Optimization and Strategic Decision Models in Industrial Engineering examines how analytical methods support decisions in production, logistics, healthcare, and infrastructure systems.
Its eleven chapters address problems ranging from delivery prediction and warehouse performance to capacity planning, industrial location selection, and green vehicle routing. Further contributions explore pharmaceutical supply chains, maintenance contractor evaluation, remanufacturing, occupational safety, post-disaster transportation resilience, and the development of generative AI products in healthcare.
The volume brings together optimization, multi-criteria decision-making, machine learning, cooperative game theory, and scenario analysis. Through modelling studies, decision frameworks, and a review of pharmaceutical supply chain optimization, it examines how costs, risks, environmental impacts, and stakeholder priorities enter the decision process.
For researchers, graduate students, and practitioners, this collection offers examples of how industrial engineering methods can be applied across sectors, with attention to the assumptions and practical conditions that shape their use.
