HETEROGENEOUS SERVER RETRIAL QUEUEING MODEL WITH FEEDBACK AND WORKING VACATION USING ARTIFICIAL BEE COLONY OPTIMIZATION ALGORITHM

This research delves into the dynamics of a retrial queueing system featuring heterogeneous servers with intermittent availability, incorporating feedback and working vacation mechanisms. Employing a matrix geometric approach, this study establishes the steady-state probability distribution for the queue size in this complex heterogeneous service model. Additionally, a range of system performance metrics is developed, alongside the formulation of a cost function to evaluate decision variable optimization within the service system. The Artificial Bee Colony (ABC) optimization algorithm is harnessed to determine service rates that minimize the overall cost. This work includes numerical examples and sensitivity analyses to validate the model's effectiveness. Also, a comparison between the numerical findings and the neuro-fuzzy results has been examined by the adaptive neuro fuzzy interface system (ANFIS)

Inventory Models for Manufacturing Process with Reverse Supply Chain

Technology innovation leading to development of new products and enhancement of features in existing products is happening at a faster pace than ever. This trend has resulted in gross increase in use of new materials and decreased customers‘ interest in relatively older products leading to the deteriorating conditions of the environment due to the reduction of non-renewable resources and steady increase in the land fill of waste. This has forced organizations and communities to consider recovery alternatives such as reuse, repair, recycle, refurbish, remanufacture and cannibalize, rather than discarding of the products after end of life. Products are retuned back or become redundant because either they do not function properly or functionally they become obsolete. The sources of these returns are Manufacturing returns, Distribution returns and Customer returns. The product recovery options in reverse supply are Repair, Refurbish, Re-manufacture, Cannibalize and Recycle. The main difference between the options is in the reprocessing techniques. Where Repair, refurbishing, and remanufacturing are involved in the up gradation of the used products in quality and/or technology with a difference with respect to the degree of up gradation(repair involves the least, and remanufacturing the largest),the cannibalization and recycling are involved in using parts ,components and materials of the used products. Although much is being disused on the different recovery options still a lot of research remains to be done for improvement of the currently available techniques. In this context the present work focuses on remanufacturing option of recovery process for return items which is the most advanced and environmentally friendly production processes in use. Therefore the broad objectives of the present work are to deal with the different models of remanufacturing either new or existing for adding new features to it and making it simple and more user oriented, to develop deterministic models using direct manufacturing and remanufacturing for profit optimization, to develop and deal with probabilistic models of inventory with demand fluctuation using direct manufacturing and remanufacturing.to select and recommend a tool for predicting various critical parameters associated with the Reverse supply chain (RSC).to make these models usable to achieve maximum advantages by reutilization of resources integrating the upstream and downstream chains. For the effective implementation of remanufacturing in Reverse supply chain, the entire work has been arranged in different chapters to present the distinct aspects of the research. Models are developed with special reference to remanufacturing. These models proposed helped in minimizing the gaps existing in the RSC in the v present scenario. The different models proposed for RSC are discussed on the basis of deterministic and probabilistic approaches. Although a lot of assumptions are intentionally made to make the models deterministic, still these models have its own identity in satisfying the needs of RSC. Two models are being discussed under deterministic approach. These models tries to find out the amount of new product supply to the market, the amount of remanufactured products supply to the market, the amount of products returned from the market and the amount of waste. Pertinent data from industry have been considered to prepare the models. The model variables are tested with adaptive-network-based fuzzy inference system (ANFIS), where the testing of the actual out come and desired outcome is done by using ANFIS. One of the proposed models is picked up to predict the critical parameters associated with RSC using remanufacturing. Although the models dealing with the deterministic RSC models are simple still it becomes difficult to deal with a situation where there is a fluctuation of demand in the market, which is a common phenomenon. Therefore, it becomes inevitable to use the probabilistic approach for sorting out it. The aim is to deal with probabilistic models of inventory and models are proposed where the uncertainty due to fluctuation of demand and uncertainty in the return rate of used products is taken care of by using the safety stock. The determination of the safety stock is done on the basis of service level approach. The model variables are optimized using mathematical models considering the profit maximization. The contribution of the present work is directed towards the environmental benefits. The manufacture of durable goods is one of the major contributors to the GNP of all developed countries. It employs large amounts of human resources, raw materials and energy. The raw materials and energy in the production of durable goods have been continually depleted. Many durable products are disposed in landfills at the end of their useful lives as well. The landfill space has been decreasing and the price charged by the landfills is increasing at a faster rate. This becomes an environmental concern. Remanufacturing, as discussed earlier is one of the predominant product recovery option for the return products. With respect to quality it is considered to be as good as new ones but with a lower cost of conversion. Therefore, focusing on remanufacturing option of product recovery not only decreases the depletion rate of virgin raw materials and rate of land fill but also contributes much towards the GDP as well as GNP. The models proposed in this work are simple and can be practically implemented to get benefits from the return items and still satisfying the market demand for sustainable production

Quasi-optimization of Neuro-fuzzy Expert Systems using Asymptotic Least-squares and Modified Radial Basis Function Models: Intelligent Planning of Operational Research Problems

The uncertainty found in many industrialization systems poses a significant challenge; partic-ularly in modelling production planning and optimizing manufacturing flow. In aggregate production planning, a key requirement is an ability to accurately predict demand from a range of influencing factors, such as consumption for example. Accurately building such causal models can be problematic if significant uncertainties are present, such as when the data are fuzzy, uncertain, fluctuate and are non-linear. AI models, such as Adaptive Neuro-Fuzzy Inference Systems (ANFIS), can cope with this better than most but even these well-established approaches fail if the data is scarce, poorly scaled and noisy. ANFIS is a combination of two approaches; Sugeno-type Fuzzy Inference System (FIS)and Artificial Neural Networks (ANN). Two sets of parameters are required to define the model: premise parameters and consequent parameters. Together, they ensure that the correct number and shape of membership functions are used and combined to produce reliable outputs. However, optimally determining values for these parameters can only happen if there are enough data samples representing the problem space to ensure that the method can converge. Mitigation strategies are suggested in the literature, such as fixing the premise parameters to avoid over-fitting, but, for many practitioners, this is not an adequate solution, as their expertise lies in the application domain, not in the AI domain. The work presented here is motivated by a real-world challenge in modelling and pre-dicting demand for the gasoline industry in Iraq, an application where both the quality and quantity of the training data can significantly affect prediction accuracy. To overcome data scarcity, we propose novel data expansion algorithms that are able to augment the original data with new samples drawn from the same distribution. By using a combination of carefully chosen and suitably modified radial basis function models, we show how robust methods can overcome problems of over-smoothing at boundary values and turning points. We further show how transformed least-squares (TLS) approximation of the data can be constructed to asymptotically bound the effect of outliers to enable accurate data expansion to take place. Though the problem of scaling/normalization is well understood in some AI applications, we assess the impact on model accuracy for two specific scaling techniques. By comparing and contrasting a range of data scaling and data expansion methods, we can evaluate their effectiveness in reducing prediction error. Throughout this work, the various methods are explained and expanded upon using the case study drawn from the oil and gas industry in Iraq which focuses on the accurate prediction of yearly gasoline consumption. This case study, and others are used to demonstrate, empirically, the effectiveness of the approaches presented when compared to current state of the art. Finally, we present a tool developed in Matlab to allow practitioners to experiment with all methods and options presented in this work

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

The Effect of Uncertainties on Multi-Echelon Serial Supply Chains

Uncertainties are the major concerns in supply chain because existence of uncertainties degrades the performance of supply chain. Hence, business executives need to seriously focus towards controlling the effect of uncertainty on supply chain performance. In this study, a four echelon serial supply chain employed with reorder-point order-up-to level inventory replenishment (s, S) policy is modeled using system dynamics approach. Manufacturing systems adopting make-to-stock (MTS) and assemble-to-stock (ATS) manufacturing policy and operating under uncertain environment are modelled through system dynamics approach. A serial two-stage MTS manufacturing system is modelled through system dynamics approach and the behaviour is studied under the influence of uncertainty in demand, lead time, supplier’s acquisition rate, processing time and delay due to machine failure. Two different improved demand forecasting models are proposed to enhance the forecasting accuracy and reduce the bullwhip effect (BWE) and net-stock amplification (NSAmp). The first proposed model is the integrated approach of autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) model denoted as ARIMA-GARCH to overcome the problem related to heteroskedastic nature of demand series. Second proposed model is the integrated approach of discrete wavelet transformation (DWT) and intelligence technique such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), least square support vector machine (LSSVM) and multi-gene genetic programming (MGGP) to deal with non-linear, non-stationary demand series. Simulation study of multi-echelon supply chain indicates that target inventory significantly influence the BWE and it can be reduced through keeping target inventory at low level when there is low uncertainty in demand and lead time. From the analysis of manufacturing supply chain, it is observed that backlog at manufacturer’s end is significantly influenced by uncertainty in processing time and delay due to machine failure. The backup strategy adopted in manufacturing supply chain reveals that performance of manufacturing system is highly affected when uncertainty in supplier’s acquisition rate increases. The study proves that maintaining high service level at the bottom echelon is required to achieve high service level at the upper echelon of a supply chain. From the forecasting study, it is found that performance of the ARIMA-GARCH model outperforms the ARIMA model. Further, it is proved through case-study example

Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development

The protection and maintenance of environmental resources for future generations require responsible interaction between humans and the environment in order to avoid wasting natural resources. According to an ancient Native American proverb, “We do not inherit the Earth from our ancestors; we borrow it from our children.” This indigenous wisdom has the potential to play a significant role in defining environmental sustainability. Recent technological advances could sustain humankind and allow for comfortable living. However, not all of these advancements have the potential to protect the environment for future generations. Developing societies and maintaining the sustainability of the ecosystem require appropriate wisdom, technology, and management collaboration. This book is a collection of 19 important articles (15 research articles, 3 review papers, and 1 editorial) that were published in the Special Issue of the journal Sustainability entitled “Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development” during 2021-2022.addresses the policymakers and decision-makers who are willing to develop societies that practice environmental sustainability, by collecting the most recent contributions on the appropriate wisdom, technology, and management regarding the different aspects of a community that can retain environmental sustainability