Abstract. Enhancements for the Benders’ decomposition algorithm can be derived from large neighbourhood search (LNS) heuristics. While mixed-integer programming (MIP) solvers are endowed with an array of LNS heuristics, their use is typically limited in bespoke Benders’ decomposition implementations. To date, only ad hoc approaches have been developed to enhance the Benders’ decomposition algorithm using large neighbourhood search techniques—namely local branching and proximity search. A general implementation of Benders’ decomposition has been developed within the MIP solver SCIP to permit a greater use of LNS heuristics with the expectation that it will enhance the solution algorithm. Benders’ decomposition is employed for all LNS heuristics to improve the quality of the identified solutions and generate additional cuts that can be used to improve the convergence of the main solution algorithm. Focusing on the heuristics of proximity search, RINS and DINS, the results will demonstrate the value of using Benders’ decomposition within LNS.
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