Large neighbourhood search applied to the efficient solution of spatially explicit strategic supply chain management problems

Supply chain management (SCM) has recently gained wider interest in both academia and industry given its potential to improve the benefits of a company through an integrated coordination of all its entities. Optimization problems in SCM are commonly cast as large scale mixed-integer linear programs (MILPs) that are hard to solve in short CPU times. This limitation is critical in spatially explicit SCM models since they require a large number of discrete variables to represent the geographical configuration of the network, which leads to complex MILPs. We present herein a novel solution method for this type of problems that combines the strengths of standard branch and cut techniques with the efficiency of large neighbourhood search (LNS). We illustrate the capabilities of this novel approach through its application to two case studies arising in energy applications: the design of supply chains (SCs) for bioethanol production and the strategic planning of hydrogen infrastructures for vehicle use.

► A hybrid metaheuristic is presented to solve supply chain management problems. ► Our algorithm identifies feasible solutions even when branch & cut method fails. ► Our method obtains near optimal solutions in less time than the branch & cut method.