Сравнение методов

	Замкнутая цепь поставок	Маршрутизация запасов
Область	Операционный менеджмент	Операционный менеджмент
Семейство	Machine learning	Machine learning
Год появления	2003	2014
Автор метода	Guide, V. D. R., & Van Wassenhove, L. N.	Coelho, L. C., Cordeau, J. F., & Laporte, G.
Тип	Supply chain strategy	Optimization problem
Основополагающий источник	Guide, V. D. R., & Van Wassenhove, L. N. (2003). Business aspects of closed-loop supply chains. Pittsburgh: Carnegie Mellon University Press.	Coelho, L. C., Cordeau, J. F., & Laporte, G. (2014). Thirty years of inventory routing. Transportation Research Part B: Methodological, 55, 28-67.
Другие названия	reverse logistics, circular economy logistics	IRP, vendor-managed logistics
Связанные	5	5
Сводка	A closed-loop supply chain (CLSC) integrates forward logistics (moving products to customers) with reverse logistics (recovering products, components, or materials from customers) to optimize resource recovery, reduce waste, and minimize environmental impact. Products flow forward for customer use, then flow backward for remanufacturing, refurbishment, recycling, or proper disposal. CLSC is driven by regulatory compliance (e.g., take-back laws), cost recovery, environmental responsibility, and increasingly, customer demand for sustainable business practices.	The Inventory Routing Problem (IRP) is an optimization problem that jointly determines inventory levels at customer locations, delivery routes, and shipment quantities to minimize total logistics and inventory holding costs. Rather than treating inventory management and vehicle routing as separate decisions, IRP recognizes that they are interdependent: larger shipments reduce routing costs but increase inventory holding costs, and vice versa. IRP is solved using mixed-integer programming, heuristics, and metaheuristics, and is a cornerstone of vendor-managed inventory (VMI) programs.