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Service composition in cloud manufacturing is a critical optimization problem that must balance multiple conflicting objectives, including service quality, cost, and service association impact. However, existing approaches often overlook the quantitative impact of service associations on composition performance, leading to suboptimal solutions. To address this issue, this study introduces a service association cost function and develops a three-objective optimization model that explicitly accounts for service quality, cost, and service association effects. To efficiently solve this model, we propose an enhanced NSGA-II algorithm with the following key improvements: (1) Good point set-based population initialization, integrating good point sets and random sampling to enhance solution diversity and search efficiency. (2) Reverse learning-based crossover operator, designed to improve exploration capability and prevent premature convergence. (3) Adaptive dynamic elitism strategy, which dynamically adjusts the elite retention ratio and adaptively incorporates local search operators to balance convergence and diversity. Extensive experiments on both benchmark problems and cloud service composition scenarios demonstrate that the proposed algorithm outperforms conventional multi-objective optimization methods in terms of convergence, diversity, and robustness. These findings confirm the effectiveness of our approach and its practical applicability in real-world cloud manufacturing environments.

【Objective】Optimizing pipe networks is a critical aspect of drip irrigation design. This paper proposes a multi-objective method to optimize pressurized drip irrigation network design and evaluate its applicability.【Method】The optimization method incorporates two objectives: minimizing the cost per unit area and improving network reliability by reducing the variance of surplus head at unfavourable nodes in the network. A mathematical model was developed, accounting for depreciation costs and operation and management expenses. The proposed model was applied to a pressurized drip irrigation network in an irrigation area in Xinjiang. The performance of the improved NSGA-II (Non-dominated Sorting Genetic Algorithm II) was compared with the original algorithm by analysing the Pareto frontier solutions.【Result】The Pareto frontier solution obtained by the improved NSGA-II algorithm is better than that obtained by the original algorithm. When the cost of a unit area was the same, the optimal network obtained by the improved NSGA-II algorithm was more reliable than that obtained by the original algorithm. Across 50 independent calculations, the uniformity index value of the improved NSGA-II and the original algorithm was 0.371 and 0.404, respectively. The cost of a unit area calculated using the improved NSGA-II algorithm was 792.92 yuan/hm2, compared to 851.89 yuan/hm2 budgeted in the original project. The optimisation calculated by the proposed method reduced the variance of surplus head at the unfavourable nodes in the network from 0.15 to 0.06.【Conclusion】The improved NSGA-II algorithm is more effective than the original algorithm, offering a practical approach to reducing costs and enhancing the reliability of pressurized drip irrigation pipe networks and similar hydraulic systems.

In order to achieve rapid and fair distribution of emergency supplies after a large-scale sudden disaster, this paper constructs a comprehensive time perception satisfaction function and a comprehensive material loss pain function to portray the perceived satisfaction of disaster victims based on objective constraints such as limited transport, multimodal transport and supply being less than demand, and at the same time considers the subjective perception of time and material quantity of disaster victims under limited rational conditions, and constructs a multi-objective optimisation model for the dispatch of multi-cycle emergency supplies by combining comprehensive rescue cost information. For the characteristics of the proposed model, based on the NSGA-II algorithm, generalized reverse learning strategy, coding repair strategy, improved adaptive crossover, variation strategy, and elite retention strategy are introduced. Based on this, we use the real data of the 2008 Wenchuan earthquake combined with simulated data to design corresponding cases for validation and comparison with the basic NSGA-II algorithm, SPEA-II and MOPSO algorithms. The results show that the proposed model and algorithm can effectively solve the large-scale post-disaster emergency resource allocation problem, and the improved NSGA- II algorithm has better performance.

The road network in rural areas is complex and the infrastructure is relatively backward. The multi-temperature zone cold chain logistics involves agricultural products with different temperature requirements, which requires considering the transportation cost, carbon emission cost, refrigeration cost, and time cost of different temperature zones during path planning, thereby increasing the difficulty of path planning. Therefore, a multi-objective optimization-based algorithm for selecting the optimal path of rural multi-temperature zone cold chain dynamic logistics intermodal transportation is proposed. Based on the analysis of the multi-temperature cold chain collection and distribution model based on multimodal transportation, a multi-objective optimization model is constructed. This model aims to minimize transportation costs, carbon emission costs, refrigeration costs, time costs, and maximize logistics quality, while satisfying constraints such as transfer schedule times, the number of transport mode conversions, transport mode selection, and time continuity. To solve this model, an improved NSGA-II algorithm is adopted, which combines an improved mutation operator, congestion distance calculation, and the C-W saving algorithm to achieve the optimal transport path solution. Additionally, ArcGIS software is used to implement the shortest path planning based on real road networks. The experimental results show that by selecting the road-rail combined transport mode and adopting the D1–D6–D10 transport path, it is possible to transport fresh agricultural products from location A to the distribution center at location B, with the lowest Pareto fitness value. Furthermore, the algorithm's effectiveness is further verified by completing the end-of-life fresh agricultural product distribution task with four multi-temperature refrigerated vehicles. The study also finds that extending or shortening the latest service time window for customers, although it leads to a decrease or increase in the optimal value of the algorithm's objective function, has little impact on the average distribution time and transport vehicles. These findings provide new theoretical and practical guidance for the path selection of multimodal transportation in multi-temperature cold chain logistics, with significant theoretical and application value.

Production scheduling and machine tool intermittent state control separately influence a workshop’s machining and intermittent energy consumption. Effective scheduling decisions and intermittent state control are crucial for optimizing the overall energy consumption in the workshop. However, the scheduling scheme determines the machine tool intermittent durations, which imposes strong constraints on the decision-making process for intermittent state control. This makes it difficult for intermittent state control to be used in providing feedback and optimizing scheduling decisions, significantly limiting the overall energy-saving potential of the workshop. To this end, a workshop energy-saving scheduling method is proposed integrating machine tool intermittent state control. Firstly, the variation characteristics of workshop machining energy consumption, machine tool intermittent durations, and intermittent energy consumption are analyzed, and an energy-saving optimization strategy is designed. Secondly, by incorporating variables such as intermittent durations, intermittent energy consumption, and variable operation start time, a multi-objective integrated optimization model is established. Thirdly, the energy-saving optimization strategy is integrated into chromosome encoding, and multiple crossover and mutation genetic operator strategies, along with a low-level selection strategy, are introduced to improve the NSGA-II algorithm. Finally, the effectiveness of the proposed method is verified through a machining case. Results show that the generated Gantt chart reflects both production scheduling and intermittent state control decision outcomes, resulting in a 1.51% reduction in makespan, and 3.90% reduction in total energy consumption.

As an important connecting channel between cities, roads are one of the main elements in urban development infrastructure. The stability evaluation of the roadbed slope runs through the entire life cycle, especially during the operation stage. However, under extreme weather conditions, especially heavy rainfall, the roadbed slope may become unstable, thus endangering operational safety. Therefore, it is necessary to conduct precise dynamic assessments of slope stability. However, due to site limitations, it is often not possible to obtain accurate mechanical parameters of a slope using traditional survey methods when deformation and failure have already occurred. In this study, building upon our existing parameter inversion model, the improved backpropagation genetic algorithm non-dominated sorting genetic algorithm II model (BPGA-NSGA-II), in-depth research was conducted on the selection of key parameters for the model. This study utilized monitoring data to perform an inversion analysis of the real-time mechanical parameters of the slope. Subsequently, the inverted parameters were applied to dynamically assess the stability of the slope. The calculation results demonstrate that the slope safety factor decreased from an initial value of 1.212 to 0.800, which aligns with actual monitoring data. This research provides a scientifically effective method for the dynamic stability assessment of slopes.

On large-scale product assembly lines, such as those used in aircraft manufacturing, multiple assembly positions and devices often coexist within a single workstation, leading to complex task interactions. As a result, the problem of parallel task execution within workstations must be effectively addressed. This study focuses on positional and equipment conflicts within workstations. To manage positional and equipment conflicts, a multi-objective optimization model is developed that integrates assembly sequence planning with the first type of assembly line balancing problem. This model aims to minimize the number of workstations, balance task loads, and reduce equipment procurement costs. An improved NSGA-II algorithm is proposed by incorporating artificial immune algorithm concepts and neighborhood search. A selection strategy based on dominance rate and concentration is introduced, and crossover and mutation operators are refined to enhance search efficiency under restrictive parallel constraints. Case studies reveal that a chromosome concentration weight of about 0.6 yields superior search performance. Compared with the traditional NSGA-II algorithm, the improved version achieves the same optimal number of workstations but provides a 5% better workload balance, 2% lower cost, a 76% larger hyper-volume, and a 133% increase in Pareto front solutions. The results demonstrate that the proposed algorithm effectively handles assembly line balancing with complex parallel constraints, improving Pareto front quality and maintaining diversity. It offers an efficient, practical optimization strategy for scheduling and resource allocation in large-scale assembly systems.

Trajectory planning and avoidance of tipping are the main keys to success in the mobile dual-arm manipulation, especially when the dual-arm or moving platform is running fast. The forces and moments between wheel-terrain and body-arm have been analyzed by kinematics and force analysis of a robot to define tip-over stability constraint. Then, an improved tip-over moment stability criterion for a wheeled mobile dual-arm robot is presented and defines tip-over stability constraint based on it. To improve the motion stability of the robot, this paper presents an optimal joint trajectory planning model based on time and energy. The quintic B-spline curve and an improved NSGA-II algorithm, which are time and energy, are applied to multi-objective optimization. The simulation results show that the motion stability of a robot is improved based on the tip-over stability constraint. This trajectory planning method based on the stability constraint can be applied to other mobile robots as well.

The dense population and the large amount of domestic waste generated make it difficult to determine the best route and departure time for waste removal trucks in a city. Aiming at the problems of municipal solid waste (MSW) removal and transportation not in time, high collection and transportation costs and high carbon emissions, this paper studies the vehicle routing problem of municipal solid waste removal under the influence of time-dependent travel time, traffic congestion and carbon emissions. In this paper, a dual objective model with the lowest total economic cost and the highest garbage removal efficiency is established, and a DCD-DE-NSGAII algorithm based on Dynamic Crowding Distance and Differential Evolution is designed to improve the search ability, improve the convergence speed and increase the diversity of the optimal solution set. The results show that: according to the actual situation of garbage collection and transportation, the method can scientifically plan the garbage collection and transportation route, give a reasonable garbage collection scheme and departure time, and effectively avoid traffic congestion time; Through algorithm comparison, the algorithm and model proposed in this paper can reduce collection and transportation costs, improve transportation efficiency and reduce environmental pollution.

Regarding enterprise service management, optimizing business processes must achieve a balance between several service quality factors such as speed, flexibility, and cost. Recent advances in industrial wireless technology and the Internet of Things (IoT) have brought about a paradigm shift in smart applications, such as manufacturing, predictive maintenance, smart logistics, and energy networks. This has been assisted by smart devices and intelligent machines that aim to leverage flexible smart Enterprise Resource Planning (ERP) regarding all the needs of the company. Many emerging research approaches are still in progress with the view to composing IoT and Cloud services for meeting the expectation of companies. Many of these approaches use ontologies and metaheuristics to optimize service quality of composite IoT and Cloud services. These approaches lack responsiveness to changing customer needs as well as changes in the power capacity of IoT devices. This means that optimization approaches need an effective adaptive strategy that replaces one or more services with another at runtime, which improves system performance and reduces energy consumption. The idea is to have a system that optimizes the selection and composition of services to meet both service quality and energy saving by constantly reacting to context changes. In this paper, we present a semantic dynamic cooperative service selection and composition approach while maximizing customer non-functional needs and quickly selecting the relevant service drive with energy saving. Particularly, we introduce a new QoS energy violation degree with a cooperative energy-saving mechanism to ensure application durability while different IoT devices are run-out of energy. We conduct experiments on a real business process of the company SETIF IRIS using different cooperative strategies. Experimental results showed that the smart ERP system with the proposed approach achieved optimized ERP performance in terms of average service quality and average energy consumption ratio equal to 0.985 and 0.057, respectively, in all simulated configurations compared to ring and maser/slave methods.