Hybrid Genetic Algorithm-Simulation Optimization Method for Proactively Planning Layout of Material Yard Laydown

Alanjari, Pejman, RazaviAlavi, SeyedReza and AbouRizk, Simaan (2015) Hybrid Genetic Algorithm-Simulation Optimization Method for Proactively Planning Layout of Material Yard Laydown. Journal of Construction Engineering and Management, 141 (10). 06015001. ISSN 0733-9364

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Abstract

This paper presents a hybrid optimization method combining a genetic algorithm (GA) and simulation for planning the layout of material yard laydown areas. An optimized material yard layout entails efficiency in terms of time and cost for decision makers who seek increased performance in material handling, availability, and accessibility. Laying out materials on yards is mostly performed reactively in current practice, where the planner decides daily where to position the incoming materials, based on the list of material arrival and required materials for consumption, received daily. This policy cannot account for the dynamism of material flow in and out of the yard during a construction project. In contrast, a proactive materials placement policy can be used to address this concern based on incoming and outgoing material schedules for a certain period of time. This paper aims to evaluate the proactive material placement policy and present an integrated framework to determine the optimum layout for placing materials resulting in minimum material haulage time. To this end, a hybrid optimization is implemented through a case study from the steel fabrication industry, where an effective materials handling method could be of great significance. The major contribution of this work is the development of an approach that performs dynamic layout optimization of materials arriving at construction yards, using GA to heuristically search for the solution, and use of simulation to model the material handling process and determine the material haulage time. Results of the analyses show clear merits of proactive material placement over the reactive strategy and demonstrate the importance of GA and simulation integration to obtain more realistic outcomes.

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