The following paper presents an innovative approach for dealing with complex capacity problems in aviation. We introduce a sliding window framework composed by an optimization method with a simulation component. By applying this framework in diverse problems that are dependent on time it is possible to find feasible and close-to-reality solutions in shorter time than the ones that could be achieved by evaluating the problem in the complete time-horizon. The framework can be applied to solve diverse problems in aviation or similar industries. We exemplify the approach with a model of Paris Charles de Gaulle Airport in France.
MULTIFILE
This deliverable focuses on the construction industry of the Netherlands.The construction industry has a reputation for being inefficient. Innovation in construction logistics is needed to ensure that cities stay liveable. To create innovation in constructionlogistics, collaboration between stakeholders is necessary. However, the lack of reliable quantitative data is a problem. Reliable quantitative data are necessary to convince stakeholders for new collaborations that are needed for innovations in construction logistics. There is, therefore, a need to examine the current state of construction logistics calculation models. The integrated logistics concept (ILC) is used to examine construction logistics processes and to address factors that obstruct the development of construction logistics calculation models.
The design of a spatial distribution structure is of strategic importance for companies, to meet required customer service levels and to keep logistics costs as low as possible. Spatial distribution structure decisions concern distribution channel layout – i.e. the spatial layout of the transport and storage system – as well as distribution centre location(s). This paper examines the importance of seven main factors and 33 sub-factors that determine these decisions. The Best-Worst Method (BWM) was used to identify the factor weights, with pairwise comparison data being collected through a survey. The results indicate that the main factor is logistics costs. Logistics experts and decision makers respectively identify customer demand and service level as second most important factor. Important sub-factors are demand volatility, delivery time and perishability. This is the first study that quantifies the weights of the factors behind spatial distribution structure decisions. The factors and weights facilitate managerial decision-making with regard to spatial distribution structures for companies that ship a broad range of products with different characteristics. Public policy-makers can use the results to support the development of land use plans that provide facilities and services for a mix of industries.
