Developing Crew Allocation System for the Precast Industry Using Genetic Algorithms

Al-Bazi, Ammar; Dawood, Nashwan

doi:10.1111/j.1467-8667.2010.00666.x

Cited by 50 publications

(36 citation statements)

References 36 publications

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“…Over the past decade, the Neural Networking (NN) has become one of the most popular optimization and event simulation methods in a number of engineering fields. In construction industry, it is mostly used for simulation of events and resources as an alternative or addition to the well-known Monte-Carlo simulation of events [30][31][32][33][34][35]. Hola and Schabowicz have made NN models for estimating the time and cost of serial operation of soil excavation machines.…”

Section: Optimization Methods For Final Selection Machinerymentioning

confidence: 99%

Scenarijski simulacijski model za optimalnu alokaciju građevinskih strojeva

2016

JCE

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Optimization methods for narrowing down the choice of resources, and for their allocation, are highly important in all phases of construction projects. This particularly concerns complex project environments where resources (construction machines) have to be allocated to several construction projects at different phases and with different priorities. The authors propose a model based on the multicriteria optimisation algorithm and the ranking of scenarios involving sub-optimal programs for serial multi-channel operation of construction machines, with a fixed number of machines for final selection, and with separation of the optimum program and/or program scenario cross-sections in complex environment. Scenarijski simulacijski model za optimalnu alokaciju građevinskih strojeva Metode optimizacije imaju veliku važnost pri sužavanju izbora resursa na konačni i pri alokaciji resursa u svim fazama projekata. To se naročito odnosi na složena projektna okruženja gdje je potrebno provesti alokaciju resursa (strojeva) na više građevinskih projekata u njihovim raznim fazama i prioritetima. U radu se predlaže model koji se zasniva na algoritmu višekriterijske optimizacije i rangiranja scenarija suboptimalnih programa serijskog višekanalnog rada građevinskih strojeva pri konačnom broju strojeva za izbor, te izlučivanje presjeka optimalnih programa i/ili scenarija programa u složenom okruženju.

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Section: Optimization Methods For Final Selection Machinerymentioning

confidence: 99%

Scenarijski simulacijski model za optimalnu alokaciju građevinskih strojeva

2016

JCE

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“…Francis Siu et al (2015) proposed a crew-job allocation model to facilitate resource management for both project and workface levels. Al-Bazi and Dawood (2010) presented a strategy to allocate crews of workers in the precast concrete industry using genetic algorithms-based simulation modeling. This work was later extended (Al-Bazi and Dawood, 2017) in a model that optimizes costs of resources while considering different crew allocation constraints such as skills of workers, crew formation details and the parallel repetitive layout of manufacturing operations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Adeli and Karim (1997) presented a general formulation for the sheduling of construction projects using a non-linear optimization approach providing the capabilities of the critical path method and linear scheduling methods for multiple construction applications. Al-Bazi and Dawood (2010) presented a strategy to allocate crews of workers in the precast concrete industry using genetic algorithms-based simulation modeling. Hegazy and Wassef (2001) developed a model for scheduling and cost optimization of repetitive construction projects by integrating the critical path and line of balance methods.…”

Section: Introductionmentioning

confidence: 99%

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Crew Allocation System for the Masonry Industry

Flórez

2017

Computer aided Civil Eng

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Khanzadi

Attar

Bagherpour

2016

Period. Polytech. Civil Eng.

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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

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Developing Crew Allocation System for the Precast Industry Using Genetic Algorithms

Cited by 50 publications

References 36 publications

Scenarijski simulacijski model za optimalnu alokaciju građevinskih strojeva

Scenarijski simulacijski model za optimalnu alokaciju građevinskih strojeva

Crew Allocation System for the Masonry Industry

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

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