scholarly journals Two-Echelon Multidepot Logistics Network Design with Resource Sharing

2021 ◽  
Vol 2021 ◽  
pp. 1-28
Author(s):  
Siyu Luo ◽  
Yong Wang ◽  
Jinjun Tang ◽  
Xiangyang Guan ◽  
Maozeng Xu
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Method ◽  
Resource Sharing ◽  
Clustering Algorithm ◽  
Operating Cost ◽  
Pickup And Delivery ◽  
Nsga Ii ◽  
Logistics Network ◽  
Logistics Networks

Resource sharing within a logistics network offers an effective way to solve problems resulting from inefficient and costly operations of individual logistics facilities. However, the existing analysis of resource sharing and profit allocation is still limited. Therefore, this study aims to model resource sharing in two-echelon delivery and pickup logistics networks to improve the overall efficiency and decrease the total network operating cost. A bi-objective integer programming model is first proposed for two-echelon collaborative multidepot pickup and delivery problems with time windows (2E-CMDPDTW) to seek the minimization of operating costs and number of vehicles. Integrating a customer clustering algorithm, a greedy algorithm, and an improved nondominated sorting genetic algorithm-II (Im-NSGA-II), a hybrid method is then designed to handle the 2E-CMDPDTW model. The customer clustering and the greedy algorithms are employed to generate locally optimized initial solutions to accelerate the calculating velocity and guarantee the diversity of feasible solutions. The Im-NSGA-II combines the order crossover operation and the polynomial mutation process to find the optimal solution of the 2E-CMDPDTW. The comparative results show that the proposed hybrid method outperforms the NSGA-II and the multiobjective genetic algorithm. Furthermore, a Shapley value method is used for allocating total profits of established alliances and finding an optimal coalition sequence of the logistics facilities joining alliances based on the strictly monotonic path strategy. Finally, a case study of 2E-CMDPDTW in Chongqing China is conducted to validate the feasibility. Results indicate that this study contributes to long-term partnerships between logistics facilities within multi-echelon logistics networks in practice and contributes to the long-term sustainability of urban logistics pickup and delivery networks’ development.

scholarly journals Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Yong Wang ◽  
Xiuwen Wang ◽  
Xiangyang Guan ◽  
Jinjun Tang
Keyword(s):  
Resource Allocation ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Resource Sharing ◽  
Clustering Algorithm ◽  
Time Window ◽  
Operational Efficiency ◽  
Nsga Ii ◽  
Routing Problem ◽  
Logistics Networks

This study aims to provide tactical and operational decisions in multidepot recycling logistics networks with consideration of resource sharing (RS) and time window assignment (TWA) strategies. The RS strategy contributes to efficient resource allocation and utilization among recycling centers (RCs). The TWA strategy involves assigning time windows to customers to enhance the operational efficiency of logistics networks. A biobjective mathematical model is established to minimize the total operating cost and number of vehicles for solving the multidepot recycling vehicle routing problem with RS and TWA (MRVRPRSTWA). A hybrid heuristic algorithm including 3D k-means clustering algorithm and nondominated sorting genetic algorithm- (NSGA-) II (NSGA-II) is designed. The 3D k-means clustering algorithm groups customers into clusters on the basis of their spatial and temporal distances to reduce the computational complexity in optimizing the multidepot logistics networks. In comparison with NSGA algorithm, the NSGA-II algorithm incorporates an elitist strategy, which can improve the computational speed and robustness. In this study, the performance of the NSGA-II algorithm is compared with the other two algorithms. Results show that the proposed algorithm is superior in solving MRVRPRSTWA. The proposed model and algorithm are applied to an empirical case study in Chongqing City, China, to test their applicability in real logistics operations. Four different scenarios regarding whether the RS and TWA strategies are included or not are developed to test the efficacy of the proposed methods. The results indicate that the RS and TWA strategies can optimize the recycling services and resource allocation and utilization and enhance the operational efficiency, thus promoting the sustainable development of the logistics industry.

scholarly journals Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yong Wang ◽  
Yaoyao Sun ◽  
Xiangyang Guan ◽  
Yanyong Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Resource Sharing ◽  
Hybrid Algorithm ◽  
Time Windows ◽  
Operating Cost ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Logistics Networks ◽  
Location Routing

In this work, a two-echelon location-routing problem with time windows and transportation resource sharing (2E-LRPTWTRS) is solved by selecting facility locations and optimizing two-echelon vehicle routes. The optimal solutions improve the efficiency of a logistics network based on the geographical distribution and service time windows of logistics facilities and customers. Furthermore, resource utilization is maximized by enabling resource sharing strategies within and among different logistics facilities simultaneously. The 2E-LRPTWTRS is formulated as a biobjective optimization model, and obtaining the smallest number of required delivery vehicles and the minimum total operating cost are the two objective functions. A two-stage hybrid algorithm composed of k-means clustering and extended multiobjective particle swarm optimization algorithm is proposed for 2E-LRPTWTRS optimization. A self-adaptive mechanism of flight parameters is introduced and adopted during the iterative process to balance the evolution of particles and improve the efficiency of the two-stage hybrid algorithm. Moreover, 20 small-scale instances are used for an algorithm comparison with multiobjective genetic algorithm and nondominated sorting genetic algorithm-II, and the solutions demonstrate the superiority of the proposed algorithm in optimizing logistics networks. The proposed optimization model and hybrid algorithm are tested by employing a real-world case of 2E-LRPTWTRS in Chongqing, China, and the optimization results verify the positive role of the developed model and algorithm in improving logistics efficiency, reducing operating cost, and saving transportation resources in the operations of two-echelon logistics networks.

scholarly journals Multi-Depot Pickup and Delivery Problem with Resource Sharing

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Yong Wang ◽  
Lingyu Ran ◽  
Xiangyang Guan ◽  
Yajie Zou
Keyword(s):  
Resource Sharing ◽  
Hybrid Algorithm ◽  
Management Tool ◽  
Pickup And Delivery ◽  
Two Stage ◽  
Nsga Ii ◽  
Logistics Network ◽  
Delivery Problem ◽  
Pickup And Delivery Problem ◽  
Nondominated Sorting

Resource sharing (RS) integrated into the optimization of multi-depot pickup and delivery problem (MDPDP) can greatly reduce the logistics operating cost and required transportation resources by reconfiguring the logistics network. This study formulates and solves an MDPDP with RS (MDPDPRS). First, a bi-objective mathematical programming model that minimizes the logistics cost and the number of vehicles is constructed, in which vehicles are allowed to be used multiple times by one or multiple logistics facilities. Second, a two-stage hybrid algorithm composed of a k-means clustering algorithm, a Clark-Wright (CW) algorithm, and a nondominated sorting genetic algorithm II (NSGA-II) is designed. The k-means algorithm is adopted in the first stage to reallocate customers to logistics facilities according to the Manhattan distance between them, by which the computational complexity of solving the MDPDPRS is reduced. In the second stage, CW and NSGA-II are adopted jointly to optimize the vehicle routes and find the Pareto optimal solutions. CW algorithm is used to select the initial solution, which can increase the speed of finding the optimal solution during NSGA-II. Fast nondominated sorting operator and elite strategy selection operator are utilized to maintain the diversity of solutions in NSGA-II. Third, benchmark tests are conducted to verify the performance and effectiveness of the proposed two-stage hybrid algorithm, and numerical results prove that the proposed methodology outperforms the standard NSGA-II and multi-objective particle swarm optimization algorithm. Finally, optimization results of a real-world logistics network from Chongqing confirm the applicability of the mathematical model and the designed solution algorithm. Solving the MDPDPRS provides a management tool for logistics enterprises to improve resource configuration and optimize logistics operation efficiency.

scholarly journals An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ze Dong ◽  
Hao Jia ◽  
Miao Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Majority Vote ◽  
Clustering Algorithms ◽  
Nsga Ii ◽  
Number Of Clusters ◽  
Automatic Data ◽  
Multiobjective Genetic Algorithm ◽  
Fuzzy Clustering Method

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

scholarly journals Thermal-economic multiobjective optimization of heat pipe heat exchanger for energy recovery in HVAC applications using genetic algorithm

2014 ◽  
Vol 18 (suppl.2) ◽  
pp. 375-391 ◽  
Author(s):  
Sepehr Sanaye ◽  
Davood Modarrespoor
Keyword(s):  
Genetic Algorithm ◽  
Heat Exchanger ◽  
Heat Pipe ◽  
Operating Cost ◽  
Design Parameters ◽  
Nsga Ii ◽  
Pipe Length ◽  
Total Cost ◽  
Continuous And Discrete Variables ◽  
Pipe Heat

Cost and effectiveness are two important factors of heat pipe heat exchanger (HPHE) design. The total cost includes the investment cost for buying equipment (heat exchanger surface area) and operating cost for energy expenditures (related to fan power). The HPHE was thermally modeled using e-NTU method to estimate the overall heat transfer coefficient for the bank of finned tubes as well as estimating pressure drop. Fast and elitist non-dominated sorting genetic algorithm (NSGA-II) with continuous and discrete variables was applied to obtain the maximum effectiveness and the minimum total cost as two objective functions. Pipe diameter, pipe length, numbers of pipes per row, number of rows, fin pitch and fin length ratio were considered as six design parameters. The results of optimal designs were a set of multiple optimum solutions, called ?Pareto optimal solutions?. The comparison of the optimum values of total cost and effectiveness, variation of optimum values of design parameters as well as estimating the payback period were also reported for various inlet fresh air volume flow rates.

scholarly journals Genetic algorithm for the cargo shunting cooperation between two hub-and-spoke logistics networks

2019 ◽  
Vol 12 (2) ◽  
pp. 356
Author(s):  
Jingjing Hu ◽  
Youfang Huang
Keyword(s):  
Genetic Algorithm ◽  
Programming Model ◽  
Northern China ◽  
Third Party ◽  
Utilization Rate ◽  
Network Resources ◽  
Hub And Spoke ◽  
Logistics Network ◽  
Logistics Networks ◽  
Hub And Spoke Networks

Purpose: The overstocked goods flow in the hub of hub-and-spoke logistics network should be disposed of in time, to reduce delay loss and improve the utilization rate of logistics network resources. The problem we need to solve is to let logistics network cooperate by sharing network resources to shunt goods from one hub-and-spoke network to another hub-and-spoke network.Design/methodology/approach: This paper proposes the hub shunting cooperation between two hub-and-spoke networks. Firstly, a hybrid integer programming model was established to describe the problem, and then a multi-layer genetic algorithm was designed to solve it and two hub-and-spoke networks are expressed by different gene segments encoded by genes. The network data of two third-party logistics companies in southern and northern China are used for example analysis at the last step. Findings: The hub-and-spoke networks of the two companies were constructed simultaneously. The transfer cost coefficient between two networks and the volume of cargo flow in the network have an impact on the computation of hubs that needed to be shunt and the corresponding cooperation hubs in the other network.Originality/value: Previous researches on hub-and-spoke logistics network focus on one logistics network, while we study the cooperation and interaction between two hub-and-spoke networks. It shows that two hub-and-spoke network can cooperate across the network to shunt the goods in the hub and improve the operation efficiency of the logistics network. 

scholarly journals Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles Under Time Windows

2019 ◽  
Vol 11 (12) ◽  
pp. 3492 ◽  
Author(s):  
Wang ◽  
Yuan ◽  
Guan ◽  
Wang ◽  
Liu ◽  
...  
Keyword(s):  
Optimization Model ◽  
Clustering Algorithm ◽  
Time Windows ◽  
Linear Optimization ◽  
Pickup And Delivery ◽  
Dijkstra Algorithm ◽  
Demand And Supply ◽  
Total Cost ◽  
Delivery Problem ◽  
Logistics Networks

The sustainability and complexity of logistics networks come from the temporally and spatially uneven distributions of freight demand and supply. Operation strategies without considering the sustainability and complexity could dramatically increase the economic and environmental costs of logistics operations. This paper explores how the unevenly distributed demand and supply can be optimally matched through collaborations, and formulates and solves a Collaborative Pickup and Delivery Problem under Time Windows (CPDPTW) to optimize the structures of logistics networks and improve city sustainability and liverability. The CPDPTW is a three-stage framework. First, a multi-objective linear optimization model that minimizes the number of vehicles and the total cost of logistics operation is developed. Second, a composite algorithm consisting of improved k-means clustering, Demand-and-Time-based Dijkstra Algorithm (DTDA) and Improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) is devised to solve the optimization model. The clustering algorithm helps to identify the feasible initial solution to INSGA-II. Third, a method based on improved Shapley value model is proposed to obtain the collaborative alliance strategy that achieves the optimal profit allocation strategy. The proposed composite algorithm outperforms existing algorithms in minimizing terms of the total cost and number of electro-tricycles. An empirical case of Chongqing is employed to demonstrate the efficiency of the proposed mechanism for achieving optimality for logistics networks and realizing a win-win situation between suppliers and consumers.

scholarly journals Design of a Multiobjective Reverse Logistics Network Considering the Cost and Service Level

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Shuang Li ◽  
Nengmin Wang ◽  
Zhengwen He ◽  
Ada Che ◽  
Yungao Ma
Keyword(s):  
Genetic Algorithm ◽  
Reverse Logistics ◽  
Heuristic Algorithms ◽  
Service Level ◽  
Weighted Sum ◽  
Nsga Ii ◽  
Logistics Network ◽  
Reverse Logistics Network ◽  
Optimisation Model ◽  
The Cost

Reverse logistics, which is induced by various forms of used products and materials, has received growing attention throughout this decade. In a highly competitive environment, the service level is an important criterion for reverse logistics network design. However, most previous studies about product returns only focused on the total cost of the reverse logistics and neglected the service level. To help a manufacturer of electronic products provide quality postsale repair service for their consumer, this paper proposes a multiobjective reverse logistics network optimisation model that considers the objectives of the cost, the total tardiness of the cycle time, and the coverage of customer zones. The Nondominated Sorting Genetic Algorithm II (NSGA-II) is employed for solving this multiobjective optimisation model. To evaluate the performance of NSGA-II, a genetic algorithm based on weighted sum approach and Multiobjective Simulated Annealing (MOSA) are also applied. The performance of these three heuristic algorithms is compared using numerical examples. The computational results show that NSGA-II outperforms MOSA and the genetic algorithm based on weighted sum approach. Furthermore, the key parameters of the model are tested, and some conclusions are drawn.

scholarly journals The Bi-Level Optimal Configuration Model of the CCHP System Based on the Improved FCM Clustering Algorithm

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 907
Author(s):  
Kaoshe Zhang ◽  
Peiji Feng ◽  
Gang Zhang ◽  
Tuo Xie ◽  
Jinwang Hou ◽  
...  
Keyword(s):  
Carbon Dioxide Emissions ◽  
Clustering Algorithm ◽  
Meteorological Data ◽  
Operating Cost ◽  
Optimal Configuration ◽  
Configuration Model ◽  
Configuration Optimization ◽  
Nsga Ii ◽  
Fcm Algorithm ◽  
Fcm Clustering

To improve the comprehensive benefits of the CCHP system, this paper proposes a bi-level optimal configuration model of the CCHP system based on the improved FCM clustering algorithm. Firstly, based on the traditional FCM clustering algorithm, the entropy method is used to introduce the PFS index and the Vp index in a weighted form to achieve a comprehensive evaluation of the clustering effect. The effectiveness of the improved FCM algorithm is verified by analyzing the clustering process of the load and meteorological data using the improved FCM algorithm. Then the best cluster number and fuzzy coefficient is found using the traversal method. Secondly, a bi-level configuration optimization model is constructed. The outer layer is the configuration optimization layer, and the inner layer is the operation optimization layer. The model is solved by combining the NSGA-II and PSO algorithms. Finally, a bi-level optimal configuration model is constructed for actual cases, and the clustering results of the improved FCM algorithm are brought into the model. The example calculation analyses show that, compared with existing methods, the proposed method significantly reduces the operating cost and carbon dioxide emissions of the CCHP microgrid.

The use of computer simulation for analysing the logistics networks resilience

2018 ◽  
Vol 120 ◽  
pp. 75-86
Author(s):  
Jerzy Feliks ◽  
Marek Karkula ◽  
Katarzyna Majewska
Keyword(s):  
Computer Simulation ◽  
Time Interval ◽  
Actual System ◽  
Practical Applications ◽  
Logistics Network ◽  
Logistics Networks ◽  
Multi Method Approach ◽  
The Relationship ◽  
Method Approach

Logistic networks are complex systems, composed of many elements connected through nonlinear relations. This property makes it difficult to study these systems with traditional analytical methods. Therefore, computer simulation is a valuable tool in the practical applications of modeling the structure of the logistics network, the relationship between its components and the rules controling its functioning. The simulation model can be run in order to imitate the operation of the actual system in a given time interval and analyze its behavior under different scenarios. In the paper resilience measures for logistic networks are proposed and a multi-method approach of computer simulation for analysing their resistance is presented. The proposed concept allows determining and checking the possibilities ways of building long-term robustness of logistics networks for serious disturbances. The application of the model is illustrated by an example from the steel industry. AnyLogic 8.2 software was used for the implementation.

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