Time-Cost Tradeoff for Multiple Paving Projects Using Genetic Algorithms

Road construction companies work on delivering multiple projects at the same time. Careful planning and allocation of each project's resources need to be determined and evaluated in order to minimize their costs and maximize their profits. In this paper, a model was developed with the objective of trading off between time and cost for Multiple Paving Projects using Genetic algorithms (MPP-GA). The MPP-GA was designed with four different modules: (1) an input module that requires the number of multiple projects in hand and their related information; including each project's contractual milestones and construction specifications. (2) An equipment database module that contains a list of the available different types of equipment used on paving projects including their production rates and their cost. (3) an Optimization engine module that works to determine the optimum equipment fleet required to be allocated on each paving project based on the total equipment cost. The optimization module performs a trade-off between the cost of resources allocated for each project and the delay damages and/or incentives calculated as a result of late, early or on time delivery of each project. and finally, (4) an output module that reports the fleet configuration, delay damages and/or incentives, total cost and expected finishing dates for each project. A case study was presented to illustrate a number of practical features of the proposed model and to demonstrate its capabilities in selecting the near optimum fleet configuration.