A survey on computational intelligence approaches for intelligent marine terminal operations

Marine container terminals (MCTs) play a crucial role in intelligent maritime transportation (IMT) systems. Since the number of containers handled by MCTs has been increasing over the years, there is a need for developing effective and efficient approaches to enhance the productivity of IMT systems. The berth allocation problem (BAP) and the quay crane allocation problem (QCAP) are two well-known optimization problems in seaside operations of MCTs. The primary aim is to minimize the vessel service cost and maximize the performance of MCTs by optimally allocating berths and quay cranes to arriving vessels subject to practical constraints. This study presents an in-depth review of computational intelligence (CI) approaches developed to enhance the performance of MCTs. First, an introduction to MCTs and their key operations is presented, primarily focusing on seaside operations. A detailed overview of recent CI methods and solutions developed for the BAP is presented, considering various berthing layouts. Subsequently, a review of solutions related to the QCAP is presented. The datasets used in the current literature are also discussed, enabling future researchers to identify appropriate datasets to use in their work. Eventually, a detailed discussion is presented to highlight key opportunities along with foreseeable future challenges in the area. This survey presents an in-depth review of computational intelligence approaches developed to optimize key terminal seaside operations such as berth allocation and quay crane scheduling. The comprehensive review and analysis make this survey manuscript useful for understanding the existing techniques and the challenges involved, as well as motivating the development of new, practical approaches for addressing seaside operational problems. The datasets used or proposed in the current literature are also discussed, enabling future researchers to identify appropriate datasets to use in their own work. image