A tactical time slot management problem under mixed logit demand

TL;DR

This paper introduces a novel optimization framework for managing delivery time slots in attended home delivery, incorporating customer heterogeneity and complex demand patterns through a mixed logit model and a simulation-based heuristic.

Contribution

It develops a mixed-integer linear programming model combined with an adaptive large neighborhood search method to efficiently solve large-scale, stochastic demand-driven time slot management problems.

Findings

Effective capture of customer preference heterogeneity

Scalable solution approach for large instances

Improved decision-making in delivery slot management

Abstract

We study the tactical time slot management problem under mixed logit demand for attended home delivery in subscription settings. We propose a static mixed-integer linear programming model that integrates delivery slot assortment, price discount decisions, and routing optimization while capturing customer heterogeneity through the mixed logit model. To overcome the computational challenges posed by simulation-based choice probabilities, we develop a simulation-based Adaptive Large Neighborhood Search method aligned with a Sample Average Approximation reformulation. Computational experiments on large-scale instances demonstrate the effectiveness of our approach in capturing stochastic customer behavior and preference heterogeneity, providing a scalable and flexible method for optimizing time slot management under complex demand structures.