Binary decision diagrams for computing the non-dominated set July 13, 2015 Antti Toppila and Ahti Salo 27th European Conference on Operational Research,

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Presentation transcript:

Binary decision diagrams for computing the non-dominated set July 13, 2015 Antti Toppila and Ahti Salo 27th European Conference on Operational Research, July 2015, University of Strathclyde, Glasgow, Scotland

Portfolio selection with interval-values 1/2 Project cost Project value 1 2 3

Portfolio selection with interval-values 2/2 Portfolio cost Portfolio value B 1 2 1&2 3 2&3 1&2&3 1&3

Non-dominated set B = dominance Infeasible = ND set Portfolio cost Portfolio value

Computing the non-dominated set 1/2 Check all pairs

Computing the non-dominated set 2/2

Our contribution

Storing sets using Binary Decision Diagrams (BDDs) BDD = compressed binary decision tree (BDT) = BDD BDT Redundant = 1 –Identical nodes merged –Redundant nodes eliminated Truthtable Identical

Efficient set operations using BDDs

Example: Compression of a ND set Project Cost

Discarding sets of projects using BDDs: Project dominance 10

Example 1/2 Total of 190 project dominances Project 1 dominates project 3, 7, and 10-30, etc. Each project dominance yields a set within which ND portfolios are –Each set stored as BDD The intersection of these sets computed using BDD set operations Value Project projects of equal cost

Example 2/2 For clarity, node 0 and arcs to it removed from BDD

Discussion: Discarding process using BDDs We implemented the discarding process using Branch- and-Bound (B&B) –Sytematically cuts sets of dominated portfolios Many of these methods BDD variants of those presented by Liesiö et al Developed a method for reducing the number of pairs to check –Extension of the Fischetti-Toth dominance procedure (1988) Preliminary results suggest that large ND sets can computed using this implementation

Conclusions Storage as BDD opens new possiblities –Deriving stronger bounds using information from the BDD –Optimizing over BDD set = solving shortest path problem Use of implied dominance checks makes it possible to pairwise compare possibly huge ND sets Possible future research directions –Zero-suppressed BDDs for improved storage (Minato 2007) –Additive linear value functions (Liesiö et al. 2007) –Uncertainty set defined by linear inequalities (Liesiö et al. 2007) –Cost efficiency (Liesiö et al. 2008)

References Brace KS, Rudell RL, Bryant RE, Efficient implementation of a BDD package. Design Automation Conference, Proceedings 27th ACM/IEEE, 40-45, Jun 24-28, 1990 Bryant RE. Graph-based algorithms for Boolean function manipulation. IEEE Transactions on Computers 100(8): , 1986 Fischetti M, Salvagnin D. Pruning moves. INFORMS Journal on Computing 22(1): , 2010 Fischetti M, Toth P. A new dominance procedure for combinatorial optimization problems. Operations Research Letters 7(4): ,1988 Liesiö J, Mild P, Salo A. Preference programming for robust portfolio modeling and project selection. European Journal of Operational Research 181(3): , 2007 Liesiö J, Mild P, Salo A. Robust portfolio modeling with incomplete cost information and project interdependencies. European Journal of Operational Research 190(3): , 2008 Minato S. Zero-suppressed BDDs for set manipulation in combinatorial problems. Proceedings of 30th Conference on Design Automation, , June 1993 Pisinger D. Where are the hard knapsack problems? Computers & Operations Research 32(9) , 2005