Search

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]

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

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. • Time-dependent speed and piecewise penalty cost are integrated to optimize MDGVRP. • A method combining bi-objective mathematical model and hybrid heuristic is designed. • Transportation resource sharing reduces travelled distance and carbon emission. • Vehicle routes are less influenced by piecewise penalty cost under unimodal traffic. • The shortest path sometimes undermines minimum cost and carbon emission objectives. [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]

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]

• 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]

• 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]

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]

• 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]

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]