A note on the prize collecting traveling salesman problem
Mathematical Programming: Series A and B
Lot-sizing with constant batches: formulation and valid inequalities
Mathematics of Operations Research
A General Approximation Technique for Constrained Forest Problems
SIAM Journal on Computing
Multiprocessor Scheduling with Rejection
SIAM Journal on Discrete Mathematics
Algorithms for facility location problems with outliers
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Improved Approximation Algorithms for Metric Facility Location Problems
APPROX '02 Proceedings of the 5th International Workshop on Approximation Algorithms for Combinatorial Optimization
Facility location with Service Installation Costs
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Primal-dual algorithms for deterministic inventory problems
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Stochastic Optimization is (Almost) as easy as Deterministic Optimization
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
A constant approximation algorithm for the one-warehouse multi-retailer problem
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Requirements Planning with Pricing and Order Selection Flexibility
Operations Research
On dependent randomized rounding algorithms
Operations Research Letters
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We consider important generalizations of a wide class of traditional deterministic inventory and facility location models that we call inventory/facility location models with market selection. Instead of the traditional setting, we are given a set of markets, each specified by a sequence of demands and associated with a revenue. Decisions are made in two stages. We first make a decision of what markets to select, where all other markets are rejected. Next we have to construct a minimum-cost production plan (facility layout) to satisfy all of the demands of all the selected markets. The goal is to minimize the overall lost revenues of rejected markets and the production (facility openings and connection) costs. We show how to leverage existing approximation results for the traditional models to corresponding results for the counterpart models with market selection. More specifically, any LP based α–approximation for the traditional model can be leveraged to a $\frac{1}{_{1-e}-\frac{1}{\alpha}}$- approximation algorithm for the counterpart model with market selection. Our techniques are also applicable to an important class of covering problems.