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Although outcome sharing in construction is a well-established concept in the literature, there is still an ongoing debate on the most effective approach for distributing project outcomes between an owner and downstream contracting parties (DCPs). To address this issue, this paper aims to investigate an optimal framework for distributing project outcomes among various levels of subcontracting in construction projects. The framework includes contractors, subcontractors, sub-subcontractors and other related parties.

To formulate the optimization problem, the principal–agent model is utilized. The theoretical development is validated through an experiment conducted with employees from road construction companies.

When distributing outcomes among various levels of subcontracting, the sharing should be determined by their contribution to the outcome, effort costs, level of outcome uncertainty and risk preference.

This paper expands on the existing principal–agent theory by incorporating multiple levels of agents, transforming the conventional view of outcome sharing among downstream subcontracting levels into testable hypotheses and well-defined concepts. The paper has practical implications for industry practitioners seeking to effectively allocate benefits and costs throughout a project's subcontracting chain.

The purpose of this paper is to propose an optimum form of incentive contracts with multiple outcomes and multiple agents.

Utility theory and principal-agent theory provide the underlying basis for this paper. A sample of 60 practitioners from public organizations and private companies participated in an exercise to validate the proposed model.

The paper shows that, in outcome sharing contracts, the contributions of agents toward outcomes are positively related, while agent effort costs, outcome uncertainty, outcome correlation and agent level of risk aversion are negatively related. The paper further demonstrates that outcome sharing is positively associated with the level of effort selected by the agents.

Outcome sharing models might be used in construction contracts to encourage the agent to act in the interests of the principal. However, few studies have looked at contracts with multiple outcomes and multiple agents. This paper contributes to the current practice of contract management through simplifying the complex nature of multiple incentive contracts and providing theoretical guidance for multi arrangements.

The purpose of this paper is to address a shortfall in the literature dealing with optimal sharing arrangements. In construction projects, where the owner is concerned about multiple project outcomes (cost, time, quality, […]), there exist no guidelines in the literature on what a sharing arrangement should be between the owner and the contractor. This paper gives that arrangement, under defined risk assumptions on the contractor (risk averse ranging to risk neutral) and the owner (risk neutral). The sharing aligns the contractor's interests with those of the owner.

The results are based on solving a constrained maximisation problem involving the expected utilities of both the owner and contractor. Construction practitioners were interviewed in a designed experiment to validate the results.

It is demonstrated that, at the optimum, the proportions of outcomes sharing to the contractor should be higher for outcomes with lower effort cost and a lower level of uncertainty, and by increasing the correlation between outcomes, the fixed component of the contractor’s fee should increase and the proportions to the contractor should decrease.

The theoretical results assume that the contractor is risk-averse ranging to risk-neutral, and that the owner is risk-neutral. The theory is supported through conducting an empirical study based on interviewing a sample of practitioners working for medium-sized contractors, and hence the support is limited to similar situations, until further data are assembled.

By providing a broader understanding of sharing arrangements within contracts, a contribution is made to the current practice of contracts management. The results may be used in the design of contracts, or as benchmarks, by which contracts designed differently, may be compared.

The results address a shortfall in the literature and are an original solution to establishing an optimal multiple-outcome sharing arrangement.

Target cost contracts are commonly used to share the monetary outcome of work or a project. However, discussion is ongoing, as to what constitutes optimal sharing. The purpose of this paper is to examine optimal sharing and derives a result for defined risk assumptions on the owner (risk neutral) and contractor (risk-averse ranging to risk neutral).

The derivation is based on solving a constrained maximization problem using ideas from principal-agent theory. Practitioners were engaged in a designed exercise in order to validate the approach and propositions. The influence of the contractor's level of risk aversion, the cost uncertainty and the contractor's effort effectiveness are examined.

The paper shows that, at the optimum, the sharing ratio between contractor and owner needs to reduce and the fixed fee needs to increase when the contractor becomes more risk-averse, the level of the cost uncertainty increases, or the effectiveness of the contractor effort decreases.

The paper's findings provide practitioners with a useful benchmark for outcome sharing in target contracts.

Existing work on outcome sharing in target contracts is limited to being qualitative and anecdotal in nature. This paper extends existing knowledge by providing a quantitative treatment of optimal sharing.

The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously.

The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method.

Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values.

The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects.

Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

Many applications of Building Information modelling (BIM) are already integrated into project management processes. However, the construction industry is suffering from poor decision-making, especially during procurement where fundamental decisions are made. To make the best decisions at earlier project stages, such as design, large amount of information needs to be processed and classified. Therefore, this study seeks to create a Decision Support System (DSS) for construction procurement through the application of existing informatics infrastructure and BIM applications.

Literature review expert interviews and case studies with complex procurement considerations were used to identify and validate attributes and criterions for procurement decision-making. Accordingly, Multi-Attribute Utility Theory (MAUT) methodology was used and mathematical models were driven as the foundation for a DSS.

Five major criterions of time, cost, relationship quality, sustainability and quality of work performed was identified for complex construction procurement decision-making. Accordingly, a DSS structure and mathematical model was proposed. Based on this a model architecture was developed for the integration of the DSS into Autodesk Revit as a BIM platform, and assist in pre-contract decision-making.

The results can be used in pre-contract selection processes via currently used BIM applications. The model architecture can integrate DSS outputs to nD models, cloud systems and potentially virtual reality facilities to facilitate better construction operations and smarter more automated processes.

This study formulates and captures complex and unstructured information on construction procurement into a practical DSS model. The study provides a link to integrate solutions with already available platforms and technologies. The study also introduces the concept of designing for procurement; which can be expanded to other challenging decisions during construction.