Metaheuristics are general frameworks used to build heuristic algorithms for hard optimization problems. In this paper, an overview of promising and widely used metaheuristic methods in solving different variants of Berth Allocation Problem is presented. | Yugoslav Journal of Operations Research 27 (2017), Number 3, 265–289 DOI: SURVEY METAHEURISTIC APPROACHES FOR THE BERTH ALLOCATION PROBLEM ˇ Nataˇsa KOVAC Maritime Faculty, University of Montenegro, Kotor, Montenegro knatasa@ Received: May 2016 / Accepted: March 2017 Abstract: Berth Allocation Problem incorporates some of the most important decisions that have to be made in order to achieve maximum efficiency in a port. Terminal manager of a port has to assign incoming vessels to the available berths, which need to be loaded/unloaded in such a way that some objective function is optimized. It is well known that even simpler variants of Berth Allocation Problem are NP-hard, and thus, metaheuristic approaches are more convenient than exact methods since they provide high quality solutions in reasonable computational time. Metaheuristics are general frameworks used to build heuristic algorithms for hard optimization problems. In this paper, an overview of promising and widely used metaheuristic methods in solving different variants of Berth Allocation Problem is presented. Keywords: Container Terminal, Assignment of Vessels, Heuristic Optimization, High Quality sub-optimal Solutions. MSC: 90-02, 90B80, 68W20. 1. INTRODUCTION In global optimization problems, an emphasis is given to finding global optimum over all input variables for some set of functions under a given set of constraints. Combinatorial optimization is a branch of global optimization where the examined set of objects is finite. Let D denote the set of feasible solutions, defined by the constraints, for some optimization problem. Then, the global optimization problem can be expressed as: min{ f (s) : s ∈ D} (1) 266 N. Kovaˇc / Metaheuristic Approaches for BAP where f (s) is a function to be minimized and s is a feasible solution of the optimization problem. A solution s∗ ∈ D is optimal if (∀s ∈ D)( f (s∗ ) ≤ f (s)). (2) Maximization problem can be defined in analogous
