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This article explores how cutting‑edge optimization and simulation techniques empower supply‑chain planning—covering network design, inventory layout, and large‑scale scenario modeling—to reduce costs, improve efficiency, and enhance user experience through fast, scalable algorithms and AI‑driven insights.
The article presents how operations‑optimization and simulation algorithms empower supply‑chain planning—covering network design, inventory layout, and large‑scale simulation—to achieve cost reduction, efficiency gains, and enhanced user experience through advanced algorithmic solutions.
This article presents the ORLM intelligent decision‑making large model, detailing how real‑world decision problems are formalized and solved, the training difficulties and data synthesis methods, the transition from academic research to commercial platforms, and future technical improvement plans.
This article presents JD Retail's award‑winning, data‑driven inventory selection and allocation framework that combines machine‑learning‑based demand forecasting, heuristic selection algorithms, and an end‑to‑end multi‑task learning model to improve fulfillment rates, reduce stock‑out loss, and lower inventory transfer costs in a large‑scale e‑commerce supply chain.
JD Retail’s supply‑chain team won the Daniel H. Wagner Prize by developing data‑driven inventory selection and allocation algorithms that optimize two‑tier RDC/FDC networks, improve order fulfillment rates, reduce stock‑out losses and costs, and have been deployed at scale across millions of orders.
The Taotian Group’s algorithm team, with Zhejiang University’s School of Management, created the E‑commerce Supply Chain Optimization Decision Support System—implemented as Taotian Inventory Optimization Engine (IOS)—which uses deep learning, robust optimization and game theory to solve NP‑hard location, inventory and transfer problems, providing warehouse and inventory plans that improve efficiency and consumer experience, earning a nomination for China Operations Research Society’s Operations Application Award.
JD's retail supply chain team presents a data‑driven framework combining inventory selection and allocation algorithms—ML‑Top‑K, Reverse‑Exclude, and an end‑to‑end multi‑task learning model—that improve local order fulfillment, reduce stockout loss and allocation costs, and have been deployed across its RDC/FDC network.
The article explains how operations research techniques such as column generation and partition solving can optimize large‑scale supply‑chain logistics networks, detailing model formulation, constraints, computational challenges, and the benefits of generating high‑quality initial solutions for faster convergence.
This article explains how operations‑research techniques such as mixed‑integer programming, column generation, and partitioned solving are applied to large‑scale supply‑chain network design, illustrating model formulation, constraints, computational challenges, and the benefits of generating high‑quality initial solutions for faster convergence.
The Zhejiang University and Ant Group collaborative project on green‑computing resource allocation and task scheduling received the Operations Application Award at ORSC 2024, highlighting its multi‑stage optimization, significant CPU and carbon savings, and related publications in top conferences.
The article presents a packaging‑material recommendation system for e‑commerce fulfillment that uses rule‑based filters and a three‑dimensional bin‑packing algorithm—both exact integer programming and fast heuristics—to select the smallest suitable box or bag, enabling cost reduction, better space utilization, and lower damage rates through applications such as box‑cutting, order merging, box‑type redesign, and AI‑driven protective‑packaging matching.
This article explains why model validation is crucial for land‑use planning, outlines five practical validation techniques—including historical data checks, sensitivity analysis, scenario analysis, stress testing, and cross‑validation—and shows how each method helps identify risks and improve model robustness before real‑world deployment.
The two‑day workshop hosted by Zhejiang University Management School and Shanghai Jiao Tong University brings leading scholars and industry experts to present cutting‑edge research on operations research, large‑scale optimization, AI, graph neural networks, and supply‑chain applications, with detailed speaker bios, talk titles, abstracts, and registration information.
This article introduces the classic Vehicle Routing Problem (VRP) model, detailing its decision variables, objective function, and constraints, and demonstrates its application to a central kitchen distribution scenario with three trucks serving ten outlets, highlighting how linear programming or heuristics can minimize travel distance and cost.
This article introduces a lightweight Go-based heuristic solver designed for a wide range of logistics optimization problems such as vehicle routing, scheduling, and warehouse planning, detailing its algorithmic repertoire, configurable architecture, and real‑world deployment cases.
This article introduces a mathematical model for designing compromise solutions in multi‑party decision making, explains the underlying multi‑objective optimization framework, presents a quadratic programming example, and discusses how adjusting indicator ranges can balance differing preferences to achieve mutually acceptable outcomes.
This article compiles a comprehensive list of evaluation and optimization model resources, covering Data Envelopment Analysis, ANP, VIKOR, grey relational analysis, linear and integer programming, and various meta‑heuristic algorithms with example implementations, providing a handy reference for scholars and practitioners.
This article compiles recent resources on evaluation and optimization models, covering fundamental concepts, data preprocessing techniques, weighting methods such as TOPSIS and entropy, as well as linear and integer programming, graph theory, network algorithms, and meta‑heuristic approaches like simulated annealing and genetic algorithms.
The source presents several modeling challenges for the COMAP contest, covering ecological sex‑ratio dynamics of marine eels, submersible navigation and rescue, Great Lakes water‑level management, sustainable property insurance under climate risk, and strategies to curb illegal wildlife trade, each requiring development and analysis of quantitative models.
Pareto optimality, also known as Pareto efficiency, describes a state where improving any individual's outcome inevitably worsens another's, serving as a key criterion in multi‑objective optimization and decision science for evaluating trade‑offs such as maximizing profit while minimizing environmental impact.