Study on the joint allocation method of berths and shore bridges in tidal harbors considering carbon emissions
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摘要: 当前内河码头为了进一步提高客户满意度,泊位计划等运营决策需要充分考虑船方成本、收益等指标,因此为解决泊位与岸桥联合分配问题,建立了以港区碳排放成本和船舶在港成本之和最小为目标函数的混合整数规划模型;同时考虑潮汐因素对内河码头泊位分配的影响,在模型中加入了潮汐约束条件。为了提高算法求解该模型的适用性,对粒子群算法进行改进,设计了一种粒子群-遗传(PSO-GA)混行优化算法,采用遗传算法的交叉、变异算子在优化过程中可以实现对群体进化的智能调节,从而提高了优化的准确性和优越性。最后,根据某集装箱码头的实际数据对算法进行算例验证,然后将算法优化后的验证结果与传统遗传算法、粒子群算法相比总成本分别降低了约12.5%和11.3%,证明了算法的优越性和模型的可行性。
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关键词:
- 内河码头 /
- 泊位与岸桥 /
- PSO-GA混行算法 /
- 碳排放 /
- 潮汐
Abstract: At present,in order to further improve the customer satisfaction of inland waterway terminals,operational decisions such as berth plans need to fully consider the ship’s costs,revenues and other indicators,so this paper,in order to solve the problem of the joint allocation of berths and shore bridges,establishes a mixed-integer planning model with the minimization of the sum of the carbon emission cost of the port and the cost of the ship’s presence in the port as the objective function;and at the same time,considering the effect of the tide factor on the allocation of berths of the inland waterway terminals,in the model tidal constraints are added into the model.In order to improve the applicability of the algorithm to solve the model,this paper improves the particle swarm algorithm and designs PSO-GA mixed row optimization algorithm,which adopts the crossover and mutation operators of the genetic algorithm to realize the intelligent regulation of the population evolution during the optimization process,so as to improve the accuracy and superiority of the optimization.Finally,this paper validates the algorithm with examples based on the actual data of a container terminal,and then the optimized validation results of the algorithm are compared with the traditional genetic algorithm and particle swarm algorithm to reduce the total cost by about 12.5% and 11.3%,respectively,which proves the superiority of the algorithm and the feasibility of the model. -
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