An optimization model for workgroup-based repetitive scheduling

2006 ◽  
Vol 33 (9) ◽  
pp. 1172-1194 ◽  
Author(s):  
Rong-yau Huang ◽  
Kuo-Shun Sun
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Construction Projects ◽  
Optimal Solution ◽  
Planning And Scheduling ◽  
Construction Scheduling ◽  
Chromosome Representation ◽  
Scheduling Method ◽  
Model Formation ◽  
System Project

Most construction repetitive scheduling methods developed so far have been based on the premise that a repetitive project is comprised of many identical production units. Recently, Huang and Sun (2005) developed a workgroup-based repetitive scheduling method that takes the view that a repetitive construction project consists of repetitive activities of workgroups. Instead of repetitive production units, workgroups with repetitive or similar activities in a repetitive project are identified and employed in the planning and scheduling. The workgroup-based approach adds more flexibility to the planning and scheduling of repetitive construction projects and enhances the effectiveness of repetitive scheduling. This work builds on previous research and develops an optimization model for workgroup-based repetitive scheduling. A genetic algorithm (GA) is employed in model formation for finding the optimal or near-optimal solution. A chromosome representation, as well as specification of other parameters for GA analysis, is described in the paper. Two sample case studies, one simple and one sewer system project, are used for model validation and demonstration. Results and findings are reported.Key words: construction scheduling, repetitive project, workgroup, optimization, genetic algorithm.

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

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.

A Bi-Objective Order Allocation Optimization Model for Horizontal Manufacturing Collaborative Alliance

2010 ◽  
Vol 102-104 ◽  
pp. 836-840 ◽  
Author(s):  
Fang Qi Cheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Optimal Solution ◽  
Order Allocation ◽  
Nsga Ii ◽  
Manufacturing Enterprises ◽  
Proposed Model ◽  
Optimal Solution Set ◽  
Simulation Results ◽  
Enterprise Community

Horizontal manufacturing collaborative alliance is a dispersed enterprise community consisting of several enterprises which produce the same kind of products. To correctly assign order among member companies of horizontal manufacturing collaborative alliance is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises. For the order allocation problem, a bi-objective optimization model is developed to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimization functions. The optimal solution set of Pareto is obtained. The simulation results indicate that the proposed model and algorithm is able to obtain satisfactory solutions.

scholarly journals An Extended Genetic Algorithm for Distributed Integration of Fuzzy Process Planning and Scheduling

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Shuai Zhang ◽  
Zhinan Yu ◽  
Wenyu Zhang ◽  
Dejian Yu ◽  
Yangbing Xu
Keyword(s):  
Genetic Algorithm ◽  
Process Planning ◽  
Optimal Solution ◽  
Planning And Scheduling ◽  
Fuzzy Process ◽  
Short Period ◽  
Mutation Strategies ◽  
Encoding Method ◽  
Machine Replacement ◽  
Crossover And Mutation

The distributed integration of process planning and scheduling (DIPPS) aims to simultaneously arrange the two most important manufacturing stages, process planning and scheduling, in a distributed manufacturing environment. Meanwhile, considering its advantage corresponding to actual situation, the triangle fuzzy number (TFN) is adopted in DIPPS to represent the machine processing and transportation time. In order to solve this problem and obtain the optimal or near-optimal solution, an extended genetic algorithm (EGA) with innovative three-class encoding method, improved crossover, and mutation strategies is proposed. Furthermore, a local enhancement strategy featuring machine replacement and order exchange is also added to strengthen the local search capability on the basic process of genetic algorithm. Through the verification of experiment, EGA achieves satisfactory results all in a very short period of time and demonstrates its powerful performance in dealing with the distributed integration of fuzzy process planning and scheduling (DIFPPS).

scholarly journals A genetic algorithm for the steel continuous casting with inter-sequence dependent setups and dedicated machines

2018 ◽  
Vol 52 (4-5) ◽  
pp. 1351-1376 ◽  
Author(s):  
Abdelkader Sbihi ◽  
Makram Chemangui
Keyword(s):  
Genetic Algorithm ◽  
Continuous Casting ◽  
Optimal Solution ◽  
Planning And Scheduling ◽  
Steel Continuous Casting ◽  
Dedicated Machines ◽  
Sequence Dependent ◽  
The Times ◽  
Commercial Solver ◽  
Hybrid Flexible Flowshop

The steel continuous casting planning and scheduling problem namely SCC is a particular hybrid (flexible) flowshop that includes stages: (i) the converters (CV), (ii) the refining stands (RS) and (iii) the continuous casting (CC) stages. In this paper we study the SCC with inter-sequence dependent setups and dedicated machines at the last stage. The batch sequences are assumed to be pre-determined for one of the CC devices with a non preemptive scheduling process. The aim is to schedule the batches for each CC machine including the times setup between two successive sequences. We model the problem as a MILP where the objective is to minimize the makespan Cmax that we formulate as the largest completion time taking account of the setup times for each CC. Then, we propose an adapted genetic algorithm that we call Regeneration GA (RGA) to solve the problem. We use a randomly generated instances of several sizes to test the model and for which we do not know an optimal solution. The method is able to solve the problems in an acceptable time for medium and large instances while a commercial solver was able to solve only small size instances.

scholarly journals Coordinated Control of PV Generation and EVs Charging Based on Improved DECell Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Guo Zhao ◽  
Xueliang Huang ◽  
Hao Qiang
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Control Strategy ◽  
Optimization Model ◽  
Pareto Front ◽  
Optimal Solution ◽  
Coordinated Control ◽  
Nsga Ii ◽  
Nondominated Solutions ◽  
Pv Generation

Recently, the coordination of EVs’ charging and renewable energy has become a hot research all around the globe. Considering the requirements of EV owner and the influence of the PV output fluctuation on the power grid, a three-objective optimization model was established by controlling the EVs charging power during charging process. By integrating the meshing method into differential evolution cellular (DECell) genetic algorithm, an improved differential evolution cellular (IDECell) genetic algorithm was presented to solve the multiobjective optimization model. Compared to the NSGA-II and DECell, the IDECell algorithm showed better performance in the convergence and uniform distribution. Furthermore, the IDECell algorithm was applied to obtain the Pareto front of nondominated solutions. Followed by the normalized sorting of the nondominated solutions, the optimal solution was chosen to arrive at the optimized coordinated control strategy of PV generation and EVs charging. Compared to typical charging pattern, the optimized charging pattern could reduce the fluctuations of PV generation output power, satisfy the demand of EVs charging quantity, and save the total charging cost.

Application of genetic algorithms to construction scheduling with or without resource constraints

2002 ◽  
Vol 29 (3) ◽  
pp. 421-429 ◽  
Author(s):  
Y Cengiz Toklu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Construction Management ◽  
Construction Projects ◽  
Resource Constraints ◽  
Fitness Function ◽  
Computer Application ◽  
Mutation Operator ◽  
Construction Problem ◽  
Construction Scheduling

The difficulties encountered in scheduling construction projects with resource constraints are highlighted by means of a simplified bridge construction problem. A genetic algorithm applicable to projects with or without resource constraints is described. In this application, chromosomes are formed by genes consisting of the start days of the activities. This choice necessitated introducing two mathematical operators (datum operator and left compression operator) and emphasizing one genetic operator (fine mutation operator). A generalized evaluation of the fitness function is conducted. The algorithm is applied to the example problem. The results and the effects of some of the parameters are discussed.Key words: scheduling, genetic algorithms, construction management, computer application.

Robust global and local search approach to resource-constrained project scheduling

2009 ◽  
Vol 36 (3) ◽  
pp. 375-388 ◽  
Author(s):  
Jin-Lee Kim ◽  
Ralph D. Ellis
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Project Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Construction Scheduling ◽  
Computationally Intensive ◽  
Search Approach

The resource-constrained project scheduling problem (RCPSP) is one of the most challenging problems in construction scheduling applications, in which optimal solutions are of great value to project planners. This paper presents a new adaptive hybrid genetic algorithm search simulator (AHGASS) for finding an optimal solution to the problem, and provides the strategies and practical procedures to develop the algorithm. Elitist genetic algorithm (EGA) developed is used for the global search, while random walk algorithm for the local search is incorporated into the EGA to overcome the drawbacks of general genetic algorithms, which are computationally intensive and premature convergence to a local solution. Computational experiments are presented to demonstrate the performance and accuracy of AHGASS. The proposed algorithm provides a comparable and competitive performance compared with the existing genetic algorithm (GA) hybrid heuristic methods. The findings demonstrate that AHGASS has significant promise for solving a large-sized RCPSP.

scholarly journals Sustainable Integrated Process Planning and Scheduling Optimization Using a Genetic Algorithm with an Integrated Chromosome Representation

2019 ◽  
Vol 11 (2) ◽  
pp. 502 ◽  
Author(s):  
Hyun Lee ◽  
Chunghun Ha
Keyword(s):  
Genetic Algorithm ◽  
Process Planning ◽  
Flexible Manufacturing ◽  
Benchmark Problems ◽  
Integrated Process ◽  
Genetic Operators ◽  
Complete Class ◽  
Planning And Scheduling ◽  
Chromosome Representation ◽  
Single String

This paper proposes a genetic algorithm (GA) to find the pseudo-optimum of integrated process planning and scheduling (IPPS) problems. IPPS is a combinatorial optimization problem of the NP-complete class that aims to solve both process planning and scheduling simultaneously. The complexity of IPPS is very high because it reflects various flexibilities and constraints under flexible manufacturing environments. To cope with it, existing metaheuristics for IPPS have excluded some flexibilities and constraints from consideration or have built a complex structured algorithm. Particularly, GAs have been forced to construct multiple chromosomes to account for various flexibilities, which complicates algorithm procedures and degrades performance. The proposed new integrated chromosome representation makes it possible to incorporate various flexibilities into a single string. This enables the adaptation of a simple and typical GA procedure and previously developed genetic operators. Experiments on a set of benchmark problems showed that the proposed GA improved makespan by an average of 17% against the recently developed metaheuristics for IPPS in much shorter computation times.

scholarly journals Performance optimization of aircraft deicing equipment based on genetic algorithm

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041987773
Author(s):  
Bin Chen ◽  
Yalei Yang ◽  
Jianhua Liu
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Flow Rate ◽  
Performance Optimization ◽  
Optimization Model ◽  
Optimal Solution ◽  
Wing Area ◽  
Energy Conversion Process ◽  
Actual Operation ◽  
Main Influence

Using deicing fluids is the main way of aircraft ground deicing, which plays an important role in ensuring flights’ safety. However, most of the airports use deicing fluids excessively to ensure the quality and efficiency of aircraft ground deicing, which will not only cause a large amount of deicing fluids wasted but also pollute water resources and the environment. Finding the optimal solution between deicing efficiency and deicing fluids consumption through effective methods is necessary. This article analyzes the energy conversion process of aircraft ground deicing, establishes multi-parameter optimization model for deicing, and optimizes the consumption of deicing fluids. The physical quantity, including the flow rate and the temperature of deicing fluids, is found as the main influence of the deicing time, which is the most concerned problem in the actual operation. Under the precondition of ensuring the deicing efficiency, the optimized parameters such as different ambient temperature, wing area, and icing thickness are obtained by genetic algorithm. The trend between the parameters with the change of environment has also been analyzed. Finally, the actual using condition in the capital airport and the optimized results are compared, and the results show that the usage of deicing fluids reduced 13% to 24%.

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