Routing and Scheduling with Time Windows: Models and Algorithms for Tramp Sea Cargos and Rail Car-Blocks
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This thesis introduces a new model formulation to solve routing and scheduling problems, with the main applications in answering routing and scheduling problems faced by a sea-cargo shipping company and a railroad company. For the work in sea-cargo routing and scheduling, we focus on the tramp shipping operation. Tramp shipping is a demand-driven type of shipping operation which does not have fixed schedules. The schedules are based on the pickup and download locations of profitable service requests. Given set of products distributed among a set of ports, with each product having pickup and download time windows and a destination port, the problem is to find the schedule for a fleet of ships that maximizes profit over a specified time horizon. The problem is modeled as a Mixed Integer Non-Linear Program and reformulated as an equivalent Mixed Integer Linear Program. Three heuristic methods, along with computational results, are presented. We also exploit the special structure enjoyed by our model and introduce an upper-bounding problem to the model. With a little modification, the model is readily extendable to reflect soft time windows and inter-ship cargo-transfers. The other part of our work deals with train routing and scheduling. A typical train shipment consists of a set of cars having a common origin and destination. To reduce the handling of individual shipments as they travel, shipments are grouped into blocks. The problem is that given sets of blocks to be carried from origins to destinations, construct the most cost effective train routes and schedules and determine block-to-train assignments, such that the number of block transfers (block swaps) between trains, the number of trains used, and some other cost measures are minimized. Incorporating additional precedence requirements, the modeling techniques from the shipping research are employed to formulate a mixed integer nonlinear program for this train routing and scheduling problem. Computational results are presented.
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