In many transportation systems, a mismatch between the associated design and planning decisions and the demand is typically encountered. A tailored system is not only appealing to operators, which could have a better knowledge of their operational costs, but also to users, since they would benefit from an increase in the level of service and satisfaction. Hence, it is important to explicitly allow for the interactions between the two in the model governing the decisions of the system. Discrete choice models (DCM) provide a disaggregate demand representation that is able to capture the impact on the behavior of these decisions by taking into account the heterogeneity of tastes and preferences of the users, as well as subjective aspects related to attitudes or perceptions. Despite their advantages, the demand expressions derived from DCM are non-linear and non-convex in the explanatory variables, which restricts their integration in optimization problems. In this paper, we overcome the probabilistic nature of DCM by relying on simulation in order to specify the demand directly in terms of the utility functions (instead of the choice probabilities). This allows us to define a mixed-integer linear formulation that characterizes the preference structure and the behavioral assumption of DCM, which can then be embedded in a mixed-integer linear programming (MILP) model. We provide an overview of the extent of the framework with an illustrative MILP model that is designed to solve a profit maximization problem of a parking services operator. The obtained results show the potential of the proposed methodology to adjust supply-related decisions to the users.

在许多运输系统中，通常会遇到相关的设计和计划决策与需求之间的不匹配。量身定制的系统不仅可以吸引运营商，他们可以更好地了解其运营成本，还可以吸引用户，因为他们将从服务水平和满意度的提高中受益。因此，重要的是在管理系统决策的模型中明确允许两者之间的交互。离散选择模型（DCM）提供了分类的需求表示形式，能够通过考虑用户口味和偏好的异质性以及与态度或看法相关的主观方面来捕获对这些决策行为的影响。尽管有优势，DCM得出的需求表达式在解释变量中是非线性和非凸的，这限制了它们在优化问题中的集成。在本文中，我们依靠仿真克服了DCM的概率性质，以便直接根据效用函数（而不是选择概率）指定需求。这使我们能够定义混合整数线性公式，该公式描述了DCM的偏好结构和行为假设，然后可以将其嵌入到混合整数线性规划（MILP）模型中。我们使用说明性的MILP模型提供了框架范围的概述，该模型旨在解决停车服务运营商的利润最大化问题。