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Due to negative environmental impacts caused by the building industry, sustainable buildings have recently become one of the most investigated fields in research. As the design technique itself is mainly responsible for building performance, building energy design optimization is of particular interest. Several studies concentrate on systems, operation, and control optimization, complemented by passive strategies, specifically related to the envelope. In building physics, different architectural considerations, in particular, the building’s shape, are essential variables, as they greatly influence the performance of a building. Most scientific work that takes into consideration building geometry explores spaces without any energy optimization or calculates optimization processes of a few basic variables of simplified space geometries. Review studies mainly discuss the historic development of optimization algorithms, building domains, and the algorithm-system and software framework performance with coupling issues. By providing a systemized clustering of different levels of shape integration intensities, space creation principals, and algorithms, this review explores the current status of sustainability related shape optimization. The review proves that geometry design variable modifications and, specifically, shape generation techniques offer promising optimization potential; however, the findings also indicate that building shape optimization is still in its infancy.

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This paper corresponds to the solution of some problems realized during ragweed identification experiments, namely the samples collected on the field by botanical experts did not match the initial conditions expected. Reflections and shadows appeared on the image, which made the segmentation more difficult, therefore also the classification was not efficient in previous study. In this work, unlike those solutions, which try to remove the shadow by restoring the illumination of image parts, the focus is on separating leaf and background points based on chromatic information, basically by examining the histograms of the full image and the border. This proposed solution filters these noises in the subspaces of hue, saturation and value space and their combination. It also describes a qualitative technique to select the appropriate values from the filtered outputs. With this method, the results of segmentation improved a lot.

The current paper is about bus transport process network synthesis. Unlike previously discussed urban traffic modelling and solution methods, here, it is presented as a novel application of the p-graph methodology, while exploiting the peculiarities of the problem. The focus is on the synthesis step, where the set of potentially feasible solutions is determined, in other words, the maximal bus transport process structure is generated. The classical process network instances together with their properties are adapted to this new application field, i.e. to meet the special requirements of the bus transport. First, the meaning of the material type nodes and the operating unit type nodes are described in details. A new axiom is given to complete the set of p-graph's axioms. In addition, the utilization of the conventional maximal structure and solution structure generation algorithms, they are extended to gain advantage of the new axiom and to generate the potential solution structure in a more effective manner. Based on the solution structures a mathematical programming model is generated containing the constraints and the objective function of the bus transport problem. Thus, the generation of the bus launching list is prepared. The solution method presented for bus transport problems meets the high level expectations of decision-makers, i.e. the resulting system is complete, flexible and robust.

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The current work focuses on a Hungarian clothing manufacturer's problem. First the industrial problem is presented; its corresponding critical pass method graph is depicted. To answer all emerging questions with respect to alternative possibilities, a large number of critical pass method problems have to be solved cumbersomely. Instead, first this graph is transformed into a process network. Alternatives specified by mainly financial necessities as well as human resource constraints can now be easily managed, namely where specific activities can be performed in different ways by various employee having different qualifications, requiring different durations and obviously respective costs can be considered within this model. These separate cases can commonly be handled within the resultant sole process network and the corresponding mathematical programming model.

This article addresses the problem of finding work assignments for employees within a given time horizon in a company using a multicommodity network flow model. The problem of human resource allocation is defined by the actual manpower demands of different periods which may vary during different periods. The investigation focuses on when workers should be called in-house and for how long to satisfy demands, while also complying with labour standards and regulations. Additional targets may also be set up, such as minimising the overall number of labour, as well as meeting “comfort” expectations, i.e. the most even working time should be realised for every worker within the event horizon. The paper describes how the multicommodity network flow model is constructed and the corresponding MILP mathematical programming model is formulated in a simple situation where there is only one position for the labour. Finally, the article explains how to construct the multicommodity network flow model and the MILP model for the general case, where there are multiple positions for the labour requiring various skills and competences per position within the periods.