A layered-encoding cascade optimization approach to product-mix planning in high-mix-low-volume manufacturing

High-mix-low-volume (HMLV) production is currently a worldwide manufacturing trend. It requires a high degree of customization in the manufacturing process to produce a wide range of products in low quantity in order to meet customers' demand for more variety and choices of products. Such a kind of business environment has increased the conversion time and decreased the production efficiency due to frequent production changeover. In this paper, a layered-encoding cascade optimization (LECO) approach is proposed to develop an HMLV product-mix optimizer that exhibits the benefits of low conversion time, high productivity, and high equipment efficiency. Specifically, the genetic algorithm (GA) and particle swarm optimization (PSO) techniques are employed as optimizers for different decision layers in different LECO models. Each GA and PSO optimizer is studied and compared. A number of hypothetical and real data sets from a manufacturing plant are used to evaluate the performance of the proposed GA and PSO optimizers. The results indicate that, with a proper selection of the GA and PSO optimizers, the LECO approach is able to generate high-quality product-mix plans to meet the production demands in HMLV manufacturing environments.

An interactive genetic algorithm approach to MMIC low noise amplifier design using a layered encoding structure

In this paper, an interactive genetic algorithm (IGA) approach is developed to optimize design variables for a monolithic microwave integrated circuit (MMIC) low noise amplifier. A layered encoding structure is employed to the problem representation in genetic algorithm to allow human intervention in the circuit design variable tuning process. The MMIC amplifier design is synthesized using the Agilent Advance Design System (ADS), and the IGA is proposed to tune the design variables in order to meet multiple constraints and objectives such as noise figure, current and simulated power gain. The developed IGA is compared with other optimization techniques from ADS. The results showed that the IGA performs better in achieving most of the involved objectives.

Benzene adsorption at the aqueous (011) α-quartz interface : is surface flexibility important?

Atomistic simulations of molecular adsorption onto inorganic substrates under aqueous conditions can be used to guide the rational design of new materials, fabricated using biomimetic methods. The success of such work depends critically on the model used. Here, we investigate the impact of using a rigid structural model of the (0 1 1) ?-quartz surface, over a fully flexible model, on the calculated free energy change in the adsorption of a single molecule of benzene (a simple analogue of the amino acid phenylalanine) from liquid water. Subtle differences in the mobility of the adsorbate close to the surface result in the free energy of adsorption being overestimated by the rigid model, relative to the fully flexible case. Moreover, we find that the distribution of bound configurations of the adsorbate at their respective free energy minima is different between the two models.

Executive Systems Constrain the Flexibility of Online Control in Children During Goal-Directed Reaching

We examined the influence of inhibitory load on online motor control in children. A sample of 129 school children was tested: younger, mid-age, and older children. Online control was assessed using a double-step perturbation paradigm across three trail types: non-jump, jump, and anti-jump. Results show that mid-aged children were able to implement online adjustments to jump trials as quickly as older children, but their performance on anti-jump trials regressed toward younger children. This suggests that rapid unfolding of executive systems during middle childhood may constrain the flexibility with which online control can be implemented, particularly when inhibitory demands are imposed.

Developing an analytical framework to assess the uncertainty and flexibility mismatches across the supply chain

Purpose: The purpose of this paper is to discuss how decisions regarding organisational flexibility can be improved through targeted resource allocation, by focusing on the supply chain's level of uncertainty exposure. Specifically, the issue of where and in what ways flexibility has been incorporated across the organisation's supply chain is addressed. Design/methodology/approach: A two-phase methodology design based on literature review and case study was used. Using 83 journal articles in the areas of uncertainty and flexibility an analytical process for assessing uncertainty-flexibility mismatches was developed. Furthermore, results from ten interviews with senior/middle managers within the Australian manufacturing sector were used to provide preliminary insights on the usefulness and importance of the analytical process and its relationship with organisational practice. Findings: The paper emphasises the importance of having a systematic and encompassing view of uncertainty-flexibility mismatches across the supply chain, as well as the significance of socio-technical engagement. The paper both conceptually and empirically illustrates how, using a structured analytical process, flexibility requirements across the supply, process, control and demand segments of a supply chain might be assessed. A four-step analytical process was accordingly developed and, its application, usefulness and importance discussed using empirical data. Practical implications: The analytical process presented in this paper can assist managers to obtain a comprehensive overview of supply chain flexibility when dealing with situations involving uncertainty. This can facilitate and improve their decision-making with respect to prioritising attention on identified flexibility gaps in order to ensure stability of their performance. Originality/value: The paper presents a supply chain-wide discussion on the difficulties that uncertainty brings to organisations, and how organisational flexibility might serve to moderate those challenges for supply chain management. It discusses how to identify the flexibility gap and proposes an original analytical process for systematic assessment of uncertainty-flexibility mismatches. © Emerald Group Publishing Limited.

A syntactic two-component encoding model for the trajectories of human actions

Human actions have been widely studied for their potential application in various areas such as sports, pervasive patient monitoring, and rehabilitation. However, challenges still persist pertaining to determining the most useful ways to describe human actions at the sensor, then limb and complete action levels of representation and deriving important relations between these levels each involving their own atomic components. In this paper, we report on a motion encoder developed for the sensor level based on the need to distinguish between the shape of the sensor's trajectory and its temporal characteristics during execution. This distinction is critical as it provides a different encoding scheme than the usual velocity and acceleration measures which confound these two attributes of any motion. At the same time, we eliminate noise from sensors by comparing temporal and spatial indexing schemes and a number of optimal filtering models for robust encoding. Results demonstrate the benefits of spatial indexing and separating the shape and dynamics of a motion, as well as its ability to decompose complex motions into several atomic ones. Finally, we discuss how this specific type of sensor encoder bears on the derivation of limb and complete action descriptions.

Disrupting the encoding of misinformation delivered in closed specific and open presumptive questions

To determine whether the encoding of misleading information presented in two different types of leading questions (closed specific and open presumptive questions) could be disrupted, participants took part in a misinformation experiment. They viewed an event before answering questions that had a closed specific structure (e.g. “Did the robber have a shotgun that had a black barrel and a brown stock?”) or an open presumptive structure (e.g. “Tell me about the shotgun”). Half the questions contained misleading information and half did not. For some of the questions, participants completed a concurrent distractor task. Finally, they completed a recognition memory test about the original event. Results showed that the concurrent distractor task during the interview reduced the size of the misinformation effect for the closed specific questions, but had no impact on the misinformation effect for the open presumptive questions. The results suggest that open presumptive questions encourage participants to encode and process the misleading information more deeply than the closed specific questions. The misinformation effect was more difficult to disrupt with a concurrent task during the encoding of the misleading information for open presumptive questions than for closed specific questions. The implications for interviewing are discussed.

How Australian manufacturing firms perceive and understand the concepts of agility and flexibility in the supply chain

Purpose: The purpose of this paper is to address an important question which centres on investigating how do manufacturing businesses perceive and understand the concepts of agility and flexibility in their supply chains (SCs).

Intelligent facial emotion recognition using a layered encoding cascade optimization model

In this research, we propose a facial expression recognition system with a layered encoding cascade optimization model. Since generating an effective facial representation is a vital step to the success of facial emotion recognition, a modified Local Gabor Binary Pattern operator is first employed to derive a refined initial face representation and we then propose two evolutionary algorithms for feature optimization including (i) direct similarity and (ii) Pareto-based feature selection, under the layered cascade model. The direct similarity feature selection considers characteristics within the same emotion category that give the minimum within-class variation while the Pareto-based feature optimization focuses on features that best represent each expression category and at the same time provide the most distinctions to other expressions. Both a neural network and an ensemble classifier with weighted majority vote are implemented for the recognition of seven expressions based on the selected optimized features. The ensemble model also automatically updates itself with the most recent concepts in the data. Evaluated with the Cohn-Kanade database, our system achieves the best accuracies when the ensemble classifier is applied, and outperforms other research reported in the literature with 96.8% for direct similarity based optimization and 97.4% for the Pareto-based feature selection. Cross-database evaluation with frontal images from the MMI database has also been conducted to further prove system efficiency where it achieves 97.5% for Pareto-based approach and 90.7% for direct similarity-based feature selection and outperforms related research for MMI. When evaluated with 90° side-view images extracted from the videos of the MMI database, the system achieves superior performances with >80% accuracies for both optimization algorithms. Experiments with other weighting and meta-learning combination methods for the construction of ensembles are also explored with our proposed ensemble showing great adpativity to new test data stream for cross-database evaluation. In future work, we aim to incorporate other filtering techniques and evolutionary algorithms into the optimization models to further enhance the recognition performance.