当前位置: X-MOL 学术Netw. Spat. Econ. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Multi-depot Two-Echelon Fuel Minimizing Routing Problem with Heterogeneous Fleets: Model and Heuristic
Networks and Spatial Economics ( IF 2.4 ) Pub Date : 2019-02-05 , DOI: 10.1007/s11067-018-9437-7
Surendra Reddy Kancharla , Gitakrishnan Ramadurai

We formulate the two-echelon routing problem considering multiple depots and heterogeneous fleets. Our study (a) presents a Mixed Integer Linear Programming (MILP) formulation with load-dependent fuel minimization objective, (b) uses driving cycles to represent speed variations along a path, (c) allows the vehicles to return to any depot/satellite, and (d) conserves the total number of vehicles at each depot/satellite. We call the problem a Multi-Depot Two-Echelon Fuel Minimizing Routing Problem (MD2E-FMRP). Prior studies assumed there is a fixed number of vehicles available at each satellite/depot, whereas we allow different number of vehicles of each vehicle type at each satellite and depot. Our formulation relaxes several unrealistic assumptions in existing two-echelon formulations and hence has greater practical application. Despite the relaxation of constraints, the running time of our model is comparable to existing formulations. Gurobi optimizer is used to find a better upper bound for up to 56 node instances within a given time limit of 10,000s. We also propose an Adaptive Large Neighborhood Search (ALNS) based heuristic solution technique that outperformed Gurobi in all the tested instances of MD2E-FMRP. We observe an average saving of 13.11% in fuel consumption by minimizing fuel consumed instead of minimizing distance. In general, adapting heterogeneous fleets results in fuel savings and consequently lower emissions compared to using a homogeneous fleet.

中文翻译:

异构舰队的多仓库两级燃料最小化路由问题:模型和启发式

考虑多个仓库和异构机队,我们制定了两级路由问题。我们的研究(a)提出了一种基于混合线性整数规划(MILP)的公式,该公式具有与负荷相关的燃料最小化目标;(b)使用行驶周期来表示路径上的速度变化;(c)允许车辆返回任何基地/卫星站,和(d)节省每个车厂/卫星站的车辆总数。我们将该问题称为多仓库两级燃料最小化路由问题(MD2E-FMRP)。先前的研究假设每个卫星/仓库都有固定数量的车辆,而我们在每个卫星和仓库允许每种车辆类型的不同数量的车辆。我们的公式放松了现有两级公式中的一些不切实际的假设,因此具有更大的实际应用价值。尽管放宽了约束,但我们模型的运行时间与现有公式相当。Gurobi优化器用于在给定的10,000s的时间限制内为多达56个节点实例找到更好的上限。我们还提出了一种基于自适应大邻域搜索(ALNS)的启发式解决方案技术,该技术在所有测试的MD2E-FMRP实例中均优于Gurobi。我们观察到,通过最小化燃料消耗而不是最小化距离,平均可节省13.11%的燃料。通常,与使用同质车队相比,适应异构车队可以节省燃料并因此降低排放。我们还提出了一种基于自适应大邻域搜索(ALNS)的启发式解决方案技术,该技术在所有测试的MD2E-FMRP实例中均优于Gurobi。我们观察到,通过最小化燃料消耗而不是最小化距离,平均可节省13.11%的燃料。通常,与使用同质车队相比,适应异构车队可以节省燃料并因此降低排放。我们还提出了一种基于自适应大邻域搜索(ALNS)的启发式解决方案技术,该技术在所有测试的MD2E-FMRP实例中均优于Gurobi。我们观察到,通过最小化燃料消耗而不是最小化距离,平均可节省13.11%的燃料。通常,与使用同质车队相比,适应异构车队可以节省燃料并因此降低排放。
更新日期:2019-02-05
down
wechat
bug