Seven practitioner-grade e-books covering mathematical optimisation with Pyomo and HiGHS, demand forecasting, transport routing, and autonomous S&OP — with Python code, real datasets, and industry case studies.
Mathnal's flagship optimisation title. A practitioner's path from linear-programming first principles to production-grade decision systems, built entirely on the free, open-source Pyomo modeling language and the HiGHS solver — no commercial solver license required. Every model is developed against a single running case study (Konrad Industries) and every numerical result is verified against the solver output before publication, so what you build in each chapter is provably correct, not just plausible.
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Mathnal's e-book library covers the full spectrum of supply chain AI — from Python forecasting fundamentals to production-grade agentic planning systems — written by practitioners for practitioners.
Supply chain AI books cover mathematical optimisation (LP, MIP, network design, vehicle routing), demand forecasting (time series, ML, deep learning), transport and logistics, inventory management, S&OP planning, and increasingly agentic AI — where autonomous agents plan, negotiate and execute supply chain decisions with minimal human intervention. Mathnal's seven titles span this full range.
Pyomo is a free, open-source Python library for building mathematical optimisation models — linear programs, mixed-integer programs and nonlinear programs — using natural Python syntax instead of a proprietary modeling language. Paired with the HiGHS solver (also free and open-source, and now competitive with commercial solvers like Gurobi and CPLEX on many problem classes), Pyomo lets supply chain teams build, validate and deploy production-grade optimisation systems — network design, production planning, vehicle routing — without paying for a commercial solver license. Optimizing Supply Chains with Pyomo and HiGHS is Mathnal's dedicated guide to this stack, from first-principles modeling through production deployment.
Python is the dominant language for data science, machine learning and optimisation in supply chain. Libraries like scikit-learn, XGBoost, Pyomo, HiGHS, PuLP, OR-Tools and LangGraph give practitioners direct access to state-of-the-art algorithms without building from scratch. Learning supply chain AI through Python means you build deployable skills, not just theory.
Agentic AI uses autonomous software agents — each with a defined persona, tools and decision authority — to handle supply chain planning tasks that traditionally require human planners. In S&OP, this means agents for demand sensing, supply allocation, constraint resolution and executive communication working together through frameworks like LangGraph and CrewAI.
Free content teaches concepts in isolation. These books are structured end-to-end programs — each chapter builds on the last, uses real supply chain datasets, includes working code, and culminates in production-ready implementations. The Agentic S&OP Handbook, for example, takes you from architecture design through five complete industry deployments.
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