Optimization under Uncertainty for E-retail Distribution: From Suppliers to the Last Mile

This thesis examines problems faced in the distribution management of e-retailers, in different stages of the supply chain, while accounting for sources of uncertainty. The first problem studies distribution planning, under stochastic customer demand, in a transshipment network. To decide on a transportation schedule that minimizes transportation, inventory and outsourcing costs, the problem is formulated as a two-stage stochastic programming model with recourse. Computational experiments demonstrate the cost-effectiveness of distribution plans generated while considering uncertainty, and provide insights on conditions under which the proposed model achieves significant cost savings. We then focus our attention on a later phase in the supply chain: last-mile same-day delivery. We specifically study crowdsourced delivery, a new delivery system where freelance drivers deliver packages to customers with their own cars. We provide a comprehensive review of this system in terms of academic literature and industry practice. We present a classification of industry platforms based on their matching mechanisms, target markets, and compensation schemes. We also identify new challenges that this delivery system brings about, and highlight open research questions. We then investigate two important research questions faced by crowdsourced delivery platforms. The second problem in this thesis examines the question of balancing driver capacity and demand in crowdsourced delivery systems when there is randomness in supply and demand. We propose models and test the use of heatmaps as a balancing tool for directing drivers to regions with shortage, with an increased likelihood, but not a guarantee, of a revenue-producing order match. We develop an MDP model to sequentially select matching and heatmap decisions that maximize demand fulfillment. The model is solved using a stochastic look-ahead policy, based on approximate dynamic programming. Computational experiments on a real-world dataset demonstrate the value of heatmaps, and factors that impact the effectiveness of heatmaps in improving demand fulfillment. The third problem studies the integration of driver welfare considerations within a platform's dynamic matching decisions. This addresses the common criticism of the lack of protection for workers in the sharing economy, by proposing compensation guarantees to drivers, while maintaining the work hour flexibility of the sharing economy. We propose and model three types of compensation guarantees, either utilization-based or wage-based. We formulate an MDP model, then utilize value function approximation to efficiently solve the problem. Computational experiments are presented to assess the proposed solution approach and evaluate the impact of the different types of guarantees on both the platform and the drivers

Distribution Planning with Consolidation - A Two-Stage Stochastic Programming Approach

The distribution planning problem with consolidation center(s) addresses the coordination of distribution activities between a set of suppliers and a set of customers, through the use of intermediate facilities in order to achieve savings in transportation cost. We study the problem from the perspective of a third-party logistics provider (3PL) that is coordinating shipments between suppliers and customers. Given customer demand of products from different suppliers, the goal is to consolidate the shipments in fewer high volume loads, from suppliers to the consolidation center(s) and from the consolidation center(s) to customer. We assume that suppliers have a finite set of transportation options, each with a given capacity and time of arrival at the consolidation center(s). Similarly, customers have a set of transportation options, each with a given capacity and dispatch time from the consolidation center(s). The 3PL wants to determine the optimal transportation options, or shipment schedule, and the allocation of shipments to transportation options from suppliers to consolidation center(s), and from consolidation center(s) to customers, that minimize the total transportation cost and holding cost at the consolidation center. The literature studies many variations of this problem, which assume deterministic demand. This thesis extends the problem for stochastic demand and formulates it as a two-stage stochastic programming model. We model the case where the choice of transportation options is a \textit{contractual} decision, and a 3PL needs to decide on which options to reserve for a given planning period subject to stochastic customer demand. Therefore, the choices of transportation options are the stage one variables in the two-stage stochastic program. The second stage variables, which are decisions that are made after the uncertainty conditions become known, represent the allocation of orders to reserved transportation options as well as shipping orders through a spot-market carrier, at a greater transportation cost. Because of the high computational demand of the model, the integer L-shaped method is applied to decompose the problem. To increase the efficiency of the algorithm, we experiment with three valid cuts with the goal of generating stronger cuts than the L-cut. We also apply three algorithm enhancement techniques to speed up the convergence of the algorithm. Numerical results show that the performance of our proposed methodology and valid cuts is comparable to that of CPLEX. We suggest promising areas for future work to further improve the computational efficiency of our decomposition algorithm

Distribution planning with random demand and recourse in a transshipment network

In this paper we consider a distribution planning problem in a transshipment network under stochastic customer demand, to account for uncertainty faced in real-life applications when planning distribution activities. To date, considerations of randomness in distribution planning networks with intermediate facilities have received very little attention in the literature. We address this gap by modeling uncertainty in a distribution network with an intermediate facility, and providing insight on the benefit of accounting for randomness at the distribution planning phase. The problem is studied from the perspective of a third-party logistics provider (3PL) that is outsourced to handle the logistics needs of its customers; the 3PL uses a consolidation center to achieve transportation cost savings. We formulate a two-stage stochastic programming model with recourse that aims to minimize the sum of transportation cost, expected inventory holding cost and expected outsourcing cost. The recourse variables ensure that the problem is feasible regardless of the realization of demand, by allowing the option of using a spot market carrier if demand exceeds capacity. We propose a flow-based formulation with a nonlinear holding cost component in the objective function. We then develop an alternative linear path-based formulation that models the movement of freight in the network as path variables. We apply Sample Average Approximation (SAA) to solve the problem, and show that it results in reasonable optimality gaps for problem instances of different sizes. We conduct extensive testing to evaluate the benefits of our proposed stochastic model compared to its deterministic counterpart. Our computational experiments provide managerial insight into the robustness and cost-efficiency of the distribution plans of our proposed stochastic model, and the conditions under which our model achieves significant distribution cost savings.Natural Sciences and Engineering Research Council of Canad