A MULTI-OBJECTIVE RISK-BASED FRAMEWORK FOR MISSION CAPABILITY PLANNING

In this paper, we propose a risk-based framework for military capability planning. Within this framework, metaheuristic techniques such as Evolutionary Algorithms are used to deal with multi-objectivity of a class of NP-hard resource investment problems, called The Mission Capability Planning Problem, under the presence of risk factors. This problem inherently has at least two conflicting objectives: minimizing the cost of investment in the resources as well as the makespan of the plans. The framework allows the addition of a risk-based objective to the problem in order to support risk assessment during the planning process. In other words, with this framework, a mechanism of progressive risk assessment is introduced to capability planning.We analyze the performance of the proposed framework under both scenarios: with and without risk. In the case of no risk, the purpose is to study several optimization-related aspects of the framework such as convergence, trade-off analysis, and its sensitivity to the algorithm parameters; while the second case is to demonstrate the ability of the framework in supporting risk assessment and also robustness analysis.

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

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

[3]  Alf Kimms,et al.  Optimization guided lower and upper bounds for the resource investment problem , 2001, J. Oper. Res. Soc..

[4]  David E. Goldberg,et al.  FOX-GA: A Genetic Algorithm for Generating and Analyzing Battlefield Courses of Action , 1999, Evolutionary Computation.

[5]  D. S. Kim,et al.  A new heuristic for the multi-mode resource investment problem , 2005, J. Oper. Res. Soc..

[6]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

[7]  R. Rockafellar,et al.  Optimization of conditional value-at risk , 2000 .

[8]  Shahram Shadrokh,et al.  A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty , 2007, Eur. J. Oper. Res..

[9]  Albert Battersby,et al.  Advances in Critical Path Methods , 1966 .

[10]  Hany H. Ammar,et al.  A Methodology for Architecture-Level Reliability Risk Analysis , 2002, IEEE Trans. Software Eng..

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

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

[13]  Amir Azaron,et al.  Multi-objective time-cost trade-off in dynamic PERT networks using an interactive approach , 2007, Eur. J. Oper. Res..

[14]  Eduardo F. Camacho,et al.  Using a risk-based approach to project scheduling: A case illustration from semiconductor manufacturing , 2008, Eur. J. Oper. Res..

[15]  S Greenland,et al.  Sensitivity Analysis, Monte Carlo Risk Analysis, and Bayesian Uncertainty Assessment , 2001, Risk analysis : an official publication of the Society for Risk Analysis.

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

[17]  Klaus Neumann,et al.  Resource levelling for projects with schedule-dependent time windows , 1999, Eur. J. Oper. Res..

[18]  Ortwin Renn Three decades of risk research: accomplishments and new challenges , 1998 .

[19]  Krzysztof Fleszar,et al.  An evolutionary algorithm for resource-constrained project scheduling , 2002, IEEE Trans. Evol. Comput..

[20]  Zbigniew Michalewicz,et al.  Parameter control in evolutionary algorithms , 1999, IEEE Trans. Evol. Comput..

[21]  Jan Karel Lenstra,et al.  Scheduling subject to resource constraints: classification and complexity , 1983, Discret. Appl. Math..

[22]  Robert H. Kewley,et al.  Computational military tactical planning system , 2002, IEEE Trans. Syst. Man Cybern. Part C.

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

[24]  Vinícius Amaral Armentano,et al.  Scatter search for project scheduling with resource availability cost , 2006, Eur. J. Oper. Res..

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

[26]  Erik Demeulemeester,et al.  Minimizing resource availability costs in time-limited project networks , 1995 .

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

[28]  Rolf H. Möhring,et al.  Resource-constrained project scheduling: Notation, classification, models, and methods , 1999, Eur. J. Oper. Res..

[29]  Ake J Holmgren,et al.  Using Graph Models to Analyze the Vulnerability of Electric Power Networks , 2006, Risk analysis : an official publication of the Society for Risk Analysis.

[30]  Rainer Kolisch,et al.  Experimental investigation of heuristics for resource-constrained project scheduling: An update , 2006, Eur. J. Oper. Res..

[31]  Vicki M. Bier Challenges to the Acceptance of Probabilistic Risk Analysis , 1999 .