Efficient hybrid local search heuristics for solving the travelling thief problem

Real-world problems often consist of several interdependent subproblems. The degree of interaction of the subproblems is associated with the complexity of the problem and solving each subproblem optimally not ensure the optimal solution of the overall problem. The Travelling Thief Problem (TTP) integrates two well-known combinatorial optimization problems namely the classical Travelling Salesman Problem (TSP) and the 0-1 Knapsack Problem (KP). TTP was introduced to represent the complication of a real-world combinatorial optimization problem. The goal of this problem is to provide a tour to a thief over all the cities and a picking plan that determines which item should be taken from which city to achieve the maximum benefits. The KP component of the TTP is more efficient as compared to the TSP component for optimization. Our proposed method mainly focuses on constructing a picking plan for a near-optimal tour generated by Chained Lin-Kernighan Heuristic (CLKH). In this picking plan, items are picked up according to their scoring value which is calculated by our proposed formulation. Additionally, bit-flip is used for a better solution that can give a more profitable picking plan. The experimental results suggest that our proposed approach can meet or beat current state-of-the-art methods for a large number of TTP instances. (C) 2020 Published by Elsevier B.V.