Dynamic Adaptive Large Neighborhood Search for the Joint Berth Allocation and Manpower Assignment Problem in Automobile Terminal

This paper studies a novel joint scheduling problem of berth allocation and manpower assignment in automobile terminals. A mixed-integer linear programming model is at first established to minimize the total service time for vessels under the constraints of independent tidal time windows for port side and starboard, manpower quantities, and physical restrictions. We present a dynamic adaptive large neighborhood search algorithm to deal with large-scale complicated instances, in which a hybrid genetic algorithm is approached to generate multiple starting points for global search and dynamic iterations perform local searches around these starting points. Furthermore, the study identifies special cases that are based on continuous berth relaxation and tidal relaxation to formulate heuristic rules, and corresponding operators are adopted by real-life scenarios. The computational experiments prove that our algorithms can efficiently generate optimal vessel berthing plans for the automobile terminal. Case studies based on real data further demonstrate the effectiveness and practicality of the proposed model and algorithms