Airport management is frequently faced with a problem of assigning flights to available stands and parking positions in the most economical way that would comply with airline policies and suffer minimum changes due to any operational disruptions. This work presents a novel approach to the most common airport problem – efficient stand assignment. The described algorithm combines benefits of data-mining and metaheuristic approaches and generates qualitative solutions, aware of delay trends and airport performance perturbations. The presented work provides promising solutions from the starting moments of computation, in addition, it delivers to the airport stakeholders delay-aware stand assignment, and facilitates the estimation of risk and consequences of any operational disruptions on the slot adherence.
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This paper presents work aimed at improved organization and performance of production in housing renovation projects. The purpose is to explore and demonstrate the potential of lean work organization and industrialized product technology to improve workflow and productive time. The research included selected case studies that have been found to implement lean work organization and industrialized product technology in an experimental setting. Adjustments to the work organization and construction technology have been implemented on site. The effects of the adjustments have been measured and were reviewed with operatives and managers. The data have been collected and analyzed, in comparison to traditional settings. Two projects were studied. The first case implied am application of lean work organization in which labor was reorganized redistributing and balancing operations among operatives of different trades. In the second case industrialized solution for prefabricated installation of prefabricated roofs. In both cases the labor productivity increased substantially compared to traditional situations. Although the limited number of cases, both situations appeared to be representative for other housing projects. This has led to conclusions extrapolated from both cases applicable to other projects, and contribution to the knowledge to improve production in construction. Vrijhoef, R. (2016). “Effects of Lean Work Organization and Industrialization on Workflow and Productive Time in Housing Renovation Projects.” In: Proc. 24 th Ann. Conf. of the Int’l. Group for Lean Construction, Boston, MA, USA, sect.2 pp. 63–72. Available at: .
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Industry 4.0 has placed an emphasis on real-time decision making in the execution of systems, such as semiconductor manufacturing. This article will evaluate a scheduling methodology called Evolutionary Learning Based Simulation Optimization (ELBSO) using data generated by a Manufacturing Execution System (MES) for scheduling a Stochastic Job Shop Scheduling Problem (SJSSP). ELBSO is embedded within Ordinal Optimization (OO), where in the first phase it uses a meta model, which previously was trained by a Discrete Event Simulation model of a SJSSP. The meta model used within ELBSO uses Genetic Programming (GP)-based Machine Learning (ML). Therefore, instead of using the DES model to train and test the meta model, this article uses historical data from a front-end fab to train and test. The results were statistically evaluated for the quality of the fit generated by the meta-model.
