Logistics planning under uncertainty

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One of the key challenges in today's society resides in managing a sustainable usage of the resources as well as of their transformation and distribution into goods and services with both low environmental impact and high positive social and economic impacts. The area of value chain management has had a major contribution for improvements at many companies. A critical part of this development is based on Decision Support System (DSSs) that are used in companies' core business systems. This development of analytical planning tools has been made possible by new information and decision-making technologies, for which operational research is critical. Another important contribution is the availability of information and data. Today, a massive amount of information, termed "big data", is generated by various information systems including public and commercial databases, sensors and geographical information systems. However, much of this data is about past activities, while planning requires data about the future. Much of the data needed by DSSs is not known exactly due to, for example, price, demand, and driving time uncertainties. Despite that, most (if not all) DSSs for logistics are based on deterministic and decoupled planning models. This drawback creates number of planning and operational challenges for logistics operations as certain anticipation and counter measures must be implemented in order to handle uncertainty. Although there are approaches available for planning under uncertainty, they are generally not adapted for practical logistic planning. These include robust optimization, stochastic programming and financial and operational hedging. In this proposal, we aim to develop efficient approaches for value chain logistics taking into account uncertainty. The results will lead to more robust and stable solutions with less need of re-planning, and have reduced risk to be stuck with a difficult position which requires expensive corrective actions, and less emergency management i.e. only solve urgent problems as they appear. All this lead to reduced costs and negative environmental impact, increased flexibility and fairness, and last but not least sustainable use of our collective resources.