Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Ejection chains, reference structures and alternating path methods for traveling salesman problems
Discrete Applied Mathematics - Special volume: first international colloquium on graphs and optimization (GOI), 1992
Vehicle scheduling in public transit and Lagrangean pricing
Management Science
Heuristics Ancient and Modern: Transport Scheduling Through the Ages
Journal of Heuristics
Simultaneous Vehicle and Crew Scheduling in Urban Mass Transit Systems
Transportation Science
Models and Algorithms for Integration of Vehicle and Crew Scheduling
Journal of Scheduling
A flexible system for scheduling drivers
Journal of Scheduling
Simultaneous Vehicle and Crew Scheduling for Extra Urban Transports
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Transit network timetabling and vehicle assignment for regulating authorities
Computers and Industrial Engineering
Truck Driver Scheduling in the European Union
Transportation Science
Combining metaheuristic algorithms to solve a scheduling problem
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
| Hi-index | 0.00 |
In this paper, we address a driver-vehicle scheduling problem in a limousine rental company. Given a set of trips to be covered, the goal consists in finding a driver-vehicle schedule that serves the maximum workload and optimizes several economic objectives while satisfying a set of imperative constraints. In this context, we propose a simultaneous scheduling of drivers and vehicles. The problem is modeled using the notion of partial consistent assignment. The solution approach is composed of two phases: the first one is based on constraint programming techniques and leads to the construction of an initial solution, improved in a second phase by a Simulated Annealing algorithm. Significant gains on the resulting solutions are systematically obtained in terms of quality, operational costs and elaboration time, compared to the current practice in the company.