Abstract
Distribution planning, which includes Vehicle Routing and Scheduling Problem (VRSP), has become an important element in Supply Chain impacting its service level and efficiency. Computer Aided Routing and Scheduling (CARS) has been developed and implemented, which can handle complicated distribution models using advanced heuristic optimization algorithms. A classification scheme is introduced to classify various types of routing and scheduling problems in a structured manner, based on the main objects of VRSP. The integrated system described in this paper can manage the dynamic aspects of the Supply Chain in practice. The modelling and solution approach in the CARS optimization engine, its user interface, sample performance measurements, and planning and operational features of the system are described in detail.
Highlights
Distribution plays an essential role in the delivery of value within the supply chain, since value is not realized until the product reaches where it is needed
In order to evaluate the performance of Computer Aided Routing and Scheduling (CARS) optimization engine, a comprehensive experiment is conducted on a set of standard problem available in the literature
This paper described the modeling and design approach as well as main features of an advanced optimization system for various types of Vehicle Routing and Scheduling Problem (VRSP)
Summary
Distribution planning, which includes Vehicle Routing and Scheduling Problem (VRSP), has become an important element in Supply Chain impacting its service level and efficiency. Computer Aided Routing and Scheduling (CARS) has been developed and implemented, which can handle complicated distribution models using advanced heuristic optimization algorithms. A classification scheme is introduced to classify various types of routing and scheduling problems in a structured manner, based on the main objects of VRSP. The integrated system described in this paper can manage the dynamic aspects of the Supply Chain in practice. The modelling and solution approach in the CARS optimization engine, its user interface, sample performance measurements, and planning and operational features of the system are described in detail
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