Data-driven local search heuristics for bilevel network design problems

Abstract

In the Network Design Problem (NDP), one aims to design the configuration of a network by installing links between a set of given nodes and determine the flow of a set of commodities over these installed links. In this thesis, we work on two bilevel NDPs where the sequential process of decision making approach is inherited. In the first bilevel NDP we model the strategic flight NDP of a small airline carrier as a network interdiction problem to analyse the maximum possible disruption in its flight network in the wake of virtual attacks performed by a competitor. We call this problem the r - Interdiction Network Design Problem with Lost Demand (RI-NDPLD). In the second problem, namely Bilevel Optimization Model for the Reconfiguration of refugee camp network (BOpt-RRC), the readjustment of configurations of refugee camp network are studied under the case of new refugee flows and possible variations in the supply of public service providers. We implement a set of generic local search matheuristics to solve both problems. In the Tabu Search (TS) proposed for the RI- NDPLD, we enhance the generic implementation with bound based pruning and regression based candidate solution set generation procedures to reduce the computational burden of explicit evaluation of all neighboring solutions, and hence, enjoy better diversification. We also implement a generic TS to solve the BOpt-RRC and devise an adaptive neighborhood selection procedure to incorporate into this implementation. In addition to the generic TS, we also implement a Variable Neighborhood Search (VNS) matheuristic and devise an association rule based injection procedure to incorporate good solution components to initial solutions obtained by usual random shaking. Experimental studies reveal promising results for the proposed methods.