This paper examines the yard truck scheduling,the yard location assignment for discharging containers,and the quay crane scheduling in container terminals.Taking into account the practical situation,we paid special at...This paper examines the yard truck scheduling,the yard location assignment for discharging containers,and the quay crane scheduling in container terminals.Taking into account the practical situation,we paid special attention to the loading and discharging precedence relationships between containers in the quay crane operations.A Mixed Integer Program（MIP） model is constructed,and a two-stage heuristic algorithm is proposed.In the first stage an Ant Colony Optimization（ACO） algorithm is employed to generate the yard location assignment for discharging containers.In the second stage,the integration of the yard truck scheduling and the quay crane scheduling is a flexible job shop problem,and an efficient greedy algorithm and a local search algorithm are proposed. Extensive numerical experiments are conducted to test the performance of the proposed algorithms.展开更多
Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles ha...Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.展开更多
基金supported by the National Nature Science Foundation of China under grant no.71102011
文摘This paper examines the yard truck scheduling,the yard location assignment for discharging containers,and the quay crane scheduling in container terminals.Taking into account the practical situation,we paid special attention to the loading and discharging precedence relationships between containers in the quay crane operations.A Mixed Integer Program（MIP） model is constructed,and a two-stage heuristic algorithm is proposed.In the first stage an Ant Colony Optimization（ACO） algorithm is employed to generate the yard location assignment for discharging containers.In the second stage,the integration of the yard truck scheduling and the quay crane scheduling is a flexible job shop problem,and an efficient greedy algorithm and a local search algorithm are proposed. Extensive numerical experiments are conducted to test the performance of the proposed algorithms.
文摘Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.
