Multiobjective Resource-Constrained Project Scheduling with a Time-Varying Number of Tasks

In resource-constrained project scheduling (RCPS) problems, ongoing tasks are restricted to utilizing a fixed number of resources. This paper investigates a dynamic version of the RCPS problem where the number of tasks varies in time. Our previous work investigated a technique called mapping of task IDs for centroid-based approach with random immigrants (McBAR) that was used to solve the dynamic problem. However, the solution-searching ability of McBAR was investigated over only a few instances of the dynamic problem. As a consequence, only a small number of characteristics of McBAR, under the dynamics of the RCPS problem, were found. Further, only a few techniques were compared to McBAR with respect to its solution-searching ability for solving the dynamic problem. In this paper, (a) the significance of the subalgorithms of McBAR is investigated by comparing McBAR to several other techniques; and (b) the scope of investigation in the previous work is extended. In particular, McBAR is compared to a technique called, Estimation Distribution Algorithm (EDA). As with McBAR, EDA is applied to solve the dynamic problem, an application that is unique in the literature.

[1]  Yan Wu,et al.  An Improved Estimation of Distribution Algorithm in Dynamic Environments , 2008, 2008 Fourth International Conference on Natural Computation.

[2]  Sanja Petrovic,et al.  SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS , 2006 .

[3]  Helen D. Karatza,et al.  Dynamic Sequencing of A Multi-Processor System: A Genetic Algorithm Approach , 1993 .

[4]  Zbigniew Michalewicz,et al.  Implicit memory-based technique in solving dynamic scheduling problems through Response Surface Methodology - Part II: Experiments and analysis , 2014, Int. J. Intell. Comput. Cybern..

[5]  A. Nagar,et al.  Multiple and bicriteria scheduling : A literature survey , 1995 .

[6]  A. Sima Etaner-Uyar,et al.  A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments , 2005, Soft Comput..

[7]  Xin Yao,et al.  Population-Based Incremental Learning With Associative Memory for Dynamic Environments , 2008, IEEE Transactions on Evolutionary Computation.

[8]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[9]  Yuping Wang,et al.  Multi-population and diffusion UMDA for dynamic multimodal problems , 2010 .

[10]  Christian Bierwirth,et al.  Production Scheduling and Rescheduling with Genetic Algorithms , 1999, Evolutionary Computation.

[11]  W. Andrew,et al.  LO, and A. , 1988 .

[12]  Hisashi Handa Solving Multi-objective Reinforcement Learning Problems by EDA-RL - Acquisition of Various Strategies , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[13]  Andrew J. Page,et al.  Dynamic task scheduling using genetic algorithms for heterogeneous distributed computing , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[14]  Jürgen Branke,et al.  Memory enhanced evolutionary algorithms for changing optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[15]  Zbigniew Michalewicz,et al.  Adaptation in Dynamic Environments: A Case Study in Mission Planning , 2012, IEEE Transactions on Evolutionary Computation.

[16]  Patrizia Beraldi,et al.  A heuristic approach for resource constrained project scheduling with uncertain activity durations , 2011, Comput. Oper. Res..

[17]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[18]  S. Baluja An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics , 1995 .

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

[20]  Christian Artigues,et al.  A polynomial activity insertion algorithm in a multi-resource schedule with cumulative constraints and multiple modes , 2000, Eur. J. Oper. Res..

[21]  Shengxiang Yang,et al.  Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments , 2008, Evolutionary Computation.

[22]  Michel Bierlaire,et al.  Real Time Recovery in Berth Allocation Problem in Bulk Ports , 2012 .

[23]  Fernando José Von Zuben,et al.  Online learning in estimation of distribution algorithms for dynamic environments , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[24]  Shengxiang Yang,et al.  Environment identification-based memory scheme for estimation of distribution algorithms in dynamic environments , 2011, Soft Comput..

[25]  Philippe Fortemps,et al.  A hybrid rank-based evolutionary algorithm applied to multi-mode resource-constrained project scheduling problem , 2010, Eur. J. Oper. Res..

[26]  Chen Fang,et al.  An effective estimation of distribution algorithm for the multi-mode resource-constrained project scheduling problem , 2012, Comput. Oper. Res..

[27]  Jacques Carlier,et al.  Handbook of Scheduling - Algorithms, Models, and Performance Analysis , 2004 .

[28]  Zbigniew Michalewicz,et al.  Implicit memory-based technique in solving dynamic scheduling problems through Response Surface Methodology - Part I: Model and method , 2014, Int. J. Intell. Comput. Cybern..

[29]  Lam Thu BUI,et al.  An adaptive approach for solving dynamic scheduling with time-varying number of tasks — Part I , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[30]  Kalyanmoy Deb,et al.  An Investigation of Niche and Species Formation in Genetic Function Optimization , 1989, ICGA.

[31]  Xin Yao,et al.  Characterizing environmental changes in Robust Optimization Over Time , 2012, 2012 IEEE Congress on Evolutionary Computation.

[32]  Erik D. Goodman,et al.  A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problem , 1997, ICGA.

[33]  Dipankar Dasgupta,et al.  Nonstationary Function Optimization using the Structured Genetic Algorithm , 1992, PPSN.

[34]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[35]  Qingfu Zhang,et al.  An estimation of distribution algorithm with guided mutation for a complex flow shop scheduling problem , 2007, GECCO '07.

[36]  Bernhard Sendhoff,et al.  Trade-Off between Performance and Robustness: An Evolutionary Multiobjective Approach , 2003, EMO.

[37]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[38]  Stephen F. Smith,et al.  A Memory Enhanced Evolutionary Algorithm for Dynamic Scheduling Problems , 2008, EvoWorkshops.

[39]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

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

[41]  Christoph F. Eick,et al.  Supporting Polyploidy in Genetic Algorithms Using Dominance Vectors , 1997, Evolutionary Programming.

[42]  Erik Demeulemeester,et al.  Proactive heuristic procedures for robust project scheduling: An experimental analysis , 2008, Eur. J. Oper. Res..

[43]  Shahram Shadrokh,et al.  Bi-objective resource-constrained project scheduling with robustness and makespan criteria , 2006, Appl. Math. Comput..

[44]  Jorge Pinho de Sousa,et al.  Using metaheuristics in multiobjective resource constrained project scheduling , 2000, Eur. J. Oper. Res..

[45]  Luo Fei,et al.  Optimal Genes Selection with a New Multi-objective Evolutional Algorithm Hybriding NSGA-II with EDA , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[46]  Sushil J. Louis,et al.  Learning with case-injected genetic algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[47]  Emma Hart,et al.  A Comparison of Dominance Mechanisms and Simple Mutation on Non-stationary Problems , 1998, PPSN.

[48]  Adel Guitouni,et al.  Multi-objectives Tabu Search based algorithm for progressive resource allocation , 2007, Eur. J. Oper. Res..

[49]  W. Punch,et al.  A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problems , 1997 .

[50]  John J. Grefenstette,et al.  Case-Based Initialization of Genetic Algorithms , 1993, ICGA.

[51]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[52]  S. Shakya Probabilistic model building Genetic Algorithm ( PMBGA ) : A survey , 2003 .

[53]  David W. Pearson,et al.  An Immune System-Based Genetic Algorithm to Deal with Dynamic Environments : Diversity and Memory , 2004 .