Optimization models for targeted offers in direct marketing: Exact and heuristic algorithms
This paper presents an optimization model for the selection of sets of clients that will receive an offer for one or more products during a promotion campaign. We show that the problem is strongly NP-hard and that it is unlikely that a constant-factor approximation algorithm can be proposed for solving this problem. We propose an alternative set-covering formulation and develop a branch-and-price algorithm to solve it. We also describe eight heuristics to approximate an optimal solution, including a depth-first branch-and-price heuristic and a tabu-search algorithm. We perform extensive computational experiments both with the exact as well as with the heuristic algorithms. Based on our experiments, we suggest the use of optimal algorithms for small and medium-size instances, while heuristics (especially tabu search and branch-and-price-based routines) are preferable for large instances and when time is an important factor.
heuristics branch-and-price algorithm tabu search direct marketing campaign integer programming non-approximability