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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 our methodology 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. The problem is that given a set of products distributed among a set of ports, with each product having pickup and download time windows and a destination port, find the schedule for a fleet of ships that maximizes profit for a specified time horizon. The problem is modeled as a Mixed Integer Non-Linear Program and reformulated as a 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 is the work in train routing and scheduling. A typical train shipment consists of a set of cars having the common origin and destination. To reduce the handling of individual shipments as they travel, shipments are grouped into blocks. The problem is that given a set 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. The same modeling technique, with extra precedence requirements, is used. Computational results are presented.
