Abstract
We introduce three mathematical models of increasing complexity for designing liner shipping services that guarantee the punctual arrival of vessels at a specified service level. On-time reliability is an important performance indicator for many liner carriers, but current approaches for creating new routes in liner shipping networks do not consider data-driven uncertainty. We perform an empirical analysis of vessel travel times in a real liner shipping network to develop probability distributions that we use within novel, chance-constrained mathematical models for liner shipping service design. Our models are also the first to support variable vessel speeds for service design. In our experiments, we use real-world data from 22 liner shipping routes and evaluate the designed services using a simulation procedure that demonstrates the effectiveness of our approach for reducing lateness. We show that our models can be effectively used for decision support at a tactical level not only for designing services, but also potentially for negotiating maximum demand transit times and prices with customers.
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