Integrating neighborhood delivery services into parcel delivery networks

Problem definition: Leveraging developments in the sharing economy, several innovative delivery models have been adopted in the parcel delivery industry. One such innovation is the neighborhood delivery service model, where local residents receive parcels and deliver them within their neighborhood. We study the integration of neighborhood delivery services into a parcel delivery network. Methodology and results: We combine distributionally robust optimization with continuous approximation to build a model that captures the interaction between demand volatility, neighborhood delivery service capacities, and the delivery performance of a parcel delivery company. Efficient lower bounding procedures determine fleet size requirements that guarantee meeting service level targets for individual neighborhoods. An algorithm based on column generation solves the problem for a network of multiple neighborhoods. Analytical results show that average demand profiles, demand volatility levels, and service level targets determine the ability of neighborhood delivery services to reduce the fleet size and save total network cost. Numerical analyses with empirical data from a case study emphasize the important role of neighborhood delivery service capacity. Even with a modest capacity of 3.3% of the peak demand, the fleet size can be reduced by 4.0%. Larger fleet size reductions, of up to 24.9%, can be achieved when capacity of a neighborhood delivery service is 33.3% of the peak demand in the neighborhood. Interestingly, these fleet size reductions also translate into total network cost savings of 1.3% and 8.6%, respectively. Managerial implications: Our study reveals two key levers for managers to recruit and retain neighborhood delivery services and negotiate for higher capacities. Of those, making minimum compensation agreements is new to the literature and can be used more liberally than the other lever: increasing unit outsourcing cost. Furthermore, we show how managers should consider the average demand, demand volatility, and service level targets in recruiting neighborhood delivery services.<br/

Consistent Time Window Assignments for Stochastic Multi-Depot Multi-Commodity Pickup and Delivery

In this paper, we present the problem of assigning consistent time windows for the collection of multiple fresh products from local farmers and delivering them to distribution centers for consolidation and further distribution in a short agri-food supply chain with stochastic demand. We formulate the problem as a two-stage stochastic program. In the first stage, the time windows are assigned from a set of discrete time windows to farmers and in the second stage, after the demand is realized, the collection routes are planned by solving yet a newly introduced multi-depot multi-commodity team orienteering problem with soft time windows. The objective is to minimize the overall travel time and the time window violations. To solve our problem, we design a (heuristic) progressive hedging algorithm to decompose the deterministic equivalent problem into subproblems for a sampled set of demand scenarios and guide the scenarios toward consensus time windows. Through numerical experiments, we show the value of considering demand uncertainty over solving the deterministic expected value problem and the superiority of our approach over benchmarks when it comes to reducing the routing cost as well as the inconvenience for farmers

A concise guide to existing and emerging vehicle routing problem variants

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges

A concise guide to existing and emerging vehicle routing problem variants

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges.</p