Research On Low-Carbon Logistics Location-Routing Problem And Hyper-Heuristic Algorithm

With the deepening of the concept of low-carbon development and the implementation of energy conservation and emission reduction policies,as one of the significant pillar industries in the national economy,the logistics industry characterized by low consumption,high quality,and high yield is extremely urgent.Given the current situation of China's logistics industry,how to improve the worse situation of the logistics industry that characterized by high emission,high energy consumption,high pollution,high cost,and low timeliness is a prominent problem.Low-carbon logistics is to use the advanced logistics technology to plan and implement logistics operations,such as packaging,transportation,handling,storage,distribution,and processing,aiming at achieving economically,environmentally,and socially sustainable development goals.Hence,driven by economic,environmental,and social benefits,researches and analyses on low-carbon logistics have important theoretical and practical significance.In the context of city logistics and low-consumption,reducing emission,and high production-oriented logistics,this paper studies and analyzes the low-carbon location routing problem of integrated logistics.Based on its theory and practical applications,the mathematical models and optimization algorithms are studied and defined in detail.The main research content of this paper is as follows:(1)A three-dimensional integer programming model was defined for the locationrouting problem.A novel hyper-heuristic algorithm is developed for the proposed problems.Based on the proposed algorithm framework,optimization frameworks based on multi-point search and single-point search were proposed.Three classification-based selection strategies and three acceptance criteria were proposed in the high-level control strategy.At the same time,14 low-level heuristics were constructed,and both have two optimization strategies.The stability,validity,and accuracy of the proposed algorithm are verified by benchmark instances of capacitied location-routing problem and the location-routing problem with simultaneous pickup and delivery.(2)For the low carbon location-routing problem,an effective estimation model is used to estimate fuel consumption and carbon emissions produced in the logistics activities.At the same time,we consider a city by considering urban structure and road characteristics in which goods need to be delivered from a depot to customers located in nested zones characterized by different speed limits.A mathematical model was defined that can simultaneously improve the logistics economy and environmental benefits.Aiming at this problem,a hyperheuristic algorithm based on classification and sharing mechanism strategy was proposed.In the experiment,the effectiveness of the proposed algorithms and model were verified.At the same time,the impact of problem parameters(client and depot distribution,time windows,and fleet composition)on logistics cost,fuel consumption and carbon emissions,travel distance and time were analyzed for providing several constructive conclusions.(3)Aiming at the problem of the bi-objective low-carbon location-routing problem in logistics,a bi-objective model that can simultaneously meet economic and environmental benefits was constructed.One of the objectives is the total logistics cost,including the fixed costs of depots,vehicle rental expenses,costs of fuel consumption and carbon emissions,while the other objective is the total waiting time of vehicles which is used to reduce the logistics cycle and improve the timeliness of the logistics network.Aiming at this problem,a multi-objective hyper-heuristic algorithm was proposed.In the high-level strategy of the proposed algorithm,a selection strategy based on quantum update mechanism and three acceptance criteria are proposed to improve the framework performance.A general multi-objective evolutionary algorithm framework and a hyper-heuristic algorithm framework were designed for the problem domain.In the experiment,the effectiveness of the proposed algorithm and model was verified.At the same time,extensive analysis was carried out to assess the effects of various problem parameters(such as the distribution of depots and clients and fleet composition)on the key performance indicators(including fuel consumption,carbon emissions,logistics costs,travel distance,and time).And several relevant conclusions are provided for logistics enterprises to plan and design distribution networks.(4)A multi-objective optimization model that can simultaneously meet economic,environmental,and social benefits was constructed for the low-carbon logistics location-routing problem considering client satisfaction and distribution cycle.The objectives include total logistics cost,delivery cycle,and customer waiting time.The total logistics cost consists of the fixed costs of depots,vehicle rental fee,fuel consumption cost,and carbon emission cost(economic benefit and environmental benefit),and the delivery cycle corresponds to the accumulated time of all vehicles for improving the timeliness of delivery network,and the third objective is the cumulative value of waiting time for all clients to improve customer satisfaction and loyalty(social benefits).For this model,a quantum selection strategy is improved based on the fifth chapter to improve the performance of the multi-objective hyper-heuristic algorithm.Besides,extensive experiments were conducted to analyze the benefits of fleet composition and joint effects of the depot and client distribution,as well as the area of speed zones on the performance indicators of Pareto fronts,which could produce several advises from managerial views for the logistics enterprises.Based on the above mathematical models and optimization algorithms,a location-routing problem-based low-carbon logistics delivery network system was developed,which integrated the proposed models and algorithms,and plenty of models and algorithms were also provided.The system could serve as proof of the algorithms' efficiency and robustness.