Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
478200 | European Journal of Operational Research | 2014 | 16 Pages |
•A component-based heuristic framework for multi-attribute vehicle routing problems is introduced.•Problem-specific attributes are confined into adaptive resolution components in relation to their structure.•A general-purpose local search based on partial route concatenations is introduced.•A Unified Hybrid Genetic Search (UHGS) with a unified Split algorithm is proposed.•UHGS matches or outperforms all current best problem-tailored algorithms on 29 notable VRP variants and 42 benchmark sets.
Vehicle routing attributes are extra characteristics and decisions that complement the academic problem formulations and aim to properly account for real-life application needs. Hundreds of methods have been introduced in recent years for specific attributes, but the development of a single, general-purpose algorithm, which is both efficient and applicable to a wide family of variants remains a considerable challenge. Yet, such a development is critical for understanding the proper impact of attributes on resolution approaches, and to answer the needs of actual applications. This paper contributes towards addressing these challenges with a component-based design for heuristics, targeting multi-attribute vehicle routing problems, and an efficient general-purpose solver. The proposed Unified Hybrid Genetic Search metaheuristic relies on problem-independent unified local search, genetic operators, and advanced diversity management methods. Problem specifics are confined to a limited part of the method and are addressed by means of assignment, sequencing, and route-evaluation components, which are automatically selected and adapted and provide the fundamental operators to manage attribute specificities. Extensive computational experiments on 29 prominent vehicle routing variants, 42 benchmark instance sets and overall 1099 instances, demonstrate the remarkable performance of the method which matches or outperforms the current state-of-the-art problem-tailored algorithms. Thus, generality does not necessarily go against efficiency for these problem classes.