Manufacturing Execution System of Bluetooth Speaker Pen Holder Assembly Line based on Genetic Algorithm

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

In response to the optimization scheduling problem in the dyeing production process, the author proposes a hierarchical scheduling method for dyeing vats based on genetic algorithm and multi-agent. In this method, a hierarchical scheduling algorithm is used to decompose production scheduling into static and dynamic strategies. The static strategy adopts a genetic algorithm that supports batch processing of multiple products, non equality of equipment, order delivery time, switching cost, and other constraints: Dynamic strategy is a coordinated dynamic optimization algorithm that uses multi-agent systems to support the running status of dye tanks based on static strategies. By solving the algorithm with multiple constraints and dynamic factors in the production process, the final result of the dyeing tank operation task is obtained. The simulation compared pure genetic algorithm with manual scheduling, and the results showed that the hierarchical dynamic scheduling strategy based on data-driven achieved the goal of optimizing the production scheduling of dyeing vats. The practical application results also demonstrate the feasibility of this method.

Production scheduling is a form of decision-making, which plays a crucial role in manufacturing as well as in service industries. Many approaches are used in practice in order to find good and feasible solutions in the scheduling problems. Approaches based on Genetic Algorithms (GAs) are becoming more and more widespread nowadays. This paper is the result of the author's extensive study on the use of advanced technologies in the field of production planning and scheduling and follows his previous research on the use of expert systems in this field (Metaxiotis et al., 2002). The present paper presents famous GAs known in the literature and current applications, analyses the relative benefits and concludes by sharing thoughts and estimations on GAs future prospects in this area.

The garment industry is a global industry that requires high agility in response to changing market demands that are quickly changing. Short product cycles with unpredictable demand often make the industry unable to meet consumer needs. In increasing the agility of production to deliver products to customers as fast as possible, the production scheduling system must be designed optimally. Recently algorithm hybridization is used because the combination of more than one algorithm is more optimal. Genetic Algorithm (GA) is a metaheuristic algorithm is applied in various production scheduling and its power can be improved by combining it with the Tabu Search (TS). The GA is the best metaheuristic algorithm to output the optimal scheduling with less execution time but has the disadvantage –easily trapped in local optimum (early convergence is faster). The TS algorithm works as a local search algorithm with a faster computation time than GA. This study aims to minimize the total time to complete the work (minimizing makespan) by combining TS into GA in conducting local searches to increase industrial agility. The results obtained are GA-TS hybridization can provide a more optimal solution for the production scheduling in the garment so that agility can increase.

The increased availability of data and computer-aided technologies such as MRPI/II, ERP and MES system, allowing producers to be more adaptive to market dynamics and to improve production scheduling. Integration of production scheduling and computer modelling, simulation and visualization systems can be useful in the analysis of production system constraints related to the efficiency of manufacturing systems. A integration methodology based on semi-automatic model generation method for eliminating problems associated with complexity of the model and labour-intensive and time-consuming process of simulation model creation is proposed. Data mapping and data transformation techniques for the proposed method have been applied. This approach has been illustrated through examples of practical implementation of the proposed method using KbRS scheduling system and Enterprise Dynamics simulation system.

BackgroundMining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network (Epi-GTBN). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm.ResultsWe compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets.ConclusionsThe presented methodology (Epi-GTBN) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses.

Optimizing production processes to conserve resources and reduce waste has become crucial in pursuing sustainable manufacturing practices. The solid wood panel industry, marked by substantial raw materials and energy consumption, stands at the forefront of addressing this challenge. This research delves into production scheduling and equipment utilization inefficiencies, offering innovative solutions for the solid wood panel processing line aimed at achieving environmental sustainability and operational efficiency. The study is articulated through two main segments: (1) an exhaustive analysis and the development of a simulation system for the solid wood panel processing line, delineating all production elements and operational logic, furnished with a user-friendly simulation interface, and (2) a comprehensive evaluation and enhancement of various scheduling algorithms specific to the Flexible Job-Shop Scheduling Problem (FJSP) encountered in solid wood panel workshops. A significant leap forward is made with the introduction of the Adaptive Intelligent Optimization Genetic Algorithm (AIOGA), an evolved version of the standard Genetic Algorithm (GA) engineered for optimal scheduling within the solid wood panel processing line. AIOGA incorporates advanced features such as encoding strategy, population initialization, objective function setting, selection strategy, crossover operation, and mutation operation, demonstrating the methodological depth of the study. We applied AIOGA in a designed FJSP, and AIOGA substantially reduced the maximum completion time to 90 min. It evidenced an improvement of 39.60% over the conventional GA, enhancing the equilibrium of the equipment workload across the system. This research presents a multifaceted strategy to address the scheduling complications inherent in solid wood panel production and highlights the extensive applicability of adaptive intelligent optimization in diverse industrial settings. This study establishes a new paradigm in manufacturing optimization, underlining the valuable integration of sustainability and efficiency in production methodologies.

Production scheduling in plants, due to their hybrid nature, can become very complex. Manufacturers developed various manufacturing operations to facilitate better scheduling, while effective production schedules ensure smoothness of downstream operations. This paper deals with a production scheduling problem in a flexible (or hybrid) job-shop with particular constraints: batch production; existence of two steps: production of several sub-products followed by the assembly of the final product; possible overlaps for the processing periods of two successive operations of a same job. Different objectives should be considered simultaneously, among the makespan, the mean completion time, the maximal tardiness, the mean tardiness. The main contribution of this paper is the presentation of a novel approach based on a genetic algorithm as a suitable tool for scheduling of hybrid systems. The major benefit of this approach is a significant reduction in complexity during problem formulation. The proposed method is explained through a mill case study.

A decision support system for production scheduling in an ion plating cell

This paper proposes three genetic algorithms with different types of crossovers dedicated to solve the problem of short-term scheduling of batch processes. The genetic algorithms suggested are able to determine the amount of batches produced, their production sequence, machinery assignments, and a different batch size for each batch. This is a more complex variation of the batch scheduling problem where genetic algorithms are usually limited to decide the batch production sequence. A manufacturing case study is used to test the performance when minimizing the makespan, in order to compare the proposed genetic algorithms against a simulated annealing implementation, a totally random search, and a simplified genetic algorithm with no crossover (to test the crossovers’ efficiency). The results show that one of the crossovers proposed has the fastest convergence in all scenarios, finding the best solutions in short searches. This crossover is slightly outperformed by the simulated annealing only in extensive searches with more iterations. The other two crossovers suggested have a good performance in certain scenarios, but show poor results in others.

Based on the SBM model including unexpected output, this paper studies the water resource utilization efficiency of 30 provinces in China from 2003 to 2019. The study found that China's water resource utilization efficiency showed obvious provincial differences. The water resource utilization efficiency of most eastern coastal provinces was relatively high, and that of most central and western inland provinces was not high. There are also significant differences among the three regions of the East, the middle, and the West. The utilization efficiency of water resources in the East is the highest, followed by the middle, and the West is the lowest. The redundancy of input factors, such as labor, capital, and water consumption, is the main reason for the low efficiency of water resource utilization, and the redundancy of wastewater discharge also affects the efficiency of water resource utilization. The clustering results show that the utilization efficiency of water resources in most provinces of China is located in medium efficiency area and low efficiency area, and the efficiency needs to be improved. There are relatively few provinces in high-efficiency areas, highlighting that China's water resource utilization still faces severe challenges.

Lean-circular maturity model (LCMM) for companies' self-assessment in terms of process, product and life cycle thinking

WITHDRAWN: Grasshopper algorithm based channel assignment for cognitive radio networks

An Open-Pit Production Scheduling (OPPS) problem focuses on specifying block production scheduling to achieve the highest possible Net Present Value (NPV). This paper presents a new mathematical model for OPPS under uncertainty. To this end, a robust box and ellipsoidal counterpart approach was used. The proposed method was implemented in a hypothetical model. A Genetic Algorithm (GA) and an exact mathematical modeling approach were used to solve the model. It was shown that the scheduling of deterministic and robust models in various conditions is different. Considering the type of robust counterparts, different production plans under various conditions were scheduled. Furthermore, the price of robustness was determined for various levels of conservation.

Nomenclature Aircraft design is a complex multidisciplinary process to determine aircraft configuration variables that satisfy a set of mission requirements. It is very hard for aircraft designers to foresee the consequences of changing certain variables. Furthermore, conventional optimization processes are limited by the type and number of parameters used, resulting in sub-optimal designs. The objective of this research is to test the functionality and implementation of a multidisciplinary aircraft conceptual design optimization method using an adaptive genetic algorithm (GA), as a feasible alternative to the existing sizing and optimization methods. To illustrate the approach the algorithm is used to optimize a medium range commercial aircraft, with takeoff weight as an optimization goal, subjected to constraints in performance and geometric parameters. Adaptive and traditional formulations for the handling of constraints by the GA are tested and compared. Results show the ability of the adaptive GA to unbiased search through the design space of aircraft conceptual designs, leading to more viable aircraft configurations than the traditional GA approach at reduced timeframes, with a lower cost than current aircraft design optimization procedures.