WA10 - Stochastic and Robust Optimization 3

We study a two-period joint inventory and pricing problem under three modeling paradigms—deterministic, stochastic, and distributionally robust—and across three pricing settings: fixed price, markdown, and markup pricing. We compare the resulting optimal decisions, expected profits, and consumer surplus across these settings and derive managerial insights into the value of distributional information and pricing flexibility.

We propose an event-driven re-optimization framework for multi-commodity flow with stochastic demand. A dual-inspired trigger controls update frequency, trading off optimality and computation. Experiments reveal near-optimal performance at small thresholds, monotone regret behavior, and structured inter-event times that admit simple statistical characterization.

This study introduces a variant of the multi-period vehicle routing problem in which collections may occur between release and due dates. The release date is defined as the time that the products become available for collection on farms. The release date for each product is stochastic, and farm products are collected over a one-week planning horizon. The due date for each collection is time-dependent, specified by the farmer, and late collections are not allowed. A fixed number of single-compartment homogeneous vehicles is booked throughout the week. Although the internal fleet size remains fixed at the weekly level, daily routes and farm assignments can be adjusted as availability information is revealed. If realized service requirements exceed the capacity of the committed internal fleet, additional transportation can be procured from a third-party logistics provider (3PL) at a higher marginal cost. The objective is to minimize the total collection cost, which includes transportation and expected 3PL costs. We propose a decomposition-based solution approach to solve the resulting two-stage stochastic vehicle routing problem with outsourcing.