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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.
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 StyleYong 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 StyleYong 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.
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.
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 StyleYong 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 StyleYong 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.
Electric vehicles have great potential in dramatically reducing environmental pollution, which has become an important strategic direction for future sustainable development. With the influence of policy support and market, the construction of new energy supply infrastructure in China has achieved remarkable outcomes. However, according to the actual use of the new energy supply facilities, there is still a severe imbalance between long queues and unattended charging stations in some areas. Therefore, this paper aims to establish a fuzzy demand-profit model to accurately optimize new energy supply from the perspective of the three-level service chain. Then, based on authoritative historical sales data of electric vehicles in China in 2011–2018, the model is used to analyze the fuzzy demand for electric vehicle charging capacity. The research results indicate that the fuzzy demand-profit model is an effective tool to promote the coordination of new energy supply, which will provide support for the sustainable development of electric vehicles in China.
Weiwei Chen; Maozeng Xu; Qingsong Xing; Ligang Cui; Liudan Jiao. A Fuzzy Demand-Profit Model for the Sustainable Development of Electric Vehicles in China from the Perspective of Three-Level Service Chain. Sustainability 2020, 12, 6389 .
AMA StyleWeiwei Chen, Maozeng Xu, Qingsong Xing, Ligang Cui, Liudan Jiao. A Fuzzy Demand-Profit Model for the Sustainable Development of Electric Vehicles in China from the Perspective of Three-Level Service Chain. Sustainability. 2020; 12 (16):6389.
Chicago/Turabian StyleWeiwei Chen; Maozeng Xu; Qingsong Xing; Ligang Cui; Liudan Jiao. 2020. "A Fuzzy Demand-Profit Model for the Sustainable Development of Electric Vehicles in China from the Perspective of Three-Level Service Chain." Sustainability 12, no. 16: 6389.
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.
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 StyleKe 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 StyleKe 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.
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.
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 StyleYong 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 StyleYong 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.
To contribute to global sustainability, many manufacturers are starting to implement green product development and trying to provide environmentally friendly products. Although green products are environmentally beneficial to our society, the performance of green product development remains poor because of cannibalization from traditional alternatives at lower prices. This is particularly the case in the current unforgiving marketing reality in which many brand retailers, such as Wal-Mart, Tesco, and Carrefour, offer their own store brands as traditional alternatives. Although a large stream of research has studied the effects of competition on manufacturers’ green design, to the best of our knowledge, there is a dearth of research on the effects of competition from retailers’ store brands on manufacturers’ green design. To fill this gap, we present two models in which the manufacturer has an incentive to design for the environment, and the retailer has the flexibility to sell store brands (Model S), or it does not (Model N). Surprisingly, our analysis indicates that the presence of store brands may stimulate the manufacturer to release a new greener version of the national brand. Moreover, we find that although the presence of store brands is beneficial to the retailer and industry, it always hurts the manufacturer’s profitability. To incentivize the manufacturer to support Model S, we propose a two-part tariff contract.
Xi Yang; Maozeng Xu; Wanleng Zhang. Can Design for the Environment be Worthwhile? Green Design for Manufacturers Brands When Confronted with Competition from Store Brands. Sustainability 2020, 12, 1078 .
AMA StyleXi Yang, Maozeng Xu, Wanleng Zhang. Can Design for the Environment be Worthwhile? Green Design for Manufacturers Brands When Confronted with Competition from Store Brands. Sustainability. 2020; 12 (3):1078.
Chicago/Turabian StyleXi Yang; Maozeng Xu; Wanleng Zhang. 2020. "Can Design for the Environment be Worthwhile? Green Design for Manufacturers Brands When Confronted with Competition from Store Brands." Sustainability 12, no. 3: 1078.
With the intensification of time-based competition, the importance of reducing lead-time by rapidly delivering multi-orders has been underscored in the process of replenishment-storage-transportation. This perception has prompted enterprises to increase expenditure on purchasing modern time-tracing technologies (e.g. RFID), and equipping facilities for item fast movement (e.g. high-rack automatic shelves) to retain customers. In this study, we explore a novel extension of the multi-item joint replenishment problem (JRP) with lead-time compressing initiatives. By assuming controllable lead-time, we construct a stochastic periodic-review joint replenishment and delivery (JRD) model to investigate impacts of capital investment in lead-time reduction to the decisions of multi-item joint replenishment and delivery. To solve the proposed JRD, two heuristics and a differential evolutionary algorithm are presented based on the model property analyses. The experimental results reveal the performance differences (e.g., searching speed, robustness and searching effectiveness) of three algorithms. Furthermore, our findings have managerial implications that proper investment in lead-time reduction not only helps shorten replenishment time and cut major ordering cost, but can also reduce the system cost.
Ligang Cui; Jie Deng; Rui Liu; Dongyang Xu; Yajun Zhang; Maozeng Xu. A stochastic multi-item replenishment and delivery problem with lead-time reduction initiatives and the solving methodologies. Applied Mathematics and Computation 2020, 374, 125055 .
AMA StyleLigang Cui, Jie Deng, Rui Liu, Dongyang Xu, Yajun Zhang, Maozeng Xu. A stochastic multi-item replenishment and delivery problem with lead-time reduction initiatives and the solving methodologies. Applied Mathematics and Computation. 2020; 374 ():125055.
Chicago/Turabian StyleLigang Cui; Jie Deng; Rui Liu; Dongyang Xu; Yajun Zhang; Maozeng Xu. 2020. "A stochastic multi-item replenishment and delivery problem with lead-time reduction initiatives and the solving methodologies." Applied Mathematics and Computation 374, no. : 125055.
In this paper, by assuming a B2C e-business company with several regional distribution centers (DCs), an extension of multi-item joint replenishment-distribution problem (JRD) is raised with stochastic lead-time and demands to investigate their mutual impacts to the JRD system. For the proposed JRD, the objective comprising four types of costs, namely, the ordering cost, the inventory holding cost, the lost sale penalties and the transportation cost is minimized by jointly deciding the basic cycle time, the replenishment frequencies and safety stock factors of all items. A hybrid differential artificial bee colony (DABC) algorithm, which combines the superiorities of the differential evolution (DE) algorithm in global search and the artificial bee colony (ABC) algorithm in fine search, is presented to solve the proposed JRD model. Numerical experiments and parameter sensitivity analyses are conducted. The computational results have testified that DABC is faster than that of DE (16%) and two hybrid ABC-DEs (18% and 9%), more effective than that of genetic algorithm (GA, 0.15%), ABC (0.03%) and ABC-DE1 (0.015%) and robust than that of GA (99%), DE (71%), ABC (0.97%) and two hybrid ABC-DEs (86% and 88%). Most importantly, different uncertainty significance of lead-time and demands to JRD are revealed and analyzed. Furthermore, management insights, such as the magnitudes of the problem-related parameters and the mutual effects of the two stochastic variables to the system cost, are indicated in the replenishment process.
Ligang Cui; Jie Deng; Yajun Zhang; Guofeng Tang; Maozeng Xu. Hybrid differential artificial bee colony algorithm for multi-item replenishment-distribution problem with stochastic lead-time and demands. Journal of Cleaner Production 2020, 254, 119873 .
AMA StyleLigang Cui, Jie Deng, Yajun Zhang, Guofeng Tang, Maozeng Xu. Hybrid differential artificial bee colony algorithm for multi-item replenishment-distribution problem with stochastic lead-time and demands. Journal of Cleaner Production. 2020; 254 ():119873.
Chicago/Turabian StyleLigang Cui; Jie Deng; Yajun Zhang; Guofeng Tang; Maozeng Xu. 2020. "Hybrid differential artificial bee colony algorithm for multi-item replenishment-distribution problem with stochastic lead-time and demands." Journal of Cleaner Production 254, no. : 119873.
In e-business running, not only uncertainties in customers’ demands cause great risks from the marketing side, but also imperfect qualities of multi-item induce great losses at the supplying side. Hence, in this paper a joint replenishment problem (IJRP) simultaneously considering the stochastic demands and random number of imperfect items for the first time is proposed and a meta-heuristic, namely, bare-bones differential evolutionary (BBDE) algorithm is redesigned based on the solution structure containing the continuous variable and discrete variable to solve the IJRP problem. Through intensive numerical experiments, it has been testified that BBDE is not only superior to genetic evolution algorithm (GA) and particle swarm optimization (PSO) algorithm at the best-found TC, the lowest mean value, and the smallest standard error for three small tested JRPs (GJRP, SJRP and IJRP), but also a competitive algorithm to differential evolution (DE) and bare bones PSO (BBPSO) for three small scale JRPs (GJRP, SJRP and IJRP) at the aspects of finding the best-found TC, the lowest mean value, the smallest standard error and the least of computation time. BBDE also shows great efficiency in solving some large scale IJRPs comparing to DE and BBPSO, the limitation of BBDE for large scale IJRP is also reported and analyzed.
Ligang Cui; Jie Deng; Yajun Zhang; Zijian Zhang; Maozeng Xu. The bare-bones differential evolutionary for stochastic joint replenishment with random number of imperfect items. Knowledge-Based Systems 2019, 193, 105416 .
AMA StyleLigang Cui, Jie Deng, Yajun Zhang, Zijian Zhang, Maozeng Xu. The bare-bones differential evolutionary for stochastic joint replenishment with random number of imperfect items. Knowledge-Based Systems. 2019; 193 ():105416.
Chicago/Turabian StyleLigang Cui; Jie Deng; Yajun Zhang; Zijian Zhang; Maozeng Xu. 2019. "The bare-bones differential evolutionary for stochastic joint replenishment with random number of imperfect items." Knowledge-Based Systems 193, no. : 105416.
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.
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 StyleYong 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 StyleYong 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.
Navigation systems can help in allocating public charging stations to electric vehicles (EVs) with the aim of minimizing EVs’ charging time by integrating sufficient data. However, the existing systems only consider their travel time and transform the allocation as a routing problem. In this paper, we involve the queuing time in stations as one part of EVs’ charging time, and another part is the travel time on roads. Roads and stations are easily congested resources, and we constructed a joint-resource congestion game to describe the interaction between vehicles and resources. With a finite number of vehicles and resources, there exists a Nash equilibrium. To realize a self-adaptive allocation work, we applied the Q-learning algorithm on systems, defining sets of states and actions in our constructed environment. After being allocated one by one, vehicles concurrently requesting to be charged will be processed properly. We collected urban road network data from Chongqing city and conducted experiments. The results illustrate the proposed method can be used to solve the problem, and its convergence performance was better than the genetic algorithm. The road capacity and the number of EVs affected the initial of Q-value, and not the convergence trends.
Li Zhang; Ke Gong; Maozeng Xu. Congestion Control in Charging Stations Allocation with Q-Learning. Sustainability 2019, 11, 3900 .
AMA StyleLi Zhang, Ke Gong, Maozeng Xu. Congestion Control in Charging Stations Allocation with Q-Learning. Sustainability. 2019; 11 (14):3900.
Chicago/Turabian StyleLi Zhang; Ke Gong; Maozeng Xu. 2019. "Congestion Control in Charging Stations Allocation with Q-Learning." Sustainability 11, no. 14: 3900.
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.
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 StyleYong 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 StyleYong 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.
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.
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 StyleYong 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 StyleYong 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.
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.
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 StyleYong 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 StyleYong 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.
The adoption of collaboration strategies among logistics facilities and the formation of one or multiple coalitions constitute a sustainable approach to vehicle routing network optimization. This paper introduces a collaborative multiple centers vehicle routing problem with simultaneous delivery and pickup (CMCVRPSDP) to minimize operating cost and the total number of vehicles in the network. Distribution and pickup centers are allowed to share vehicles and customers in order to increase the entire network's efficiency and maximize profit. To provide the coalition coordinators with good routing solutions, we propose a hybrid heuristic algorithm which properly combines k-means and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Based on clustering solutions, the proposed Hybrid NSGA-II (HNSGA-II) first generates a real coded population to bind our mathematical model constraints and to obtain a large number of feasible solutions which converge to optimality. Chromosomes are divided for genetic operations with partial mapped crossover and swap mutation algorithms, before their recombination to ensure the quality of our results. Comparisons with the traditional NSGA-II and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm indicate better performances of HNSGA-II in terms of objective function values. We also apply Cost Gap Allocation method (CGA) and the strictly monotonic path selection principle to examine profit allocation schemes. Numerical analyses on part of Chongqing city's logistics network show the superiority of HNSGA-II over MOPSO and NSGA-II on the practical case study, as well that of CGA over the Minimum Costs-Remaining Savings (MCRS), Shapley and Game Quadratic Programming (GQP) methods. In addition, the proposed profit allocation approach has supported the establishment of a grand coalition instead of two sub-coalitions. CMCVRPSDP optimization reduces long-haul transportation, improves the vehicle loading rate and facilitates sustainable development. Through the rational allocation of profits, the proposed solution methodology assures the stability and fairness among coalition members. The implementation is also important to design sustainable urban transportation networks.
Yong Wang; Jie Zhang; Kevin Assogba; Yong Liu; Maozeng Xu; Yinhai Wang. Collaboration and transportation resource sharing in multiple centers vehicle routing optimization with delivery and pickup. Knowledge-Based Systems 2018, 160, 296 -310.
AMA StyleYong Wang, Jie Zhang, Kevin Assogba, Yong Liu, Maozeng Xu, Yinhai Wang. Collaboration and transportation resource sharing in multiple centers vehicle routing optimization with delivery and pickup. Knowledge-Based Systems. 2018; 160 ():296-310.
Chicago/Turabian StyleYong Wang; Jie Zhang; Kevin Assogba; Yong Liu; Maozeng Xu; Yinhai Wang. 2018. "Collaboration and transportation resource sharing in multiple centers vehicle routing optimization with delivery and pickup." Knowledge-Based Systems 160, no. : 296-310.
The formation of a cooperative alliance is an effective means of approaching the vehicle routing optimization in two-echelon reverse logistics networks. Cooperative mechanisms can contribute to avoiding the inefficient assignment of resources for the recycling logistics operations and reducing long distance transportation. With regard to the relatively low performance of waste collection, this paper proposes a three-phase methodology to properly address the corresponding vehicle routing problem on two echelons. First, a bi-objective programming model is established to minimize the total cost and the number of vehicles considering semitrailers and vehicles sharing. Furthermore, the Clarke–Wright (CW) savings method and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) are combined to design a hybrid routing optimization heuristic, which is denoted CW_NSGA-II. Routes on the first and second echelons are obtained on the basis of sub-optimal solutions provided by CW algorithm. Compared to other intelligent algorithms, CW_NSGA-II reduces the complexity of the multi-objective solutions search and mostly converges to optimality. The profit generated by cooperation among retail stores and the recycling hub in the reverse logistics network is fairly and reasonably distributed to the participants by applying the Minimum Costs-Remaining Savings (MCRS) method. Finally, an empirical study in Chengdu City, China, reveals the superiority of CW_NSGA over the multi-objective particle swarm optimization and the multi objective genetic algorithms in terms of solutions quality and convergence. Meanwhile, the comparison of MCRS method with the Shapley value model, equal profit method and cost gap allocation proves that MCRS method is more conducive to the stability of the cooperative alliance. In general, the implementation of cooperation in the optimization of the reverse logistics network effectively leads to the sustainable development of urban and sub-urban areas. Through the reasonable reorganization of the entire network, recycling companies can provide more reliable services, contribute to the reduction of environmental pollution, and guarantee significant profits. Thus, this paper provides manufacturing companies, logistics operators and local governments with tools to protect the environment, while still making profits.
Yong Wang; Shouguo Peng; Kevin Assogba; Yong Liu; Haizhong Wang; Maozeng Xu; Yinhai Wang. Implementation of Cooperation for Recycling Vehicle Routing Optimization in Two-Echelon Reverse Logistics Networks. Sustainability 2018, 10, 1358 .
AMA StyleYong Wang, Shouguo Peng, Kevin Assogba, Yong Liu, Haizhong Wang, Maozeng Xu, Yinhai Wang. Implementation of Cooperation for Recycling Vehicle Routing Optimization in Two-Echelon Reverse Logistics Networks. Sustainability. 2018; 10 (5):1358.
Chicago/Turabian StyleYong Wang; Shouguo Peng; Kevin Assogba; Yong Liu; Haizhong Wang; Maozeng Xu; Yinhai Wang. 2018. "Implementation of Cooperation for Recycling Vehicle Routing Optimization in Two-Echelon Reverse Logistics Networks." Sustainability 10, no. 5: 1358.