Dynamic facility layout problem in footwear industry

Dynamic facility layout problem (DFLP) deals with the arrangement of machines in a site as to minimize the sum of materials handling and re-layout costs by considering multi periods. The DFLP studies in the literature provide several different algorithms and utilize the well known test problems to assess their performance. However, real life applications are overlooked. The industries such as footwear and clothing are prone to seasonal demand changes. Therefore, time horizons and layout/re-layout of the machines within the facility should be studied carefully. This study considers a footwear facility and several scenarios are generated by using the real life data. A clonal selection based algorithm is proposed to solve the real life DFLP. The performance of the algorithm, further the effect of time periods on solution quality and applicability of the results are tested and promising results are obtained.     

A new MM algorithm for constrained estimation in the proportional hazards model

The constrained estimation in Cox’s model for the right-censored survival data is studied and the asymptotic properties of the constrained estimators are derived by using the Lagrangian method based on Karush–Kuhn–Tucker conditions. A novel minorization–maximization (MM) algorithm is developed for calculating the maximum likelihood estimates of the regression coefficients subject to box or linear inequality restrictions in the proportional hazards model. The first M-step of the proposed MM algorithm is to construct a surrogate function with a diagonal Hessian matrix, which can be reached by utilizing the convexity of the exponential function and the negative logarithm function. The second M-step is to maximize the surrogate function with a diagonal Hessian matrix subject to box constraints, which is equivalent to separately maximizing several one-dimensional concave functions with a lower bound and an upper bound constraint, resulting in an explicit solution via a median function. The ascent property of the proposed MM algorithm under constraints is theoretically justified. Standard error estimation is also presented via a non-parametric bootstrap approach. Simulation studies are performed to compare the estimations with and without constraints. Two real data sets are used to illustrate the proposed methods.     

A New Time-Aware Collaborative Filtering Intelligent Recommendation System

Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy, this paper introduces project attribute fuzzy matrix, measures the project relevance through fuzzy clustering method, and classifies all project attributes. Then, the weight of the project relevance is introduced in the user similarity calculation, so that the nearest neighbor search is more accurate. In the prediction scoring section, considering the change of user interest with time, it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation, so that the newer evaluation information in the system has a relatively large weight. The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.     

低碳时变城配送车辆路径-发车调度集成优化

针对交通拥挤环境下日益增长的城市配送需求，通过分析时序依赖对成本和碳排放的影响，引入车辆在节点等待和离散调度策略，研究基于时序依赖的低碳城市配送车辆路径与离散调度问题。为求解该问题，设计基于遗传算法与局部搜索相结合的混合进化搜索算法对模型求解，用积极的局部搜索机制替代随机的变异操作，并通过可行解构造算法、变概率交叉和多种局部搜索策略来提高算法求解质量和求解效率。通过对比仿真实验对算法和模型的有效性进行了验证。     

Remaining useful life prediction for degrading systems with random shocks considering measurement uncertainty

Degradation data have been widely used for the remaining useful life (RUL) prediction of systems. Most existing works apply a preset model to capture the degradation process and focus on the degradation process without shocks or constant shock effects. More generally, the actual degradation path is unobservable due to the existence of measurement uncertainty, which interferes with the determination of the degradation model. Besides, the effect of random shocks is usually fluctuating. Given these problems, a general degradation model with the random shock fluctuant effects considering the measurement uncertainty is first developed to describe the degradation process, and a two-step approach combining the arithmetic average filter and the Bayesian information criterion is adopted to identify the degradation path. Subsequently, the transfer processes of the actual degradation state and the abrupt change caused by shocks are depicted using a two-dimensional state-space model, and an expectation-maximization algorithm combined with the particle filtering is developed for parameter estimation. Furthermore, the explicit solution of RUL distribution is obtained when only considering harmful shocks, while a simulation method of RUL distribution is provided when both harmful and beneficial shocks exist. Finally, the effectiveness of the proposed method is verified by a numerical example and two practical case studies.     

Model-Based Design Space Exploration for FPGA-based Image Processing Applications Employing Parameterizable Approximations

The increasing demand for low power consumption and high computational performance is outpacing available technological improvements in embedded systems. Approximate computing is a novel design paradigm trying to bridge this gap by leveraging the inherent error resilience of certain applications and trading in quality to achieve reductions in resource usage. Numerous approximation methods have emerged in this research field. While these methods are commonly demonstrated in isolation, their combination can increase the achieved benefits in complex systems. However, the propagation of errors throughout the system necessitates a global optimization of parameters, leading to an exponentially growing design space. Additionally, the parameterization of approximated components must consider potential cross-dependencies between them. This work proposes a systematic approach to integrate and optimally configure parameterizable approximate components in FPGA-based applications, focusing on low-level but high-bandwidth image processing pipelines. The design space is explored by a multi-objective genetic algorithm which takes parameter dependencies between different components into account. During the exploration, appropriate models are used to estimate the quality-resource trade-off for probed solutions without the need for time-consuming synthesis. We demonstrate and evaluate the effectiveness of our approach on two image processing applications that employ multiple approximations. The experimental results show that the proposed methods are able to produce a wide range of Pareto-optimal solutions, offering various choices regarding the desired quality-resource trade-off.     

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance.     

Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework

Predictive maintenance is one of the important technical means to guarantee and improve the normal industrial production. The existing bottlenecks for popularization and application are analyzed. In order to solve these problems, a cooperative awareness and interconnection framework across multiple organizations for total factors that affect prediction maintenance decision-making is discussed. Initially, the structure and operation mechanism of this framework are proposed. It is designed to support the sharing of data, knowledge and resources. As a key supporting technology, the digital twin is also integrated into it to improve the accuracy of fault diagnosis and prediction and support making a maintenance plan with higher accuracy and reliability. Then, under this framework, an integrated mathematical programming model is established with considering the parameter uncertainty and an NSGA-II hybrid algorithm is utilized to solve it. Moreover, an adjustment strategy for a maintenance plan is discussed in response to the dynamic characteristics of the actual maintenance environment. Finally, a case, prediction maintenance decision-making for bearings in grinding rolls of the large vertical mill, is studied. Analysis results verify the advantage of the integrated solving mechanism based on the proposed framework. The framework and integrated decision-making approach can guide the implementation of predictive maintenance with higher accuracy and reliability for industrial enterprises.     

A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.     

Modeling,design, and optimization of a cost‐effective and reliable hybrid renewable energy system integrated with desalination using the division algorithm

People in the Middle East are facing the problem of freshwater shortages. This problem is more intense for a remote region, which has no access to the power grid. The use of seawater desalination technology integrated with the generated energy unit by renewable energy sources could help overcome this problem. In this study, we refer a seawater reverse osmosis desalination (SWROD) plant with a capacity of 1.5 m³/h used on Larak Island, Iran. Moreover, for producing fresh water and meet the load demand of the SWROD plant, three different stand‐alone hybrid renewable energy systems (SAHRES), namely wind turbine (WT)/photovoltaic (PV)/battery bank storage (BBS), PV/BBS, and WT/BBS are modeled and investigated. The optimization problem was coded in MATLAB software. Furthermore, the optimized results were obtained by the division algorithm (DA). The DA has been developed to solve the sizing problem of three SAHRES configurations by considering the object function's constraints. These results show that this improved algorithm has been simpler, more precise, faster, and more flexible than a genetic algorithm (GA) in solving problems. Moreover, the minimum total life cycle cost (TLCC = 243 763$), with minimum loss of power supply probability (LPSP = 0%) and maximum reliability, was related to the WT/PV/BBS configuration. WT/PV/BBS is also the best configuration to use less battery as a backup unit (69 units). The batteries in this configuration have a longer life cycle (maximum average of annual battery charge level) than two other configurations (93.86%). Moreover, the optimized results have shown that utilizing the configuration of WT/PV/BBS could lead to attaining a cost‐effective and green (without environmental pollution) SAHRES, with high reliability for remote areas, with appropriate potential of wind and solar irradiance.