Supply Chain Management

Recursive Partitioning and Batching for Network Design with Service Time Guarantees at Massive Scale

  • Myungeun Eom
  • Alan L. Erera
  • Alejandro Toriello
    • Categories Production and Logistics, Supply Chain Management, Transportation Tags , , , ,

    Motivated by the parcel delivery industry, we study a network design problem with service time guarantees at industrial scale. This tactical service network design problem determines primary paths and delivery schedules for commodities to minimize transportation and handling costs while ensuring committed service times. To construct a solution for a real-world instance with over 1,000 … Read more

    Reduction from the partition problem: Dynamic lot sizing problem with polynomial complexity

  • Chee-Khian Sim
    • Categories Dynamic Programming, Production and Logistics, Supply Chain Management

    In this note, we polynomially reduce an instance of the partition problem to a dynamic lot sizing problem, and show that solving the latter problem solves the former problem. By solving the dynamic program formulation of the dynamic lot sizing problem, we show that the instance of the partition problem can be solved with pseudo-polynomial … Read more

    Relay-Hub Network Design for Consolidation Planning Under Demand Variability

  • Onkar Anand Kulkarni
  • Mathieu Dahan
    • Categories Branch and Cut Algorithms, Supply Chain Management, Transportation Tags , , , , , ,

    Problem description: We study the problem of designing large-scale resilient relay logistics hub networks. We propose a model of Capacitated Relay Network Design under Stochastic Demand and Consolidation-Based Routing (CRND-SDCR), which aims to improve a network’s efficiency and resilience against commodity demand variability through integrating tactical decisions. Methodology: We formulate CRND-SDCR as a two-stage stochastic … Read more

    Multistage Stochastic Facility Location under Facility Disruption Uncertainty

  • Bonn Kleiford Seranilla
  • Nils Löhndorf
    • Categories Production and Logistics, Stochastic Programming, Supply Chain Management Tags , , ,

    We consider a multistage variant of the classical stochastic capacitated facility location problem under facility disruption uncertainty. Two solution algorithms for this problem class are presented: (1) stochastic dual dynamic integer programming (SDDiP), the state-of-the-art algorithm for solving multistage stochastic integer programs, and (2) shadow price approximation (SPA), an algorithm utilizing trained parameters of the … Read more

    Store Fulfillment with Autonomous Mobile Robots and In-Store Customers

  • Joyjit Bhowmick
  • Jennifer Pazour
  • Iman Dayarian
    • Categories Production and Logistics, Supply Chain Management, Transportation

    Omnichannel services, such as buy-online-pickup-in-store, curbside pickup, and ship-from-store, have shifted the order-picking tasks previously completed by in-store customers doing their own shopping to the retailer’s responsibility. To fulfill these orders, many retailers have deployed a store fulfillment strategy, where online orders are picked from brick-and-mortar retail store shelves. We focus on the design of … Read more

    Data-Driven Reliable Facility Location Design

  • Hao Shen
  • Mengying Xue
  • Z.J. Max Shen
    • Categories Data Science Applications, Network Optimization, Supply Chain Management Tags , , ,

    We study the reliable (uncapacitated) facility location (RFL) problem in a data-driven environment where historical observations of random demands and disruptions are available. Owing to the combinatorial optimization nature of the RFL problem and the mixed-binary randomness of parameters therein, the state-of-the-art RFL models applied to the data-driven setting either suggest overly conservative solutions, or … Read more

    Integrating Order-to-Delivery Time Sensitivity in E-Commerce Middle-Mile Consolidation Network Design

  • Lacy M. Greening
  • Mathieu Dahan
  • Alan L. Erera
    • Categories Production and Logistics, Supply Chain Management, Transportation Tags , , ,

    This paper proposes an approach that leverages data on customer purchasing sensitivity to quoted order-to-delivery times (ODTs) when designing middle-mile consolidation networks to maximize the profit of e-commerce retailers. Our approach integrates quoted ODT-dependent sales volume predictions into a new mixed-integer program (MIP) that simultaneously determines ODT quotes and a consolidation plan, characterized by the … Read more

    Pricing and Demand Management for Integrated Same-Day and Next-Day Delivery Systems

  • Dipayan Banerjee
  • Alan L. Erera
  • Alejandro Toriello
    • Categories Applications - OR and Management Sciences, Production and Logistics, Supply Chain Management Tags , ,

    We study a system in which a common delivery fleet provides service to both same-day delivery (SDD) and next-day delivery (NDD) orders placed by e-retail customers who are sensitive to delivery prices. We develop a model of the system and optimize with respect to two separate objectives. First, empirical research suggests that fulfilling e-retail orders … Read more

    A Robust Location-Allocation Model for Optimizing a Multi-Echelon Blood Supply Chain Network Under Uncertainty

  • Masoud Asadpour Ahmadchali
  • Mahdi Ebrahimzadeh Afrouzi
    • Categories Robust Optimization, Supply Chain Management Tags , , , , ,

    Designing and planning blood supply chains is very complicated due to its uncertain nature, such as uncertain blood demand, high vulnerability to disruptions, irregular donation, and blood perishability. In this vein, this paper seeks to optimize a multi-echelon blood supply chain network under uncertainty by designing a robust location-allocation model. The magnitude of the earthquake … Read more

    Using Neural Networks to Guide Data-Driven Operational Decisions

  • Ningyuan Chen
  • Saman Lagzi
    • Categories Applications - OR and Management Sciences, Supply Chain Management, Yield Management Tags , , ,

    We propose to use Deep Neural Networks to solve data-driven stochastic optimization problems. Given the historical data of the observed covariate, taken decision, and the realized cost in past periods, we train a neural network to predict the objective value as a function of the decision and the covariate. Once trained, for a given covariate, … Read more