Juraj Fosin

A Modelling and Optimization Framework for Real-World Vehicle Routing Problems

The globalisation of the economy leads to a rapidly growing exchange of goods on our planet. Limited commodities and transportation resources, high planning complexity and the increasing cost pressure through the strong competition between logistics service providers make it essential to use computer-aided systems for the planning of the transports. An important subtask in this context is the operational planning of trucks or other specialized transportation vehicles. These optimization tasks are called Vehicle Routing Problems (VRP). Over 1000 papers about a huge variety of Vehicle Routing Problems indicate the practical and theoretical importance of this NP-hard optimization problem. Therefore, many specific solvers for different Vehicle Routing Problems can be found in the literature. The drawback is that most of these solvers are high specialized and inflexible and it needs a lot of effort to adapt them to modified problems. Additionally, most real world problems are often much more complex than the idealized problems out of literature and they also change over time. To face this issue, we present an integrated modelling and optimization framework for solving complex and practical relevant Vehicle Routing Problems. The modular structure of the framework, a script based modelling language, a library of VRP related algorithms and a graphical user interface give the user both reusable components and high flexibility for rapid prototyping of complex Vehicle Routing Problems.

Empirical analysis of two different metaheuristics for real-world vehicle routing problems

We present two hybrid Metaheuristics, a hybrid Iterated Local Search and a hybrid Simulated Annealing, for solving real-world extensions of the Vehicle Routing Problem with Time Windows. Both hybrid Metaheuristics are based on the same neighborhood generating operators and local search procedures. The initial solutions are obtained by the Coefficient Weighted Distance Time Heuristics with automated parameter tuning. The strategies are compared in an empirical study on four real-world problems. A performance measure is used that also considers multiple restarts of the algorithms.

Comparison of Calculated and Measured Travel Times with PND Prediction

Correct estimation of the travel time is a rather difficult task because it relies on many factors which act in an unpredictable way. To verify predicted travel time results obtained by calculation from historic data, we have tried to verify those results by measurement and comparison with industrial personal navigation devices available on the market. With the limited time and resources we were able to cover only a finite number of routes with different parameters. Comparison of results show that there are many factors which influence the travel time on short routes and results suffer from higher inaccuracy. On longer routes or on combination of more short routes results show much smaller deviation and are better comparable to the predicted ones.

Vehicle Routing Optimization Using Multiple Local Search Improvements

Combinatorial optimization problems on graphs arise in many practical applications. One of the most studied practical combinatorial optimization problem is the Vehicle Routing Problem (VRP). When coupled with modern in-car navigation and fleet management software, real world applications of VRP optimization result in significant cost savings. In this paper novel multiple improvements pivoting rule for Capacitated VRP (CVRP) is proposed. Its application significantly reduces computational time needed for CVRP optimization. A novel pivoting rule is implemented as part of the search step selection mechanism in the Iterated Local Search algorithm. Augmented iterated local search algorithm is tested on 4 large scale real-world problems in Croatia with up to 7,065 customers and 236 vehicles, and on standard CVRP benchmark sets. Real-world problem data was obtained from a large Croatian logistics company. Comparison of well known first and best pivoting rules with proposed novel multiple improvements pivoting rule regarding travel distance, number of search moves and computational time is given. Achieved computational speed-ups are up to 29 times compared to the first improvement pivoting rule and 9 times compared to the best improvement pivoting rule, without any substantial degradation in quality of the obtained solution.

Point-based estimation of calculated speed profiles for road segments

Route planning for freight and personal navigation in urban areas is rather difficult because optimal route is not only distance-but also time-dependent. This task often depends on varying traffic conditions which can be influenced by different factors. Optimal route planning is therefore dependent on the time of the day and of the day in the week for different urban areas. One of the usual solutions to this problem is calculation of speed profiles from GPS traces gathered from different vehicles. Speed profiles are calculated estimates of average speed which can be achieved on a specific road segment during one day. To calculate speed profiles we used gathered GPS data collected during five years and 4,000 vehicles, mostly in urban areas in Croatia. There are different approaches considering time and distance averaging, and final results depend of the chosen values. Actual speed estimation can be made using radar measurements which are point-based but can give a good estimate for the chosen segment. In this paper we compare measurements obtained using Frequency Modulated Continuous Wave (FMCW) radars with calculated speed profiles and present difference for the four road segments.