take account of cost and preference trade-offs not handled by those methods. With successive refinements, the algorithm eventually took only a few seconds to run, the time depending on the version of the algorithm and the scheduling problem. The scheduling models and algorithms developed and tested here can be used on their own, or as key components for a more general system for train scheduling for a rail line or network.Train scheduling for a busy station includes ensuring that there are no conflicts between several hundred trains per day going in and out of the station on intersecting paths from multiple in-lines and out-lines to multiple platforms, while ensuring that each train is allowed at least its minimum required headways, dwell time, turnaround time and trip time. This has to be done while minimizing (costs of) deviations from desired times, platforms or lines, allowing for conflicts due to through-platforms, dead-end platforms, multiple sub-platforms, and possible constraints due to infrastructure, safety or business policy.

Dynamic Knowledge Creation Through Value Management Teams

With the intention of introducing unique and value-added products to the market, organizations have become more conscious of how to best create knowledge as reported by Ganesh Bhatt in 2000 in 'Information dynamics, learning and knowledge creation in organizations'. Knowledge creation is recognized as having an important role in generating and sustaining a competitive advantage as well as in meeting organizational goals, as reported by Aleda Roth and her colleagues in 1994 in 'The knowledge factory for accelerated learning practices.' One of the successful ingredients of value management (VM) is its utilization of diverse knowledge resources, drawing upon different organizational functions, professional disciplines, and stakeholders, in a facilitated team process. Multidisciplinary VM study teams are viewed as having high potential to innovate due to their heterogeneous nature. This paper looks at one of the VM workshop's major benefits, namely, knowledge creation. A case study approach was used to explore the nature, processes, and issues associated with fostering a dynamic knowledge creation capability within VM teams. The results indicate that the dynamic knowledge creating process is embedded in and influenced by managing team constellation, creating shared awareness, developing shared understanding, and producing aligned action. The catalysts that can speed up the processes are open dialogue and discussion among participants. This process is enhanced by the use of facilitators, skilled at extracting knowledge.