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This article makes an attempt to build on the extant literature on time–cost trade-off in a transportation problem. The idea is to agglomerate the world of multiple shipment options along with the uncertainty in demand/supply requirements to the traditional set-up. The article introduces a bi-objective transportation model with multi-choice cost and time coefficients along with interval demand and supply constraints. Such a transportation framework is more generalized and provides a much broader view to a decision-maker. The research proposes an efficient iterative algorithm for generating the Pareto frontier that solves a minimum cost flow problem at each iteration. The special structure of this network flow problem is exploited to obtain computationally efficient results. The proposed method is applied to a real-world case study of UPS Express Critical in the United States. The analysis suggests that significant savings in cost and time objectives can be achieved over the traditional set-ups.

Optimizing time–cost trade-off decisions in an interval transportation problem with multiple shipment options