The four-way shuttle-based storage and retrieval system (FSS/RS) handles retrieval transactions through shuttles moving horizontally in four directions and lifts moving vertically. The scheduling process and working r...
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The four-way shuttle-based storage and retrieval system (FSS/RS) handles retrieval transactions through shuttles moving horizontally in four directions and lifts moving vertically. The scheduling process and working route of both are important problems for daily operations. This article constructs a scheduling model for the tier-captive FSS/RS with multiple lifts and designs a two-stage optimization heuristic algorithm (OHA). A heuristic assignment algorithm is proposed to determine the retrieval request sequence, and the route-generation stage adopts an improved A-star algorithm with a multi-phase conflict avoidance method to generate shuttle conflict-free routes. Simulation results demonstrate that OHA can reliably generate high-quality solutions, with a low degree of fluctuation in the results, not exceeding 0.5%. The proposed scheduling method outperforms its competitors in all instances, reducing the makespan by 8%. Moreover, the evidence shows that the proposed method can obtain the most competitive scheduling scheme in FSS/RS with more shuttles and lifts.
This paper studies the problem of considering customer satisfaction in the no-battery-swap mode and in the power-swap mode. First, with the goal of maximizing customer satisfaction, the total cost of charging and disc...
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This paper studies the problem of considering customer satisfaction in the no-battery-swap mode and in the power-swap mode. First, with the goal of maximizing customer satisfaction, the total cost of charging and discharging and the minimum construction cost of swapping stations, the customer time window, and the load constraints of electric vehicles are considered. A model of electric vehicle charging and discharging route optimization and replacement station location without battery swapping behavior, considering customer satisfaction, is established, and then, a two-stage improved ant colony-genetic algorithm is designed to solve the model, and finally, the comparative analysis considers customer satisfaction. Based on the path optimization results and location decisions considering the cost of charging and discharging, the following conclusions are obtained: 1) electric vehicle route optimization and swap station location planning considering customer satisfaction can not only effectively reduce logistics distribution costs and replacement costs but also improve customer satisfaction levels. 2) Reducing the number of route crossings in the process of logistics distribution routes can save electricity costs for electric vehicles and logistics distribution costs, and help reduce the total cost of the entire logistics distribution network. 3) The gradient setting of the electricity price for electricity exchange will reduce the cost of electricity exchange, improve the utilization efficiency of the battery, reduce the cost of logistics and distribution, and improve the electricity exchange revenue of the electricity exchange station.
Making operational plans for Yard Cranes (YCs) to enhance port efficiency has become vital issues for the container terminals. This paper discusses the load-scheduling problem of multiple YCs. The problem is to schedu...
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Making operational plans for Yard Cranes (YCs) to enhance port efficiency has become vital issues for the container terminals. This paper discusses the load-scheduling problem of multiple YCs. The problem is to schedule two YCs at different container blocks, which serve the loading operations of one quay crane so as to minimize the total distance of visiting paths and the make-span at stack area. We consider the container handling time, the YC visiting time, and the waiting time of each YC when evaluating the make-span of the loading operation by YCs. Both the container bay visiting sequences and the number of containers picked up at each visit of the two YCs are determined simultaneously. A mathematical model, which considers interference between adjacent YCs, is provided by means of time-space network to formulate the problem and a two-stage hybrid algorithm composed of greedy algorithm and dynamic programming is developed to solve the proposed model. Numerical experiments were conducted to compare performances of the algorithm in this study with actual scheduling rules.
This paper presents a novel two-stagehybrid swarm intelligence optimization algorithm called GA-PSO-ACO algorithm that combines the evolution ideas of the genetic algorithms, particle swarm optimization and ant colon...
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This paper presents a novel two-stagehybrid swarm intelligence optimization algorithm called GA-PSO-ACO algorithm that combines the evolution ideas of the genetic algorithms, particle swarm optimization and ant colony optimization based on the compensation for solving the traveling salesman problem. In the proposed hybridalgorithm, the whole process is divided into twostages. In the first stage, we make use of the randomicity, rapidity and wholeness of the genetic algorithms and particle swarm optimization to obtain a series of sub-optimal solutions (rough searching) to adjust the initial allocation of pheromone in the ACO. In the second stage, we make use of these advantages of the parallel, positive feedback and high accuracy of solution to implement solving of whole problem (detailed searching). To verify the effectiveness and efficiency of the proposed hybridalgorithm, various scale benchmark problems from TSPLIB are tested to demonstrate the potential of the proposed two-stagehybrid swarm intelligence optimization algorithm. The simulation examples demonstrate that the GA-PSO-ACO algorithm can greatly improve the computing efficiency for solving the TSP and outperforms the Tabu Search, genetic algorithms, particle swarm optimization, ant colony optimization, PS-ACO and other methods in solution quality. And the experimental results demonstrate that convergence is faster and better when the scale of TSP increases.
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