Facility Location: Concepts, Models, Algorithms and Case Studies

The mathematical science of facility locating has attracted much attention in d- crete and continuous optimization over nearly last four decades. Investigators have focused on both algorithms and formulations in diverse settings in both the private sectors (e.g., industrial plants, banks, retail facilities, etc.) and the public sectors (e.g., hospitals, post stations, etc.). Facility location problems locate a set of facilities (resources) to minimize the cost ofsatisfying someset ofdemands(ofthecustomers)with respectto some set of constraints. Facility location decisions are critical elements in strategic planning for awiderangeofprivateandpublic?rms.Thebranchesoflocatingfacilities arebroad and long-lasting, in?uencing numerous operational and logistical decisions. High costs associated with property acquisition and facility construction make facility location or relocation projects long-term investments. Decision makers must select sites that will not only perform well according to the current system state, but also willcontinuetobepro?tableforthefacility¿slifetime,evenas environmentalfactors change,populationsshift,andmarkettrendsevolve.Findingrobustfacilitylocations is thus a dif?cult task, demanding decision makers to account for uncertain future events. Locationscience is an areaof analyticalstudythat can be tracedbackto Pierrede Fermat, Evagelistica Torricelli (a student of Galileo), and Battista Cavallieri. Each one independently proposed (and some say solved) the basic Euclidean spatial - dian problem early in the seventeenth century.