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Using new optimization methods and information-communications technology has become the key issue in the competition among the distributers of fast-moving consumer goods. Introducing a delivery optimization system instead of manual routing enables significant cost savings. The prerequisites for optimization are stable information system and efficient company management. The rich vehicle routing problem model is discussed and the effects of implementing the delivery optimization system are presented. For four years of continuous utilisation, the system has helped the distributer to reduce the overall distribution costs. It also made possible to close down several depots and handle more customer requests without investing in the vehicle fleet. The developed optimization system enabled the distributer to adapt to the new distribution schedule and react to first indicators of recession very fast.

This paper addresses the Vehicle Routing Problem with Time Windows (VRPTW) and shows that implementing algorithms for solving various instances of VRPs can significantly reduce transportation costs that occur during the delivery process. Two metaheuristic algorithms were developed for solving VRPTW: Simulated Annealing and Iterated Local Search. Both algorithms generate initial feasible solution using constructive heuristics and use operators and various strategies for an iterative improvement. The algorithms were tested on Solomon’s benchmark problems and real world vehicle routing problems with time windows. In total, 44 real world problems were optimized in the case study using described algorithms. Obtained results showed that the same distribution task can be accomplished with savings up to 40% in the total travelled distance and that manually constructed routes are very ineffective.