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In vehicle routing problems with time windows (VRPTW), a set of vehicles with limits on capacity and travel time are available to service a set of customers with demands and earliest and latest time for servicing. The objective is to minimize the number of vehicles and the distance traveled for servicing the set of customers without being tardy or exceeding the capacity or travel time of the vehicles. As finding a feasible solution to the problem is NP-hard, szarch methods based upon heuristics are most promising for problems of practical size. In this paper we describe GIDEON, a genetic algorithm system for solving the VRPTW. On a standard set of 56 VRPTW problems obtained from the literature, GIDEON did better than the alternate methods on 41 of them, with an average reduction of 3.9% in fleet size and 4.4% in distance traveled for the 56 problems. AI TOPIC: Genetic Algorithms. DOMAIN AREA: Vehicle Routing Problems with Time Windows. LANGUAGED'OOL: C Language/GENESIS. STATUS: Implemented. EFFORT: Two and a half person years.IMPACT: Genetic algorithms used as a meta-level search strategy in routing and scheduling problems can obtain near optimal solutions for dynamic environments in real time. IntroductionThe problem we address is the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW involves routing a fleet of vehicles, of limited capacity and travel time, from a central depot to a set of geographically dispersed customers with known demands within specified time windows. The time windows are two-sided, meaning a customer must be serviced at or after its earliest time and before its latest time. If a vehicle reaches a customer before the earliest time it results in idle or waiting time. A vehicle that reaches a customer after the latest time is tardy. A service time is also associated with servicing each customer. The route cost of a vehicle is the total of the traveling time (proportional to the distance), waiting time and service time taken to visit a set of customers.The VRPTW arises in a wide array of practical decision making problems. Instances of the VRPTW occur in retail distribution, school bus routing, mail and newspaper delivery, municipal waste collection, fuel oil delivery, dial-a-ride service, airline and railway fleet routing and scheduling. Efficient routing and scheduling of vehicles can potentially save govemment and industry many millions of dollars a year. The current status of vehicle routing research is available in [3] and [l]. Solomon and Desrosiers[l9] provide an excellent survey on vehicle routing problems with time windows.Savelsbergh[l6] has shown that finding a feasible solution for a VRPTW using a fixed fleet size is NP-hard. Due to the intrinsic difficulty of the problem, search methods based upon heuristics are most promising for solving practical size problems [21,[18] and [20].In this paper we describe GIDEON, a genetic algorithm system to heuristically solve the VRPTW. GIDEON consists of two distinct modules, a global clustering module that assigns customers to vehi...

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