A Self-Adaptive Evolutionary Algorithm for the Berth Scheduling Problem: Towards Efficient Parameter Control  
[PERSON]  
1 Department of Civil & Environmental Engineering, Florida A&M University-Florida State University,  
Tallahassee, FL 32310-6046, USA; [EMAIL_ADDRESS] (M.K. ); [EMAIL_ADDRESS] (O.A. ); [EMAIL_ADDRESS] (J.P.)  
1  
[PERSON]  
1 Department of Civil & Environmental Engineering, Florida A&M University-Florida State University,  
Tallahassee, FL 32310-6046, USA; [EMAIL_ADDRESS] (M.K. ); [EMAIL_ADDRESS] (O.A. ); [EMAIL_ADDRESS] (J.P.)  
1  
[PERSON]  
1 Department of Civil & Environmental Engineering, Florida A&M University-Florida State University,  
Tallahassee, FL 32310-6046, USA; [EMAIL_ADDRESS] (M.K. ); [EMAIL_ADDRESS] (O.A. ); [EMAIL_ADDRESS] (J.P.)  
1  
[PERSON]  
1 Department of Civil & Environmental Engineering, Florida A&M University-Florida State University,  
Tallahassee, FL 32310-6046, USA; [EMAIL_ADDRESS] (M.K. ); [EMAIL_ADDRESS] (O.A. ); [EMAIL_ADDRESS] (J.P.)  
1  
Footnote 1: email: {[[http://www.mdpi.com/journal/algorithms/article/pdf/100](http://www.mdpi.com/journal/algorithms/article/pdf/100)]([http://www.mdpi.com/journal/algorithms/article/pdf/100](http://www.mdpi.com/journal/algorithms/article/pdf/100))  
of waterborne trade increased by 33.6% from 7.7 billion tons in 2006 to 10.3 billion tons in 2016 [5]. Moreover, the volumes of all major types of cargo, transported by vessels, have significantly increased over the last ten years. Specifically, containerized trade increased by 59.9%, while major bulk cargo, dry bulk cargo, and oil/gas increased by 74.9%, 10.8%, and 13.2%, respectively, from 2006 to 2016. The general consumption goods and high-value cargo are typically transported in containerized form. Containers are transferred among different continents by vessels, which are served at marine container terminals (MCTs). Rapid growth of waterborne trade requires MCT operators upgrading the existing terminal infrastructure and improving operations planning. MCT operations can be categorized in the following three types [1; 6]: (1) seaside operations, which focus on service of arriving vessels (i.e., loading containers on vessels and unloading containers from vessels); (2) marshaling yard operations, which focus on temporary storage of containers in yard blocks of the MCT marshaling yard; and (3) landslide operations, which focus on pick-up and delivery of containers by the inland transportation modes (generally, on-dock rail and/or drayage trucks). Efficient seaside operations are critical for MCT performance, as disruptions in the seaside operations may significantly delay service of the arriving vessels. A significant amount of previously conducted studies primarily focused on the berth scheduling problem (BSP), aiming to improve the seaside operations at MCTs [6]. In BSP, the MCT operator aims to assign arriving vessels for service at available MCT berthing positions and determine the service order of vessels at each berthing position. The BSP is a challenging decision problem, which can be reduced to the unrelated machine scheduling problem [6; 7; 8; 9; 10; 11; 12]. As underlined by [PERSON] [13], the unrelated machine scheduling problem (therefore, the BSP as well) has NP-hard complexity. Hence, the realistic size problem instances of BSPs typically cannot be solved using the exact optimization algorithms to the global optimality within an acceptable computational time. To obtain good-quality berth schedules within a reasonable computational time, many different heuristic and metaheuristic algorithms have been presented in the BSP literature. For a detailed review of the BSP mathematical models and solution algorithms this study refers to the literature survey, conducted by [PERSON] and [PERSON] [6]. Many BSP studies applied Evolutionary Algorithms (EAs) and demonstrated their efficiency based on the numerical experiments [6; 7; 8; 9; 10; 11; 12; 14; 15; 16; 17; 18; 19].