论文标题
离散混合logit需求下的静态定价问题的精确算法
An exact algorithm for the static pricing problem under discrete mixed logit demand
论文作者
论文摘要
价格差异是许多市场中的常见策略。在本文中，我们研究了一个静态的多授权价格优化问题，其需求是由离散混合多项式logit模型给出的。通过考虑在实用程序规范中包含个人特定变量的混合logit模型，我们的定价问题很好地反映了实践中使用的离散选择模型。为了解决这个定价问题，我们设计了一种有效的迭代优化算法，该算法渐近地收敛到最佳解决方案。为此，基于信任区域的方法制定了线性优化（LO）问题，以找到“良好”可行解决方案，并从下面近似该问题。另一个LO问题是使用分段线性放松设计的，以近似上面的优化问题。然后，我们开发了一种新的分支方法来加强最佳差距。在停车服务定价案例中证明了我们算法的有效性，并对求解器进行了基准和文献中的现有贡献。
Price differentiation is a common strategy in many markets. In this paper, we study a static multiproduct price optimization problem with demand given by a discrete mixed multinomial logit model. By considering a mixed logit model that includes individual-specific variables in the utility specification, our pricing problem reflects well the discrete choice models used in practice. To solve this pricing problem, we design an efficient iterative optimization algorithm that asymptotically converges to the optimal solution. To this end, a linear optimization (LO) problem is formulated, based on the trust-region approach, to find a "good" feasible solution and approximate the problem from below. Another LO problem is designed using piecewise linear relaxations to approximate the optimization problem from above. Then, we develop a new branching method to tighten the optimality gap. The effectiveness of our algorithm is demonstrated on a parking services pricing case, and a benchmark against solvers and existing contributions in the literature are performed.
