Solving Multi-Objective Multi-Constraint Optimization Problems using Hybrid Ants System and Tabu Search
نویسندگان
چکیده
Many real-world optimization problems today are multi-objective multi-constraint generalizations of NP-hard problems. A classic case we study in this paper is the Inventory Routing Problem with Time Windows (IRPTW). IRPTW considers inventory costs across multiple instances of Vehicle Routing Problem with Time Windows (VRPTW). The latter is in turn extended with time-windows constraints from the Vehicle Routing Problem (VRP), which is extended with optimal fleet size objective from the single-objective Traveling Salesman Problem (TSP). While single-objective problems like TSP are solved effectively using meta-heuristics, it is not obvious how to cope with the increasing complexity systematically as the problem is compounded with additional objectives and constraints.
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