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handle: 10725/4554
Abstract Competitive bidding is the main mechanism of allocating projects in the construction market. In the traditional single criterion bidding method, the markup decision has a significant impact on a contractor's business success. Contractors usually take into consideration several factors in the process of determining their markup. This study has reviewed the literature and identified a range of contractors' behaviors when making their markup decision within a competitive bidding environment. An additive markup function consisting of three components, namely competition, risk, and need for work, was developed in order to replicate markup behaviors of contractors. Then, agent-based modeling has been employed for simulating the bidding process within a market formed of a set of heterogeneous contractors with different risk attitudes and defined markup behaviors. This model was used to study the impact of considering need for work and risk allowance in markup determination on financial performance of contractors in various market scenarios. Results suggest that the optimal policy is moderation in both dimensions of risk attitude and need for work.
690, Need for work, Agent-based modeling, Multi-attribute decision making, Construction Engineering and Management, Competitive bidding, Risk attitude, Markup, Computer-aided simulation, Civil Engineering
690, Need for work, Agent-based modeling, Multi-attribute decision making, Construction Engineering and Management, Competitive bidding, Risk attitude, Markup, Computer-aided simulation, Civil Engineering
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 32 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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