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	Problem trasowania pojazdów (VRP)	Programowanie całkowitoliczbowe	Modele lokalizacyjno-alokacyjne	Analiza obszaru obsługi
Dziedzina	Optymalizacja	Optymalizacja	Analiza przestrzenna	Analiza przestrzenna
Rodzina	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Rok powstania	1959	1958	1963	2001
Twórca	George Dantzig & John Ramser	Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)	Leon Cooper; S. L. Hakimi	Harvey Miller & Shih-Lung Shaw
Typ	Combinatorial optimization problem	Mathematical optimisation — exact combinatorial method	Spatial facility-location optimization	Network GIS pipeline
Źródło pierwotne	Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, 6(1), 80–91.	Wolsey, L.A. (1998). Integer Programming. Wiley. ISBN: 9780471283669	Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.	Miller, H. J., & Shaw, S.-L. (2001). Geographic Information Systems for Transportation: Principles and Applications. Oxford University Press. ISBN: 978-0-19-512394-4
Inne nazwy	Capacitated Vehicle Routing Problem, Fleet Routing Problem, Multi-Vehicle Routing Problem, Araç Rotalama Problemi	IP, MIP, mixed-integer programming, mixed-integer linear programming	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri	Isochrone Analysis, Network Catchment Area Analysis, Travel-Time Polygon Analysis, Hizmet Alanı Analizi
Pokrewne	3	4	4	3
Podsumowanie	The Vehicle Routing Problem (VRP) seeks the minimum-cost set of routes for a fleet of vehicles to serve a collection of geographically dispersed customers, each with a known demand, departing from and returning to a central depot. Originally formulated as the Truck Dispatching Problem by Dantzig and Ramser in 1959, VRP is a foundational model in logistics, supply chain management, and operations research, applicable whenever goods or services must be delivered efficiently across multiple stops.	Integer programming (IP), also called mixed-integer programming (MIP) when only some variables are restricted to whole numbers, is a branch of mathematical optimisation in which some or all decision variables must take integer or binary values. Building on linear programming, it was formalised through Ralph Gomory's cutting-plane method (1958) and the Land-and-Doig branch-and-bound algorithm (1960), and it has since become the standard exact framework for scheduling, assignment, routing, and resource-allocation problems.	Location-allocation models decide where to place a set of facilities and simultaneously assign demand points to them so as to optimize an objective such as total travel cost, worst-case distance, or population covered. Rooted in the operations-research work of Cooper (1963) and Hakimi (1964) and central to network GIS, they answer questions like where to site warehouses, hospitals, fire stations, or schools to best serve a spatially distributed population.	Service Area Analysis delineates the geographic region reachable from one or more origin facilities within a specified travel cost — typically time, distance, or generalized impedance — by traversing a real road or transit network. It is widely used by urban planners, public health officials, logistics managers, and emergency response coordinators who need to understand actual accessibility rather than simple straight-line buffers.