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Understanding the influence mechanism of landscape urbanization on ecosystem services (ESs) will provide useful guides for ecosystem conservation and urban sustainable management. However, the models used in existing studies tended to evaluate the various impacts of landscape urbanization on ES in the same period and fail to reveal the dynamic response of ES to landscape urbanization and the contribution of each impact variable, which is not conducive to the long‐term planning and management of urban ecosystems. To fill the gap, based on the panel data of the 141 counties in the Chengdu–Chongqing urban agglomeration (CCUA) from 2000 to 2015, this study applied the panel vector autoregressive (PVAR) model to investigate the dynamic impact of different aspects (i.e., urban scale, density, shape, and compactness) of landscape urbanization on ES multifunctionality (ESM) and identified the key impact factors. The results showed that (1) the proportion of high‐level ESM decreased by 9.13% in 2000–2010 and then increased by 19.14% in 2010–2015; (2) the ESM of the CCUA existed significant spatial agglomeration, and the high‐level areas were concentrated in the southwestern mountains, while the low‐level areas were mainly located in the central plains and urban areas; (3) There was a strong non‐linear relationship between landscape urbanization and ESM, with variability in the intensity and direction of ESM's response to landscape urbanization shocks as time. Specifically, urban landscape scale and shape (i.e., percentage of landscape, largest patch index (LPI), and landscape shape index) were generally more influential on the ESM than urban landscape density and compactness (i.e., patch density and pacth cohesion index), with LPI making the greatest contribution. These findings will help to clarify the dynamic relationship between landscape urbanization and ESM, which could provide important guidance for ecosystem conservation and urban planning. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The identification and analysis of the spatiotemporal dynamic traffic patterns in citywide road networks constitute a crucial process for complex traffic management and control. However, city-scale and synchronal traffic data pose challenges for such kind of quantification, especially during peak hours. Traditional studies rely on data from road-based detectors or multiple communication systems, which are limited in not only access but also coverage. To avoid these limitations, we introduce real-time, traffic condition digital maps as our input. The digital maps keep spatiotemporal urban traffic information in nature and are open to access. Their pixel colors represent traffic conditions on corresponding road segments. We propose a stacked convolutional autoencoder-based method to extract a low-dimension feature vector for each input. We compute and analyze the distances between vectors. The statistical results show different traffic patterns during given periods. With the actual data of Chongqing city, we compare the feature extraction performance between our proposed method and histogram. The result shows our proposed method can extract spatiotemporal features better. For the same data set, there is little difference in the number distribution of red pixels found in the statistics result of the histogram, while differences do exist in the results of our proposed method. We find the most fluctuated morning is on Friday; the most fluctuated evening is on Tuesday; and the most stable evening is on Wednesday. The distance captured by our method can represent the evolution of different traffic conditions during the morning and evening peak hours. Our proposed method provides managers with assistance to sense the dynamics of citywide traffic conditions in quantity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the growing interest in win–win cooperation within the logistics industry recently, collaborative multicenter logistics networks have witnessed the establishment of numerous alliances aimed at achieving cost savings. However, alliance members may choose to withdraw from an alliance due to changes in market positioning strategies and short-term profit considerations. Such withdraws often lead to the disintegration of the alliance and result in financial losses for the remaining non-defaulting members. This study focuses on addressing the issues of compensation and profit allocation in a specific scenario known as the collaborative multicenter vehicle routing problem with time windows and defaulting members withdrawal (CMVRPTWDMW). The objective is to ensure alliance stability and fair profit distribution among its members. To achieve this, a tri-objective mathematical model is proposed to optimize the re-formed alliances by considering the total operating cost, number of vehicles, and service waiting time. Additionally, an improved minimum cost remaining saving method is introduced to accurately quantify the deserved profits of non-defaulting members. Compensation quantification and default cost allocation models are then developed based on this analysis. To solve the CMVRPTWDMW and obtain Pareto optimal solutions, a hybrid heuristic algorithm is designed. This algorithm combines a 3D k -means clustering algorithm for customer reassignment after the withdrawal of defaulting members, effectively reducing the complexity of the problem. Furthermore, the algorithm employs a Clarke-Wright saving method-based non-dominated sorting genetic algorithm-III (CW-NSGA-III) to search for optimal solutions. To enhance the algorithm's search ability, an elite alteration strategy and a penalty-based boundary intersection approach are incorporated into CW-NSGA-III. Finally, a real-world case study conducted in Chongqing City, China, is presented to demonstrate the feasibility of the proposed models and solution algorithms. The study contributes valuable insights into the sustainable operation of urban logistics networks that may experience unpredictable defaults, providing guidance for the stability of logistics alliances in the transportation sector. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Logistics distribution using electric vehicles (EVs) is gradually becoming popular in urban logistics networks with the promise of carbon peak and carbon neutrality. A collaborative mechanism among multiple depots can effectively increase the efficiency and sustainability of a logistics network. Sharing charging stations (CSs) is presented as a critical strategy to improve the efficiency of logistics networks and facilitate collaboration further. Solving a collaborative multidepot EV routing problem with time windows and shared charging stations (CMEVRPTW-SCS) involves centralized transportation services among multiple depots, coordinated delivery services among multiple depots and the charging needs of EVs. A bi-objective nonlinear programming model for CMEVRPTW-SCS is formulated to minimize the total operating cost and number of EVs, wherein the operating cost contains transportation costs among depots and vehicle routing costs in each depot. Gaussian mixture clustering algorithm is developed to cluster customers and assign them to depots probabilistically to reduce the computational complexity, and an improved multi-objective genetic algorithm with tabu search (IMOGA-TS) is designed to optimize the EV routes and charging locations in CMEVRPTW-SCS. With the combination of local and global searches and the improvement of solutions at each iteration by TS, the proposed hybrid algorithm can significantly accelerate the efficient exploration of optimal solutions. The comparative results of the IMOGA-TS with multi-objective genetic algorithm, non-dominated sorting genetic algorithm-II and multi-objective harmony search algorithm show the superiority of the proposed algorithm in solving CMEVRPTW-SCS. Then, a Shapley value model based on game theoretical methods is proposed to find the best profit allocation scheme. A real-world case study of CMEVRPTW-SCS in Chongqing City, China is conducted, and three scenarios of various collaborative alliances and 11 schemes with and without shared CSs are further discussed. The experimental results show that the proposed methods are of practical significance in reducing operating costs, improving transportation efficiency, and achieving sustainable operation in EV-based logistics distribution networks. [ABSTRACT FROM AUTHOR]

• A collaborative emergency logistics network with TWA is studied. • A tri-objective optimization model is proposed for emergency logistics delivery. • A multi-objective ALNS with split algorithm is devised to solve the model. • A case study is explored to show the applicability of proposed methods. • Comparative analyses with different TWA and sharing strategies are tested. During natural disasters or accidents, an emergency logistics network aims to ensure the distribution of relief supplies to victims in time and efficiently. When the coronavirus disease 2019 (COVID-19) emerged, the government closed the outbreak areas to control the risk of transmission. The closed areas were divided into high-risk and middle-/low-risk areas, and travel restrictions were enforced in the different risk areas. The distribution of daily essential supplies to residents in the closed areas became a major challenge for the government. This study introduces a new variant of the vehicle routing problem with travel restrictions in closed areas called the two-echelon emergency vehicle routing problem with time window assignment (2E-EVRPTWA). 2E-EVRPTWA involves transporting goods from distribution centers (DCs) to satellites in high-risk areas in the first echelon and delivering goods from DCs or satellites to customers in the second echelon. Vehicle sharing and time window assignment (TWA) strategies are applied to optimize the transportation resource configuration and improve the operational efficiency of the emergency logistics network. A tri-objective mathematical model for 2E-EVRPTWA is also constructed to minimize the total operating cost, total delivery time, and number of vehicles. A multi-objective adaptive large neighborhood search with split algorithm (MOALNS-SA) is proposed to obtain the Pareto optimal solution for 2E-EVRPTWA. The split algorithm (SA) calculates the objective values associated with each solution and assigns multiple trips to shared vehicles. A non-dominated sorting strategy is used to retain the optimal labels obtained with the SA algorithm and evaluate the quality of the multi-objective solution. The TWA strategy embedded in MOALNS-SA assigns appropriate candidate time windows to customers. The proposed MOALNS-SA produces results that are comparable with the CPLEX solver and those of the self-learning non-dominated sorting genetic algorithm-II, multi-objective ant colony algorithm, and multi-objective particle swarm optimization algorithm for 2E-EVRPTWA. A real-world COVID-19 case study from Chongqing City, China, is performed to test the performance of the proposed model and algorithm. This study helps the government and logistics enterprises design an efficient, collaborative, emergency logistics network, and promote the healthy and sustainable development of cities. [ABSTRACT FROM AUTHOR]

Since the multidepot vehicle routing problem with time windows and three-dimensional loading constraints (MDVRPTW-TDLC) is a multiconstraint and combinatorial optimization problem, this study proposes a resource sharing scheme on the basis of customer clustering to achieve a balance of spatial resource spatial distributions, thereby reducing the operating costs in a collaborative multidepot logistics network. Moreover, this study proposes a vehicle compartment partition strategy based on cargo characteristics such as type and sizes to increase vehicle loading rates, after which the proposed MDVRPTW-TDLC is formulated as a bi-objective mixed-integer programming model to minimize the total operating costs while maximizing the average loading rate of vehicles. Subsequently, a two-stage hybrid algorithm combining a three-dimensional (3D) k -harmonic means clustering algorithm and extended nondominated sorting genetic algorithm-II (ENSGA-II) is developed to find the Pareto optimal solutions and solve the MDVRPTW-TDLC optimization model, followed by an introduction of the 3D k -harmonic means clustering algorithm to cluster customers, thereby reducing computational complexity. The proposed ENSGA-II, integrating the improved Clarke–Wright savings algorithm and NSGA-II is then used to obtain the final optimal solutions, followed by the application of a selective exchange mechanism between the clustering algorithm and ENSGA-II to enhance the global and local optimization capability, enabling fast and efficient computation of optimal solutions. Based on our investigations revealed through an algorithm comparison using the multiobjective harmony search algorithm, multiobjective evolution algorithm, multiobjective particle swarm optimization, monarch butterfly optimization, Runge Kutta optimizer, and Harris hawks optimization, ENSGA-II demonstrates better performance in reaching high-quality objective function values. Finally, a real-world case study of Chongqing City, China is employed to verify the efficiency of the proposed model and optimization algorithm, after which a comparison of the optimization results among different vehicle compartment partition strategies demonstrates the efficiency of the proposed method. We also discuss 16 scenarios with and without the vehicle compartment partition strategy with computational results showing an improved vehicle loading rate and reduced logistics operating costs with the adopted vehicle compartment partition strategy. Therefore, our results confirm that the proposed MDVRPTW-TDLC can facilitate the sustainable development of logistics operations, and providing decision support for constructing intelligent logistics systems smart cities. • A MDVRPTW with three-dimensional loading constraints is studied. • A bi-objective mixed-integer programming model is proposed for the MDVRPTW-TDLC. • A hybrid method with 3D k -harmonic means and ENSGA-II is devised to solve the model. • A real-world case study is conducted to test the applicability of proposed approach. • Vehicle loading rates with different compartment partition scenarios are explored. [ABSTRACT FROM AUTHOR]

• Optimize a collaborative multicenter vehicle routing problem with DCDs. • Propose a bi-objective optimization model for the CMVRPTWDCD. • Design a hybrid method with k -medoids clustering and ER-NSGA-III to solve the model. • Conduct a practical case study to show the applicability of proposed approach. • Implement a series of comparative analyses in different collaborative scenarios. The vehicle routing problem for waste recycling has been created to improve resource utilization and reduce environmental pollution as a result of the rapid consumption of natural resources and the negative impact of waste on the environment. This study investigates a collaborative multicenter vehicle routing problem with time windows and dynamic customer demands. The collaboration among logistics facilities is an effective mechanism to reduce the number of vehicles and improve resource utilization in a multicenter reverse logistics network. A multi-objective mathematical programming model is proposed to coordinate static and dynamic customer demands while minimizing the total operating cost, the number of vehicles, and the total waiting time. A hybrid heuristic algorithm comprising an improved k -medoids clustering algorithm and an extended reference point-based non-dominated genetic algorithm- III, is designed to solve the multi-objective optimization model. The clustering algorithm is implemented to reduce the network complexity. The extended reference point-based non-dominated genetic algorithm- III with a dynamic insertion strategy is proposed to adjust service routes for dynamic customer demands and find the Pareto optimal solution for the multi-objective optimization model. The performance of the proposed algorithm is evaluated, and results suggest that the extended reference point-based non-dominated genetic algorithm- III has superior solutions in solving the collaborative multicenter vehicle routing problem with time windows and dynamic customer demands compared with other algorithms. The cost savings resulting from optimization are fairly distributed among participants in a collaborative alliance via the minimum cost remaining savings and principle of strictly monotonic path selection. The proposed methods are applied in a case study of a realistic reverse logistics network in Chongqing City, China. Different service periods and optimization strategies are discussed and compared to verify the effectiveness of the proposed solution methods. The optimization results indicate that the collaborative mechanism and dynamic insertion strategy can improve resource utilization and transportation efficiency and contribute to a sustainable urban logistics transportation system. [ABSTRACT FROM AUTHOR]

• A truck-drone hybrid routing problem with time-dependent travel time is studied. • A mixed-integer programming model is developed for TDHRP-TDRTT. • An iterated local search heuristic combing multi-phase approaches is devised. • The model and algorithm are tested by a case study in a terrain-constraint network. • A sensitivity analysis is implemented with four road congestion indices. Combining trucks and drones in package delivery provides a promising venue for a future logistics system that is more efficient and sustainable than the existing one. However, how to coordinate trucks and drones, particularly under uncertain traffic conditions (thus, travel time), remains a critical question in this field. To address this challenge, this study proposes and solves a truck–drone hybrid routing problem with time-dependent road travel time (TDHRP-TDRTT) to address the truck–drone cooperation issue. TDHRP-TDRTT is formulated as a cost minimization problem with constraints associated with logistics demand and supply. An iterative local search heuristic algorithm based on intra-pair and inter-pair customer exchanges and link re-optimization is developed to solve TDHRP-TDRTT. Our results on small-scale and benchmark instances show that the proposed algorithm has better computational performance than CPLEX solver, the adaptive large neighborhood search, hybrid genetic-sweep algorithm, and variable neighborhood search. A case study using traffic data from Chongqing, China shows that the truck–drone solution improves the timeliness of delivery, undertakes sensitivity analysis considering four road congestion states, significantly reduces trucking mileage, and facilitates overcoming terrain limitations. Therefore, the proposed model and algorithm are of practical significance in reducing operating cost, improving transportation efficiency, and facilitating a smart and sustainable urban logistics distribution system. [ABSTRACT FROM AUTHOR]

• Study a collaborative mechanism in a CMVRPTW with mixed deliveries and pickups. • Propose a mixed-integer optimization model for the CMVRPMDPTW. • Devise a hybrid solution algorithm that combines improved 3D k -means and GA-PSO. • Conduct a case study to show the applicability of proposed model and algorithms. • Implement a comparison analysis in five collaborative open-closed scenarios. This study focuses on the collaborative multicenter vehicle routing problem with time windows and mixed deliveries and pickups (CMVRPTWMDP), which is a variant of the vehicle routing problem (VRP) with mixed deliveries and pickups, and VRP with simultaneous deliveries and pickups and time windows. Collaboration and transportation resource sharing are adopted to optimize vehicle routes in the CMVRPTWMDP, to integrate the delivery and pickup services with time windows, and to construct open–closed mixed vehicle routes. First, the CMVRPTWMDP is formulated as a mixed-integer programming model to minimize logistics operating costs. The effect of transportation resource sharing on reducing the number of needed vehicles and the maintenance cost is considered in the model formulation. Second, a two-stage hybrid algorithm combining customer clustering and vehicle routing optimization is designed to solve the CMVRPTWMDP. An improved 3D k -means clustering algorithm based on space–time distances and the customer demand is proposed to reassign customers to logistics facilities (e.g., delivery or pickup centers). Furthermore, a hybrid heuristic algorithm that combines the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, called GA–PSO, is designed to optimize the vehicle routes. A coordination operator between GA and PSO is designed to allow particles and chromosomes to interact, increasing the diversity of particle swarms and the possibility of finding a feasible solution. Third, the performance and effectiveness of the proposed approach are tested by comparing them with the CPLEX solver using 30 small-scale instances and other existing algorithms using 25 benchmark instances. Fourth, the minimum costs-remaining savings (MCRS) model is adopted to design a fair and reasonable profit allocation scheme for participants in the collaborative alliance and maintain alliance stability. Finally, the optimization results of a real-world case study from Chongqing, China, show that transportation resource misuse and logistics operating costs are significantly reduced, demonstrating the proposed approach's effectiveness and applicability. This study provides insights for logistics enterprises and transportation departments on effectively allocating and utilizing the transportation resources and optimizing the local logistics network. [ABSTRACT FROM AUTHOR]

Collaborative multicenter pickup and delivery problems with time window assignment (CMPDPTWA) are introduced in this paper and formulated and solved as a two-stage optimization problem. First, the collaborative mechanism achieves optimal transportation resource configuration in a multicenter logistics network. Second, open-closed mixed pickup and delivery routes satisfying the demands and time windows of customers are proposed to enhance transportation efficiency and reduce the total logistics operating cost. A set of candidate-time windows is assigned to the corresponding customers through time window assignment (TWA) strategy to increase the operational efficiency of logistics network. A bi-objective programming model for CMPDPTWA is formulated to minimize the number of vehicles and total operating cost. An improved k -means clustering algorithm is used to cluster customers according to their proximities to pickup and delivery centers for reducing the computational complexity to solve the abovementioned problem. A self-learning non-dominated sorting genetic algorithm-II (SNSGA-II) is proposed to optimize the open-closed mixed pickup and delivery routes in CMPDPTWA. An update mechanism for the probabilities of crossover and mutation is devised to improve the efficiency of searching the solution space, and thus to accelerate the convergence of SNSGA-II. The performance of the proposed algorithm is compared with that of other algorithms, and the results indicate that it can generate near-optimal solutions for CMPDPTWA. A game theoretical method involving a cost gap allocation model and the strictly monotonic path strategy is proposed to find the best profit allocation scheme and the optimal coalition sequence for stabilizing the collaborative coalition. Finally, the proposed method is applied to a case study using the real-world logistics network in Chongqing City, China. Six scenarios with and without the collaborative mechanism and TWA strategy are simulated and compared with one another to verify the efficacy of the proposed method. The results show that the collaborative mechanism and TWA strategy can improve the utilization of transportation resources and operational efficiency. Thus, they can facilitate an efficient and sustainable urban logistics network. • Collaborative multicenter pickup and delivery problems with TWA is studied. • A bi-objective optimization model is developed based on collaborative networks. • A hybrid method with k -means clustering and SNSGA-II is devised to solve the model. • Profit allocation and coalition stability are tested under different game methods. • A case study is conducted to show the applicability of proposed approach. [ABSTRACT FROM AUTHOR]

Optimization of collaborative multi-depot pickup and delivery logistics networks (CMDPDLN) with split loads and time windows involves a customer demand splitting strategy and a multi-depot pickup and delivery vehicle routing problem under time window constraints. In the collaborative network, the customer demand splitting scheme based on customer clustering aims to achieve the balance of demands' spatial distribution and improve the efficiency of logistics transportation. The multi-depot pickup and delivery vehicle routing problem focuses on establishing a collaborative network optimization model to coordinate the pickup and delivery services among multiple depots and determine the optimal routes with reduced operating cost through logistics resource sharing. A 3D customer clustering algorithm with split load strategies is developed to reassign each customer to its favorable service provider considering multiple customer service characteristics. A hybrid genetic algorithm with tabu search is designed to optimize the pickup and delivery routes and maximize the logistics resource utilization. A realistic logistics network in Chongqing, China is used to test the performance of the proposed solution methods for the CMDPDLN optimization. Computational results show the effectiveness of customer clustering and demand splitting in simplifying and improving the large-scale collaborative network, and the adaptability of the hybrid algorithm in finding the minimal-cost vehicle routes. Therefore, the collaboration and demand split strategy adopted in network optimization can provide a reference for logistics operational management and facilitate sustainable pickup and delivery networks. • Study a collaborative pickup and delivery logistics network with split loads. • Propose a mixed-integer programming model for a multi-depot split logistics network. • Devise a hybrid method with 3D k -means clustering, split load strategies and GA-TS. • Conduct different vehicle capacity types to test logistics operation efficiency. • Explore loading rate means and variations in non- and collaborative networks. [ABSTRACT FROM AUTHOR]

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. • 2E-MPLRP with shared transportation resource is studied. • A bi-objective mixed-integer programming model is developed for the 2E-MPLRP. • A hybrid algorithm combining 3D k -means and MOIPSO is proposed to solve the model. • A real-world case study is conducted to show the applicability of proposed approach. • A comparison analysis in location schemes of different service periods is conducted. [ABSTRACT FROM AUTHOR]

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. • Study a collaborative emergency logistics network based on resource sharing. • Propose a bi-objective optimization model for emergency logistics delivery. • Devise a hybrid algorithm with k-means clustering and CW_NSGA-II to solve the model. • Conduct a case study to show the applicability of proposed model and algorithms. [ABSTRACT FROM AUTHOR]

• Collaborative logistics pickup and delivery problem with eco-packages is studied. • A multi-objective mixed-integer programming model is developed based on TS network. • A hybrid method combining multidimensional K -means, DP and RP-NSGA-II is devised. • The collaborative profit and alliance stability are tested under different methods. • Limited truck storage space sometimes undermines logistics operation efficiency. 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. [ABSTRACT FROM AUTHOR]

• Study collaborative mechanisms in a 2E-CMDPVRP with resource sharing. • Establish a multi-objective optimization model for the 2E-CMDPVRP. • Design a hybrid heuristic algorithm that combines 3D k-means and IR-NSGA-III. • Explore synchronization and coalition sequences in the 2E-CMDPLN. • Conduct a practical study to show the applicability of proposed approach. 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. [ABSTRACT FROM AUTHOR]

• Propose a collaborative mechanism to coordinate logistics participants in an FLDN. • Establish a multi-objective linear optimization model to formulate the CMCVRP-RSTC. • Design a hybrid method including extended k-means and TS-NSGA-II to solve the model. • Conduct an empirical study to demonstrate the applicability of proposed approach. • Implement a sensitivity analysis to identify the optimal controlled temperatures. 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. [ABSTRACT FROM AUTHOR]

• The VORSB farmwork can displayed the actual EH status than traditional method. • Land use change had significant spatiotemporal differences under a multi-scenario perspective. • The land development scenarios with target ecological protection increased EH. • The conversion of farmland to forest land had the greatest positive contribution to EH. • Future attention should be paid to the deterioration of EH caused by urban sprawl in metropolitan developed circle. Land-use changes dominated by high-intensity human activities have seriously threatened regional ecosystem health (EH) and will continue to increase in the future, especially in mountainous cities. Therefore, there is a need to systematically assess the contribution and spatial effects of future land use conversion on EH from a multi-scenario simulation perspective, which is essential for ecological conservation and management. For this purpose, taking Chongqing as an example, this study assessed the static distribution and dynamic evolution of EH over historical periods (2000–2020) and future multi-scenarios in 2030 using the vigor-organization-resilience-ecosystem services balance (VORSB) framework and patch-level land use simulation (PLUS) model, then analyzed the influence of land use conversion on EH and identified sensitive areas under multiple scenarios based on the contribution index and ecological sensitivity index. The results showed that Chongqing ecosystems were generally at a moderate healthy level during 2000–2020, with an increasing trend in EH level, with high-EH located in the eastern mountainous areas, while low-EH clustered in the western urban core and its peripheral areas. From 2020 to 2030, the EH level was improved by about 0.21% under the ecological protection scenario due to the transformation from farmland to forestland, which has the largest positive contribution of 0.111. In contrast, the EH level under the natural development and economic development scenarios declined by 1.95% and 3.07%, especially in the metropolitan developed circle, which was significantly related to the occupation of farmland resources by urban land, with negative contribution of −0.103 and −0.189, respectively. In the future, these regions should strictly control the urban expansion scale and strengthen high-quality farmland protection to achieve the balance of ecological protection and economic development. The findings will provide scientific guidance for the regional ecosystem conservation, land use planning, and urban sustainable development of mountainous cities. [ABSTRACT FROM AUTHOR]

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. • Formulate the two-echelon multicenter vehicle routing problem in SST networks. • Study collaborative mechanisms in two-echelon SST networks. • Establish a bi-objective optimization model for the above problems. • Design an integrated algorithm that includes SST-based DP, K-means and Im-NSGAII. • Conduct a case study to test the proposed approach in real world. [ABSTRACT FROM AUTHOR]

• Customer with time window assignment is considered and modeled after collaboration. • A multi-objective optimization model is established to study the CMDVRPTWA. • A hybrid algorithm includes K -means, CW and E-NSGA-II is devised to solve the model. • A practical study is conducted to show the applicability of proposed mechanism. 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. [ABSTRACT FROM AUTHOR]

Exploring the patterns of ecological security and its driving mechanisms is crucial for ecosystem management and regional development. However, few studies have conducted the ecological security evaluation from multiple perspectives of ecosystems, and the relative importance and spatial heterogeneity impacts of human activities, natural and landscape structural on ecological security changes need to be further explored. In this study, a health-services-risk framework was developed to evaluate ecological security pattern of the Chengdu-Chongqing urban agglomeration (CCUA) from 2000 to 2015. Then, the geo-detector method and geographically weighted regression model were employed to investigate the relative importance and spatial heterogeneity of various factors on ecological security changes. The results showed that (1) the comprehensive ecological security index (CESI) pattern in the CCUA increased from the central plains to the surrounding mountains. About 14.61% of the regional ecological security had declined from 2000 to 2015, which was concentrated in the plains and around the core cities, while the southwest and northeast experienced a evident increase in CESI. (2) Human activity were the dominant factors in CESI changes, followed by natural factors and landscape structural factors. The great influence of natural and landscape structural concentrated in the southwestern and northeastern mountainous areas, while the significant influence of human activities occurred in the plains areas in the central and western parts. The proposed framework can be applied to the security evaluation of other rapidly urbanizing areas, and the findings will help the ecological conservation and sustainable management of the CCUA. [ABSTRACT FROM AUTHOR]

Urban landscape patterns serve as the spatial carriers of emission sources and influencing factors of air contaminants, which inevitably impact submicron particle (PM 1) pollution. However, most studies ignore the spatial nonstationary and nonlinear effects of urban landscape patterns on the PM 1 concentration. In this study, a novel framework was developed that integrates a multiscale geographically weighted regression (MGWR) model and Shapley Additive exPlanations (SHAP) machine learning method to explore the spatial heterogeneity effect, relative contribution and nonlinear impact of landscape patterns on the PM 1 concentration at the national and urban agglomeration scales. The results indicated that urban landscape patterns were closely related to PM 1 pollution and exhibited obvious spatial differences. The disorderly expansion of urban built-up areas and an irregular urban morphology could aggravate PM 1 pollution, while urban fragmentation and connectivity could impose reducing effects. Dispersed urban landscape patterns could reduce the PM 1 concentration in the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Chengdu-Chongqing (CDCQ) regions, whereas compact and continuous landscape patterns positively affected PM 1 pollution mitigation in the Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZH) urban agglomerations. The impact of urban landscape patterns on PM 1 pollution was greater in urban agglomerations than elsewhere. The ENN_MN landscape index exhibited the highest feature importance and interpretability. The threshold effects between the urban shape indicators and PM 1 concentration were more complex than those with the other landscape indices. This critical knowledge provides a scientific basis for further understanding the correlation mechanism between PM 1 pollution and the landscape pattern, urban sustainable planning and air pollution control. [Display omitted] • We developed a novel research framework that integrates the MGWR and SHAP models. • PM 1 pollution was closely related to urban landscape patterns, with remarkable space heterogeneity. • The polycentric and dispersed landscape metrics were conducive to reducing PM 1 concentration in YRD, PRD and CDCQ. • The compact and continuous landscape dynamics can effectively mitigate PM 1 pollution in BTH and GZH. • The relative importance and threshold effect of ENN_MN on PM 1 concentration are stronger and more complex. [ABSTRACT FROM AUTHOR]

During the Marine Isotope Stage (MIS) 5/4 transition, the global climate changed from an interglacial to a glacial state, and hence it was characterized by high-amplitude climate changes. High-resolution stalagmite records can potentially improve our understanding of climate change during this transition. Here we use a high-precision 230Th-dated, 50-yr-resolution stalagmite δ18O record from Yangkou Cave, in Chongqing, southwestern China, to reveal centennial- to millennial-scale changes in Asian summer monsoon (ASM) intensity during the interval of 98.8–59.3 kyr B.P. (thousands of years before 1950 CE). The record reveals five Chinese Interstadial events, namely CIS 18–22, and pronounced centennial-scale oscillations are evident and verified within these millennial-scale events. There are four centennial-scale events of monsoon strengthening in both CIS 21 and CIS 22, corresponding to Greenland Interstadial events GI 21 and GI 22. By contrast, CIS 18, CIS 19 and CIS 20 differ in both structure and onset time relative to the corresponding GI events during MIS 4. During MIS 5, reduced ice sheet and sea-ice cover and strong Atlantic Meridional Overturning Circulation (AMOC) forced the Intertropical Convergence Zone (ITCZ) towards its northern limit, which enhanced the teleconnection between the ASM and climate change in northern high latitudes. During MIS 5, the millennial-scale events (CIS 21 and 22) show a rapid atmospheric teleconnection between the ASM and the climate of northern high latitudes, but this coupling did not exist during MIS 4. The weakening of Northern Hemisphere summer insolation, the expansion of ice sheets and sea ice, combined with the increased influence of Antarctica, may have led to the decoupling of the Asian summer monsoon and climate change in high northern latitudes. • An accurate dating and high resolution stalagmite record cover MIS4 and MIS5. • The CIS 18, 19 and 20 differ to the corresponding GI events. • The climate of the northern high latitudes decouples from the ASM during MIS 4. [ABSTRACT FROM AUTHOR]

• Define key congestion points and convert it to a feature selection problem. • Propose an expert system to discover key congestion points of urban traffic. • Revise BSSReduce, which runs 15 times faster than BSSReduce for this data. • Discover 75 and 300 key congestion points from over 10,000 and 50,000 points. 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. [ABSTRACT FROM AUTHOR]