The objective of this paper is to investigate the importance of taking uncertainty explicitly into account for service network design. We study how solutions based on uncertain demand differ from solutions based on deterministic demand and provide qualitative descriptions of the structural differences. Some of these structural differences provide a hedge against uncertainty by using consolidation. This way we get consolidation as output from the model rather than as an a priori required property. Service networks with such properties are robust, as seen by the customers, by providing operational flexibility.
This paper considers the time-dependent service network design problem with stochastic demand represented by scenarios. To our knowledge, this is the first attempt to address real life-size instances of this problem. The model integrates the balancing of empty vehicles, the cost of handling freight in intermediate terminals, the costs associated with moving freight using the selected services, and the penalty costs of not being able to deliver freight. A metaheuristic is presented and computational results are reported on a set of large new problem instances.
Deterministic models, even if used repeatedly, will not capture the essence of planning in an uncertain world. Flexibility and robustness can only be properly valued in models that use stochastics explicitly, such as stochastic optimization models. However, it may also be very important to capture how the random phenomena are related to one another. In this article we show how the solution to a stochastic service network design model depends heavily on the correlation structure among the random demands. The major goal of this paper is to discuss why this happens, and to provide insights into the effects of correlations on solution structures. We illustrate by an example.
We present a new model to support strategic planning by actors in the liquefied natural gas market. The model takes an integrated portfolio perspective and addresses uncertainty in future prices. Decision variables include investments and disinvestments in infrastructure and vessels, chartering of vessels, the timing of contracts, and spot market trades. The model accounts for various contract types and vessels, and it addresses losses. The underlying mathematical model is a multistage stochastic mixed-integer linear problem. Industry-motivated numerical cases are discussed as benchmarks for the potential increases in profits that can be obtained by using the model for decision support. These examples illustrate how a portfolio perspective leads to decisions different than those obtained using the traditional net present value approach. We show how explicitly considering uncertainty affects investment and contracting decisions, leading to higher profits and better utilization of capacity. In addition, model run times are competitive with current business practices of manual planning.
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