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Purpose One of the long-standing issues in the field of corporate real estate management is the alignment of an organisation’s real estate to its corporate strategy. To date, 14 models for corporate real estate (CRE) alignment have been made, as well as four comparative studies about CRE alignment. Some of the CRE alignment models indicate that they strive for maximum or optimum added value. However, because most models take a so-called procedural rationality approach, where the focus is not on the content of the decision but on the way that the decision is made, “how a CRE manager can select an (optimum) alternative” stays a black box. The purpose of this paper is to open the black box and offer a Preference-based Accommodation Strategy (PAS) design procedure that enables CRE managers to design a real estate portfolio, makes use of scales for direct measurement of added value/preference, and allows the aggregation of individual ratings into an overall performance rating. This procedure can be used as add-on to existing alignment models. Design/methodology/approach The objective of this paper is to test if participants are able to successfully perform the PAS procedure in practice. The PAS procedure is in essence a design methodology that aims to solve strategic portfolio design/decision-making problems. In accordance with problem-solving methodology, mathematical models are made for two pilot studies at the Delft University of Technology. This paper describes a second test of the proposed procedure for designing a real estate strategy. The application of real estate strategy design methods in practice is very context-dependent. Applying the PAS procedure to multiple context-dependent cases yields more valuable results than just applying it to one case. Findings The PAS design procedure enables CRE managers to select the (optimal) solution and thereby enhances CRE decision-making. The pilot study results reveal that, by completing the steps in the PAS procedure, the participants are able to express their preferences accordingly. They designed an alternative portfolio with substantially more added value, i.e. a higher overall preference score, than their current real estate portfolio. In addition, they evaluated the design method positively. Research limitations/implications The positive results suggest that designing a strategy by using the PAS design procedure is a suitable approach to alignment. Practical implications The PAS design procedure enables CRE managers to determine the added value of a real estate strategy and quickly and iteratively design many alternatives. Moreover, the PAS design method is generic, it can be used for a wide range of real estate portfolio types. Originality/value The PAS procedure is original because it considers CRE alignment as a combined design and decision problem. The use of operational design and problem-solving methodologies along with an iterative procedure, instead of empirical/statistical methods and procedures, is a novel approach to CRE alignment. The PAS procedure is tested in a second pilot study to provide an assessment of the methodology through the study by testing it under different conditions to the first study. The novelty of this pilot is also that it allowed testing the procedure in its purest form, as the problem structure did not require the additional use of linear programming.

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Probabilistic Monte Carlo simulations are often used to determine a project's completion time given a required probability level. During project execution, schedule changes negatively affect the probability of meeting the project's completion time. A manual trial and error approach is then conducted to find a set of mitigation measures to again arrive at the required probability level. These are then implemented as scheduled activities. The mitigation controller (MitC) proposed in this paper automates the search for finding the most costeffective set of mitigation measures using multiobjective linear optimization so that the probability of timely completion remains at the required level. It considers different types of uncertainties and risk events in the probabilistic simulation. Moreover, it removes the fundamental modeling error that exists in the traditional probabilistic approach by incorporating human control and adaptive behavior in the simulation. Its usefulness is demonstrated using an illustrative example derived from a recent Dutch construction project in which delay is not permitted. It is shown that the MitC is capable of identifying the most effective mitigation strategies allowing for substantial cost savings.

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