from the conferences organized by TANGER Ltd.
In our prior research, we formulated a technique for planning distribution strategy amid future uncertainties. Conventional planning methods, which involve designing potential scenarios and defining probabilities, frequently face difficulties because of unforeseeable future events. Suboptimal strategies can emerge if the probabilities were assigned incorrectly at the beginning. The contribution of this article is a new method that avoids making rigid assumptions about the exact probability of each future scenario. Instead, it explores the entire allowable probability space and selects an optimal strategy in most situations. In the case study, the method’s usability is shown on a real-world company operating in Prague. We use it to model the impact of a city policy on the company’s operations within the city limits. Due to the frequent traffic restrictions in other major European cities and the overall trend of following more environmentally friendly policies, Prague is also expected to take this path. Accordingly, the company's operations could be significantly affected. By utilizing historical delivery data from the company and specialized simulation software, we model diverse distribution network scenarios under potential traffic restrictions, already in effect in other European cities, and probabilistically assess the future the company is facing.
Keywords: Distribution strategy planning, concurrent optimization model, last mile delivery, uncertainty© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.