Genetic Algorithm Heuristics for the Static and Dynamic Berth Allocation Problems in Container Terminals

  • Ahmed S. A. Simrin

Student thesis: Master's Thesis


The current decade sees a considerable growth in worldwide container transportation and with it a critical need for optimization, as ports today are gearing up to meet the challenge of handling mega-vessels capable of carrying 10000-12000 TEU (Twentyfoot equivalent unit). One of the important seaside operations problems that received a lot of attention in the literature is the assignment of quay space and service time to vessels that have to be unloaded and loaded at a terminal. This problem is commonly referred to as the berth allocation problem (BAP). A more cost-effective way of increasing berth capacity and productivity, rather than building a bigger berth, is to utilize it more efficiently. Therefore, much research focuses on optimizing the BAP. Different approaches to berth allocation exist in literature; some of them consider the static berth allocation problem (SBAP) in which vessels arrive before a schedule plan is constructed, while other approaches consider the dynamic berth allocation problem (DBAP) which has no restrictions on the arrival time of vessels. In this thesis, we modeled and solved both problems - static and dynamic. The problems are non-deterministic polynomial-time (NP) which means that they cannot be solved in polynomial time; therefore, we developed genetic algorithm (GA) based heuristics that are capable of obtaining good-quality solutions within reasonable computational time. We applied those heuristics on different instances of the problems, and through computational experiments we analyzed the best, average, and worst case performances to determine the efficiency of the algorithms.
Date of Award2013
Original languageAmerican English
SupervisorAli Diabat (Supervisor)


  • Maritime Transport
  • Logistics; Container Terminals (CT); Berth Allocation Problem (BAP).

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