Variable-Relationship Guided LNS for the Car Sequencing Problem

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Souza, Filipe
Grimes, Diarmuid
O'Sullivan, Barry
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Springer Cham
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Large Neighbourhood Search (LNS) is a powerful technique that applies the "divide and conquer" principle to boost the performance of solvers on large scale Combinatorial Optimization Problems. In this paper we consider one of the main hindrances to the LNS popularity, namely the requirement of an expert to define a problem specific neighborhood. We present an approach that learns from problem structure and search performance in order to generate neighbourhoods that can match the performance of domain specific heuristics developed by an expert. Furthermore, we present a new objective function for the optimzation version of the Car Sequencing Problem, that better distinguishes solution quality. Empirical results on public instances demonstrate the effectiveness of our approach against both a domain specific heuristic and state-of-the art generic approaches.
LNS , Neighbourhood Selection , Car Sequencing Problem , Large Neighbourhood Search (LNS) , Artificial intelligence
Souza, F., Grimes, D. and O’Sullivan, B. (2023) ‘Variable-relationship guided lns for the car sequencing problem’, AICS2022, in L. Longo and R. O’Reilly (eds) Artificial Intelligence and Cognitive Science. Cham: Springer Nature Switzerland, pp. 437–449.