Dynamic Store Fulfillment with Collaborative Robots and In-Store Customers

Omnichannel services, such as buy-online-pickup-in-store, curbside pickup, and ship-from-store, have shifted the order-picking tasks that used to be completed by in-store customers doing their own shopping to the responsibility of retailers. To fulfill these orders, many retailers have deployed a store fulfillment strategy, where online orders are picked from inventory in brick-and-mortar stores. As store … Read more

Branch and Price for the Length-Constrained Cycle Partition Problem

The length-constrained cycle partition problem (LCCP) is a graph optimization problem in which a set of nodes must be partitioned into a minimum number of cycles. Every node is associated with a critical time and the length of every cycle must not exceed the critical time of any node in the cycle. We formulate LCCP … Read more

Crew Scheduling and Routing Problem in Road Restoration via Branch-and-Price Algorithms

This paper addresses the single crew scheduling and routing problem in the context of road network repair and restoration, which is critical in assisting complex post-disaster decisions in humanitarian logistics settings. We present three novel formulations for this problem, which are the first suitable for column generation and branch-and-price (BP) algorithms. Specifically, our first formulation … Read more

Submodular Dispatching with Multiple Vehicles

Motivated by applications in e-commerce logistics and production planning where orders (or items, or jobs) arrive at different times and must be dispatched or processed in batches, we consider a multi-vehicle dispatching problem that captures the tension between waiting for orders to arrive and the economies of scale due to batching. Our model extends the … Read more

Neural Approximate Dynamic Programming for the Ultra-fast Order Dispatching Problem

Same-Day Delivery (SDD) services aim to maximize the fulfillment of online orders while minimizing delivery delays but are beset by operational uncertainties such as those in order volumes and courier planning. Our work aims to enhance the operational efficiency of SDD by focusing on the ultra-fast Order Dispatching Problem (ODP), which involves matching and dispatching … Read more

Solving Various Classes of Arc Routing Problems with a Memetic Algorithm-based Framework

Arc routing problems are combinatorial optimization problems that have many real-world applications, such as mail delivery, snow plowing, and waste collection. Various variants of this problem are available, as well as algorithms intended to solve them heuristically or exactly. Presented here is a generic algorithmic framework that can be applied to a variety of arc … Read more

Facets of the knapsack polytope from non-minimal covers

We propose two new classes of valid inequalities (VIs) for the binary knapsack polytope, based on non-minimal covers. We also show that these VIs can be obtained through neither sequential nor simultaneous lifting of well-known cover inequalities. We further provide conditions under which they are facet-defining. The usefulness of these VIs is demonstrated using computational … Read more

A variable neighborhood search for the green vehicle routing problem with two-dimensional loading constraints and split delivery

\(\) We address the Green Vehicle Routing Problem with Two-Dimensional Loading Constraints and Split Delivery (G2L-SDVRP), which extends the split delivery vehicle routing problem to include customer demands represented by two-dimensional, rectangular items. We aim to minimize carbon dioxide (CO\(_2\)) emissions instead of travel distance, a critical issue in contemporary logistics activities. The CO\(_2\) emission … Read more

The Robust Bike Sharing Rebalancing Problem: Formulations and a Branch-and-Cut Algorithm

Bike Sharing Systems (BSSs) offer a sustainable and efficient urban transportation solution, bringing flexible and eco-friendly alternatives to city logistics. During their operation, BSSs may suffer from unbalanced bike distribution among stations, requiring rebalancing operations throughout the system. The inherent uncertain demand at the stations further complicates these rebalancing operations, even when performed during downtime. … Read more

Data-driven Stochastic Vehicle Routing Problems with Deadlines

Vehicle routing problems (VRPs) with deadlines have received significant attention around the world. Motivated by a real-world food delivery problem, we assume that the travel time depends on the routing decisions, and study a data-driven stochastic VRP with deadlines and endogenous uncertainty. We use the non-parametric approaches, including k-nearest neighbor (kNN) and kernel density estimation … Read more