Vehicle routing problem with time window (VRPTW) is the most representative optimization problem in logistics researches, which appears to be a multi-objective optimization problem in essence. However, most previous studies transform it into a single objective optimization problem to deal with. Also, the vehicle routing models in related studies focus on minimizing the cost. However, the efficiency (e.g., the waiting time due to early arrivals) and customer satisfaction equally important as the cost for the long-term development of logistics companies. In this article, a corresponding tri-objective VRPTW model for logistics dispatching is introduced, and a decomposition-based multi-objective variable neighborhood descent algorithm (D-VND) is proposed to solve the tri-objective VRPTW. In D-VND, the tri-objective VRPTW is decomposed into multiple single-objective sub-problems via a set of uniformly distributed weight vectors and then the route-based evolutionary operator is used to generate the offspring. Meanwhile, the objective-wise neighborhood operators are incorporated into the variable neighborhood descent method as local searches to improve qualities of sub-problems. Moreover, we adopt a heuristic initialization strategy and an external archive mechanism based on fast sorting and crowding distance methods to improve the performance of D-VND. Experimental results on benchmarking datasets with different scales show that D-VND outperforms two other representative algorithms regarding convergence and diversity and yields better solutions.

Wireless Sensor Networks (WSNs), as an important part of the Internet of Things (IoT), have developed rapidly in recent years. WSNs comprises of spatially distributed sensor nodes to collect the information from the environment. The energy of nodes is usually limited, so the speed of energy consumption determines the lifespan of the network. This paper proposed an energy-efficient adaptive clustering routing algorithm named MPDE&VSSLS-SIACR to minimize the energy consumption of WSNs and prolong the network lifespan. Here, SIACR is a swarm-intelligence-based adaptive clustering routing protocol, while MPDE&VSSLS is a novel Memetic Algorithm that belongs to the swarm intelligence algorithm. The SIACR protocol classifies WSN nodes into three categories: cluster heads, cluster members, and free nodes, and employs the MPDE&VSSLS to optimize clustering, with the objectives of reducing energy consumption and balancing the residual energy among nodes. In the comparison experiments, the algorithm presented in this study was compared to the LEACH, LEACH-C, and PSO-SIACR algorithms for two network topologies with three data fusion rates of 0, 0.5, and 1, respectively. The experimental results revealed that MPDE&VSSLS-SIACR outperformed the other algorithms in terms of network lifetime and residual energy. Meanwhile, it is demonstrated that the proposed algorithm is highly adaptable to different topologies and different data fusion rates. Furthermore, the PSO-SIACR algorithm performed similarly to the MPDE&VSSLS-SIACR algorithm, indicating that the SIACR is an effective framework for clustering routing optimization using swarm intelligence algorithms.

Recently, a two-echelon city dispatching model with mobile satellites (2ECD-MS) has been proposed to reduce costs effectively. However, in addition to costs, speeds of delivery to customers are increasingly demanding in urban dispatching. This work extends 2ECD-MS to 2ECD-MS-CS by adopting the crowd-shipping model in the second-echelon dispatching, which uses occasional drivers of private vehicles to deliver parcels to improve the delivery speed. Furthermore, existing works generally consider the optimization from a single aspect, e.g., the delivery company. However, the sustainable development of a logistics company must also focus on other subjects in logistics activities, such as customers and delivery employees. So, we define a multi-objective model considering company cost, customer satisfaction, and income satisfaction of crowd-shippers simultaneously. The multi-objective optimization problem of 2ECD-MS-CS is solved by a multi-directional evolutionary algorithm (MDEA). In MDEA, multiple neighborhood operators are designed and combined with the multi-directional search strategy to fully explore the Pareto Front. Finally, we generate 40 new 2ECD-MS-CS instances based on existing common vehicle routing datasets. Experimental results show that 2ECD-MS-CS reduces the average cost by 3.4% and improves the delivery speed by 42% against 2ECD-MS in 40 instances with different customer scales, numbers of mobile satellites, and geographic scopes. The proposed MDEA outperforms several popular multi-objective optimization algorithms in both convergence and diversity. These illustrate the advantages of 2ECD-MS-CS especially in terms of delivery speed and the effectiveness of the proposed MDEA.

At present, city logistics mostly adopts a two-echelon dispatching model which combines distribution centers located in suburbs and fixed satellites located in urban areas for distribution. However, both expensive rental fees and daily changes of customer demand in metropolitan areas make dispatching route generated by fixed satellites inefficient. Moreover, the existing mobile depot model needs a large investment for facilities. In this paper, we propose a two-echelon city dispatching model with mobile satellites (2ECD-MS) which locations of mobile satellites change according to demands of customers to ensure the efficiency of delivery routes in every day. A cluster-based variable neighborhood search scheduling algorithm is proposed to determine locations of mobile satellites and dispatching routes of trucks and tricycles. Then, the 2ECD-MS is extended to 2ECD-MS-TDD to allow trucks dispatching directly (TDD) for further cost reduction. Experimental results show that the 2ECD-MS significantly reduces the total cost against the model using fixed satellites mode by 3.5% while the 2ECD-MS-TDD further reduces the total cost against the 2ECD-MS significantly by 3.25% in 54 cases with different customer scales, geographical scopes, and distribution types. These show the superiority of the proposed methods in cost reduction for city logistics in comparison to the traditional fixed model.

In many fields, including management, computer, and communication, Large-Scale Global Optimization (LSGO) plays a critical role. It has been applied to various applications and domains. At the same time, it is one of the most challenging optimization problems. This paper proposes a novel memetic algorithm (called MPCE & SSALS) based on multiparent evolution and adaptive local search to address the LSGO problems. In MPCE & SSALS, a multiparent crossover operation is used for global exploration, while a step-size adaptive local search is utilized for local exploitation. A new offspring is generated by recombining four parents. In the early stage of the algorithm execution, global search and local search are performed alternately, and the population size gradually decreases to 1. In the later stage, only local searches are performed for the last individual. Experiments were conducted on 15 benchmark functions of the CEC′2013 benchmark suite for LSGO. The results were compared with four state-of-the-art algorithms, demonstrating that the proposed MPCE & SSALS algorithm is more effective.