Multi-period resource allocation for estimating project costs in competitive bidding
- 601 Downloads
In competitive bidding for project contracts, contractors estimate the cost of completing a project and then determine the bid price. Accordingly, the bid price is markedly affected by the inaccuracies in the estimated cost. To establish a profit-making strategy in competitive bidding, it is crucial for contractors to estimate project costs accurately. Although allocating a large amount of resources to cost estimates allows contractors to prepare more accurate estimates, there is usually a limit to available resources in practice. To the best of our knowledge, however, none of the existing studies have addressed the resource allocation problem for estimating project costs in competitive bidding. To maximize a contractor’s expected profit, this paper develops a multi-period resource allocation method for estimating project costs in a sequential competitive bidding situation. Our resource allocation model is posed as a mixed integer linear programming problem by making piecewise linear approximations of the expected profit functions. Numerical experiments examine the characteristics of the optimal resource allocation and demonstrate the effectiveness of our resource allocation method.
KeywordsResource allocation Cost estimation Project management Competitive bidding Mixed-integer linear programming
This work was supported by Grant-in-Aid for Scientific Research (C) 25350455 by the Japan Society for the Promotion of Science.
- Beale EML, Tomlin JA (1970) Special facilities in a general mathematical programming system for non-convex problems using ordered sets of variables. In: Proceedings of the 5th international conference on operations research, pp 447–454Google Scholar
- Christensen P, Dysert LR (1997) Cost estimate classification system. In: AACE international recommended practice 17R–97Google Scholar
- Ibaraki T, Katoh N (1988) Resource allocation problems: algorithmic approaches. MIT Press, CambridgeGoogle Scholar
- Ishii N, Takano Y, Muraki M (2015) A heuristic bidding price decision algorithm based on cost estimation accuracy under limited engineering man-hours in EPC projects. In: Obaidat MS, Koziel S, Kacprzyk J, Leifsson L, Ören T (eds) Simulation and modeling methodologies, technologies and applications. Springer, Cham, pp 101–118Google Scholar
- Karande C, Mehta A, Srikant R (2013) Optimizing budget constrained spend in search advertising. In: Proceedings of the 6th ACM international conference on web wearch and data mining, pp 697–706Google Scholar
- Li H, Womer K (2006) Project scheduling in decision-theoretic competitive bidding. In: Proceedings of the 2006 IEEE congress on evolutionary computation, pp 3042–3049Google Scholar
- Soma T, Kakimura N, Inaba K, Kawarabayashi K (2014) Optimal budget allocation: theoretical guarantee and efficient algorithm. In: Proceedings of the 31st international conference on machine learning, pp 351–359Google Scholar
- Towler G, Sinnott RK (2012) Chemical engineering design, 2nd edn. Butterworth-Heinemann, WalthamGoogle Scholar
- Zhang W, Zhang Y, Gao B, Yu Y, Yuan K, Liu TY (2012) Joint optimization of bid and budget allocation in sponsored search. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1177–1185Google Scholar