HYBRID GENETIC ALGORITHM PARAMETER EFFECTS FOR OPTIMIZATION OF CONSTRUCTION RESOURCE ALLOCATION PROBLEM. JinLee KIM 1, M. ASCE


 Oswin Lyons
 2 years ago
 Views:
Transcription
1 1560 HYBRID GENETIC ALGORITHM PARAMETER EFFECTS FOR OPTIMIZATION OF CONSTRUCTION RESOURCE ALLOCATION PROBLEM JinLee KIM 1, M. ASCE 1 Assistant Professor, Department of Civil Engineering and Construction Engineering Management, California State University, 1250 Bellflower Blvd., Long Beach, CA 90840, Phone: (562) , Fax: (562) , ABSTRACT The optimal solutions for the resource allocation problem are of great significant to project planners for distributing their available resources into the activities most effectively. Many studies have been undertaken to solve the resourceconstrained project scheduling problems using genetic algorithms, which have been proven as an effective and efficient optimization tool to solve difficult and complex problems. One of the trends in the genetic algorithm research study is to develop a hybrid metaheuristic method using artificial intelligence and biologicallyinspired techniques. In an effort to address this issue, the author developed a new hybrid genetic algorithm to solve the construction resourceconstrained project scheduling problems. This paper evaluates the parameter effects of the hybrid genetic algorithm for optimization because optimal settings of the genetic algorithm parameters such as population size, crossover probability, and mutation probability, are critical conditions in producing the best value for the outcomes. KEY WORDS Optimization, Heuristics, Resource allocation, Scheduling, Algorithm INTRODUCTION Construction scheduling must include resource allocation to avoid waste and shortage of resources on an actual construction jobsite, since resources for construction activities are limited in the real world. Thus, the resourceconstrained project scheduling problem attempts to allocate the available resources to construction activities so as to find the shortest duration of a project within the constraints of precedence relationships. The optimal solutions for the resourceconstrained project scheduling problem are of great significant to project planners for distributing their available resources into the activities most effectively. Many studies have been undertaken to solve the resourceconstrained project scheduling problem using Genetic algorithms (GAs), which have been proven as an effective and efficient optimization tool to solve difficult and complex problems. Numerous studies have been done to solve the standard resourceconstrained project scheduling problem using a GA. Specifically, several works of research conducted by Brucker et al. (1998), Demeulemeester and Herroelen (1997), Mingozzi et al. (1998), Sprecher and Drexl (1998), Brucker et al. (1999), and Klein and Scholl (1999) have been considered to be the currently most powerful exact procedures for solving the resourceconstrained project scheduling problem. One of the trends in the GA research study is to develop a new hybrid metaheuristic method using artificial intelligence and biologically
2 1561 inspired techniques. The concept of a hybrid genetic algorithm has been successfully applied to many engineering optimization problems, such as aerodynamic design (Foster and Dulikravitch 1997), signal analysis (Sabatini 2000), and water resources planning and management (Espinoza et al. 2005), and others (Lobo and Goldberg 1997; Goldberg and Voessner 1999; Kapelan et al. 2000; Sinha and Goldberg 2001; Kapelan et al. 2002; and Hsiao and Chang 2002). Like general genetic algorithm, optimal settings of the GA parameters such as population size, crossover probability, and mutation probability, are critical conditions in producing the best value for the outcomes. In an effort to address this issue, the author developed a new hybrid genetic algorithm to solve the construction resourceconstrained project scheduling problems. This paper evaluates the parameter effects of the hybrid genetic algorithm for optimization of construction resourceconstrained project scheduling problems. The following section briefly introduces the problem definition of the resourceconstrained project scheduling problem, followed by the hybrid genetic algorithm, developed in the previous stage of this research. Next, the methodology for selecting various input parameters that affect the performance of the hybrid algorithm is examined in the experimental design section, followed by the concluding remarks and future studies. RESOURCE ALLOCATION PROBLEM DEFINITION A project that includes a finite set of activities is considered here, where N activities labeled i = 1,, n are given. The objective function for a hybrid genetic algorithm to the resourceconstrained project scheduling problem is to minimize the project duration when constrained by precedence relationships among project activities and the availability of renewable resources. The mathematical expressions of the objective function associated with resource and precedence constraints are given as follows (Kim and Ellis 2009): Minimize f ( i) = max { ti + d i i = 1,2,... n} (1) subject to t t d j S (2) P M N p = 1 m = 1 i = 1 j i i, i Η RR imt P M p = 1 m = 1 Ξ RA mt f ( i) 0 (4) HYBRID GENETIC ALGORITHM This section introduces the hybrid genetic algorithm (HGA) for an optimal solution to the resourceconstrained project scheduling problem of construction project networks. Figure 1 shows the operation of HGA. The elitist genetic algorithm (EGA), which is used for base platform, employs four basic operators: elite selection, roulette wheel selection, onepoint crossover, and uniform mutation. The initial population of possible solutions to the resourceconstrained project scheduling problems is created to apply the algorithm in the very first step of global search. A fitness value of an individual in an initial population is calculated using three different schedule generation schemes. Evaluation of local search is achieved before the move to the selection operator embedded in the EGA. If local search is needed, it (3)
3 1562 occurs following the selection operator. Otherwise, local search is not implemented. The selection of the parent individuals is made through the elitist roulette wheel selection operator for the next generation. Using the parent individuals obtained from the selection operator, onepoint crossover operator is performed by exchanging parent individual segments and then recombining them to produce two resulting offspring individuals. The uniform mutation operator is performed to play the role of random local search, which searches much smaller portion than the random walk algorithm. Figure 1: Operation of hybrid genetic algorithm (After Kim and Ellis 2009) EXPERIMENTAL DESIGN FOR PARAMETER VALUES The fifteen numbers of combinations are designed based on various input parameter values for the hybrid genetic algorithm in order to show the overall performances, which measure the average percentage deviation and the average CPU runtime. All of input parameter values of local search are set as follows: Variation threshold, local search probability, adaptive parameter, maximum number of local search iterations, and local search proportion are set to 0.75, 0.2, 0.5, 20, and 0.2, respectively, for the hybrid genetic algorithm. The population size varies 30, 50, and 100 for each schedule of 1000 and The crossover probability varies ranging from 0.5 to 0.9 by the increase of 0.1, which produces
4 1563 five different combinations per each population size. Transformation power and mutation probability are set to 1.6 and 0.03, respectively, for the comparison purpose. Then, the hybrid genetic algorithm is tested with 30activity instance sets of 480 problems using the serial schedule generation scheme as a function of the uniquely generate schedules 1000 and 5000, respectively. We compare the results obtained from the hybrid genetic algorithm to those obtained from stateoftheart algorithms. The number of uniquely generated schedules is adopted as the stopping criteria in order to form the basis for the comparison. A unique schedule means that it is possible for several individuals to have the same fitness value (makespan, the term used in many resourceconstrained project scheduling problem studies), but their starting time should be totally different. This measurement is reasonable because of the assumption that the computation effort for constructing one schedule is similar in most heuristics. It is independent of the computer platform so hybrid genetic algorithm can be tested with the original implementation and the best configuration of various input parameters. It is also independent of compilers and implementation skills so that heuristic concepts can be evaluated rather than program codes. It is notable that this stopping criterion has a few drawbacks: it cannot be applied to all heuristics and different heuristics may require different computation times to generate one schedule (Kolisch and Hartmann 2005). Regardless of these drawbacks, the limitation of the number of schedules is the best criterion available for such a broad comparison. Therefore, 1,000 and 5,000 unique schedules are selected as stopping criteria in order to make use of the benchmark results presented in Kolisch and Hartmann (2005). It is very important to note that if one makes use of the results in their works, a schedule generated from hybrid genetic algorithm should be a unique schedule. It is necessary to set the number of a unique schedule as a termination condition, but it is not necessary to set the number of generations as a termination condition in the input parameter value of the hybrid genetic algorithm. In this experiment, the average deviation method is used to compare the performance of the hybrid genetic algorithm to that of the existing algorithms. The average deviation is the precision of measurement. The average deviations for the existing algorithms are known and they are relatively easy to calculate even though standard deviation is a more accurate method of finding the error margin. The average deviation is an estimate of how far off the observed values are from the average value with the assumption that the results computed from the hybrid genetic algorithm are accurate. As shown in Eq. 5, the average deviation can be calculated by finding the average of the percentage of the deviations, subtracting the average values from the observed values. n = i= 1 ( β i α i ) α i n 100 (5)
5 1564 where, = the average percentage deviation of a problem instance i, i = 1, 2,.., n, α i = the optimal value for 30activity instance sets, α i = the optimal value for 30activity instance sets, β i = the observed value obtained from the hybrid genetic algorithm, and N = the number of the problem instance. All optimal solutions for the set with 30 nondummy activities are known. Tables 1 and 2 show the computation results obtained from the hybrid genetic algorithm for 1000 and 5000 unique schedules, respectively. Test No. Test No. Table 1. Results for 30activity Instance Sets of 480 Problems with 1000 Schedules Parameters for Combinations Unique Schedules Population size Cp Average fitness values Experimental results Average CPU runtime (sec.) Average deviation (%) from PSPLIB (ave_opt: 58.99) Table 2. Results for 30activity Instance Sets of 480 Problems with 5000 Schedules Parameters for Combinations Unique Schedules Population size Cp Average fitness values Experimental results Average CPU runtime (sec.) Average deviation (%) from PSPLIB (ave_opt: 58.99)
6 1565 The hybrid genetic algorithm produces consistent and correct solutions for the resourceconstrained project scheduling problem with a little difference among the average fitness values regardless of the combination considered for 30activity instance sets of 480 problems. The hybrid genetic algorithm seems likely that it produces comparable solutions for all the problem sets because the location of average deviation obtained from hybrid genetic algorithm takes a middle position among other heuristics. Parameter effect analysis is conducted to examine the behaviors of the hybrid genetic algorithm according to either the population size or the crossover probability. Figure 2 shows the behaviors of average fitness values according to the different population size using results for 30activity instance sets of 480 problems. It is shown that the hybrid genetic algorithm produces consistent solutions for the resourceconstrained project scheduling problems with a little difference among the average fitness values regardless of the combination considered. However, it is observed that as the population size increases, the hybrid genetic algorithm is likely to obtain better solutions. Figure 3 also shows the behaviors of average fitness values according to the different crossover probability. It is observed that as the crossover probability increases, the hybrid genetic algorithm is not likely to show similar patterns as population size. Therefore, it can be concluded that the selection of the population size is a more critical element than that of the crossover probability. Figure 2. Comparison of average fitness values by population sizes
7 1566 Figure 3. Comparison of average fitness values by crossover probabilities Figure 4 shows the behaviors of the average CPU runtime in seconds per instance according to the different population size using results for 30activity instance sets of 480 problems. It was shown that the hybrid genetic algorithm requires a similar computation time to obtain a fitness value for a resourceconstrained project scheduling problems with little difference among the average CPU time in seconds regardless of the population size. Figure 5 also shows the behaviors of the average CPU runtime in seconds per instance according to the different crossover probability. It was observed that as the crossover probability increases, the hybrid genetic algorithm is likely to require less computation time to obtain a fitness value for a resourceconstrained project scheduling problems. Therefore, it can be concluded that the selection of the crossover probability with a high value is likely to decrease the computation time of the hybrid genetic algorithm. Figure 4. Comparison of average CPU runtime by population sizes
8 1567 Figure 5. Comparison of average CPU runtime by crossover probabilities CONCLUDING REMARKS This paper presented the evaluation of parameter effects on performance of the hybrid genetic algorithm that searches the optimal and/or near optimal solutions to the construction resource allocation problems. The parameters considered in this paper include population size, crossover and mutation probability. As the population size increases, the hybrid genetic algorithm is likely to obtain better solutions. However, as the crossover probability increases, the hybrid genetic algorithm is not likely to show similar patterns as population size, which means that the selection of the population size is a more critical element than that of the crossover probability. The hybrid genetic algorithm requires a similar computation time to obtain a fitness value with little difference among the average CPU time in seconds regardless of the population size. The selection of the crossover probability with a high value is likely to decrease the computation time of the hybrid genetic algorithm. A trialanderror calibration approach is generally used to determine the best configuration of the parameter values for any arbitrary genetic algorithm. The algorithm outcomes using this approach sometimes result in an inconsistent and timeconsuming process. Therefore, the outcomes of this study will be further used to propose a design and analysis methodology that aims to develop response surface design and to employ the response optimization method to jointly optimize two responses, which are minimum project duration and minimal algorithm runtime. The response optimization method, if developed, will be a valuable tool for GA users to control the design variables simultaneously, as it compromises the two responses.
9 1568 REFERENCES Brucker, P., Knust, S., Schoo, A., and Thiele, O. (1998). A branch and bound algorithm for the resourceconstrained project scheduling problem. European Journal of Operational Research, 107(2), Brucker, P., Drexl, A., Mohring, R., Neumann, K., and Pesch, E. (1999). Resourceconstrained project scheduling: notation, classification, models, and methods. European Journal of Operational Research, 112, Demeulemeester, E. L., and Herroelen, W. S. (1997). New benchmark results for the resourceconstrained project scheduling problem. Management Science, 43(11), Espinoza, F. P., Minsker, B. S., and Goldberg, D. E. (2005). Adaptive hybrid genetic algorithm for groundwater remediation design. Journal of Water Resources Planning and Management, 131(1), Foster, N., and Dulikravich, G. (1997). Threedimensional aerodynamic shape optimization using genetic and gradient search algorithms. Journal of Spacecraft Rockets, 34(1), Goldberg, D. E., and Voessner, S. (1999). Optimizing globallocal search hybrids. Illinois Genetic Algorithms Laboratory, ILLIGAL Rep , Urbana, Ill. Hsiao, C., and Chang, L. (2002). Dynamic optical ground water management with inclusion of fixed costs. Journal of Water Resource Planning and Management, 128(1), Kapelan, Z. S., Savic, D. A., and Walters, G. A. (2000). Inverse transient analysis in pipe networks for leakage detection and roughness calibration. In Proceedings of International Symposium CWS2000, Water Network Modeling for Optimal Design and Management, Exeter, UK, September Edited by A. Savic and G. A. Walters, 1, Kapelan, Z. S., Savic, D. A., and Walters, G. A. (2002). Hybrid GA for calibration of water distribution hydraulic models. In Proceedings of the 1st Annual Environmental & Water Resources Systems Analysis (EWRSA) Symposium, Roanoke, Virginia. Edited by D. F. Kibler, 10. Kim, J.L., and Ellis, R. D. (2009). Robust global and local search approach to resourceconstrained project scheduling. Canadian Journal of Civil Engineering, 36(3), Klein, R., and Scholl, A. (1999). Computing lower bounds by destructive improvement an application to resourceconstrained project scheduling. European Journal of Operational Research, 112, Kolisch, R., and Hartmann, S. (2005). Experimental investigation of heuristics for resourceconstrained project scheduling: An update. European Journal of Operational Research, 174, Lobo, F., and Goldberg, D. E. (1997). Decision making in a hybrid genetic algorithm. In Proceedings of IEEE Conference on Evolutionary Computation, Indianapolis, IN, IEEE, New York, Mingozzi, A., Maniezzo, V., Ricciardelli, S., and Bianco, L. (1998). An exact algorithm for the resourceconstrained project scheduling problem based on a new mathematical formulation. Management Science, 44(5),
10 1569 Sabatini, A. (2000). A hybrid genetic algorithm for estimating the optimal time scale of linear systems approximations using Laguerre models. IEEE Transactions on Automatic Control, 45(5), Sinha, A., and Goldberg, D. E. (2001). Verification and extension of the theory of globallocal hybrids. Illinois Genetic Algorithms Laboratory, ILLIGAL Rep. No , Urbana, Ill. Sprecher, A., and Drexl, A. (1998). Multimode resourceconstrained project scheduling by a simple, general and powerful sequencing algorithm. European Journal of Operational Research, 107,
Costefficient project management based on critical chain method with partial availability of resources
Control and Cybernetics vol. 43 (2014) No. 1 Costefficient project management based on critical chain method with partial availability of resources by Grzegorz Pawiński and Krzysztof Sapiecha Department
More informationResourceconstrained Scheduling of a Real Project from the Construction Industry: A Comparison of Software Packages for Project Management
Resourceconstrained Scheduling of a Real Project from the Construction Industry: A Comparison of Software Packages for Project Management N. Trautmann, P. Baumann Department of Business Administration,
More informationA Hybrid Heuristic Rule for Constrained Resource Allocation in PERT Type Networks
World Applied Sciences Journal 7 (10): 13241330, 2009 ISSN 18184952 IDOSI Publications, 2009 A Hybrid Heuristic Rule for Constrained Resource Allocation in PERT Type Networks Siamak Baradaran and S.M.T.
More informationAbstract Title: Planned Preemption for Flexible Resource Constrained Project Scheduling
Abstract number: 0150551 Abstract Title: Planned Preemption for Flexible Resource Constrained Project Scheduling Karuna Jain and Kanchan Joshi Shailesh J. Mehta School of Management, Indian Institute
More informationA genetic algorithm for resource allocation in construction projects
Creative Construction Conference 2015 A genetic algorithm for resource allocation in construction projects Sofia Kaiafa, Athanasios P. Chassiakos* Sofia Kaiafa, Dept. of Civil Engineering, University of
More informationReview on Multimode Resource Constrained Project Scheduling Problem
Review on Multimode Resource Constrained Project Scheduling Problem Prof. Dinesh B. Hanchate Assistant Professor, Computer Engineering (PG) Maharashtra, India. dinesh_b_hanchate@yahoo.com Mr. Yogesh A.
More informationThe Project Portfolio Selection and Scheduling Problem: Mathematical Model and Algorithms
Journal of Optimization in Industrial Engineering 3 (03) 657 The Project Portfolio Selection and Scheduling Problem: Mathematical Model and Algorithms Bahman Naderi * Assistant Professor, Young Researchers
More informationResearch on Project Scheduling Problem with Resource Constraints
2058 JOURNAL OF SOFTWARE, VOL. 8, NO. 8, AUGUST 2013 Research on Project Scheduling Problem with Resource Constraints Tinggui Chen College of Computer Science & Information Engineering, Zhejiang Gongshang
More informationA Multiobjective Scheduling Model for Solving the Resourceconstrained Project Scheduling and Resource Leveling Problems. Jia Hu 1 and Ian Flood 2
A Multiobjective Scheduling Model for Solving the Resourceconstrained Project Scheduling and Resource Leveling Problems Jia Hu 1 and Ian Flood 2 1 Ph.D. student, Rinker School of Building Construction,
More informationOperations research and dynamic project scheduling: When research meets practice
Lecture Notes in Management Science (2012) Vol. 4: 1 8 4 th International Conference on Applied Operational Research, Proceedings Tadbir Operational Research Group Ltd. All rights reserved. www.tadbir.ca
More informationManagement of Software Projects with GAs
MIC05: The Sixth Metaheuristics International Conference 11521 Management of Software Projects with GAs Enrique Alba J. Francisco Chicano Departamento de Lenguajes y Ciencias de la Computación, Universidad
More informationA Framework of Critical Resource Chain in Project Scheduling
A Framework of Critical Resource Chain in Project Scheduling S. S. Liu, 1 and K. C. Shih 2 1Assistant Professor, Department of Construction Engineering, National Yunlin University of Science and Technology,
More informationA Computer Application for Scheduling in MS Project
Comput. Sci. Appl. Volume 1, Number 5, 2014, pp. 309318 Received: July 18, 2014; Published: November 25, 2014 Computer Science and Applications www.ethanpublishing.com Anabela Tereso, André Guedes and
More informationTimeConstrained Project Scheduling
TimeConstrained Project Scheduling T.A. Guldemond ORTEC bv, PO Box 490, 2800 AL Gouda, The Netherlands J.L. Hurink, J.J. Paulus, and J.M.J. Schutten University of Twente, PO Box 217, 7500 AE Enschede,
More informationMultiMode Resource Constrained MultiProject Scheduling and Resource Portfolio Problem
MultiMode Resource Constrained MultiProject Scheduling and Resource Portfolio Problem Umut Beşikci a, Ümit Bilgea, Gündüz Ulusoy b, a Boğaziçi University, Department of Industrial Engineering, Bebek,
More informationA Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem
A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem Sayedmohammadreza Vaghefinezhad 1, Kuan Yew Wong 2 1 Department of Manufacturing & Industrial Engineering, Faculty of Mechanical
More informationA Proposed Scheme for Software Project Scheduling and Allocation with Event Based Scheduler using Ant Colony Optimization
A Proposed Scheme for Software Project Scheduling and Allocation with Event Based Scheduler using Ant Colony Optimization Arjita sharma 1, Niyati R Bhele 2, Snehal S Dhamale 3, Bharati Parkhe 4 NMIET,
More informationSTUDY ON APPLICATION OF GENETIC ALGORITHM IN CONSTRUCTION RESOURCE LEVELLING
STUDY ON APPLICATION OF GENETIC ALGORITHM IN CONSTRUCTION RESOURCE LEVELLING N.Satheesh Kumar 1,R.Raj Kumar 2 PG Student, Department of Civil Engineering, Kongu Engineering College, Perundurai, Tamilnadu,India
More informationAn Improved Ant Colony Optimization Algorithm for Software Project Planning and Scheduling
An Improved Ant Colony Optimization Algorithm for Software Project Planning and Scheduling Avinash Mahadik Department Of Computer Engineering Alard College Of Engineering And Management,Marunje, Pune Emailavinash.mahadik5@gmail.com
More informationA very brief introduction to genetic algorithms
A very brief introduction to genetic algorithms Radoslav Harman Design of experiments seminar FACULTY OF MATHEMATICS, PHYSICS AND INFORMATICS COMENIUS UNIVERSITY IN BRATISLAVA 25.2.2013 Optimization problems:
More informationExpert Systems with Applications
Expert Systems with Applications 38 (2011) 8403 8413 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa A knowledgebased evolutionary
More informationLab 4: 26 th March 2012. Exercise 1: Evolutionary algorithms
Lab 4: 26 th March 2012 Exercise 1: Evolutionary algorithms 1. Found a problem where EAs would certainly perform very poorly compared to alternative approaches. Explain why. Suppose that we want to find
More informationGenetic Algorithms commonly used selection, replacement, and variation operators Fernando Lobo University of Algarve
Genetic Algorithms commonly used selection, replacement, and variation operators Fernando Lobo University of Algarve Outline Selection methods Replacement methods Variation operators Selection Methods
More information11/14/2010 Intelligent Systems and Soft Computing 1
Lecture 9 Evolutionary Computation: Genetic algorithms Introduction, or can evolution be intelligent? Simulation of natural evolution Genetic algorithms Case study: maintenance scheduling with genetic
More informationA GENETIC ALGORITHM FOR RESOURCE LEVELING OF CONSTRUCTION PROJECTS
A GENETIC ALGORITHM FOR RESOURCE LEVELING OF CONSTRUCTION PROJECTS Mahdi Abbasi Iranagh 1 and Rifat Sonmez 2 Dept. of Civil Engrg, Middle East Technical University, Ankara, 06800, Turkey Critical path
More informationEffect of Using Neural Networks in GABased School Timetabling
Effect of Using Neural Networks in GABased School Timetabling JANIS ZUTERS Department of Computer Science University of Latvia Raina bulv. 19, Riga, LV1050 LATVIA janis.zuters@lu.lv Abstract:  The school
More informationAnt Colony Optimization for ResourceConstrained Project Scheduling
Ant Colony Optimization for ResourceConstrained Project Scheduling Daniel Merkle, Martin Middendorf 2, Hartmut Schmeck 3 Institute for Applied Computer Science and Formal Description Methods University
More informationInteger Programming: Algorithms  3
Week 9 Integer Programming: Algorithms  3 OPR 992 Applied Mathematical Programming OPR 992  Applied Mathematical Programming  p. 1/12 DantzigWolfe Reformulation Example Strength of the Linear Programming
More informationHYBRID GENETIC ALGORITHMS FOR SCHEDULING ADVERTISEMENTS ON A WEB PAGE
HYBRID GENETIC ALGORITHMS FOR SCHEDULING ADVERTISEMENTS ON A WEB PAGE Subodha Kumar University of Washington subodha@u.washington.edu Varghese S. Jacob University of Texas at Dallas vjacob@utdallas.edu
More informationLecture 9 Evolutionary Computation: Genetic algorithms
Lecture 9 Evolutionary Computation: Genetic algorithms Introduction, or can evolution be intelligent? Simulation of natural evolution Genetic algorithms Case study: maintenance scheduling with genetic
More informationCLIENTCONTRACTOR BARGAINING ON NET PRESENT VALUE IN PROJECT SCHEDULING WITH LIMITED RESOURCES
CLIENTCONTRACTOR BARGAINING ON NET PRESENT VALUE IN PROJECT SCHEDULING WITH LIMITED RESOURCES Nursel Kavlak 1, Gündüz Ulusoy 1 Funda Sivrikaya Şerifoğlu 2, Ş. Đlker Birbil 1 1 Sabancı University, Orhanlı,
More informationRESOURCE ALLOCATION AND PLANNING FOR PROGRAM MANAGEMENT. Kabeh Vaziri Linda K. Nozick Mark A. Turnquist
Proceedings of the 005 Winter Simulation Conference M. E. Kuhl, N. M. Steiger, F. B. Armstrong, and J. A. Joins, eds. RESOURCE ALLOCATION AND PLANNING FOR PROGRAM MANAGEMENT Kabeh Vaziri Linda K. Nozick
More informationA Study of Crossover Operators for Genetic Algorithm and Proposal of a New Crossover Operator to Solve Open Shop Scheduling Problem
American Journal of Industrial and Business Management, 2016, 6, 774789 Published Online June 2016 in SciRes. http://www.scirp.org/journal/ajibm http://dx.doi.org/10.4236/ajibm.2016.66071 A Study of Crossover
More informationA Fast Computational Genetic Algorithm for Economic Load Dispatch
A Fast Computational Genetic Algorithm for Economic Load Dispatch M.Sailaja Kumari 1, M.Sydulu 2 Email: 1 Sailaja_matam@Yahoo.com 1, 2 Department of Electrical Engineering National Institute of Technology,
More informationBook Review: Evolutionary Computation. A Unified Approach, by. Kennet A. De Jong.
Book Review: Evolutionary Computation. A Unified Approach, by Kennet A. De Jong. Efrén MezuraMontes National Laboratory on Advanced Informatics (LANIA A.C.) Rébsamen 80, Centro, Xalapa, Veracruz, 91000,
More informationSolving Method for a Class of Bilevel Linear Programming based on Genetic Algorithms
Solving Method for a Class of Bilevel Linear Programming based on Genetic Algorithms G. Wang, Z. Wan and X. Wang Abstract The paper studies and designs an genetic algorithm (GA) of the bilevel linear programming
More informationOptimizing CPU Scheduling Problem using Genetic Algorithms
Optimizing CPU Scheduling Problem using Genetic Algorithms Anu Taneja Amit Kumar Computer Science Department Hindu College of Engineering, Sonepat (MDU) anutaneja16@gmail.com amitkumar.cs08@pec.edu.in
More informationProject management and scheduling
Flex Serv Manuf J (2013) 25:1 5 DOI 10.1007/s106960129168x EDITORIAL Project management and scheduling Erik Demeulemeester Rainer Kolisch Ahti Salo Published online: 2 November 2012 Ó Springer Science+Business
More informationWeiNeng Chen, Student Member, IEEE, Jun Zhang, Senior Member, IEEE, Henry ShuHung Chung, Senior Member, IEEE, RuiZhang Huang, and Ou Liu
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART C: APPLICATIONS AND REVIEWS 1 Optimizing Discounted Cash Flows in Project Scheduling An Ant Colony Optimization Approach WeiNeng Chen, Student Member,
More informationA Beam Search Heuristic for MultiMode Single Resource Constrained Project Scheduling
A Beam Search Heuristic for MultiMode Single Resource Constrained Project Scheduling Chuda Basnet Department of Management Systems The University of Waikato Private Bag 3105 Hamilton chuda@waikato.ac.nz
More informationModelbased Parameter Optimization of an Engine Control Unit using Genetic Algorithms
Symposium on Automotive/Avionics Avionics Systems Engineering (SAASE) 2009, UC San Diego Modelbased Parameter Optimization of an Engine Control Unit using Genetic Algorithms Dipl.Inform. Malte Lochau
More informationAlpha Cut based Novel Selection for Genetic Algorithm
Alpha Cut based Novel for Genetic Algorithm Rakesh Kumar Professor Girdhar Gopal Research Scholar Rajesh Kumar Assistant Professor ABSTRACT Genetic algorithm (GA) has several genetic operators that can
More informationSolving a multimode biobjective resource investment problem using metaheuristic algorithms
Advanced Computational Techniques in Electromagnetics 0 No. (0) 8 Available online at www.ispacs.com/acte Volume 0, Issue, Year 0 Article ID acte009, 8 Pages doi:0.899/0/acte009 Research Article Solving
More informationInternational Journal of Software and Web Sciences (IJSWS) www.iasir.net
International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 22790063 ISSN (Online): 22790071 International
More informationUsing Genetic Programming to Learn Probability Distributions as Mutation Operators with Evolutionary Programming
Using Genetic Programming to Learn Probability Distributions as Mutation Operators with Evolutionary Programming James Bond, and Harry Potter The University of XXX Abstract. The mutation operator is the
More informationSCHEDULING RESOURCE CONSTRAINED PROJECT PORTFOLIOS WITH THE PRINCIPLES OF THE THEORY OF CONSTRAINTS 1
Krzysztof Targiel Department of Operations Research University of Economics in Katowice SCHEDULING RESOURCE CONSTRAINED PROJECT PORTFOLIOS WITH THE PRINCIPLES OF THE THEORY OF CONSTRAINTS 1 Introduction
More informationStillwater, November 2010
A Hybrid Genetic Algorithm for the Periodic Vehicle Routing Problem with Time Windows Michel Toulouse 1,2 Teodor Gabriel Crainic 2 Phuong Nguyen 2 1 Oklahoma State University 2 Interuniversity Research
More informationAn Ant Colony Optimization Approach to the Software Release Planning Problem
SBSE for Early Lifecyle Software Engineering 23 rd February 2011 London, UK An Ant Colony Optimization Approach to the Software Release Planning Problem with Dependent Requirements Jerffeson Teixeira de
More informationA SIMULATION MODEL FOR RESOURCE CONSTRAINED SCHEDULING OF MULTIPLE PROJECTS
A SIMULATION MODEL FOR RESOURCE CONSTRAINED SCHEDULING OF MULTIPLE PROJECTS B. Kanagasabapathi 1 and K. Ananthanarayanan 2 Building Technology and Construction Management Division, Department of Civil
More informationSkillbased Resource Allocation using Genetic Algorithms and Ontologies
Skillbased Resource Allocation using Genetic Algorithms and Ontologies Kushan Nammuni 1, John Levine 2 & John Kingston 2 1 Department of Biomedical Informatics, Eastman Institute for Oral Health Care
More informationSTUDY OF PROJECT SCHEDULING AND RESOURCE ALLOCATION USING ANT COLONY OPTIMIZATION 1
STUDY OF PROJECT SCHEDULING AND RESOURCE ALLOCATION USING ANT COLONY OPTIMIZATION 1 Prajakta Joglekar, 2 Pallavi Jaiswal, 3 Vandana Jagtap Maharashtra Institute of Technology, Pune Email: 1 somanprajakta@gmail.com,
More informationThe Project Scheduling and Decision Mechanism Based on the MultiResource Leveling
EPPM, Singapore, 2021 Sep 2011 The Project Scheduling and Decision Mechanism Based on the MultiResource Leveling Abstract HsiangHsi Huang 1, JiaChen Shiu 2, TaiLin Chen 3 Except for optimizing the
More informationA Service Revenueoriented Task Scheduling Model of Cloud Computing
Journal of Information & Computational Science 10:10 (2013) 3153 3161 July 1, 2013 Available at http://www.joics.com A Service Revenueoriented Task Scheduling Model of Cloud Computing Jianguang Deng a,b,,
More informationGenetic algorithm evolved agentbased equity trading using Technical Analysis and the Capital Asset Pricing Model
Genetic algorithm evolved agentbased equity trading using Technical Analysis and the Capital Asset Pricing Model Cyril Schoreels and Jonathan M. Garibaldi Automated Scheduling, Optimisation and Planning
More informationSimulating the Multiple TimePeriod Arrival in Yield Management
Simulating the Multiple TimePeriod Arrival in Yield Management P.K.Suri #1, Rakesh Kumar #2, Pardeep Kumar Mittal #3 #1 Dean(R&D), Chairman & Professor(CSE/IT/MCA), H.C.T.M., Kaithal(Haryana), India #2
More informationIntroduction To Genetic Algorithms
1 Introduction To Genetic Algorithms Dr. Rajib Kumar Bhattacharjya Department of Civil Engineering IIT Guwahati Email: rkbc@iitg.ernet.in References 2 D. E. Goldberg, Genetic Algorithm In Search, Optimization
More informationThe effectiveness of resource levelling tools for Resource Constraint Project Scheduling Problem
Available online at www.sciencedirect.com International Journal of Project Management 27 (2009) 493 500 www.elsevier.com/locate/ijproman The effectiveness of resource levelling tools for Resource Constraint
More informationEvolutionary Computation
Evolutionary Computation Cover evolutionary computation theory and paradigms Emphasize use of EC to solve practical problems Compare with other techniques  see how EC fits in with other approaches Definition:
More informationA Brief Study of the Nurse Scheduling Problem (NSP)
A Brief Study of the Nurse Scheduling Problem (NSP) Lizzy Augustine, Morgan Faer, Andreas Kavountzis, Reema Patel Submitted Tuesday December 15, 2009 0. Introduction and Background Our interest in the
More informationA Mathematical Programming Solution to the Mars Express Memory Dumping Problem
A Mathematical Programming Solution to the Mars Express Memory Dumping Problem Giovanni Righini and Emanuele Tresoldi Dipartimento di Tecnologie dell Informazione Università degli Studi di Milano Via Bramante
More informationGenetic algorithms with shrinking population size
Comput Stat (2010) 25:691 705 DOI 10.1007/s0018001001971 ORIGINAL PAPER Genetic algorithms with shrinking population size Joshua W. Hallam Olcay Akman Füsun Akman Received: 31 July 2008 / Accepted:
More informationA Hybrid Tabu Search Method for Assembly Line Balancing
Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization, Beijing, China, September 1517, 2007 443 A Hybrid Tabu Search Method for Assembly Line Balancing SUPAPORN
More informationCopyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and
Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and private study only. The thesis may not be reproduced elsewhere
More informationA Robust Method for Solving Transcendental Equations
www.ijcsi.org 413 A Robust Method for Solving Transcendental Equations Md. Golam Moazzam, Amita Chakraborty and Md. AlAmin Bhuiyan Department of Computer Science and Engineering, Jahangirnagar University,
More informationComparison of Evolutionary Algorithms in Nondominated Solutions of TimeCostResource Optimization Problem
Comparison of Evolutionary Algorithms in Nondominated Solutions of TimeCostResource Optimization Problem Mehdi Tavakolan,Ph.D.Candidate Columbia University New York, NY Babak Ashuri,Ph.D. Georgia Institute
More informationA Hybrid Technique for Software Project Scheduling and Human Resource Allocation
A Hybrid Technique for Software Project Scheduling and Human Resource Allocation A. Avinash, Dr. K. Ramani Department of Information Technology Sree Vidyanikethan Engineering College, Tirupati Abstract
More informationTimeconstrained project scheduling
J Sched (2008) 11: 137 148 DOI 10.1007/s1095100800597 Timeconstrained project scheduling T.A. Guldemond J.L. Hurink J.J. Paulus J.M.J. Schutten Published online: 14 February 2008 The Author(s) 2008
More informationGA as a Data Optimization Tool for Predictive Analytics
GA as a Data Optimization Tool for Predictive Analytics Chandra.J 1, Dr.Nachamai.M 2,Dr.Anitha.S.Pillai 3 1Assistant Professor, Department of computer Science, Christ University, Bangalore,India, chandra.j@christunivesity.in
More informationIntroduction to Scheduling Theory
Introduction to Scheduling Theory Arnaud Legrand Laboratoire Informatique et Distribution IMAG CNRS, France arnaud.legrand@imag.fr November 8, 2004 1/ 26 Outline 1 Task graphs from outer space 2 Scheduling
More informationAsexual Versus Sexual Reproduction in Genetic Algorithms 1
Asexual Versus Sexual Reproduction in Genetic Algorithms Wendy Ann Deslauriers (wendyd@alumni.princeton.edu) Institute of Cognitive Science,Room 22, Dunton Tower Carleton University, 25 Colonel By Drive
More informationMETHOD OF REDUCING RESOURCE FLUCTUATIONS IN RESOURCE LEVELING
METHOD OF REDUCING RESOURCE FLUCTUATIONS IN RESOURCE LEVELING FIELD OF INVENTION [0001] The present invention relates to a method for resource leveling, with the aim of providing practitioners with schedules
More informationPLAANN as a Classification Tool for Customer Intelligence in Banking
PLAANN as a Classification Tool for Customer Intelligence in Banking EUNITE World Competition in domain of Intelligent Technologies The Research Report Ireneusz Czarnowski and Piotr Jedrzejowicz Department
More informationSoftware Project Planning and Resource Allocation Using Ant Colony Optimization with Uncertainty Handling
Software Project Planning and Resource Allocation Using Ant Colony Optimization with Uncertainty Handling Vivek Kurien1, Rashmi S Nair2 PG Student, Dept of Computer Science, MCET, Anad, Tvm, Kerala, India
More informationGenetic Algorithms. Part 2: The Knapsack Problem. Spring 2009 Instructor: Dr. Masoud Yaghini
Genetic Algorithms Part 2: The Knapsack Problem Spring 2009 Instructor: Dr. Masoud Yaghini Outline Genetic Algorithms: Part 2 Problem Definition Representations Fitness Function Handling of Constraints
More informationResource Allocation in Construction Scheduling based on Multi Agent Negotiation
Resource Allocation in Construction Scheduling based on Multi Agent Negotiation T. Horenburg, J. Wimmer & W. A. Günthner Institute for Materials Handling, Material Flow, Logistics, Technische Universität
More informationCopyright by Guidong Zhu 2005
Copyright by Guidong Zhu 2005 The Dissertation Committee for Guidong Zhu certifies that this is the approved version of the following dissertation: Disruption Management for Project Scheduling Problem
More informationAn Optimal Project Scheduling Model with LumpSum Payment
Rev Integr Bus Econ Res Vol 2(1) 399 An Optimal Project Scheduling Model with LumpSum Payment Shangyao Yan Department of Civil Engineering, National Central University, Chungli 32001, Taiwan t320002@ccncuedutw
More informationDEPARTMENT OF DECISION SCIENCES AND INFORMATION MANAGEMENT (KBI)
Faculty of Business and Economics DEPARTMENT OF DECISION SCIENCES AND INFORMATION MANAGEMENT (KBI) KBI On the interaction between railway scheduling and resource flow networks Wendi Tian 1, 2 and Erik
More informationA GENETIC ALGORITHM FOR THE RESOURCE CONSTRAINED MULTIPROJECT SCHEDULING PROBLEM
A GENETIC ALGORITHM FOR THE RESOURCE CONSTRAINED MULTIPROJECT SCHEDULING PROBLEM J. F. GONÇALVES, J. J. M. MENDES, AND M.G.C. RESENDE ABSTRACT. This paper presents a genetic algorithm (GA) for the Resource
More informationMulti Objective Project Scheduling Under Resource Constraints Using Algorithm of Firefly
Jurnal UMP Social Sciences and Technology Management Vol. 3, Issue. 1,2015 Multi Objective Project Scheduling Under Resource Constraints Using Algorithm of Firefly Saeed Yaghoubi, School of Industrial
More informationHighMix LowVolume Flow Shop Manufacturing System Scheduling
Proceedings of the 14th IAC Symposium on Information Control Problems in Manufacturing, May 2325, 2012 HighMix LowVolume low Shop Manufacturing System Scheduling Juraj Svancara, Zdenka Kralova Institute
More informationExtended FiniteState Machine Inference with Parallel Ant Colony Based Algorithms
Extended FiniteState Machine Inference with Parallel Ant Colony Based Algorithms Daniil Chivilikhin PhD student ITMO University Vladimir Ulyantsev PhD student ITMO University Anatoly Shalyto Dr.Sci.,
More informationANT COLONY OPTIMIZATION ALGORITHM FOR RESOURCE LEVELING PROBLEM OF CONSTRUCTION PROJECT
ANT COLONY OPTIMIZATION ALGORITHM FOR RESOURCE LEVELING PROBLEM OF CONSTRUCTION PROJECT Ying XIONG 1, Ya Ping KUANG 2 1. School of Economics and Management, Being Jiaotong Univ., Being, China. 2. College
More informationHow Can Metaheuristics Help Software Engineers
and Software How Can Help Software Engineers Enrique Alba eat@lcc.uma.es http://www.lcc.uma.es/~eat Universidad de Málaga, ESPAÑA Enrique Alba How Can Help Software Engineers of 8 and Software What s a
More informationGenetic Algorithms and Sudoku
Genetic Algorithms and Sudoku Dr. John M. Weiss Department of Mathematics and Computer Science South Dakota School of Mines and Technology (SDSM&T) Rapid City, SD 577013995 john.weiss@sdsmt.edu MICS 2009
More informationMultiresource Shop Scheduling With Resource Flexibility and Blocking Yazid Mati and Xiaolan Xie
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 1 Multiresource Shop Scheduling With Resource Flexibility and Blocking Yazid Mati and Xiaolan Xie Abstract This paper proposes a general scheduling
More informationMULTIPROJECT SCHEDULING WITH 2STAGE DECOMPOSITION 1
MULTIPROJECT SCHEDULING WITH 2STAGE DECOMPOSITION 1 Anıl Can Sabancı University, Orhanlı, Tuzla 34956 Istanbul, Turkey anilcan@sabanciuniv.edu Gündüz Ulusoy Sabancı University, Orhanlı, Tuzla 34956 Istanbul,
More informationMultiobjective Multicast Routing Algorithm
Multiobjective Multicast Routing Algorithm Jorge Crichigno, Benjamín Barán P. O. Box 9  National University of Asunción Asunción Paraguay. Tel/Fax: (+9) 89 {jcrichigno, bbaran}@cnc.una.py http://www.una.py
More informationBMOA: Binary Magnetic Optimization Algorithm
International Journal of Machine Learning and Computing Vol. 2 No. 3 June 22 BMOA: Binary Magnetic Optimization Algorithm SeyedAli Mirjalili and Siti Zaiton Mohd Hashim Abstract Recently the behavior of
More informationGenetic Algorithm Based Interconnection Network Topology Optimization Analysis
Genetic Algorithm Based Interconnection Network Topology Optimization Analysis 1 WANG Peng, 2 Wang XueFei, 3 Wu YaMing 1,3 College of Information Engineering, Suihua University, Suihua Heilongjiang, 152061
More informationDeveloping Robust Project Scheduling Methods for Uncertain Parameters
Amirkabir University of Technology (Tehran Polytechnic) Vol. 47, No. 1, spring 215, pp. 2132 Amirkabir International Journal of Science & Research )AIJMISC) Developing Robust Project Scheduling Methods
More informationUn algorithme génétique hybride à gestion adaptative de diversité pour le problème de tournées de véhicules et ses variantes
Un algorithme génétique hybride à gestion adaptative de diversité pour le problème de tournées de véhicules et ses variantes Thibaut VIDAL LOSI et CIRRELT Université de Technologie de Troyes et Université
More informationA Genetic Algorithm for ResourceConstrained Scheduling
A Genetic Algorithm for ResourceConstrained Scheduling by Matthew Bartschi Wall B.S. Mechanical Engineering Massachusetts Institute of Technology, 1989 M.S. Mechanical Engineering Massachusetts Institute
More informationAS part of the development process, software needs to
Dynamic WhiteBox Software Testing using a Recursive Hybrid Evolutionary Strategy/Genetic Algorithm Ashwin Panchapakesan, Graduate Student Member, Rami Abielmona, Senior Member, IEEE, and Emil Petriu,
More informationMemory Allocation Technique for Segregated Free List Based on Genetic Algorithm
Journal of AlNahrain University Vol.15 (2), June, 2012, pp.161168 Science Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm Manal F. Younis Computer Department, College
More informationTimes. Chunxiao Ding, Xingfang Zhang. School of Mathematical Sciences, Liaocheng University, Liaocheng , China
Project Scheduling Problem with Uncertain Activity Duration Times Chunxiao Ding, Xingfang Zhang School of Mathematical Sciences, Liaocheng University, Liaocheng 252059, China dingchunxiao1987@163.com zhangxingfang2005@126.com,
More informationNAIS: A Calibrated Immune Inspired Algorithm to solve Binary Constraint Satisfaction Problems
NAIS: A Calibrated Immune Inspired Algorithm to solve Binary Constraint Satisfaction Problems Marcos Zuñiga 1, MaríaCristina Riff 2 and Elizabeth Montero 2 1 Projet ORION, INRIA SophiaAntipolis Nice,
More informationResource Dedication Problem in a MultiProject Environment*
Noname manuscript No. (will be inserted by the editor) Resource Dedication Problem in a MultiProject Environment* Umut Beşikci Ümit Bilge Gündüz Ulusoy Abstract There can be different approaches to the
More informationUsing Ant Colony Optimization for Infrastructure Maintenance Scheduling
Using Ant Colony Optimization for Infrastructure Maintenance Scheduling K. Lukas, A. Borrmann & E. Rank Chair for Computation in Engineering, Technische Universität München ABSTRACT: For the optimal planning
More information