Data-Driven Optimization for Meal Delivery: A Reinforcement Learning Approach for Order-Courier Assignment and Routing at Meituan

The rapid growth of online meal delivery has introduced complex logistical challenges, where platforms must dynamically assign orders to couriers while accounting for demand uncertainty, courier autonomy, and service efficiency. Traditional dispatching methods, often focused on short-term cost minimization, fail to capture the long-term implications of assignment decisions on system-wide performance. This paper presents a … Read more

blockSQP 2: exploiting structure to improve performance

Abstract One approach to solving optimal control problems is Bock’s direct multiple shoot- ing method. This method yields lifted nonlinear optimization problems (NLPs) with a spe- cific block structure. Exploiting this structure via tailored optimization algorithms can be computationally beneficial. We propose such methods, primarily within the framework of fil- ter line search sequential quadratic … Read more

Isotonic Optimization with Fixed Costs

This paper introduces a generalized isotonic optimization framework over an arborescence graph, where each node incurs state-dependent convex costs and a fixed cost upon strict increases. We begin with the special case in which the arborescence is a path and develop a dynamic programming (DP) algorithm with an initial complexity of $O(n^3)$, which we improve … Read more

Investment and Operational Planning for an electric market with massive entry of renewable energy

In this paper, we study a joint problem in which the Independent System Operator (ISO) intends to minimize the joint cost of operation and investment in a network structure. The problem is formulated through operational and investment control variables; we discuss the hierarchy between them and use the so-called Day Ahead Problem to find an … Read more

A Survey on the Applications of Stochastic Dual Dynamic Programming and its Variants

Stochastic Dual Dynamic Programming (SDDP) is widely recognized as the predominant methodology for solving large-scale multistage stochastic linear programming (MSLP) problems. This paper aims to contribute to the extant literature by conducting a comprehensive survey of the literature on SDDP within the realm of practical applications. We systematically identify and analyze the various domains where … Read more

Time Complexity and Optimality of Inventory and Production Policies for a Dynamic Lot Sizing Model with Remanufacturing and Separate Setup Costs

We consider a dynamic lot sizing model in which end products to satisfy demands are obtained by remanufacturing m types of cores or manufacturing from raw materials. We consider separate setup costs for manufacturing and remanufacturing in our model. It is conjectured in [21], with reference to [24], that finding an optimal policy to the … Read more

Rounding the Lovasz Theta Function with a Value Function Approximation

The Lovasz theta function is a semidefinite programming (SDP) relaxation for the maximum weighted stable set problem, which is tight for perfect graphs. However, even for perfect graphs, there is no known rounding method guaranteed to extract an optimal stable set from the SDP solution. In this paper, we develop a novel rounding scheme for … Read more

Multi-Stage Selection under Bounded Variation

We investigate a multi-stage version of the selection problem where the variation between solutions in consecutive stages is either penalized in the objective function or bounded by hard constraints. While the former problem turns out to be tractable, the complexity of the latter problem depends on the type of bounds imposed: When bounding the number … Read more

On liftings that improve convergence properties of Newton’s Method for Boundary Value Optimization Problems

The representation of a function in a higher-dimensional space is often referred to as lifting. Liftings can be used to reduce complexity. We are interested in the question of how liftings affect the local convergence of Newton’s method. We propose algorithms to construct liftings that potentially reduce the number of iterations via analysis of local … Read more