Jian Li

River–sea direct transport systems emerging along the Pinglu Canal connect coastal and inland port clusters through shared lock facilities. When berth and lock resources are planned independently, uneven port workloads, mismatched vessel flows, and avoidable waiting may arise. This study formulates a coordinated scheduling problem for a multi-port–lock network operating under direct transshipment. The mixed-integer programming model jointly determines port assignment, berth service sequences, lockage plans, and vessel placement within lock chambers, while balancing lock-operation expenditure against the port dwell time of seagoing and river vessels. To address large instances, a hierarchical iterative collaborative optimization algorithm (HICOA) is developed. Its iterative mechanism exchanges information between berth and lock decisions, and a packing heuristic and adaptive large neighborhood search are applied to the associated subproblems. Computational results show that HICOA recovers the optimum for small instances and produces feasible solutions efficiently when CPLEX is constrained by a 5-h time limit. Compared with independent scheduling, coordinated optimization reduces total port time by up to 55.69%. The sensitivity analysis further indicates that river-vessel arrival dispersion and berth distribution across ports materially influence system performance.

Dairy cold-chain network planning requires coordinated decisions under demand variability, product perishability, and environmental constraints. To address these interrelated challenges, this study formulates an order-driven multi-objective mixed-integer nonlinear programming (MINLP) model for the tactical planning of a three-echelon dairy cold-chain network. The model coordinates distribution-center selection, inventory, transportation allocation, vehicle configuration, and refrigeration decisions to minimize total cost, transportation-related carbon emissions, and the quantity- and importance-weighted average freshness-loss rate. Demand variability is represented through service-level-based safe demand, whereas product freshness is evaluated using Weibull-based shelf-life reliability and inventory–transportation exposure. Transportation congestion is further incorporated to capture its effects on travel time, refrigeration emissions, and freshness deterioration. NSGA-II is employed to generate Pareto solutions, with entropy-weighted TOPSIS used for compromise-solution selection and MOEA/D serving as the benchmark algorithm. Numerical results indicate that NSGA-II achieves favorable convergence performance and comparable solution diversity relative to MOEA/D, while small-scale mixed-integer approximation tests support the quality of the obtained solutions. Multi-scale experiments demonstrate stable computational performance as network size increases. Sensitivity and scenario analyses further reveal distinct effects of service levels, shelf-life characteristics, and road capacity on economic, environmental, and freshness performance. The proposed framework provides tactical decision support for coordinating demand-responsive supply, low-carbon operations, and freshness preservation in dairy cold-chain networks.

This study addresses the problems of vessel congestion and inefficient resource utilization caused by the independent scheduling of port berths and locks in river–sea intermodal transport. Moreover, this study explores the joint scheduling optimization problem for a single port and a single lock under the direct transshipment mode of seagoing and river vessels. First, considering the spatiotemporal coupling characteristics of the port–lock system, a mixed-integer linear programming model integrating seaside and riverside berth allocation, lock batch scheduling, and 2D layout constraints of the lock chambers is constructed. The objective is to minimize the time costs of seagoing vessels in a port, river vessels in a port, and river vessels passing through the locks. Second, a hierarchical iterative collaborative optimization algorithm is designed. The outer layer uses an iterative algorithm framework, while the inner layer utilizes a heuristic algorithm to address the lock scheduling problem. An adaptive large neighborhood search algorithm is used to address the berth scheduling problem. Finally, the model and algorithm are validated through numerical instances. Compared with the commercial solver CPLEX, the proposed algorithm HICOA achieves up to 81.45% lower costs in medium-to-large instances. Compared with independent scheduling, joint scheduling reduces the total vessel dwell time cost by up to 66.22%, demonstrating the significant value of port–lock collaboration. Furthermore, the arrival time window of river vessels and the ratio of the number of vessels to the number of berths have a significant influence on system performance.

The joint optimization of storage location assignment and order picking efficiency for fresh products has become a vital challenge in intelligent warehousing because of the perishable nature of goods, strict temperature requirements, and the need to balance cost and efficiency. This study proposes a comprehensive mathematical model that integrates five critical cost components: picking path, storage layout deviation, First-In-First-Out (FIFO) penalty, energy consumption, and picker workload balance. To solve this NP-hard combinatorial optimization problem, we develop a Particle Swarm-guided hybrid Genetic-Simulated Annealing (PS-GSA) algorithm that synergistically combines global exploration by Particle Swarm Optimization (PSO), population evolution of Genetic Algorithm (GA), and the local refinement and probabilistic acceptance of Simulated Annealing (SA) enhanced with Variable Neighborhood Search (VNS). Computational experiments based on real enterprise data demonstrate the superiority of PS-GSA over benchmark algorithms (GA, SA, HPSO, and GSA) in terms of solution quality, convergence behavior, and stability, achieving 4.08–9.43% performance improvements in large-scale instances. The proposed method not only offers a robust theoretical contribution to combinatorial optimization but also provides a practical decision-support tool for fresh e-commerce warehousing, enabling managers to flexibly weigh efficiency, cost, and sustainability under different strategic priorities.

Assembly gap of structure often occurs during the manufacture of civil aircraft, which may cause additional stress, structure failure or even airplane disaster. This article introduces 2 types of methods for solving the problem. The article studies forced assembly and shim installation procedure and certain criteria. In the end, the article figures out static strength evaluation for free shim and fixed shim.

Aiming to improve the timeliness of logistics distribution and render the optimized route scheme effective under the real traffic network, we study the green vehicle routing problem with dynamic travel speed from both dimensions of time and space. A discrete formulation is proposed to calculate the travel time based on periods and arcs, which allows a vehicle to travel across an arc in multiple periods. Then, we establish a mixed-integer nonlinear programming model with minimum distribution costs including transportation costs, carbon emissions costs, and penalty costs on earliness and tardiness. A hybrid adaptive genetic algorithm with elite neighborhood search is developed to solve the problem. In the algorithm, a neighborhood search operator is employed to optimize elite individuals so that the algorithm can stimulate the intensification and avoid falling into a local optimum. Experimental instances are constructed based on benchmark instances of vehicle routing problem. The numerical results indicate that the proposed algorithm is rather effective in global convergence. Compared with the routing schemes in which travel speed merely varies with time periods or locations, the vehicle route optimized on spatiotemporal-varying speed outperforms them in terms of carbon emissions and timeliness. The research can provide a scientific and reasonable method for logistics enterprises to plan the vehicle schedule focusing on spatiotemporal-dependent speed of the road network.

This paper examines the waste of electrical and electronic equipments (WEEE) and draws on variational inequalities to model the closed-loop supply chain network. The network consists of manufacturers, retailers and consumer markets engaging in a Cournot-Nash game. Retailers are responsible for collecting WEEE in the network. It is assumed that the price of the remanufactured goods is different from that of the newly manufactured ones. The network equilibrium occurs when all players agree on volumes and prices. Several properties of the model are examined and the modified projection method is utilized to obtain the optimal solutions. Numerical examples are provided to illustrate the impact of CLSC parameters on the profits of channel members and consumer benefits, and to provide policy support for governments. We find that it is necessary to regulate a medium collection rate and a certain minimum recovery rate. This is also advantageous to manufacturers in producing new manufactured products. The impact of collection rate and recovery rate on manufacturers are greater than that on retailers. Consumers can benefit from the increase of the recovery rate as well as the collection rate.