A Randomized Concave Programming Method for Choice Network Revenue Management

摘要: The randomized linear programming (RLP) proposed in [23] is a very simple and fast simulation-based method that has been found to be surprisingly effective robust for generating upper bounds bid-price controls network revenue management (see [26] simulations comparing the alternatives). RM incorporating more realistic models of customer behavior, as customers choosing from an offer set, have recently become popular [24]). Many extensions such ([8], [15], [12], [28], [17]) subsequently proposed. choice model behavior however are considerably difficult solve. formulations exponential number columns solution strategy use column generation. But finding entering computationally easy only limited cases. Given difficulties solving these methods, it natural explore RLP methodology model. In this paper we first give segment-based deterministic concave-program (SDCP) bound dynamic program, coincides with CDLP upper-bound [8] [15] non-overlapping segments. We then tighten by concave (RCP) method, similar independent-class advantage (i) get tighter segment model, (ii) able solve larger classes (with overlapping segments). If elements consideration set not large, both can applied any whatsoever, expanding well beyond tractable-but-restrictive ones multinomial-logit.