Consolidated optimization algorithm for resource-constrained project scheduling problems

Abstract Resource-constrained project scheduling problems (RCPSPs) represent an important class of practical problems. Over the years, many optimization algorithms for solving them have been proposed, with their performances evaluated using well-established test instances with various levels of complexity. While it is desirable to obtain a high-quality solution and fast rate of convergence from an optimization algorithm, no single one performs well across the entire space of instances. Furthermore, even for a given algorithm, the optimal choice of its operators and control parameters may vary from one problem to another. To deal with this issue, we present a generic framework for solving RCPSPs in which various meta-heuristics, each with multiple search operators, are self-adaptively used during the search process and more emphasis is placed on the better-performing algorithms, and their underlying search operators. To further improve the rate of convergence and introduce good-quality solutions into the population earlier, a local search approach is introduced. The experimental results clearly indicate the capability of the proposed algorithm to attain high-quality results using a small population. Compared with several state-of-the-art algorithms, the proposed one delivers the best solutions for problems with 30 and 60 activities, and is very competitive for those involving 120 activities.

[1]  Erik Demeulemeester,et al.  A branch-and-bound procedure for the multiple resource-constrained project scheduling problem , 1992 .

[2]  Chen Fang,et al.  An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem , 2012, Comput. Oper. Res..

[3]  Marco Dorigo,et al.  Ant colony optimization for continuous domains , 2008, Eur. J. Oper. Res..

[4]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[5]  Fei Peng,et al.  Population-Based Algorithm Portfolios for Numerical Optimization , 2010, IEEE Transactions on Evolutionary Computation.

[6]  Reza Zamani,et al.  A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem , 2013, Eur. J. Oper. Res..

[7]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[8]  Xiaodong Li,et al.  Particle swarm optimization-based schemes for resource-constrained project scheduling , 2005 .

[9]  Concepción Maroto,et al.  A Robust Genetic Algorithm for Resource Allocation in Project Scheduling , 2001, Ann. Oper. Res..

[10]  Konstantinos P. Anagnostopoulos,et al.  Resource-Constrained Critical Path Scheduling by a GRASP-Based Hyperheuristic , 2012, J. Comput. Civ. Eng..

[11]  J. M. Tamarit,et al.  Project scheduling with resource constraints: A branch and bound approach , 1987 .

[12]  Chen Fang,et al.  A hybrid estimation of distribution algorithm for solving the resource-constrained project scheduling problem , 2012, Expert Syst. Appl..

[13]  V. Valls,et al.  A Hybrid Genetic Algorithm for the RCPSP with the Peak Crossover Operator , 2002 .

[14]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[15]  Robert J Willis,et al.  An iterative scheduling technique for resource-constrained project scheduling , 1992 .

[16]  Rainer Kolisch,et al.  Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem , 2000, Eur. J. Oper. Res..

[17]  Alex S. Fukunaga,et al.  Success-history based parameter adaptation for Differential Evolution , 2013, 2013 IEEE Congress on Evolutionary Computation.

[18]  Francisco Ballestín,et al.  A hybrid genetic algorithm for the resource-constrained project scheduling problem , 2008, Eur. J. Oper. Res..

[19]  Ruhul A. Sarker,et al.  Multi-operator based evolutionary algorithms for solving constrained optimization problems , 2011, Comput. Oper. Res..

[20]  Anurag Agarwal,et al.  A Neurogenetic approach for the resource-constrained project scheduling problem , 2011, Comput. Oper. Res..

[21]  Hartmut Schmeck,et al.  Ant colony optimization for resource-constrained project scheduling , 2000, IEEE Trans. Evol. Comput..

[22]  Jürgen Zimmermann,et al.  Scheduling Tests in Automotive R&D Projects Using a Genetic Algorithm , 2015 .

[23]  Patrick De Causmaecker,et al.  An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem , 2014, Eur. J. Oper. Res..

[24]  Rainer Kolisch,et al.  PSPLIB - A project scheduling problem library: OR Software - ORSEP Operations Research Software Exchange Program , 1997 .

[25]  Tapabrata Ray,et al.  A Differential Evolution Algorithm for Solving Resource Constrained Project Scheduling Problems , 2016, ACALCI.

[26]  Hong Zhang,et al.  Particle swarm optimization for resource-constrained project scheduling , 2006 .

[27]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[28]  Konstantinos P. Anagnostopoulos,et al.  A genetic hyperheuristic algorithm for the resource constrained project scheduling problem , 2010, IEEE Congress on Evolutionary Computation.

[29]  Charles S. Newton,et al.  Evolutionary Optimization (Evopt): A Brief Review And Analysis , 2003, Int. J. Comput. Intell. Appl..

[30]  Rainer Kolisch,et al.  Benchmark instances for project scheduling problems , 1999 .

[31]  M. Cheng,et al.  Using a fuzzy clustering chaotic-based differential evolution with serial method to solve resource-constrained project scheduling problems , 2014 .

[32]  Tapabrata Ray,et al.  Memetic algorithm for solving resource constrained project scheduling problems , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[33]  Shelvin Chand Evolutionary algorithms for resource constrained project scheduling problems: Current issues & future directions , 2016, 2016 IEEE Region 10 Conference (TENCON).

[34]  Rainer Kolisch Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation , 1994 .

[35]  Rainer Kolisch,et al.  Efficient priority rules for the resource-constrained project scheduling problem , 1996 .

[36]  Wang Chen,et al.  An efficient hybrid algorithm for resource-constrained project scheduling , 2010, Inf. Sci..

[37]  Alex S. Fukunaga,et al.  Improving the search performance of SHADE using linear population size reduction , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[38]  Reza Akbari,et al.  On the performance of bee algorithms for resource-constrained project scheduling problem , 2011, Appl. Soft Comput..

[39]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[40]  Yoon Ho Seo,et al.  An improved particle swarm optimization for the resource-constrained project scheduling problem , 2013 .

[41]  Mauricio G. C. Resende,et al.  A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem , 2011, J. Heuristics.

[42]  Ruey-Maw Chen,et al.  Particle swarm optimization with justification and designed mechanisms for resource-constrained project scheduling problem , 2011, Expert Syst. Appl..

[43]  Mauricio G. C. Resende,et al.  A random key based genetic algorithm for the resource constrained project scheduling problem , 2009, Comput. Oper. Res..

[44]  Christian Artigues,et al.  Resource-Constrained Project Scheduling: Models, Algorithms, Extensions and Applications , 2007 .

[45]  Konstantinos P. Anagnostopoulos,et al.  A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem , 2014, Inf. Sci..

[46]  Mario Vanhoucke,et al.  A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem , 2007, Oper. Res..