This research addresses the infrastructure maintenance scheduling problems under budget uncertainty. Infrastructure agencies usually face budget uncertainties that will eventually lead to suboptimal planning if maintenance decisions are made without taking the uncertainty into consideration. It is important for decision makers to adopt maintenance scheduling policies that take future budget uncertainty into consideration. The author proposes a multistage, stochastic linear programming model to address this problem. The author also develops solution procedures using the augmented Lagrangian decomposition algorithm and scenario reduction method. A case study exploring the computational characteristics of the proposed methods is conducted and the benefit of using the stochastic programming approach is discussed. In the case study, the road network in Dallas District is used with data taken from the Texas Department of Transportation’s Pavement Management Information System. The case study results reveal that the stochastic programming solutions tend to allocate more resources to preventive maintenance than deterministic solutions that ignore the uncertainty information. The proposed methodology can help decision makers effectively obtain optimal maintenance plan under budget uncertainty. / text

In the beginning of 2020, the world was struck by the Covid-19 pandemic that inhibited many economies. Sweden was chosen because of its unique case when responding to the Covid-19 spread by applying an open policy in contrast to the implementation of total lockdowns.   The aim of this study is to explore the governmental budgetary responses to the Covid-19 pandemic in Sweden. To fulfil this aim, the study focuses on the multi-level governance relationships between different national and local actors in Sweden. In order to explore Sweden’s unique case, an inductive approach was carried out through netnography, mass-media analysis and document analysis in the period between January 31 and May 15. The theoretical frame of reference is based on multi-level governance and actor network theory.   The study concludes that technology as a non-human actor has played a vital role when enhancing collaborations between different actors such as government, regions, municipalities, companies and universities, in order to minimize the infection spread and economic consequences of Covid-19. The study is a basis for continued research on the unexplored topic of crisis management of Covid-19 in Sweden.

In the beginning of 2020, the world was struck by the Covid-19 pandemic that inhibited many economies. Sweden was chosen because of its unique case when responding to the Covid-19 spread by applying an open policy in contrast to the implementation of total lockdowns.   The aim of this study is to explore the governmental budgetary responses to the Covid-19 pandemic in Sweden. To fulfil this aim, the study focuses on the multi-level governance relationships between different national and local actors in Sweden. In order to explore Sweden’s unique case, an inductive approach was carried out through netnography, mass-media analysis and document analysis in the period between January 31 and May 15. The theoretical frame of reference is based on multi-level governance and actor network theory.   The study concludes that technology as a non-human actor has played a vital role when enhancing collaborations between different actors such as government, regions, municipalities, companies and universities, in order to minimize the infection spread and economic consequences of Covid-19. The study is a basis for continued research on the unexplored topic of crisis management of Covid-19 in Sweden.