Discrete multi-objective artificial bee colony algorithm for green co-scheduling problem of ship lift and ship lock. (January 2023)

• Record Type: Journal Article
• Authors: Zheng, Qian-Qian; Zhang, Yu; He, Li-Jun; Tian, Hong-Wei
• Is Part Of: Advanced engineering informatics. Volume 55(2023)
• Publication Date: 2023-01
• Subjects: Three Gorges Cascade Hub -- Multi-objective optimization -- Discrete artificial bee colony algorithm -- Green co-scheduling -- Ship lift -- Five-stage ship lock; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2023.101897
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: A green co-scheduling problem of ship lift and ship lock is studied. A new multi-objective mathematical model is proposed for this problem. The synchronous moving process of vessels is considered. An effective discrete multi-objective artificial bee colony (DMOABC) algorithm is developed. Results confirm the effectiveness of the proposed DMOABC. Abstract: This paper investigates a multi-objective green co-scheduling problem of ship lift and ship lock (GCP-SL&SL) at the Three Gorges Cascade Hub (TGCH). A mathematical model of GCP-SL&SL with objectives of the average utilizations rate of the lock chamber, average waiting time and total energy consumption of vessels, is proposed by separating it into three sub-problems: the facility assignment, lockage assignment and lockage operation scheduling. To solve this problem, a discrete multi-objective artificial bee colony (DMOABC) algorithm is developed. Within the DMOABC, a two-dimensional matrix encoding scheme is designed to encode and a group right-shift decoding scheme is specifically proposed to decode each food source. Then, a novel fitness evaluation mechanism based on fuzzy relative entropy is introduced to hand this multi-objective problem. Next, the food sources are improved from three aspects: (1) the employed bee phase uses new evolutionary operators for fast local search; (2) the onlooker bee phase adopts a modified tabu search for strong global search; (3) the scout bee phase embeds chemical reaction optimization for disturbing population. Finally, extensive experiments are conducted with the real data from historical traffic at TGCH. The results demonstrate our proposed algorithm is significantly better at solving the GCP-SL&SL than other five well-known multi-objective algorithms. The effect analysis under different scenarios indicates that the average waiting time of vessels at the dam is greatly reduced because of considering the synchronous moving process.