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The purpose of this paper is to introduce a new approach for finding the most appropriate risk allocation among construction contract parties. Although risk allocation is a strategic decision that can greatly affect the cost and time of the project, it is often made based on the personal judgments of employers. That is why owners usually find it a very time-consuming and expensive decision process, while most contractors feel that risk sharing is not done fairly. In this paper, a quantitative model for risk allocation is introduced to fulfill clients’ conflicting expectations in the risk allocation process.

By defining conflicting expectation of owners in the form of three quantitative objectives (lowest cost, maximum reliability and minimum risk exposure), a multi-objective optimization algorithm was developed to select the most appropriate risk allocation. Using experts’ knowledge through fuzzy set theory, a multi-objective decision-making model is developed based on an ant colony optimization algorithm. The proposed model is able to find the optimum risk allocation at the lowest cost and highest reliability while protecting the client against risk exposure within multiple parties projects.

The proposed model has the ability to select the most appropriate risk allocation in multi-parties projects (such as public–private partnerships) and quantitatively measure the impact of each employer’s choice on project results in the form of cost and time. The results of implementing the proposed model in a case study project revealed that optimum risk allocation requires a balanced attitude, and the transfer of all risks to the other parties will not necessarily lead to the lowest cost. The client should bear more responsibilities in risk management to avoid extreme time delay and cost overrun.

The proposed model can be implemented in multi-parties projects (such as public–private partnership), while other methods introduced in previous studies can only be used for projects with two party (client and contractor).

By implementing the proposed model in a real project in this article and comparing its results with previous works, it has been shown that the proposed model has a good performance. Using this model can help clients drastically reduce cost and time and ultimately successfully conclude risk allocation negotiations (which is the most difficult part of any contract negotiation).

In this paper, the risk allocation process is modeled in the form of a multi-objective decision problem. This method helps employers to measure their conflicting goals and choose the most appropriate risk allocation according to their objectives. Also, in this article, the conflicting expectations of the employer in the process of contract negotiations are introduced as three measurable goals, which gives the decision-maker the ability to balance his expectations and not miss an objective. In the final step, an optimization model is developed to select the best option, which can choose the most appropriate party to bear the risk in a short time and among an unlimited number of participants.

Nowadays, engineering, procurement and construction (EPC) contracts are being widely used to perform industrial and infrastructure projects because of several reasons like high speed of implementation. However, these contracts are always accompanied by high risks and uncertainties. Thus, selection of the right EPC contractor has significant importance. This paper aims to present a fuzzy multi-criteria decision-making (MCDM) model for EPC contractor prequalification.

First, the EPC contractor prequalification criteria are defined by using literature review and interviewing experts. Second, the weights of criteria are determined by interviewing experts. Then, each EPC contractor is evaluated in each criterion. Finally, fuzzy weighted average (FWA) approach is employed to select the right contractor among potential EPC contractors.

The proposed model is prepared as an applicable model for clients to select the right EPC contractors among contractors who want to conduct the project.

As a lack of applicable model does exist to assign the prequalification of EPC contractors, this study is one of the first research studies which proposed a fuzzy MCDM model for evaluation of EPC contractors. To cope with the uncertainty of the prequalification problem, fuzzy logic has been used. Using fuzzy sets leads to reaching more reliable results. Also, a real case study is provided to explain the proposed model.

Water and wastewater (WW) projects are gaining attention in Iran because of shortages of water resources. However, these projects are lengthy and they are accompanied by numerous risks, such as lack of sufficient financial resources. Public–private partnerships (PPPs) are taken into account as a constructive approach to deal with the problem of insufficient government funds and they are increasingly being implemented to construct the required infrastructures in different countries. Although WW projects in PPPs can reduce the government’s debt, investors are still uncertain about this approach. Hence, this study aims to identify and evaluate the risk of all parties involved in WW-PPP projects, from the viewpoint of investor.

First, the risk factors which are involved in WW projects are identified by interviewing experts and reviewing the literature by means of fault tree analysis (FTA) tool. Second, the probability and effects of the risky factors which are related to specific event are evaluated and analyzed by hybridization of interval fuzzy Type-2 sets (IT2FS) and risk score formulation. Finally, some solutions are proposed to deal with the most challenging risks.

Six gate events, namely, risks which are related to investors such as investor’s consultant-related risks, risky conditions from engineering, procurement, and construction (EPC) contractors’ point of view, risk factors which public sector takes into account, public sector’s consultant-related risks which public sector’s consultant consider challenging and external factors were defined according to the literature. From FTA tool and by interviewing the experts, 94 basic events were identified. Finally, from hybridization of IT2FSs and risk score formulation, top five risks are determined as “Difficulty of injecting financial resources into the project,” “Fluctuation in inflation rate,” “Poor decision-making process” in public sector, “Difficulty of importing the equipment which are required for the project (such as pumps, grain catchers, garbage collectors, etc.) from other countries” and “Impact of risky conditions in other projects on operation of PPP project.”

In the absence of a constructive approach for risk identification and a reliable model for evaluating the identified risks in PPP projects, this research project is one of the first research studies which used FTA for identifying risks and hybridization of IT2FSs and risk score formulation for evaluating the risks.