Process Planning and Scheduling in Distributed Manufacturing System Using Multiobjective Genetic Algorithm

SummaryInternet of Things (IoT) is a platform which connects the objects through the Internet. The IoT devices have limited processing capabilities. This makes the processing and analyzing huge amount of data as a challenging task in IoT environment. Due to this disadvantage of IoT devices, it cannot be used in many useful applications where they are mostly required. Hence, a technique which helps in overcoming this disadvantage is mostly desired. One of the desired technologies is the cloud computing. The cloud computing has rich set of resources, and it is efficient in processing and analyzing data irrespective of its type and quantity. The services of the cloud will be provided by a cloud service provider (CSP). Selecting an appropriate cloud service provider is a major issue in an integrated environment of IoT and Cloud. Therefore, the focus of this paper is on cloud collaboration with IoT environment with suitable CSPs. A multi‐objective genetic algorithm to find the optimized solution is proposed in this paper. The Non‐dominated Sorting Genetic Algorithm 2 (NSGAII) and Strength Pareto Evolutionary Algorithm 2 (SPEA2) are used to analyze the performance of the proposed algorithm. The Profit Information (PI) to the individual and Trust Relationship (TR) value are considered as the objectives for selecting the CSPs. The results demonstrated that the proposed Multi‐objective Genetic Algorithm (MGA) algorithm along with NSGAII performs more efficiently than MGA algorithm with SPEA2.

Solving a tri-objective supply chain problem with modified NSGA-II algorithm

Multi-objective optimization of the shell in autonomous intelligent argo profiling float

Non-orthogonal multiple access (NOMA) deployment in future wireless networks has been recently considered a promising radio access technology to enhance spectral efficiency (SE). However, gain in SE comes always with the cost of energy efficiency (EE). In this paper, we investigate the SE and EE tradeoff in downlink NOMA with the consideration of quality of service (QoS) requirements based on three population-based multi-objective evolutionary algorithms (MOEAs): multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm-II (NSGA-II) and strength Pareto evolutionary algorithm-2 (SPEA2). The tradeoff is optimized and Pareto optimal solutions are obtained through MOEAs. The effectiveness of the algorithms is evaluated based on the hypervolume metric and the capability of solving multi-objective optimization problems. Simulation results reveal that SPEA2 outperforms NSGA-II and MOPSO. Furthermore, NSGA-II is the loser among all algorithms in terms of finding Pareto optimal results.

Gene selection from microarray gene expression datasets and clustering of samples into different groups are important data mining tasks for disease identification. Selection of more interpretable genes from the gene expression dataset is an essential data-preprocessing task, which helps to study on cancer diseases. Gene selection during sample clustering is inherently a difficult task as there is no obvious criterion to guide the search. Simultaneous gene selection and sample clustering is a two-way data analysis technique which has recently gained attention in research area. The traditional clustering techniques are unable to handle noisy data properly. So, effective clustering algorithms are more desirable which can deal with the relevant and noise free data. Therefore, target genes selection before sample clustering is essential and of course effective if both the tasks are done simultaneously. In this chapter, optimal gene subset is selected and sample clustering is performed simultaneously using Multi-Objective Genetic Algorithm (MOGA). Different versions of MOGA are employed to choose the optimal gene subset, where natural number of optimal clusters of samples is automatically obtained at the end of the process. Non-dominated sorting genetic algorithm (NSGA), Strength pareto evolutionary algorithm (SPEA) and its modified version SPEA2 are applied for the purpose. The methods use nonlinear hybrid uniform cellular automata for generating initial population, tournament selection strategy, two-point crossover operation, and a suitable jumping gene mutation mechanism to maintain diversity in the population. It uses mutual correlation coefficient; internal and external cluster validation indices as objective functions to find out the non-dominated solutions. To measure the cluster validation indices, clustering algorithm is applied on data subset associated to chromosomes in the population to find out different clusters. After the convergence of genetic algorithm, the best solution from the non-dominated solutions is identified that provides the important genes and categorizes the samples into clusters. The experimental results express the correctness of the proposed simultaneous gene selection and sample categorization method. The goodness of optimality of the clusters obtained using different genetic algorithms is expressed by comparing various cluster validation indices.

Analyzing and optimizing the impact of economic restructuring on Shanghai’s carbon emissions using STIRPAT and NSGA-II

With the emergence of the concept of green manufacturing, more manufacturers have attached importance to energy consumption indicators. The process planning and shop scheduling procedures involved in manufacturing processes can both independently achieve energy savings, however independent optimization approaches limit the optimization space. In order to achieve a better optimization effect, the optimization of energy savings for integrated process planning and scheduling (IPPS) was studied in this paper. A mathematical model for multi-objective optimization of IPPS was established to minimize the total energy consumption, makespan, and peak power of the job shop. A hierarchical multi-strategy genetic algorithm based on non-dominated sorting (NSHMSGA) was proposed to solve the problem. This algorithm was based on the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) framework, in which an improved hierarchical coding method is used, containing a variety of genetic operators with different strategies, and in which a population degradation mechanism based on crowding distance is adopted. The results from the case study in this paper showed that the proposed method reduced the energy consumption by approximately 15% for two different scheduling schemes with the same makespan. The computational results for NSHMSGA and NSGA-Ⅱ approaches were evaluated quantitatively in the case study. The C-metric values for NSHMSGA and NSGA-Ⅱ were 0.78 and 0, the spacing metric values were 0.4724 and 0.5775, and the maximum spread values were 1.6404 and 1.3351, respectively. The evaluation indexes showed that the NSHMSGA approach could obtain a better non-dominated solution set than the NSGA-Ⅱ approach in order to solve the multi-objective IPPS problem proposed in this paper.

In this study, the particle swarm optimization (PSO) and back propagation neural network (BPNN) surrogate model in combination with a multi-objective genetic algorithm are developed for the design optimization of a bionic liquid cooling plate with a spider-web channel structure. The single-factor sensitivity analysis is first conducted based on the numerical simulation approach, identifying three key factors as design variables for optimizing design objectives such as maximum temperature (Tmax), maximum temperature difference (ΔTmax), and pressure drop (ΔP). Subsequently, the PSO algorithm is used to optimize the parameters of the BPNN structure, thereby constructing the PSO-BPNN surrogate model. Next, the non-dominated sorting genetic algorithm II (NSGA-II) is employed to obtain the Pareto optimal set, and the TOPSIS with the entropy weight method is used to determine the optimal solution, eliminating subjective preferences in decision-making. The results show that the PSO-BPNN model outperforms the traditional BPNN in prediction accuracy for all three objectives. Compared to the initial structure, the Tmax and ΔTmax are reduced by 1.09 °C and 0.41 °C in the optimized structure, respectively, with an increase in ΔP by 21.24 Pa.

Product platform two-stage quality optimization design based on multiobjective genetic algorithm

Recently, many model quantization approaches have been investigated to reduce the model size and improve the inference speed of convolutional neural networks (CNNs). However, these approaches usually inevitably lead to a decrease in classification accuracy. To address this problem, this paper proposes a mixed precision quantization method combined with channel expansion of CNNs by using a multi-objective genetic algorithm, called MOGAQNN. In MOGAQNN, each individual in the population is used to encode a mixed precision quantization policy and a channel expansion policy. During the evolution process, the two polices are optimized simultaneously by the non-dominated sorting genetic algorithm II (NSGA-II). Finally, we choose the best individual in the last population and evaluate its performance on the test set as the final performance. The experimental results of five popular CNNs on two benchmark datasets demonstrate that MOGAQNN can greatly reduce the model size and improve the classification accuracy at the same time.

This article aims to investigate the means to obtain optimal hot stamping process parameters and the influence of the stochastic variability of these parameters on forming quality. A multi-objective stochastic approach, integrating response surface methodology (RSM), multi-objective genetic algorithm optimization non-dominated sorting genetic algorithm II (NSGA-II) and the Monte Carlo simulation (MCS) method is proposed in this article to achieve this goal. RSM was used to establish the relationship between the process parameters and forming quality indices. NSGA-II was utilized to obtain a Pareto frontier, which consists of a series of optimal process parameters. The MCS method was employed to study and reduce the influence of a stochastic property of these process parameters on forming quality. The results confirmed the efficiency of the proposed multi-objective stochastic approach during optimization of the hot stamping process. Robust optimal process parameters guaranteeing good forming quality were also obtained using this approach.

Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD) technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Nondominated Sorting Genetic Algorithm II (NSGA-II). The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.

Supply-chain scheduling in modern production systems faces significant challenges due to the complexity of balancing cost efficiency, delivery lead times, and order variability—especially under stochastic demand and uncertain transportation conditions. Conventional metaheuristic approaches, such as the standard Non-dominated Sorting Genetic Algorithm II (NSGA-II), often struggle with premature convergence, limited population diversity, and rigid parameter settings that hinder adaptability in dynamic environments. To address these limitations, this study proposes a novel Hybrid Adaptive NSGA-II (HA-NSGA-II) framework, enhanced by two key innovations: (1) a Global-Intensity Mutation Operator (GIMO) that strengthens population diversity by dynamically adjusting mutation intensity across the entire solution space; and (2) an Adaptive Parameter Self-Tuning (APST) module based on reinforcement learning, which intelligently regulates crossover and mutation rates in response to evolutionary progress and environmental changes. A tri-objective optimization model is formulated to simultaneously minimize total supply chain cost, order quantity variance (mitigating the bullwhip effect), and customer delivery delays. The proposed HA-NSGA-II demonstrates superior convergence and diversity performance compared to traditional NSGA-II, offering a more robust and adaptive solution for complex, real-world supply chain scheduling under uncertainty. The model is tested on a simulated supply chain with 5 factories, 15 warehouses, and 50 customers facing random demand and Gaussian-distributed lead times. The new HA-NSGA-II is compared with traditional NSGA-II, SPEA2, and MOEA/D based on Hypervolume (HV), Inverted Generational Distance (IGD), Spread (Δ), Total Inventory Cost, Bullwhip Index (BI), and Robustness Index (RI). Results of 50 simulation runs demonstrate that HA-NSGA-II surpasses all the baseline algorithms with 12.9% enhancement in HV, 41.1% improvement in IGD, and 13.7% reduction in convergence time. In addition, it delivers a 14.6% supply chain cost reduction, a 20.4% bullwhip effect suppression, and 25% accelerated times of fulfillment. The research concludes that the HA-NSGA-II provides a scalable and effective scheduling approach applicable for real-time Industry 4.0 supply chains. Work in the future involves expanding the framework towards multi-period dynamic environments using IoT and blockchain-based visibility.

This paper proposes an evolutionary alternative to conventional two-phase planning methods for solving the integrated crew scheduling (ICS) problem. The approach models and formulates the ICS problem as a combinational optimization problem with multiple constraints and objectives. An integrated evolutionary framework is proposed for simultaneously considering crew pairing and crew rostering subproblems. To improve the efficiency of the Pareto set explorer, the solution methodology applies a novel variant of the nondominated sorting genetic algorithm II (NSGA-II), one of the most popular multiobjective optimization evolutionary algorithms. The proposed variant features problem-dependent constraint handling and a bounded front policy to reserve diverse individuals. The proposed approach is verified and validated in a case study of a real-world short-haul airline crew scheduling problem. The experimental results obtained by the proposed integrated approach are then compared with a real-world airline plan generated by the conventional sequential method. An aircraft schedule recovery problem is also studied to compare solution performance between the conventional NSGA-II method and the proposed NSGA-II variant. The comparison results confirm that the proposed variant obtains solutions that are superior in two aspects: First, the proposed NSGA-II variant obtains better convergence in the studied problems compared with the original version; second, the results explored by the variant enable decision makers to select from multiple crew schedules, which are superior to the real-world airline crew plan while considering the same objectives and constraints.

Considering uncertainties in engineering optimum design is often a requirement. Here, the use of the deterministic optimum design as the reference point in g-dominance is proposed. The multiobjective optimum robust design in a structural engineering test case where uncertainties in the external loads are taken into account is proposed as application, where the simultaneous minimization of the constrained weight average and the standard deviation of the constraints violation are the objective functions. Results include a comparison between both non-dominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm (SPEA2), including S-metric (hypervolume) statistical comparisons with and without the g-dominance approach. The methodology is capable to provide robust optimum structural frame designs successfully.