Summary
After a disruption in an interconnected set of systems, it is necessary to restore service. This requires the determination of the tasks that need to be undertaken to restore service, and then scheduling those tasks using the available resources. This chapter discusses combining mathematical programming and constraint programming into multiple objective restoration planning in order to schedule the tasks that need to be performed. There are three classic objectives involved in scheduling problems: the cost, the tardiness, and the make span. Efficient solutions for the multiple objective function problem are determined using convex combinations of the classic objectives. For each combination, a mixed integer program is solved using a Benders decomposition approach. The master problem assigns tasks to work groups, and then subproblems schedule the tasks assigned to each work group. Hooker has proposed using integer programming to solve the master problem and constraint programming to solve the subproblems when using one of the classic objective functions. We show that this approach can be successfully generalized to the multiple objective problem. The speed at which a useful set of points on the efficient frontier can be determined should allow the integration of the determination of the tasks to be performed with the evaluation of the various costs of performing those tasks.
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Refereneces
E.E. Lee, J.E. Mitchell, W.A. Wallace. Restoration of services in interdependent infrastructure systems: A network flows approach. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37(6):1303-1317, 2007.
E.E. Lee. Assessing vulnerability and managing disruptions to interdependent infrastructure systems: A network flows approach. Ph.D. Thesis, Rensselaer Polytechnic Institute, 2006.
I.J. Lustig, J.F. Puget. Program does not equal program: Constraint programming and its relationship to mathematical programming. Interfaces, 31:29-53, 2001.
P.V. Hentenryck, L. Perron, J.F. Puget. Search and strategies in OPL. ACM Transactions on Computational Logic, 1:282-315, 2000.
H.J. Kim, J.N. Hooker. Solving fixed-charge network flow problems with a hybrid optimization and constraint programming approach. Annals of Operations Research, 115:95-124, 2002.
J.N. Hooker. Logic, optimization and constraint programming. INFORMS Journal on Computing, 14:295-321, 2002.
J.N. Hooker. A search-infer-and-relax framework for integrating solution methods. In Roman Bartà k and Michela Milano, editors, Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR), pages 243-257. Springer, 2005.
J.N. Hooker. Logic-based Methods for Optimization: Combining Optimization and Constraint Satisfaction. John Wiley, 2000.
M. Pinedo. Scheduling: Theory, Algorithms and Systems. Prentice Hall, 2002.
V. Jain, I.E. Grossmann. Algorithms for hybrid MILP/CP models for a class of optimization problems. INFORMS Journal on Computing, 13:258-276, 2001.
I. Harjunkoski, I. E. Grossmann. Decomposition techniques for multistage scheduling problems using mixed-integer and constraint programming methods. Computers and Chemical Engineering, 26:1533-1552, 2002.
C.T. Maravelias, I.E. Grossmann. A hybrid MILP/CP decomposition approach for the continuous time scheduling of multipurpose batch plants. Computers and Chemical Engineering, 28:1921-1949, 2004.
J.N. Hooker. Planning and scheduling by logic-based benders decomposition. Operations Research, 55(3):588-602, 2007.
J.N. Hooker. A hybrid method for planning and scheduling. Constraints, 10:385-401, 2005.
J.N. Hooker. Integrated Methods for Optimization. Springer, 2007.
J. Hu, J.E. Mitchell, J.S. Pang, K.P. Bennett, G. Kunapuli. On the global solution of linear programs with linear complementarity constraints. SIAM Journal on Optimization, 19(1):445-471, 2008.
ILOG Inc. ILOG OPL Studio 3.7.1 Language Manual. ILOG Inc. Mountain View, 2002.
ILOG Inc. ILOG OPL Studio 3.7.1 User’s Manual. ILOG Inc. Mountain View, 2002.
ILOG Inc. ILOG CPLEX 8.0 User’s Manual. ILOG Inc. Mountain View, 2002.
I. Das, J. Dennis. A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Structural Optimization, 14:63-69, 1997.
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This research is supported by NSF grant CMS 0301661, Decision Technologies for Managing Critical Infrastructure Interdependencies
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Gong, J., Lee, E.E., Mitchell, J.E., Wallace, W.A. (2009). Logic-based MultiObjective Optimization for Restoration Planning. In: Chaovalitwongse, W., Furman, K., Pardalos, P. (eds) Optimization and Logistics Challenges in the Enterprise. Springer Optimization and Its Applications, vol 30. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-88617-6_11
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