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Yong Wang
School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China

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Research article
Published: 01 June 2021 in Journal of Advanced Transportation
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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.

ACS Style

Yong Wang; Lingyu Ran; Xiangyang Guan; Yajie Zou. Multi-Depot Pickup and Delivery Problem with Resource Sharing. Journal of Advanced Transportation 2021, 2021, 1 -22.

AMA Style

Yong Wang, Lingyu Ran, Xiangyang Guan, Yajie Zou. Multi-Depot Pickup and Delivery Problem with Resource Sharing. Journal of Advanced Transportation. 2021; 2021 ():1-22.

Chicago/Turabian Style

Yong Wang; Lingyu Ran; Xiangyang Guan; Yajie Zou. 2021. "Multi-Depot Pickup and Delivery Problem with Resource Sharing." Journal of Advanced Transportation 2021, no. : 1-22.

Journal article
Published: 25 May 2021 in Applied Soft Computing
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Cooperation is a powerful strategy to reduce the costs of pickup and delivery services in the conventional two-echelon pickup and delivery problems (2E-PDPs). This study develops a cooperative 2E-PDP with state–space–time network (C2E-PDPSST) and incorporates vehicle routing problem and profit allocation optimization. Transportation resource sharing (trucks/vehicles) is presented as a major cooperative strategy in C2E-PDPSST to reduce the number of vehicles and improve resource utilization. A bi-objective mixed integer model is proposed to minimize total operating costs and number of vehicles and is formulated based on state–space–time networks. A novel methodology, which combines the time-dependent forward dynamic programming algorithm and a hybrid heuristic algorithm comprising modified k-means clustering algorithm and improved multi-objective particle swarm optimization (IMOPSO) algorithm, is designed for solving C2E-PDPSST. The clustering process speeds up the solution by reducing the computational complexity. The IMOPSO algorithm combines inter- and intra-route operations to find the Pareto optimal solutions with predefined iteration and termination rules. The inter-route operations will improve the population diversity, while the intra-route operations will generate an optimal solution for each vehicle route. Thus, an effective combination of local and global solution search can be achieved. Profit allocation schemes are investigated using the criteria of minimum cost remaining savings. Our results on benchmark instances show that the proposed IMOPSO has better computational performance than the conventional MOPSO and multi-objective genetic algorithm. A case study based on the realistic logistics network in Chengdu City, China is conducted for validation. The proposed methodology considering state–space–time network performs better in terms of reducing traveling costs, and improving the flexibility and efficiency of the entire network.

ACS Style

Yong Wang; Shuanglu Zhang; Xiangyang Guan; Jianxin Fan; Haizhong Wang; Yong Liu. Cooperation and profit allocation for two-echelon logistics pickup and delivery problems with state–space–time networks. Applied Soft Computing 2021, 109, 107528 .

AMA Style

Yong Wang, Shuanglu Zhang, Xiangyang Guan, Jianxin Fan, Haizhong Wang, Yong Liu. Cooperation and profit allocation for two-echelon logistics pickup and delivery problems with state–space–time networks. Applied Soft Computing. 2021; 109 ():107528.

Chicago/Turabian Style

Yong Wang; Shuanglu Zhang; Xiangyang Guan; Jianxin Fan; Haizhong Wang; Yong Liu. 2021. "Cooperation and profit allocation for two-echelon logistics pickup and delivery problems with state–space–time networks." Applied Soft Computing 109, no. : 107528.

Journal article
Published: 25 May 2021 in Knowledge-Based Systems
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Solving a two-echelon multi-period location routing problem (2E-MPLRP) involves facility location selection and two-echelon vehicle routing optimization. Based on the periodic time characteristics of logistics facilities and customers, the optimal solutions provide periodic location decisions and vehicle routing schemes simultaneously in each service period of the planning horizon. Transportation resource configuration is tweaked by enabling resource sharing across multiple service periods to maximize resource utilization in the context of growing emphasis on sustainable development. A bi-objective mathematical model is developed to formulate the 2E-MPLRP to obtain the minimum total operating cost and number of vehicles. A two-stage hybrid algorithm including three-dimensional (3D) k-means clustering and multi-objective improved particle swarm optimization (MOIPSO) algorithm is proposed to solve the 2E-MPLRP. The 3D k-means clustering algorithm is adapted to assign customers to distribution centers (DCs) to receive service in multiple service periods, and the MOIPSO algorithm is then designed to optimize the vehicle routes and find the Pareto optimal solutions. With an external repository strategy and a rapidly decreasing mutation strategy incorporated in the iterative process, the proposed hybrid algorithm performs well in expanding the particles’ searching region and achieving robust optimal results. An algorithm comparison demonstrates the superiority of the proposed hybrid algorithm over other existing algorithms. A real-world case study of 2E-MPLRP in Chongqing, China is conducted, and results show that the proposed model and algorithm are of practical significance in minimizing operating cost, improving transportation efficiency, and contributing to sustainable two-echelon logistics network operations.

ACS Style

Yong Wang; Yaoyao Sun; Xiangyang Guan; Jianxin Fan; Maozeng Xu; Haizhong Wang. Two-echelon multi-period location routing problem with shared transportation resource. Knowledge-Based Systems 2021, 226, 107168 .

AMA Style

Yong Wang, Yaoyao Sun, Xiangyang Guan, Jianxin Fan, Maozeng Xu, Haizhong Wang. Two-echelon multi-period location routing problem with shared transportation resource. Knowledge-Based Systems. 2021; 226 ():107168.

Chicago/Turabian Style

Yong Wang; Yaoyao Sun; Xiangyang Guan; Jianxin Fan; Maozeng Xu; Haizhong Wang. 2021. "Two-echelon multi-period location routing problem with shared transportation resource." Knowledge-Based Systems 226, no. : 107168.

Research article
Published: 17 May 2021 in Journal of Advanced Transportation
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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.

ACS Style

Yong Wang; Xiuwen Wang; Xiangyang Guan; Jinjun Tang. Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment. Journal of Advanced Transportation 2021, 2021, 1 -21.

AMA Style

Yong Wang, Xiuwen Wang, Xiangyang Guan, Jinjun Tang. Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment. Journal of Advanced Transportation. 2021; 2021 ():1-21.

Chicago/Turabian Style

Yong Wang; Xiuwen Wang; Xiangyang Guan; Jinjun Tang. 2021. "Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment." Journal of Advanced Transportation 2021, no. : 1-21.

Research article
Published: 09 May 2021 in Journal of Advanced Transportation
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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.

ACS Style

Yong Wang; Yaoyao Sun; Xiangyang Guan; Yanyong Guo. Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing. Journal of Advanced Transportation 2021, 2021, 1 -20.

AMA Style

Yong Wang, Yaoyao Sun, Xiangyang Guan, Yanyong Guo. Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing. Journal of Advanced Transportation. 2021; 2021 ():1-20.

Chicago/Turabian Style

Yong Wang; Yaoyao Sun; Xiangyang Guan; Yanyong Guo. 2021. "Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing." Journal of Advanced Transportation 2021, no. : 1-20.

Journal article
Published: 18 April 2021 in Knowledge-Based Systems
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The occurrence of natural disasters or accidents causes the obstruction or interruption of road traffic connectivity and affects the transportation of essential materials, especially for cross-regional delivery under emergency situations. Affected by COVID-19, government administrators establish cross-regional quarantine roadblocks to reduce the risk of virus transmission caused by cross-regional transportation. In this study, we propose an emergency logistics network design problem with resource sharing under collaborative alliances. We construct a state–space–time network-based bi-objective mixed integer programming model to optimize the vehicle routes in order to meet customer demands for essential materials with the lowest cost and highest emergency response speed under limited transportation resources. A two-stage hybrid heuristic algorithm is then proposed to find good-quality solutions for the problem. Clustering results are obtained using a 3D k-means clustering algorithm with the consideration of time and space indices. The optimization of the initial population generated by the improved Clarke and Wright saving method and improved nondominated sorting genetic algorithm-II with elite retention strategy provides stable and excellent performance for the searching of Pareto frontier. The cost difference of the entire emergency logistics network before and after collaboration, i.e., the profit, is fairly allocated to the participants (i.e., logistics service providers) through the Shapley value method. A real-world case in Chongqing City, China is used to validate the effectiveness of the proposed model and algorithm. This study contributes to smart transportation and logistics system in emergency planning and has particular implications for the optimal response of existing logistics system to the current COVID-19 pandemic.

ACS Style

Yong Wang; Shouguo Peng; Min Xu. Emergency logistics network design based on space–time resource configuration. Knowledge-Based Systems 2021, 223, 107041 .

AMA Style

Yong Wang, Shouguo Peng, Min Xu. Emergency logistics network design based on space–time resource configuration. Knowledge-Based Systems. 2021; 223 ():107041.

Chicago/Turabian Style

Yong Wang; Shouguo Peng; Min Xu. 2021. "Emergency logistics network design based on space–time resource configuration." Knowledge-Based Systems 223, no. : 107041.

Journal article
Published: 05 January 2021 in Expert Systems with Applications
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Collaboration among logistics companies offers a simple and effective way of increasing logistics operation efficiency. This study designs an optimal collaboration strategy by solving the collaborative logistics pickup and delivery problem with eco-packages (CLPDPE). This problem seeks to minimize the total operational costs by forming collaborative alliances and allocating trucking resources based on time–space (TS) network properties. The synchronization of two-echelon logistics networks is improved by solving this problem. Moreover, this study considers the stability of collaboration (i.e., the willingness of logistics companies to join and remain in collaborative alliances) by comparing different profit allocation strategies in the CLPDPE solving process. A novel methodology that combines multi-objective mixed integer programming, multidimensional K-means clustering, reference point based non-dominated sorting genetic algorithm-II (RP-NSGA-II), forward dynamic programming and improved Shapley value method is developed to formulate and solve CLPDPE. Our results show that the proposed algorithm outperforms most other algorithms in minimizing the total cost, waiting time and number of vehicles. An empirical case study in Chongqing city, China suggests that the proposed collaborative mechanism and transportation resource sharing strategy based on TS network can reduce cost, improve distribution efficiency, and contribute to efficient, smart, intelligent and sustainable urban logistics and transportation systems.

ACS Style

Yong Wang; Shouguo Peng; Xiangyang Guan; Jianxin Fan; Zheng Wang; Yong Liu; Haizhong Wang. Collaborative logistics pickup and delivery problem with eco-packages based on time–space network. Expert Systems with Applications 2021, 170, 114561 .

AMA Style

Yong Wang, Shouguo Peng, Xiangyang Guan, Jianxin Fan, Zheng Wang, Yong Liu, Haizhong Wang. Collaborative logistics pickup and delivery problem with eco-packages based on time–space network. Expert Systems with Applications. 2021; 170 ():114561.

Chicago/Turabian Style

Yong Wang; Shouguo Peng; Xiangyang Guan; Jianxin Fan; Zheng Wang; Yong Liu; Haizhong Wang. 2021. "Collaborative logistics pickup and delivery problem with eco-packages based on time–space network." Expert Systems with Applications 170, no. : 114561.

Research article
Published: 23 November 2020 in PLOS ONE
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Collaboration among logistics facilities in a multicenter logistics delivery network can significantly improve the utilization of logistics resources through resource sharing including logistics facilities, vehicles, and customer services. This study proposes and tests different resource sharing schemes to solve the optimization problem of a collaborative multicenter logistics delivery network based on resource sharing (CMCLDN-RS). The CMCLDN-RS problem aims to establish a collaborative mechanism of allocating logistics resources in a manner that improves the operational efficiency of a logistics network. A bi-objective optimization model is proposed with consideration of various resource sharing schemes in multiple service periods to minimize the total cost and number of vehicles. An adaptive grid particle swarm optimization (AGPSO) algorithm based on customer clustering is devised to solve the CMCLDN-RS problem and find Pareto optimal solutions. An effective elite iteration and selective endowment mechanism is designed for the algorithm to combine global and local search to improve search capabilities. The solution of CMCLDN-RS guarantees that cost savings are fairly allocated to the collaborative participants through a suitable profit allocation model. Compared with the computation performance of the existing nondominated sorting genetic algorithm-II and multi-objective evolutionary algorithm, AGPSO is more computationally efficient. An empirical case study in Chengdu, China suggests that the proposed collaborative mechanism with resource sharing can effectively reduce total operational costs and number of vehicles, thereby enhancing the operational efficiency of the logistics network.

ACS Style

Shejun Deng; Yingying Yuan; Yong Wang; Haizhong Wang; Charles Koll. Collaborative multicenter logistics delivery network optimization with resource sharing. PLOS ONE 2020, 15, e0242555 .

AMA Style

Shejun Deng, Yingying Yuan, Yong Wang, Haizhong Wang, Charles Koll. Collaborative multicenter logistics delivery network optimization with resource sharing. PLOS ONE. 2020; 15 (11):e0242555.

Chicago/Turabian Style

Shejun Deng; Yingying Yuan; Yong Wang; Haizhong Wang; Charles Koll. 2020. "Collaborative multicenter logistics delivery network optimization with resource sharing." PLOS ONE 15, no. 11: e0242555.

Journal article
Published: 17 November 2020 in Expert Systems with Applications
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The customized bus (CB) is an emerging type of public transportation system, which not only provides a flexible and reliable demand-responsive service, but also reduces the usage of private car to alleviate traffic congestion in metropolitan cities. The customized bus route design problem (CBRDP) is a crucial procedure in the CB service system designing. In this work, we develop a new type of problem scenario: Multi-Trip Multi-Pickup and Delivery Problem with Time Windows, to describe CBRDP by simultaneously optimizing the operating cost and passenger profit, where excess travel time is introduced to estimate passenger extra cost compared with taxi service, and each vehicle is allowed to perform multiple trips for operational cost savings. To solve this problem, a constructive two-stage heuristic algorithm is presented to obtain the Pareto solution. Taking a benchmark problem and Beijing commuting corridor as case studies, we calculate and compare the monetary and travel costs of CB with other travel modes, and quantitatively confirm that the CB can be a cost-effective choice for passengers.

ACS Style

Xi Chen; Yinhai Wang; Yong Wang; Xiaobo Qu; Xiaolei Ma. Customized bus route design with pickup and delivery and time windows: Model, case study and comparative analysis. Expert Systems with Applications 2020, 168, 114242 .

AMA Style

Xi Chen, Yinhai Wang, Yong Wang, Xiaobo Qu, Xiaolei Ma. Customized bus route design with pickup and delivery and time windows: Model, case study and comparative analysis. Expert Systems with Applications. 2020; 168 ():114242.

Chicago/Turabian Style

Xi Chen; Yinhai Wang; Yong Wang; Xiaobo Qu; Xiaolei Ma. 2020. "Customized bus route design with pickup and delivery and time windows: Model, case study and comparative analysis." Expert Systems with Applications 168, no. : 114242.

Journal article
Published: 10 November 2020 in Expert Systems with Applications
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Collaboration among logistics operators offers an effective way to improve customer service and freight transportation efficiency. One form of collaboration is the sharing of logistics resources (e.g., delivery vehicles). Existing studies on collaboration and resource sharing have not sufficiently accounted for the time frame within which collaboration happens, and they mostly assume that collaboration among logistics operators occurs in a single time period. This study addresses the issue of collaboration across multiple time periods, in which logistics resources can be shared between different service time periods, by formulating and solving a two-echelon collaborative multi-depot multi-period vehicle routing problem (2E-CMDPVRP). The 2E-CMDPVRP is formulated as a multi-objective integer programming model that minimizes logistics operational costs, service waiting times, and number of vehicles in multiple service periods. A hybrid heuristic algorithm with three-dimensional k-means clustering and improved reference point-based non-dominated sorting genetic algorithm-III (IR-NSGA-III) is proposed to solve the multi-objective optimization model. Comparative analysis results show that the proposed IR-NSGA-III outperforms existing algorithms in terms of the minimization of logistics operational costs, service waiting times, and number of vehicles. The minimum costs remaining saving method and strictly monotonic path selection principle are combined to determine the best profit allocation schemes and the optimal coalition sequences. An empirical case study of a multi-depot multi-period logistics network in Chongqing, China, is used to validate the proposed model and solution algorithm. Results suggest that the proposed collaborative mechanism with multi-depot and multi-period resource sharing can improve the degree of synchronization within a collaborative logistics network, and thus contribute to sustainable development of urban logistics distribution networks.

ACS Style

Yong Wang; Qin Li; Xiangyang Guan; Maozeng Xu; Yong Liu; Haizhong Wang. Two-echelon collaborative multi-depot multi-period vehicle routing problem. Expert Systems with Applications 2020, 167, 114201 .

AMA Style

Yong Wang, Qin Li, Xiangyang Guan, Maozeng Xu, Yong Liu, Haizhong Wang. Two-echelon collaborative multi-depot multi-period vehicle routing problem. Expert Systems with Applications. 2020; 167 ():114201.

Chicago/Turabian Style

Yong Wang; Qin Li; Xiangyang Guan; Maozeng Xu; Yong Liu; Haizhong Wang. 2020. "Two-echelon collaborative multi-depot multi-period vehicle routing problem." Expert Systems with Applications 167, no. : 114201.

Research article
Published: 20 October 2020 in Journal of Advanced Transportation
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Energy supply is an important system that affects the overall efficiency of urban transportation. To improve the system operational efficiency and reduce costs, we formulate and solve a collaborative multidepot petrol station replenishment problem with multicompartments and time window assignment by establishing a mixed-integer linear programming model. The hybrid heuristic algorithm composed of genetic algorithm and particle swarm optimization is used as a solution, and then the Shapley value method is applied to analyze the profit allocation of each petrol depot under different coalitions. The optimal membership sequence of the cooperation is determined according to the strict monotone path. To analyze and verify the effectiveness of the proposed method, a regional petrol supply network in Chongqing city in China is investigated. Through cooperation between petrol depots in the supply network, the utilization of customer clustering, time window coordination, and distribution truck sharing can significantly reduce the total operation costs and improve the efficiency of urban transportation energy supply. This approach can provide theoretical support for relevant government departments and enterprises to make optimal decisions. The implementation of the joint distribution of energy can promote the sustainable development of urban transportation.

ACS Style

Guangcan Xu; Maozeng Xu; Yong Wang; Yong Liu; Qiguang Lv. Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment. Journal of Advanced Transportation 2020, 2020, 1 -22.

AMA Style

Guangcan Xu, Maozeng Xu, Yong Wang, Yong Liu, Qiguang Lv. Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment. Journal of Advanced Transportation. 2020; 2020 ():1-22.

Chicago/Turabian Style

Guangcan Xu; Maozeng Xu; Yong Wang; Yong Liu; Qiguang Lv. 2020. "Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment." Journal of Advanced Transportation 2020, no. : 1-22.

Journal article
Published: 09 August 2020 in Expert Systems with Applications
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Collaboration such as resource sharing among logistics participants (LPs) can effectively increase the efficiency and sustainability of logistics operations, especially in the transportation and distribution of fresh and perishable products that require special infrastructure (e.g., refrigerated trucks/vehicles). This study tackles a collaborative multi-center vehicle routing problem with resource sharing and temperature control constraints (CMCVRP-RSTC). Solving the CMCVRP-RSTC by minimizing the total cost and the number of refrigerated vehicles returns a fresh logistics operational strategy that pinpoints how a multi-center fresh logistics distribution network can be reorganized to highlight potential collaboration opportunities. To find the solution to the CMCVRP-RSTC, we develop a hybrid heuristic algorithm that combines the extended k-means clustering and tabu search non-dominated sorting genetic algorithm-II (TS-NSGA-II) to search a large solution space. This hybrid heuristic algorithm ensures that the optimal solution is found efficiently through initial solution filtering and the combination of local and global searches. Furthermore, we explore how to motivate individual LPs to collaborate by analyzing the benefits of collaboration to each LP. Using the minimum costs remaining savings method and the strictly monotonic path rule, a cost saving calculation model is proposed to find the best profit allocation scheme where each collaborating LP keeps benefiting from long-term collaboration. An empirical case study of Chongqing City, China indicates the efficiency of our proposed collaborative mechanism and optimization algorithms. Our study will help improve the efficiency of logistics operation significantly and contribute to the development of more intelligent logistics systems and smart cities.

ACS Style

Yong Wang; Jie Zhang; Xiangyang Guan; Maozeng Xu; Zheng Wang; Haizhong Wang. Collaborative multiple centers fresh logistics distribution network optimization with resource sharing and temperature control constraints. Expert Systems with Applications 2020, 165, 113838 .

AMA Style

Yong Wang, Jie Zhang, Xiangyang Guan, Maozeng Xu, Zheng Wang, Haizhong Wang. Collaborative multiple centers fresh logistics distribution network optimization with resource sharing and temperature control constraints. Expert Systems with Applications. 2020; 165 ():113838.

Chicago/Turabian Style

Yong Wang; Jie Zhang; Xiangyang Guan; Maozeng Xu; Zheng Wang; Haizhong Wang. 2020. "Collaborative multiple centers fresh logistics distribution network optimization with resource sharing and temperature control constraints." Expert Systems with Applications 165, no. : 113838.

Journal article
Published: 24 July 2020 in Sustainability
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In this work, a multidepot multiperiod vehicle routing problem with pickups and deliveries (MDPVRPPD) is solved by optimizing logistics networks with collaboration and resource sharing among logistics service providers. The optimal solution can satisfy customer demands with periodic time characteristics and incorporate pickup and delivery services with maximum resource utilization. A collaborative mechanism is developed to rearrange both the open and closed vehicle routes among multiple pickup and delivery centers with improved transportation efficiency and reduced operational costs. The effects of resource sharing strategies combining customer information sharing, facility service sharing, and vehicle sharing are investigated across multiple service periods to maximize resource utilization and refine the resource configuration. A multiobjective optimization model is developed to formulate the MDPVRPPD so that the minimum total operational costs, waiting time, and the number of vehicles are obtained. A hybrid heuristic algorithm incorporating a 3D clustering and an improved multiobjective particle swarm optimization (IMOPSO) algorithm is introduced to solve the MDPVRPPD and find Pareto optimal solutions. The proposed hybrid heuristic algorithm is based on a selective exchange mechanism that enhances local and global searching capabilities. Results demonstrate that the proposed IMOPSO outperforms other existing algorithms. We also study profit allocation issues to quantify the stability and sustainability of long-term collaboration among logistics participants, using the minimum costs remaining savings method. The proposed model and solution methods are validated by conducting an empirical study of a real system in Chongqing City, China. This study contributes to the development of efficient urban logistics distribution systems, and facilitates the expansion of intelligent and sustainable supply chains.

ACS Style

Yong Wang; Qin Li; Xiangyang Guan; Jianxin Fan; Yong Liu; Haizhong Wang. Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries. Sustainability 2020, 12, 5966 .

AMA Style

Yong Wang, Qin Li, Xiangyang Guan, Jianxin Fan, Yong Liu, Haizhong Wang. Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries. Sustainability. 2020; 12 (15):5966.

Chicago/Turabian Style

Yong Wang; Qin Li; Xiangyang Guan; Jianxin Fan; Yong Liu; Haizhong Wang. 2020. "Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries." Sustainability 12, no. 15: 5966.

Journal article
Published: 04 June 2020 in Expert Systems with Applications
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Discovering key congestion points periodically in traffic jams is a critical issue. It supports road managers to make sense of the situations, and rule out the congestion economically and efficiently. However, city-scale and synchronal traffic data bring hardships for such kind of analyses. With recent developments in data science, the availability of traffic conditions data generated by the rising digital map applications makes this issue feasible. Therefore, we firstly propose a digital map data-driven expert system to discover and measure the city-scale key congestion points. It is based on a state-of-the-art feature selection method, BSSReduce (Bijective soft set based feature selection). Data from Baidu Map for Chongqing and Beijing are collected as a case to conduct this study. The results indicate that our proposed method helps the road managers recognize 75 and 300 key congestion points from over 10,000 and 50,000 points of the urban roads each month. The visualized results, as well as the significance measurements, provide road managers an expert system to quickly rule out congestion and work out new solutions to future traffic management.

ACS Style

Ke Gong; Li Zhang; Du Ni; Huamin Li; Maozeng Xu; Yong Wang; Yuanxiang Dong. An expert system to discover key congestion points for urban traffic. Expert Systems with Applications 2020, 158, 113544 .

AMA Style

Ke Gong, Li Zhang, Du Ni, Huamin Li, Maozeng Xu, Yong Wang, Yuanxiang Dong. An expert system to discover key congestion points for urban traffic. Expert Systems with Applications. 2020; 158 ():113544.

Chicago/Turabian Style

Ke Gong; Li Zhang; Du Ni; Huamin Li; Maozeng Xu; Yong Wang; Yuanxiang Dong. 2020. "An expert system to discover key congestion points for urban traffic." Expert Systems with Applications 158, no. : 113544.

Journal article
Published: 18 February 2020 in Journal of Cleaner Production
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Collaboration among service providers in a logistics network can greatly increase their operation efficiencies and reduce transportation emissions. This study proposes, formulates and solves a collaborative two-echelon multicenter vehicle routing problem based on a state–space–time (CTMCVRP-SST) network to facilitate collaboration and resource sharing in a multiperiod state–space–time (SST) logistics network. The CTMCVRP-SST aims to facilitate collaboration in logistics networks by leveraging the spatial-temporal properties of logistics demands and resources to optimize the distribution of logistics resources in space and time to meet logistics demands. A three-component solution framework is proposed to solve CTMCVRP-SST. First, a bi-objective linear programming model based on resource sharing in a multiperiod SST network is formulated to minimize the number of vehicles and the total cost of the collaborative operation. Second, an integrated algorithm consisting of SST-based dynamic programming (DP), improved K-means clustering and improved non-dominated sorting genetic algorithm-II (Im-NSGAII) is developed to obtain optimal routes. Third, a cost gap allocation model is employed to design a collaborative mechanism that encourages cooperation among logistics service providers. Using this solution framework, the coalition sequences (i.e., the order of each logistics provider joining a collaborative coalition) are designed and the stability of the coalitions based on profit allocations is studied. Results show that the proposed algorithm outperforms existing algorithms in minimizing the total cost with all other constraints being the same. An empirical case study of a logistics network in Chongqing suggests that the proposed collaboration mechanism with SST network representation can reduce costs, improve transportation efficiency, and contribute to efficient and sustainable logistics network operations.

ACS Style

Yong Wang; Yingying Yuan; Xiangyang Guan; Maozeng Xu; Li Wang; Haizhong Wang; Yong Liu. Collaborative two-echelon multicenter vehicle routing optimization based on state–space–time network representation. Journal of Cleaner Production 2020, 258, 120590 .

AMA Style

Yong Wang, Yingying Yuan, Xiangyang Guan, Maozeng Xu, Li Wang, Haizhong Wang, Yong Liu. Collaborative two-echelon multicenter vehicle routing optimization based on state–space–time network representation. Journal of Cleaner Production. 2020; 258 ():120590.

Chicago/Turabian Style

Yong Wang; Yingying Yuan; Xiangyang Guan; Maozeng Xu; Li Wang; Haizhong Wang; Yong Liu. 2020. "Collaborative two-echelon multicenter vehicle routing optimization based on state–space–time network representation." Journal of Cleaner Production 258, no. : 120590.

Journal article
Published: 02 September 2019 in Expert Systems with Applications
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In logistics operation, delivery times are often uncertain for customers, and accommodating this uncertainty poses operation challenges as well as extra cost for logistics service providers. The delivery time uncertainty is particularly an issue if there are multiple service providers in a logistics network. To address this issue, we formulate and solve a collaborative multi-depot vehicle routing problem with time window assignment (CMDVRPTWA) to effectively reduce the impact of changing time windows on operating costs. This paper establishes a bi-objective programming model that optimize the total operating cost and the total number of delivery vehicles. A hybrid heuristic algorithm consisting of K-means clustering, Clarke–Wright (CW) saving algorithm and an Extended Non-dominated Sorting Genetic Algorithm-II (E-NSGA-II) is presented to efficiently solve CMDVRPTWA. The clustering and CW saving algorithm are employed to increase the likelihood of finding the optimal vehicle routes by identifying a feasible initial solution. The E-NSGA-II procedure combines partial-mapped crossover (PMC), relocation, 2-opt* exchange and swap mutation operations to find the optimal solution with pre-defined iteration and termination rules. Profit allocation schemes are then analyzed using the Game Quadratic Programming (GQP) method, and the optimal sequences of joining coalitions are obtained based on the principle that coalition participants’ benefits should be non-decreasing when a new participant joins the coalition. We conduct three empirical studies on a small-scale example, on several benchmark datasets and on a large-scale logistics network in Chongqing city, China. Further comparative analysis indicates that E-NSGA-II outperforms most other algorithms in solving CMDVRPTWA. This novel approach identifies profit allocation strategies that ensure the stability and reliability of the collaborative coalitions in the context of flexible customer service time windows, and can be utilized to improve the efficiency of urban logistics and intelligent transportation networks.

ACS Style

Yong Wang; Shuanglu Zhang; Xiangyang Guan; Shouguo Peng; Haizhong Wang; Yong Liu; Maozeng Xu. Collaborative multi-depot logistics network design with time window assignment. Expert Systems with Applications 2019, 140, 112910 .

AMA Style

Yong Wang, Shuanglu Zhang, Xiangyang Guan, Shouguo Peng, Haizhong Wang, Yong Liu, Maozeng Xu. Collaborative multi-depot logistics network design with time window assignment. Expert Systems with Applications. 2019; 140 ():112910.

Chicago/Turabian Style

Yong Wang; Shuanglu Zhang; Xiangyang Guan; Shouguo Peng; Haizhong Wang; Yong Liu; Maozeng Xu. 2019. "Collaborative multi-depot logistics network design with time window assignment." Expert Systems with Applications 140, no. : 112910.

Article
Published: 01 July 2019 in International Journal of Fuzzy Systems
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Emergency alternative evaluation (EAE) is crucial to improve emergency management performance, which is considered an important factor in achieving sustainable development. This paper proposes an integrated fuzzy method to deal with the EAE problem. The existence of information uncertainties and decision-makers (DMs) psychological behavior are considered due to the complexity of emergency environments. By utilizing trapezoidal intuitionistic fuzzy numbers to depict the fuzziness and uncertainty of information, prospect theory is developed to portray DMs’ heterogeneous psychological behavior and convert the initial decision matrixes into decision prospect matrixes. A thermodynamic approach that comprises operations on entropy, energy, and exergy is introduced to maximize the use of decision-making information, determine each criterion weight value, and assess information quantity and quality. Trapezoidal intuitionistic fuzzy Choquet integral operator and weighted averaging operator are presented to integrate the overall prospect value of each alternative. A score function with risk attitudinal parameter of DMs is introduced to obtain the final ranking order of the alternatives. A multi-step framework and solution procedure is then presented, and an illustrative case study is followed to investigate the EAE problem by selecting a provincial highway department in China, which confirms the practicability of our proposed solution approach.

ACS Style

Yong Liu; Yong Wang; Maozeng Xu; Guangcan Xu. Emergency Alternative Evaluation Using Extended Trapezoidal Intuitionistic Fuzzy Thermodynamic Approach with Prospect Theory. International Journal of Fuzzy Systems 2019, 21, 1801 -1817.

AMA Style

Yong Liu, Yong Wang, Maozeng Xu, Guangcan Xu. Emergency Alternative Evaluation Using Extended Trapezoidal Intuitionistic Fuzzy Thermodynamic Approach with Prospect Theory. International Journal of Fuzzy Systems. 2019; 21 (6):1801-1817.

Chicago/Turabian Style

Yong Liu; Yong Wang; Maozeng Xu; Guangcan Xu. 2019. "Emergency Alternative Evaluation Using Extended Trapezoidal Intuitionistic Fuzzy Thermodynamic Approach with Prospect Theory." International Journal of Fuzzy Systems 21, no. 6: 1801-1817.

Journal article
Published: 29 June 2019 in Sustainability
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This research examines the overall performance achievement of social media marketing (SMM) in Bangladesh by determining whether social media is successful in creating brand consciousness (i.e., brand preference, brand attachment, brand association, and brand loyalty) toward online consumers, which in turn may lead to buying commitment. In total, 564 Bangladeshi consumers were surveyed to monitor their responsiveness toward social media-aided motivations. We selected the online buying environment in Bangladesh, which is an emerging market established less than one decade ago. We specifically choose the entire local fashion industry as our target market, excluding the websites of international fashion brands operated overseas. We used the holistic concept of the five aspects of SMM, namely, interaction, entertainment, customization, electronic word of mouth (eWOM), and trendiness. Moreover, we statistically calculated the performance of social media through the consequences of five measures, namely, brand loyalty, brand preference, brand attachment, brand association, and buying commitment. We used regular linear multiple regression, correlation, and descriptive statistics to obtain statistical results. The study found strong evidence that SMM efforts (SMMEs) of the local Bangladeshi fashion industry are successful in establishing consumer attachment and preference. However, they fail to secure committed buyers when the measurement scale is below 50%. In line with the results of previous studies on consumer loyalty, our results demonstrate that SMMEs fail to create committed buyers. Lack of loyalty and association drive consumers to become uncommitted buyers.

ACS Style

Yong Wang; Shamim Chowdhury Ahmed; Shejun Deng; Haizhong Wang. Success of Social Media Marketing Efforts in Retaining Sustainable Online Consumers: An Empirical Analysis on the Online Fashion Retail Market. Sustainability 2019, 11, 3596 .

AMA Style

Yong Wang, Shamim Chowdhury Ahmed, Shejun Deng, Haizhong Wang. Success of Social Media Marketing Efforts in Retaining Sustainable Online Consumers: An Empirical Analysis on the Online Fashion Retail Market. Sustainability. 2019; 11 (13):3596.

Chicago/Turabian Style

Yong Wang; Shamim Chowdhury Ahmed; Shejun Deng; Haizhong Wang. 2019. "Success of Social Media Marketing Efforts in Retaining Sustainable Online Consumers: An Empirical Analysis on the Online Fashion Retail Market." Sustainability 11, no. 13: 3596.

Journal article
Published: 25 June 2019 in Sustainability
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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.

ACS Style

Yong Wang; Yingying Yuan; Xiangyang Guan; Yong Liu; Maozeng Xu. Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles Under Time Windows. Sustainability 2019, 11, 3492 .

AMA Style

Yong Wang, Yingying Yuan, Xiangyang Guan, Yong Liu, Maozeng Xu. Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles Under Time Windows. Sustainability. 2019; 11 (12):3492.

Chicago/Turabian Style

Yong Wang; Yingying Yuan; Xiangyang Guan; Yong Liu; Maozeng Xu. 2019. "Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles Under Time Windows." Sustainability 11, no. 12: 3492.

Journal article
Published: 29 May 2019 in Journal of Cleaner Production
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The control of the environmental impacts is a considerable challenge to the daily operations of modern logistics companies, especially under the current trend of increasing carbon dioxide emission. This paper focusses on freight distribution, introduces a transportation resource sharing strategy to address the multi-depot green vehicle routing problem, and incorporates the time-dependency of speed as well as piecewise penalty costs for earliness and tardiness of deliveries. Transportation resource sharing is proposed to eliminate long and empty-vehicle trips, improve the network’s fluidity and the efficiency of resource management. A bi-objective model is proposed to minimize total carbon emission and operating cost, while enforcing piecewise penalty costs on earliness and tardiness to reduce waiting time and improve customer satisfaction. Further, we combine the Clarke and Wright Savings Heuristic Algorithm (CWSHA), the Sweep Algorithm (SwA) and the Multi-Objective Particle Swarm Optimization algorithm (MOPSO) to design a hybrid heuristic algorithm for the vehicle routing optimization. CWSHA and SwA are consecutively used to generate the initial population, and MOPSO is employed for local and global solution search. Computational experiments reveal that sharing transportation resource reduces the total travelled distance, the number of vehicles, and facilitates a cost effective and environment-friendly distribution network. In addition, we also observe that the shortest path sometimes undermines minimum cost and carbon emission objectives. Moreover, sensitivity analyses reveal that vehicle routes are less influenced by piecewise penalty costs under unimodal traffic flows, while bimodal traffic flows would require more investment to reduce carbon emission.

ACS Style

Yong Wang; Kevin Assogba; Jianxin Fan; Maozeng Xu; Yong Liu; Haizhong Wang. Multi-depot green vehicle routing problem with shared transportation resource: Integration of time-dependent speed and piecewise penalty cost. Journal of Cleaner Production 2019, 232, 12 -29.

AMA Style

Yong Wang, Kevin Assogba, Jianxin Fan, Maozeng Xu, Yong Liu, Haizhong Wang. Multi-depot green vehicle routing problem with shared transportation resource: Integration of time-dependent speed and piecewise penalty cost. Journal of Cleaner Production. 2019; 232 ():12-29.

Chicago/Turabian Style

Yong Wang; Kevin Assogba; Jianxin Fan; Maozeng Xu; Yong Liu; Haizhong Wang. 2019. "Multi-depot green vehicle routing problem with shared transportation resource: Integration of time-dependent speed and piecewise penalty cost." Journal of Cleaner Production 232, no. : 12-29.