Linear Scheduling Optimization Model for Planning Repetitive Construction Projects

Prefabricated buildings are the direction of the future development of the construction industry and have received widespread attention. The effective execution of prefabricated construction project scheduling should consider resource constraints and the supply arrangement of prefabricated components. However, the traditional construction resource-constrained project scheduling implementation method cannot simultaneously consider the characteristics of the linkage between component production and on-site assembly construction. It cannot also fully adapt to the scheduling implementation method of the prefabricated construction projects. It is difficult to work out a reasonable project schedule and resource allocation table. In order to determine the relevant schedule parameters that can reflect the actual construction situation of the prefabricated building and meet the scheduling requirements of the prefabricated project, this study proposes a prefabricated construction project scheduling model that considers project resource constraints and prefabricated component supply constraints. Additionally, it improves the design of traditional genetic algorithms (GAs). Research results of the experimental calculation and engineering application show that the proposed project scheduling optimization model and GA are effective and practical, which can help project managers in effectively formulating prefabricated construction project scheduling plans, reasonably allocating resources, reducing completion time, and improving project performance.

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Optimization Model of Information Flow in Power Grid Engineering Construction Projects Based on Improved DSM

2015 5th International Conference on IT Convergence and Security (ICITCS) ◽

10.1109/icitcs.2015.7293003 ◽

2015 ◽

Author(s):

A-yong Li ◽

Li-rong Wei

Keyword(s):

Information Flow ◽

Optimization Model ◽

Construction Projects ◽

Power Grid ◽

Engineering Construction

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A Multiobjective Scheduling Optimization Model for Multienergy Complementary System Integrated by Wind-Photovoltaic-Convention Gas Turbines considering Demand Response

Mathematical Problems in Engineering ◽

10.1155/2018/3208934 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16

Author(s):

Zhang Lihui ◽

Xin He ◽

Ju Liwei

Keyword(s):

Demand Response ◽

Optimization Model ◽

Gas Turbines ◽

Risk Level ◽

Objective Functions ◽

Entropy Weight ◽

Scheduling Optimization ◽

Power Sources ◽

Complementary System ◽

Scheduling Model

To utilize the complementary feature of different power sources, wind power plant (WPP), and solar photovoltaic power (PV), convention gas turbines (CGT) and incentive-based demand response (IBDR) are integrated into a multienergy complementary system (MECS) with the implementation of price-based demand response (PBDR). Firstly, the power output model of WPP, PV, and CGT is constructed and the mathematical model of DR is presented. Then, a multiobjective scheduling model is proposed for MECS operation under the objective functions of the maximum economic benefit, the minimum abandoned energy, and the minimum risk level. Thirdly, the payoff table of objective functions is put forward for converting the multiobjective model into a single objective model by using entropy weight method to calculate weighting coefficients of different objective functions. Finally, the improved IEEE 30 bus system is taken as the simulation system with four simulation scenarios for comparatively analyzing the influence of PBDR and IBDR on MECS operation. The simulation results show the following: (1) The MECS fully utilized the complementarity of different power sources; CGT and IBDR can provide peaking service for WPP and PV to optimize overall system operation. (2) The proposed algorithm can solve the MECS multiobjective scheduling optimization model, and the system scheduling results in the comprehensive optimal mode can take into account different appeal. And the total revenue, abandoned energy capacity, and load fluctuation are, respectively, 108009.30¥, 11.62 MW h, and 9.74 MW. (3) PBDR and IBDR have significant synergistic optimization effects, which can promote the grid connection of WPP and PV. When they are both introduced, the peak-to-valley ratio of the load curve is 1.19, and the abandoned energy is 5.85 MW h. Therefore, the proposed MECS scheduling model and solution algorithm could provide the decision basis for decision makers based on their actual situation.

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Optimization model for resource assignment problems of linear construction projects

Automation in Construction ◽

10.1016/j.autcon.2006.08.004 ◽

2007 ◽

Vol 16 (4) ◽

pp. 460-473 ◽

Cited By ~ 40

Author(s):

Shu-Shun Liu ◽

Chang-Jung Wang

Keyword(s):

Optimization Model ◽

Construction Projects ◽

Resource Assignment ◽

Assignment Problems

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Optimization of Earthwork Allocation Path as Vehicle Route Problem Based on Genetic Algorithm

E3S Web of Conferences ◽

10.1051/e3sconf/202016504057 ◽

2020 ◽

Vol 165 ◽

pp. 04057

Author(s):

Naifu Deng ◽

Xuyang Li ◽

Yanmin Su

Keyword(s):

Genetic Algorithm ◽

Optimization Model ◽

Construction Projects ◽

Vehicle Route Problem ◽

Vehicle Route ◽

Route Problem ◽

Modified Genetic Algorithm ◽

Global Optimal ◽

The Cost ◽

Engineering Projects

In civil engineering, earthwork, prior to the construction of most engineering projects, is a lengthy and time-consuming work involving iterative processes. The cost of many AEC (Architecture, Engineering and Construction) projects is highly dependent on the efficiency of earthworks (e.g. road, embankment, railway and slope engineering). Therefore, designing proper earthwork planning is of importance. This paper simplifies the earthwork allocation problem to Vehicle Route Problem (VRP) which is commonly discussed in the field of transportation and logistics. An optimization model for the earthwork allocation path based on the modified Genetic Algorithm with a self-adaptive mechanism is developed to work out the global optimal hauling path for earthwork. The research results also instruct the initial topographic shaping of the Winter Olympic Skiing Courses Project. Furthermore, this optimization model is highly compatible with other evolutionary algorithms due to its flexibility, therefore, further improvement in this model is feasible and practical.

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