A Neighborhood Search for Sequence-dependent Setup Time in Flow Shop Fabrics Making of Textile Industry

Abstract This paper proposes a neighborhood search to solve scheduling for fabrics making in a textile industry. The production process consists of three production stages from spinning, weaving, and dyeing. All stages have one processor. Setup time between two consecutive jobs with different color is considered. This paper also proposes attribute’s decomposition of a single job to classify available jobs to be processed and to consider setup time between two consecutive jobs. Neighborhood search (NS) algorithm is proposed in which the permutation of set of jobs with same attribute and the permutation among set of jobs is conducted. Solution obtained from neighborhood search, which might be trapped in local solution, then is compared with other known optimal methods

Sustainable Manufacturing: Application of Optimization to Textile Manufacturing Plants

The main goal of manufacturing industry is to produce the end products on time with good quality and keep the resource wastage low. However, manufacturing industry face several challenges such as bottle necks in the workflow, unsynchronized production, and sudden increase in product demands. In this paper, we are proposing a management platform for textile manufacturing plants with following modules: (1) sewing workflow optimization (2) quality assurance workflow optimization and (3) finishing workflow optimizations. We have used Genetic Programming (GP) approach, to optimize the workflows, considering different factors that affect each workflow. Our results show that, using our proposed platform, the manufacturing workflows can be optimized and reduce the bottle necks in the workflows and resource wastage in the manufacturing plant

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

A Deep Reinforcement Learning Based Multi-Criteria Decision Support System for Textile Manufacturing Process Optimization

Textile manufacturing is a typical traditional industry involving high complexity in interconnected processes with limited capacity on the application of modern technologies. Decision-making in this domain generally takes multiple criteria into consideration, which usually arouses more complexity. To address this issue, the present paper proposes a decision support system that combines the intelligent data-based random forest (RF) models and a human knowledge based analytical hierarchical process (AHP) multi-criteria structure in accordance to the objective and the subjective factors of the textile manufacturing process. More importantly, the textile manufacturing process is described as the Markov decision process (MDP) paradigm, and a deep reinforcement learning scheme, the Deep Q-networks (DQN), is employed to optimize it. The effectiveness of this system has been validated in a case study of optimizing a textile ozonation process, showing that it can better master the challenging decision-making tasks in textile manufacturing processes.Comment: arXiv admin note: text overlap with arXiv:2012.0110

A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics

In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance.This work was carried out within the project "TexBoost: less Commodities more Specialities" reference POCI-01-0247-FEDER-024523, co-funded by Fundo Europeu de Desenvolvimento Regional (FEDER), through Portugal 2020 (P2020)

Bidirectional optimization of the melting spinning process

This is the author's accepted manuscript (under the provisional title "Bi-directional optimization of the melting spinning process with an immune-enhanced neural network"). The final published article is available from the link below. Copyright 2014 @ IEEE.A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.National Nature Science Foundation of China, Ministry of Education of China, the Shanghai Committee of Science and Technology), and the Fundamental Research Funds for the Central Universities

Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill: A Case Study

In the Flexible Manufacturing System (FMS), where material processing is carried out in the form of tasks from one department to another, the use of Automated Guided Vehicles (AGVs) is significant. The application of multiple-load AGVs can be understood to boost FMS throughput by multiple orders of magnitude. For the transportation of materials and items inside a warehouse or manufacturing plant, an AGV, a mobile robot, offers extraordinary industrial capabilities. The technique of allocating AGVs to tasks while taking into account the cost and time of operations is known as AGV scheduling. Most research has exclusively addressed single-objective optimization, whereas multi-objective scheduling of AGVs is a complex combinatorial process without a single solution, in contrast to single-objective scheduling. This paper presents the integrated Local Search Probability-based Memetic Water Cycle (LSPM-WC) algorithm using a spinning mill as a case study. The scheduling model’s goal is to maximize machine efficiency. The scheduling of the statistical tests demonstrated the applicability of the proposed model in lowering the makespan and fitness values. The mean AGV operating efficiency was higher than the other estimated models, and the LSPM-WC surpassed the different algorithms to produce the best result

Genetic optimization of energy- and failure-aware continuous production scheduling in pasta manufacturing

Energy and failure are separately managed in scheduling problems despite the commonalities between these optimization problems. In this paper, an energy- and failure-aware continuous production scheduling problem (EFACPS) at the unit process level is investigated, starting from the construction of a centralized combinatorial optimization model combining energy saving and failure reduction. Traditional deterministic scheduling methods are difficult to rapidly acquire an optimal or near-optimal schedule in the face of frequent machine failures. An improved genetic algorithm (IGA) using a customized microbial genetic evolution strategy is proposed to solve the EFACPS problem. The IGA is integrated with three features: Memory search, problem-based randomization, and result evaluation. Based on real production cases from Soubry N.V., a large pasta manufacturer in Belgium, Monte Carlo simulations (MCS) are carried out to compare the performance of IGA with a conventional genetic algorithm (CGA) and a baseline random choice algorithm (RCA). Simulation results demonstrate a good performance of IGA and the feasibility to apply it to EFACPS problems. Large-scale experiments are further conducted to validate the effectiveness of IGA