Stochastic Optimal Haul-Route Searching Method Based on Genetic Algorithm and Simulation

Gwak, Han-Seong; Yi, Chang-Yong; Lee, Dong‐Eun

doi:10.1061/(asce)cp.1943-5487.0000496

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…For example, the edge cut of connected component 2 in Fig. 4(1) includes eleven edges, namely: (3,2), (3,4), (3,6), (3,8), (7, 2), (7, 4) (7, 6) (7, 8) (7,10) (7, 11) (7,12). δ(S) is used to denote the edge cut set of S; x m (δ(S)) is to add up the road type variable (0/1) associated with each edge in δ(S) .…”

Section: Cutting Plane Methodsmentioning

confidence: 99%

“…Optimization of the operational cost in mass earthworks has been focused on: (1) fleet balancing for achieving the maximum earthmoving productivity [7][8][9][10][11]; (2) investigating hauling strategies under geological constraints [12][13][14][15]; (3) determining earthmoving volumes from cut areas to fill areas on site [11,[16][17][18]; and (4) identifying the proper sequence to execute earthmoving jobs [2,14,19,20]. It is worth mentioning that temporary haul road alignment design has not been explicitly defined as an optimization problem in previous construction engineering research.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A mixed-integer linear programming approach for temporary haul road design in rough-grading projects

2016

Automation in Construction

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Section: Cutting Plane Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A mixed-integer linear programming approach for temporary haul road design in rough-grading projects

2016

Automation in Construction

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“…They developed a software package based on their proposed method to automate the earthmoving and paving planning process. Gwak et al (2016) proposed a genetic algorithm-based method that incorporates stochastic variables for the haul-route optimization based on the equipment travel time and fuel consumption. They improved the computation speed of the earthmoving problem compared to past research.…”

Section: State Of Knowledge In Materials Logistics Planningmentioning

confidence: 99%

Improving materials logistics plan in road construction projects using discrete event simulation

Alvanchi

Baniassadi

Shahsavari

et al. 2021

ECAM

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PurposeMotivated by the high cost of material movements in road construction projects, past studies have used analytical methods to optimize materials logistics plans. A key shortcoming of these methods is their inability to capture the uncertain, dynamic and complex characteristics of the road construction material logistics. Failure to incorporate these characteristics can lead to sub-optimal results. The purpose of this study is to propose the use of discrete event simulation (DES) to address the existing shortfall.Design/methodology/approachDespite the powerful capabilities of DES models in capturing the operational complexities of construction projects, they have not been previously utilized to optimize the material logistics of road construction projects. The proposed DES-based method in this research captures the operational details of material logistics and uses a heuristic approach to overcome the combinatorial problem of numerous choices. The method was applied to a 63.5 km real-world road construction project case to demonstrate its capabilities.FindingsSix different material types from 28 material sources were used in the case. Approximately 1.5% of the material logistics costs were saved by following the proposed method and choosing appropriate material sources.Originality/valueThis research contributes to the body of knowledge by leveraging the capabilities of DES and presenting a novel method for improving the materials logistics plan of road construction projects. The proposed method provides practitioners with the basis for capturing the key operational details that were overlooked in the past. The proposed method can be adopted in road construction projects to reduce the overall material procurement cost.

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“…GA is an adaptive stochastic searching algorithm with overall searching strategy (Gwak et al , 2016). It imitates the principle of “natural selection and survival of the fittest” in nature and forms a genetic mechanism of free selection, crossover, mutation and iteration that conforms to the algorithm requirements.…”

Section: Objective Functionmentioning

confidence: 99%

Construction of a 5D duration and cost optimisation model based on genetic algorithm and BIM

Shi

Kong

2019

JEDT

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Purpose The construction industry is characterized by a long construction period, high cost and many uncontrollable factors. The owners and contractors are increasingly focusing on the efficiency of their construction and costs in pursuit of greater economic benefits. However, current methods used in the construction period and cost optimization analysis with multiple constraints the have their own limitations. Therefore, this study aims to propose a combination of genetic algorithm (GA) and building information modeling (BIM) to construct a five-dimensional construction duration-cost optimization model with the advantages of optimization and simulation for optimization. Design/methodology/approach This design first analyzed the characteristics of changing construction period and cost and then improved the genetic mechanism and the data processing method in the GA according to the aforementioned characteristics. Then, BIM technology was combined with GA to testify the feasibility of the model in the practical engineering project. Findings The result proved that this new method was reasonable and effective in dealing with the complicated problem of period and cost. GA accelerated the optimization process and yielded a reliable Pareto solution. BIM technology simulated the construction process before construction to increase the feasibility of the construction scheme. Originality/value This method not only can rapidly provide the best construction period/cost decision to the architect according to the previous working period/cost or contract data that can meet the demands of the architect but also visualize the construction and give a dynamic schedule of the project.

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Stochastic Optimal Haul-Route Searching Method Based on Genetic Algorithm and Simulation

Cited by 10 publications

References 12 publications

A mixed-integer linear programming approach for temporary haul road design in rough-grading projects

A mixed-integer linear programming approach for temporary haul road design in rough-grading projects

Improving materials logistics plan in road construction projects using discrete event simulation

Construction of a 5D duration and cost optimisation model based on genetic algorithm and BIM

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