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2010 IEEE World Congress on Computational Intelligence

作者： Anagnostopoulos, Konstantinos P. Koulinas, Georgios K. Democritus Univ Thrace Dept Prod & Management Engn GR-67100 Xanthi Greece

ISBN: (纸本)9781424481262

The resource constrained project scheduling problem is one of the most important issues that project managers have to deal with during the project implementation, as constrained resource availabilities very often lead to delays in project completion and budget overruns. For solving this NP-hard optimization problem, we propose a genetic based hyperheuristic, i.e. an algorithm controlling a set of low-level heuristics which work in the solution domain. Chromosomes impose the sequence that the algorithm applies the low level heuristics. Implemented within a commercial project management software system, the hyperheuristic operates on the priority values that the software uses for scheduling activities. We perform a series of computational experiments with random generated projects. The results show that the algorithm is very promising for finding good solutions in reasonable time.

关键词： NP-hard optimization problem chromosome sequence computational complexity genetic algorithms genetic hyperheuristic algorithm project management resource allocation resource constrained project scheduling problem scheduling

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A Particle Swarm Optimization Algorithm with Crossover for resource constrained project scheduling problem

A Particle Swarm Optimization Algorithm with Crossover for R...

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IITA International Conference on Services Science, Management and Engineering

作者： Li, Ming Zhang, Yuanbiao Jiang, Weigang Xie, Jianwen Zhuhai Coll Jinan Univ Zhuhai Peoples R China

ISBN: (纸本)9780769537290

resource and project optimization scheduling has become the key of the success of researching project in the enterprises. In order to solve the mass resource constrained project scheduling problem, in this paper, an improved particle swarm algorithm (PSO) called particle swarm algorithm with crossover (CPSO) was presented. This improved algorithm is based on PSO and genetic algorithm (GA). Through comparing with SPSO and GA on RCPSP, it is indicated that CPSO not only avoids premature convergence to a high degree, but also keeps a faster convergence rate than SPSO and GA.

关键词： resource constrained project scheduling problem particle swarm algorithm with crossover genetic algorithm

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A New resource constrained project scheduling problem

A New Resource Constrained Project Scheduling Problem

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Asia-Pacific Conference on Information Processing (APCIP 2009)

作者： Liu, Xiaoxiang Jiang, Weigang Xie, Jianwen Jia, Yitian Jinan Univ Zhuhai Coll Dept Comp Sci Zhuhai Peoples R China Jinan Univ Dept Accounting Zhuhai Peoples R China

ISBN: (纸本)9780769536996

The optimal scheduling of resource and program has became the key to the success of enterprise development projects. The conventional resource constrained project scheduling problem seeks a solution which satisfies the time and resource constraints on purpose of a minimal construction period. With the development of the society, the realistic problems trend to be more and more complicated, and the RCPSP model is thereupon complex. This paper proposes a new project scheduling model with success rate and it is suitable for large-scale project scheduling problems, then, an improved particle swarm optimization called CPSO based on PSO and GA is used to solve the problems. The application of solving examples with CPSO demonstrates that this algorithm is effective.

关键词： resource constrained project scheduling problem particle swarm algorithm with crossover genetic algorithm

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An Immune Genetic Algorithm for resource constrained project scheduling problem with Discounted Cash Flows

An Immune Genetic Algorithm for Resource Constrained Project...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Asadujjaman, Md Rahman, Humyun Fuad Chakrabortty, Ripon Kumar Ryan, Michael J. Univ New South Wales Capabil Syst Ctr Sch Engn & Informat Technol Canberra ACT Australia Rajshahi Univ Engn & Technol Dept Ind & Prod Engn Rajshahi Bangladesh

ISBN: (纸本)9781538672204

The resource constrained project scheduling problem with discounted cash flows (RCPSPDC) is a well-known complex optimization problem. In this paper, an immune genetic algorithm (IGA) is proposed to solve the RCPSPDC with the objective of maximizing the net present value (NPV) to ensure project profitability. Hybridizing a genetic algorithm with immune algorithm enhances the overall performance of the IGA. The performance of the proposed algorithm has been evaluated by solving 2,160 problem instances from the standard benchmark dataset and the results have been compared with existing state-of-the-art algorithms. Extensive experiments demonstrate that the overall performance of the IGA is quite satisfactory for 50 activities dataset.

关键词： resource constrained project scheduling problem Discounted Cash Flows Net Present Value Immune Genetic Algorithm

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IP-based energetic reasoning for the resource constrained project scheduling problem

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Electronic Notes in Discrete Mathematics 2010年第C期36卷 359-366页

作者： Kooli, Anis Haouari, Mohamed Hidri, Lotfi Néron, Emmanuel Combinatorial Optimization Research Group - ROI La Marsa Tunisia Departement of Industrial Engineering Ozyegin University 34662 Istanbul Turkey Combinatorial Optimization Research Group - ROI La Marsa Tunisia Laboratoire d'Informatique - LI Université François Rabelais Tours France

In this paper, we consider the resource constrained project scheduling problem (RCPSP). New feasibility tests for the energetic reasoning are introduced based on new integer programming (IP) formulations. Experimental... 详细信息

关键词： Energetic Reasoning Lower Bound resource constrained project scheduling problem

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Using Firefly Algorithm to Solve resource constrained project scheduling problem

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7th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012)

作者： Sanaei, Pejman Akbari, Reza Zeighami, Vahid Shams, Sheida Shiraz Univ Dept Math Shiraz Iran Shiraz Univ Technol Dept Comp Engn & Informat Technol Shiraz Iran Shiraz Univ Dept Management Shiraz Iran

ISBN: (纸本)9788132210375

The Firefly Algorithm (FA) is among the most recently introduced meta-heuristics. This work aims at study the application of FA algorithm to solve the resource constrained project scheduling problem (RCPSP). The algorithm starts by generating a set of random schedules. After that, the initial schedules are improved iteratively using the flying approach proposed by the FA. By termination of algorithm, the best schedule found by the method is returned as the final result. The results of the state-of-art algorithms are used in this work in order to evaluate the performance of the proposed method. The comparison study shows the efficiency of the proposed method in solving RCPSP. The proposed method has competitive performance compared to the other RCPSP solvers.

关键词： Firefly Algorithm resource constrained project scheduling problem

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Discrete Cuckoo Search for resource constrained project scheduling problem 18

Discrete Cuckoo Search for Resource Constrained Project Sche...

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18th IEEE International Conference on Computational Science and Engineering (CSE)

作者： Bibiks, Kirils Hu, Fun Li, Jian-Ping Smith, Aleister Univ Bradford Fac Engn & Informat Bradford W Yorkshire England Thales Avion Ltd Thales Res & Technol Crawley England

ISBN: (纸本)9781467382977

In this paper, a novel population-based meta-heuristic algorithm, Discrete Cuckoo Search (DCS), is applied to solve resource constrained project scheduling problem (RCPSP). The DCS is a modification of the existing Cuckoo Search which was adapted for solving combinatorial optimization problems. Application of the DCS for solving RCPSP was achieved by changing some of the algorithm's core elements, such as nest, egg, objective function, and movement through the search space. The proposed DCS is then tested on a set of benchmark instances and its performances are compared against other existing meta-heuristic algorithms. The numerical results have shown that the proposed algorithm is very efficient and outperforms several other popular meta-heuristic algorithms, both in terms of quality of the results and execution time.

关键词： Cuckoo Search Discrete Cuckoo Search Combinatorial optimization resource constrained project scheduling problem

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New Cuckoo Search Algorithm for the resource constrained project scheduling problem

New Cuckoo Search Algorithm for the Resource Constrained Pro...

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RIVF International Conference on Computing and Communication Technologies (RIVF)

作者： Huu Dang Quoc Loc Nguyen The Cuong Nguyen Doan Toan Phan Thanh Thuong Mai Univ Hanoi Vietnam Ha Noi Natl Univ Educ Hanoi Vietnam Mil Inst Sci & Technol Hanoi Vietnam

ISBN: (数字)9781728153773

ISBN: (纸本)9781728153773

The aim of this paper is to suggest novel Cuckoo Search algorithm to solving Multi-Skill resource constrained project scheduling problem (MS-RCPSP). Our proposed evolutionary algorithm, which called CSM, inspired by Cuckoo Search algorithm. CSM fast converge to global extremum, moreover our algorithm also avoid getting trapped in local extremum. Experimental results and comparisons with conventional optimization algorithms show that the proposed Cuckoo Search algorithm can effectively enhance the searching efficiently.

关键词： Evolutionary Computing resource constrained project scheduling problem Cuckoo Search Algorithm Optimization Methods

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Particle swarm optimization for multi resource constrained project scheduling problem with varying resource levels

Particle swarm optimization for multi resource constrained p...

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International Conference on Sustainable Materials, Manufacturing and Renewable Technologies (I-SMaRT)

作者： Joy, Jiby Rajeev, Srijith Abraham, Eldhose C. Fed Inst Sci & Technol Dept Mech Engn Ernakulam 683577 Kerala India Baselios Paulose II Catholicos Coll Dept Elect Illikkamukkada 686664 Kerala India

This paper deals with particle swarm optimization for multi resource constrained project scheduling problem with varying resource levels (MRCPSPVRL), and it is an updated version of particle swarm optimization for resource constrained project scheduling problem with varying resource levels (RCPSPVRL). In a project scheduling problem, there are many activities with certain durations and resource requests, which are connected by successor and predecessor relations. RCPSPVRL uses only one type of resource. However, in realistic project scheduling problems, different types of resources are used to solve the task. MRCPSPVRL uses various types of resources with limited availability by varying resource levels in a predetermined fashion. In MRCPSPVRL, maximum time required for completing the task is divided into different time periods, and resources are varied within these different time periods. The objective of this system is to schedule all the activities effectively by utilizing all types of resources. MRCPSPVRL is tested using PSO (Particle Swarm Optimization) on MATLAB R2020b. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the International Conference on Sustainable materials, Manufacturing and Renewable Technologies 2021.

关键词： Particle swarm optimisation resource constrained project scheduling problem resource constrained project scheduling problem with varying resource levels

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Development of a method based on particle swarm optimization to solve resource constrained project scheduling problem

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SCIENTIA IRANICA 2013年第6期20卷 2123-2137页

作者： Zeighami, V. Akbari, R. Ziarati, K. Shiraz Univ Dept Math Shiraz Iran Shiraz Univ Technol Dept Comp Engn & Informat Technol Shiraz Iran Shiraz Univ Dept Comp Sci & Engn & Informat Technol Shiraz Iran

This work presents an efficient hybrid method based on Particle Swarm Optimization (PSO) and Termite Colony Optimization (TCO) for solving resource constrained project scheduling problem (RCPSP). The search process of this hybrid method employs PSO iterations for global search and TCO iterations for local search. The proposed method works by interleaving the PSO and TCO search processes. The PSO method update schedules by considering the best solution found by the TCO approach. Next the TCO approach picks the solutions found by PSO search and perform local search around each solution. Each individual in TCO approach moves randomly but it is biased towards locally best observed solutions. Apart from hybridization, a new constraint handling approach is proposed to convert the infeasible solutions to the feasible ones. The standard benchmark problems of size j30, j60, j90, and j120 from PSPLIB are used to show the efficiency of the proposed method. The results showed that although PSO and TCO methods independently give good solutions, the hybrid of PSO and TCO gives better solution. (C) 2013 Sharif University of Technology. All rights reserved.

关键词： Particle swarm optimization Termite colony optimization resource constrained project scheduling problem

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