Real-time rescheduling and disruption management for public transit

This research is motivated by the operations of a public transit company in Hong Kong. We investigate how real-time information can be utilized in combination with historical data to improve routing and scheduling decisions practically. A dynamic integrated vehicle and crew scheduling problem is studied where travel times are stochastic and time-dependent. The objective is to maximize the route frequencies and mileage to provide good passenger service and simultaneously minimize crew overtime and meal-break delays. To mitigate unexpected delays due to uncertainties in operations, various mathematical models are proposed for revising the schedules in real time under a rolling-horizon framework. Their efficiency and effectiveness are evaluated via simulation using real-world data. The simulation results also identify the potential benefits of revising the schedule dynamically in real time using optimization models. The results show that the proposed approaches can significantly reduce motormen overtime and meal-break delays while maintaining coverage and route frequency requirements

A graph-based formulation for the shift rostering problem

This paper investigates a shift rostering problem – the assignment of staff to shifts over a planning horizon such that work rules are observed. Traditional integer-programming models are not able to solve shift rostering problems effectively for large number of staff and feasible shift patterns. We formulate work rules in terms of newly-proposed prohibited meta-sequences and resource constraints. A graph-based formulation and a specialized graph construction algorithm are proposed where the set of feasible shift patterns is represented by paths of a graph. The formulation size depends on the structure of the work-rule constraints and is independent of the number of staff. This approach results in smaller networks allowing large-scale rostering problems with hard constraints to be solved efficiently using standard commercial solvers. Moreover, it allows finding multiple optimal solutions which are beneficial for managerial decision makers. Computational results show that the proposed approach can obtain new best-known solutions and identify proven optimal solutions for almost all NSPLIB instances at significantly lower CPU times

Markdown pricing strategy under a dual-channel supply chain with strategic consumers

This study investigates the markdown pricing strategies for a manufacturer and a retailer in a twoperiod dual-channel supply chain, where the manufacturer sells its products via its own direct channel and an independent retail channel to strategic consumers who may wait for markdowns. A two-period game is developed to systematically study the optimal regular prices and markdown prices under four cases, i.e., no markdown in both channels, markdown only in the direct channel, markdown only in the retail channel, and markdowns in both channels. By comparing the different cases, we find that the manufacturer benefits most from the case with markdowns in both channels, where the markdown rate of the retail channel is lower than that of the direct channel. On the other hand, the results indicate that the retailer may also profit most from the case with markdowns in both channels when the consumer acceptance of the direct channel is sufficiently high; otherwise, the retailer enjoys the highest profit under the case with markdown only in the retail channel. Finally, it is found that strategic consumer behavior has a positive impact on the retailer’s profit but a negative impact on the manufacturer’s profit

How do missing patients aggravate emergency department overcrowding? A real case and a simulation study

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A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph

We study a time-constrained heterogeneous vehicle routing problem on a multigraph where parallel arcs between pairs of vertices represent different travel options based on criteria such as time, cost, and distance. We formulate the problem as a mixed-integer linear programming model and develop a tabu search heuristic that efficiently addresses computational challenges due to parallel arcs. Numerical experiments show that the heuristic is highly effective and that freight operators can achieve advantages in cost and customer service by considering alternative paths, especially when route duration limits are restrictive and/or when vehicles of smaller capacity are dispatched to serve remote customers

Real-Time Integrated Re-scheduling for Tramway Operations

Our work aims to develop practical solution approaches for real-time dispatch of crews and vehicles for disruption management. The practical motivation for our research arose from the operations of a public tramway system in Hong Kong. The tram system shares the road with other vehicular traffic in an urban area of the city, and thus is subject to congestion and other disruptions (unexpected traffic conditions, accidents, etc.), making it a challenge to run to schedule. Delays accumulating and propagating over the course of a day can lead to poor service and high operational cost. In this research, we investigate how the availability of historical and real-time auto-sensed location and traffic information can be utilized to improve the real-time scheduling decisions. The historical information is used to estimate the travel times for each route during different periods of the day, while the real-time information about the tram locations is utilized to update the expected completion times of the current assignments for each motorman. Updated estimated travel times and completion times of tasks are fed to a mixed-integer programming model for re-optimization of the schedule. The dynamic and integrated vehicle and crew scheduling problem for real-time control studied in our research has the following characteristics: 1) the actual travel times may deviate from the planned times and are dependent on the time of day and 2) while the on-going route/activity assigned to a motorman cannot be revised, the future assignments can be re-optmized when unexpected events occur. We adopt a rolling horizon approach to re-optimizing the future activities of the motormen from time to time. Upon an arrival of a motorman at a tram terminus or depot, he will be given a sequence of future task assignments, consisting of the routes to run and the scheduled departure times. The motormen will follow his revised sequence of future task assignments until the next re-optimization is performed. The objective is to achieve the target route frequencies in order to provide good quality of services to passengers, and minimize the violation of staff regulations (meal-break delays and overtime). While our application is motivated by tram services, our model can also be extended for other logistics services that suffer from daily transportation disruptions and require prompt recovery of schedules, particularly for those in an urban city setting

A Real-Time Decision Support Tool for Disaster Response: A Mathematical Programming Approach

© 2015 IEEE. Disasters are sudden and calamitous events that can cause severe and pervasive negative impacts on society and huge human losses. Governments and humanitarian organizations have been putting tremendous efforts to avoid and reduce the negative consequences due to disasters. In recent years, information technology and big data have played an important role in disaster management. While there has been much work on disaster information extraction and dissemination, real-Time optimization for decision support for disaster response is rarely addressed in big data research. In this paper, we propose a mathematical programming approach, with real-Time disaster-related information, to optimize the post-disaster decisions for emergency supplies delivery. This decision support tool can provide rapid and effective solutions, which are essential for disaster response.Link_to_subscribed_fulltex