基于碳排放的开放选址-路径问题及算法

基于物流对节能减排的重大影响及第三方物流的广泛应用,本文建立了与配送中心规模、配送路径相关的低碳开放选址-路径(OLRP)问题模型,并设计量子进化算法(QEA)进行求解.算法采用先确定车辆及其顾客集,再选择配送中心的策略,并运用局部优化算子进行解的改善.通过目标值与CPU的综合分析,确定重要参数旋转角变化值△θ,最大迭代次数itermax,种群Popsize的取值范围,并应用Barreto、Prins及Tuzun案例进行实验验证,结果显示碳排放目标的OLRP一定程度上会增大成本,量子进化算法在Barreto案例中的解均值优于LB、CPLEX及SA算法,在Prins案例中的求解效果与CPLEX相近,在Tuzun案例中绝大多数问题的求解结果优于CPLEX,在小规模问题中,优于SA算法,因此QEA是求解OLRP问题的一种有效算法.

Minimizing the carbon emission for the open location-routing problem and algorithm

It is important to study the carbon emissions of location-routing problems for reducing the carbon emission of logistics. This paper establishes an open location-routing problem model (OLRP), the goal of OLRP is to minimize the carbon emission, considering of facility construction carbon emission and distribution carbon emission. We propose a quantum-inspired evolutionary algorithm (QEA) for solving the model. The algorithm adopts the strategy of determining the vehicles and their paths first, and then selecting the distribution center. Two routing local searches and two distribution local searches are used to improve the solution. Three important parameters (Δθ、iter_max、Popsize) are determined by considering the carbon emission and CPU in Prins problem. The algorithm is tested on the benchmark of Barreto、Prins and Tuzun problems, the results show that the low carbon emission target of OLRP will increase the cost. The quantum-inspired evolutionary algorithm can obtain better average results than LB、CPLEX and SA in the Barreto problem, similar results to CPLEX in the Prins problem, and better than CPLEX in most Tuzun problem, which mean that QEA is an effective algorithm to the problem of OLRP.