Abstract
We extend the Multiple Minimum Latency Problem (mMLP) by ignoring the back-walking costs; the resulting problem is called the Multiple Minimum Back-Walk-Free Latency Problem (mMBLP). In this article, we provide a two-phase metaheuristic algorithm for this problem. In a first phase, the Insertion Heuristic (IH) builds an initial solution while the Randomized Variable Neighborhood Search (RVND) combines with the perturbation and Adaptive Memory (AM) techniques to generate numerous neighborhoods in a second phase. This combination prevents the search from local optima. The algorithm is implemented with benchmark dataset. The results indicate that the problems with up to 76 vertices can be found exactly in a short time. Moreover, the algorithm is comparable with the other metaheuristic algorithms in accordance with the solution quality.
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