Purpose -This paper aims to present a new method to circumvent the limitations of current schedule compression methods which reduce schedule crashing to the traditional time-cost trade-off analysis, where only cost is considered. Design/methodology/approach -The schedule compression process is modeled as a multi-attributed decision making problem in which different factors contribute to priority setting for activity crashing. For this purpose, a modified format of the Multiple Binary Decision Method (MBDM) along with iterative crashing process is utilized. The method is implemented in MATLAB, with a dynamic link to MS-Project to facilitate the needed iterative rescheduling. To demonstrate the use of the developed method and to present its capabilities, a numerical example drawn from literature was analysed. Findings -When considering cost only, the generated results were in good agreement with those generated using the harmony search (HS) method, particularly in capturing the project least-cost duration. However, when other factors in addition to cost were considered, as expected, different project least-cost and associated durations were obtained.Research limitations/implications -The developed method is not applicable, in its present formulation, to what is known as "linear projects" such as construction of highways and pipeline infrastructure projects which exhibit high degree of repetitive construction. Originality/value -The novelty of the developed method lies in its capacity to allow for the consideration of a number of factors in addition to cost in performing schedule compression. Also through its allowance for possible variations in the relative importance of these factors at the individual activity level, it provides contractors with flexibility to consider a number of compression execution plans and identifies the most suitable plan. Accordingly, it enables the integration of contractors' judgment and experience in the crashing process and permits consideration of different project environments and constraints.

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This paper presents a new method for schedule compression of construction projects using the analytical hierarchy process (AHP). The method utilizes a multi‐objective decision environment in which activities are queued for crashing based on the priorities established in that environment. Schedule compression is commonly needed in management of engineering, procurement and construction projects. A wide range of methods are introduced in the literature to perform schedule compression utilizing genetic algorithms, heuristic rules, near‐optimum solutions using harmony search and analogy with the direct stiffness method for structural analysis. Although all these methods consider only cost in the process of schedule compression, a recently conducted survey, by the authors, indicates that project managers consider more than one factor in this process. In fact, the lack of consideration of factors beyond cost has been attributed to the limited use of existing methods. The method presented in this paper aims to circumvent the limitation of the existing methods. It utilizes the findings of a recently conducted survey questionnaire as well as the AHP to develop a multi‐objective decision environment to perform schedule compression in a practical and flexible manner. It further allows for consideration of risk associated with the individual attributes considered in setting priorities for activity crashing. A numerical example is analysed to demonstrate the use of the developed method and to illustrate its practical features. Copyright © 2011 John Wiley & Sons, Ltd.

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Most decision making in construction projects is accomplished by human beings based on manually collected information. This process is labor-intensive, error-prone, and heavily reliant on the knowledge and experience of the decision makers as well as the quality and reliability of the available information. Cyber-physical systems (CPS) offer the potential to transform traditional data collection approach and strengthen the coordination between cyber models and physical assets in a construction project. This paper describes a knowledge-based CPS architecture that aims at augmenting human decision making in construction. Domain knowledge, such as site policies, safety rules, and workflow logic, is elicited and compiled in an ontology-based knowledge hub. With the support of bi-directional cyber-physical communication, the knowledge hub inquiries relevant data from cyber models and physical assets, integrates realtime project data, and synthesizes context-specific information to facilitate human decision-making. This paper outlines the key challenges and technical requirements for creating the knowledge hub and its linkages with traditional CPS systems. It also presents two deployment scenarios in highway and building construction projects to demonstrate the realization a knowledge-based cyber-physical system and its potential in facilitating coordination and improving project performances in safety and efficiency.

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