Finding Optimum Resource Allocation to Optimizing Construction Project Time/Cost through Combination of Artificial Agents CPM and GA

Abstract

In order to plan a construction project, computer simulations are frequently used to predict the performance of the operations through simulating the process flows and resource selection procedure. However IntroductionDespite the fact that different robust methods have been developed for project scheduling and planning, in the construction industry, Critical Path Method (CPM) is still the most popular method. The first major cause of this popularity is the simplicity of the CPM. On the other hand, for finding the optimum allocation, planners must test all of the alternate construction technologies and resource allocations, including crew sizes for all of the project activities. In each activity, each of its alternatives cause specific duration and cost. In the complicated projects, with increase in the number of activities and the types of resources, these alternatives increase progressively. Therefore, the effective technique is necessary to analyze and determine the optimal resource allocation to complete a project with the minimum cost or time. Several researchers have used discrete event simulation (DES) to develop such a technique for analyzing the effect of different resource allocations. Examples include evaluating the effect of different resource allocation on the concrete batching operations, [1], earthmoving operations [2], residential construction inspection process [3], precast concrete workshops [4], and tunnel planning [5].Although these simulation techniques could find optimum resource allocation, examination of all resource allocation combinations to determine the best solution is too lengthy. Therefore, sensitivity analysis was proposed by [6] to facilitate such enumerations. However, with increase in the complexity of simulation model and number of available resource combinations, sensitivity analysis becomes an extremely time-consuming process. In this regard, different researchers have used heuristic algorithms (HAs) to efficiently search for appropriate resource allocation under specified objectives. For instance, AbouRizk and Shi (1994) used a heuristic algorithm (HA) to find the best resource allocation in the simulation system [7]. Several researchers proposed a hybrid model with combining genetic algorithms (GAs) and simulation for optimizing resource allocation regarding minimum unit cost or maximum productivity rate of the simulation model [8][9][10]. Senouci and Adeli (2001) used dynamic neural network to optimize resource allocation subjected to project network and available resource constrains that was

Application of Fuzzy Modelling to Predict Construction Projects Cash Flow

“…A project can be a unique service or product. In each project there are some constraints including but not limited to time, cost, and scope [2][3][4]. The project manager must balance between the mentioned constraints as each project has a different condition and different objectives [5,6].…”

Scientometric Review of Construction Project Schedule Studies: Trends, Gaps and Potential Research Areas

“…- lated to identification, cause-effect and their analysis (Choudhry et al, 2014;Shen et al, 2017;Xu et al, 2018); -Schedule optimization algorithms and models: A number of CPS optimization algorithms have been studied to solve multiple CPS problems, either single objective or multiple objectives, with single or multiple constraints such as genetic algorithm (Long & Ohsato, 2009;Zou et al, 2016), fuzzy stochastic network (Radziszewska-Zielina et al, 2017), Monte Caro simulation (Indhu & Farhan, 2015), fuzzy mathematical models (Castro-Lacouture et al, 2009), constraint programming, linear programming, and symbiotic organisms search (Cheng et al, 2016); -CPS emerging technologies: CPS emerging technologies have been examined in previous studies such as 4D-BIM (Mirzaei et al, 2018) to detect time-space problems, geographical information system (GIS) (Bansal & Pal, 2009) to identify logical error and missed activities, integration of BIM with primavera (Subramani & Ammai, 2018) to manage risks and BIM schedule (Chen et al, 2011;Ding et al, 2015) to assign crew and other resources; -Resource scheduling: One of the most research areas in CPS is to solve resource constrained scheduling problems (Christodoulou et al, 2012;Giran et al, 2017). It includes but not limited to, resource allocation (Khanzadi et al, 2016), resource levelling (El-Rayes & Jun, 2009), resource utilization (Siu et al, 2016); -Lean project scheduling: The application of lean principle (e.g. Last Planner System (LPS)) has been widely studied to improve performance of project scheduling.…”