Search results for: optimization framework

Authors: Bharat Singh Om Prakash Vyas

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Now a day’s applications deal with High Dimensional Data have tremendously used in the popular areas. To tackle with such kind of data various approached has been developed by researchers in the last few decades. To tackle with such kind of data various approached has been developed by researchers in the last few decades. One of the problems with the NMF approaches, its randomized valued could not provide absolute optimization in limited iteration, but having local optimization. Due to this, we have proposed a new approach that considers the initial values of the decomposition to tackle the issues of computationally expensive. We have devised an algorithm for initializing the values of the decomposed matrix based on the PSO (Particle Swarm Optimization). Through the experimental result, we will show the proposed method converse very fast in comparison to other row rank approximation like simple NMF multiplicative, and ACLS techniques.

Keywords: ALS, NMF, high dimensional data, RMSE

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Authors: Nuray Gökçek Karaca, Berrin Gökçek

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Employers occupational safety and health training obligations are regulated in 89/391/EEC Framework Directive and also in 6331 numbered Occupational Health and Safety Law in Turkey. The main objective of this research is to determine and evaluate the employers’ occupational health and safety training obligations in Framework Directive in comparison with the 6331 numbered Occupational Health and Safety Law and to examine training principles in Turkey. For this purpose, employers’ occupational health and safety training obligations examined in Framework Directive and Occupational Health and Safety Law. This study carried out through comparative scanning model and literature model. The research data were collected through European Agency and ministry legislations. As a result, employers’ occupational health and safety training obligations in the 6331 numbered Occupational Health and Safety Law are compatible with the 89/391/EEC numbered Framework Directive and training principles are determined by in different ways like the trained workers, training issues, training period, training time, and trainers. In this study, employers’ training obligations are evaluated in detail.

Keywords: directive, occupational health and safety, training, work accidences

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Authors: Arbnor Pajaziti, Hasan Cana

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In this paper, the structural genetic algorithm is used to optimize the neural network to control the joint movements of robotic arm. The robotic arm has also been modeled in 3D and simulated in real-time in MATLAB. It is found that Neural Networks provide a simple and effective way to control the robot tasks. Computer simulation examples are given to illustrate the significance of this method. By combining Genetic Algorithm optimization method and Neural Networks for the given robotic arm with 5 D.O.F. the obtained the results shown that the base joint movements overshooting time without controller was about 0.5 seconds, while with Neural Network controller (optimized with Genetic Algorithm) was about 0.2 seconds, and the population size of 150 gave best results.

Keywords: robotic arm, neural network, genetic algorithm, optimization

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Authors: Jennifer E. Lynette

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This study describes the development process of a methodological framework for quality standards used to measure the efficiency and quality of response efforts of trained personnel at emergency events. This paper describes the techniques used to develop the initial framework and its potential application to professions under the broader field of emergency management. The example described in detail in this paper applies the framework specifically to fire response activities by firefighters. Within the quality standards framework, the fire response process is chronologically mapped. Individual variables within the sequence of events are identified. Through in-person data collection, questionnaires, interviews, and the expansion of the incident reporting system, this study identifies and categorizes previously unrecorded variables involved in the response phase of a fire. Following a data analysis of each variable using a quantitative or qualitative assessment, the variables are ranked pertaining to the magnitude of their impact to the event outcome. Among others, key indicators of quality performance in the analysis involve decision communication, resource utilization, response techniques, and response time. Through the application of this framework and subsequent utilization of quality standards indicators, there is potential to increase efficiency in the response phase of an emergency event; thereby saving additional lives, property, and resources.

Keywords: emergency management, fire, quality standards, response

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Authors: Tewfik Bouchaour, Salah Hamri, Yasmina Houda Bendahma, Ulrich Maschke

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Stimuli-responsive materials based on UV crosslinked acrylic polymer networks are fabricated. A various kinds of polymeric systems, hydrophilic polymers based on 2-Hydroxyethyl methacrylate have been widely studied because of their ability to simulate biological tissues, which leads to many applications. The acrylic polymer network PHEMA developed by UV photopolymerization has been used for dye retention. For these so-called smart materials, the properties change in response to an external stimulus. In this contribution, we report the influence of some parameters (initial composition, temperature, and nature of components) in the properties of final materials. Optimization of different parameters is examined by experimental design.

Keywords: UV photo-polymerization, PHEMA, external stimulus, optimization

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Authors: Joseph C. Chen, Venkata Karthik Jakka

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The main objective of this research is to optimize tensile strength and dimensional accuracy in injection molding processes using Taguchi Parameter Design. An L16 orthogonal array (OA) is used in Taguchi experimental design with five control factors at four levels each and with non-controllable factor vibration. A total of 32 experiments were designed to obtain the optimal parameter setting for the process. The optimal parameters identified for the shrinkage are shot volume, 1.7 cubic inch (A4); mold term temperature, 130 ºF (B1); hold pressure, 3200 Psi (C4); injection speed, 0.61 inch3/sec (D2); and hold time of 14 seconds (E2). The optimal parameters identified for the tensile strength are shot volume, 1.7 cubic inch (A4); mold temperature, 160 ºF (B4); hold pressure, 3100 Psi (C3); injection speed, 0.69 inch3/sec (D4); and hold time of 14 seconds (E2). The Taguchi-based optimization framework was systematically and successfully implemented to obtain an adjusted optimal setting in this research. The mean shrinkage of the confirmation runs is 0.0031%, and the tensile strength value was found to be 3148.1 psi. Both outcomes are far better results from the baseline, and defects have been further reduced in injection molding processes.

Keywords: injection molding processes, taguchi parameter design, tensile strength, high-density polyethylene(HDPE)

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Authors: A. Nuchitprasittichai, N. Lerdritsirikoon, T. Khamsing

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Simulation modeling can be used to solve real world problems. It provides an understanding of a complex system. To develop a simplified model of process simulation, a suitable experimental design is required to be able to capture surface characteristics. This paper presents the experimental design and algorithm used to model the process simulation for optimization problem. The CO₂ liquefaction based on external refrigeration with two refrigeration circuits was used as a simulation case study. Latin Hypercube Sampling (LHS) was purposed to combine with existing Central Composite Design (CCD) samples to improve the performance of CCD in generating the second order model of the system. The second order model was then used as the objective function of the optimization problem. The results showed that adding LHS samples to CCD samples can help capture surface curvature characteristics. Suitable number of LHS sample points should be considered in order to get an accurate nonlinear model with minimum number of simulation experiments.

Keywords: central composite design, CO2 liquefaction, latin hypercube sampling, simulation-based optimization

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Authors: Devatha Kalyan Kumar, R. Poovarasan

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In this paper, we have taken certain important factors and health parameters of diabetes patients especially among children by birth (pediatric congenital) where using the above three metrics methods we are going to assess the importance of each attributes in the dataset and thereby determining the most highly responsible and co-related attribute causing diabetics among young patients. We use cost optimization, control chart and Spearmen methodologies for the real-time application of finding the data efficiency in this diabetes dataset. The Spearmen methodology is the correlation methodologies used in software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher, then there is a higher chance of occurrence of the risk in the software. With the use of control; chart mean, variance and standard deviation of data are calculated. With the use of Cost optimization model, we find to optimize the variables. Hence we choose the Spearmen, control chart and cost optimization methods to assess the data efficiency in diabetes datasets.

Keywords: correlation, congenital diabetics, linear relationship, monotonic function, ranking samples, pediatric

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Authors: Reyhaneh Soltanshah, Hamid R. Zarandi

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In order to be more efficient in industries that are related to computer systems, software testing is necessary despite spending time and money. In the embedded system software test, complete knowledge of the embedded system architecture is necessary to avoid significant costs and damages. Software tests increase the price of the final product. The aim of this article is to provide a method to reduce time and cost in tests based on program structure. First, a complete review of eleven white box test methods based on ISO/IEC/IEEE 29119 2015 and 2021 versions has been done. The proposed algorithm is designed using two versions of the 29119 standards, and some white-box testing methods that are expensive or have little coverage have been removed. On each of the functions, white box test methods were applied according to the 29119 standard and then the proposed algorithm was implemented on the functions. To speed up the implementation of the proposed method, the Unity framework has been used with some changes. Unity framework can be used in embedded software testing due to its open source and ability to implement white box test methods. The test items obtained from these two approaches were evaluated using a mathematical ratio, which in various software mining reduced between 50% and 80% of the test cost and reached the desired result with the minimum number of test items.

Keywords: embedded software, reduce costs, software testing, white-box testing

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Authors: S. Curteanu, F. Leon, M. Gavrilescu, S. A. Floria

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Optimization algorithms inspired from human behavior were applied in this approach, associated with neural networks models. The algorithms belong to human behaviors of learning and cooperation and human competitive behavior classes. For the first class, the main strategies include: random learning, individual learning, and social learning, and the selected algorithms are: simplified human learning optimization (SHLO), social learning optimization (SLO), and teaching-learning based optimization (TLBO). For the second class, the concept of learning is associated with competitiveness, and the selected algorithms are sports-inspired algorithms (with Football Game Algorithm, FGA and Volleyball Premier League, VPL) and Imperialist Competitive Algorithm (ICA). A real process, the synthesis of polyacrylamide-based multicomponent hydrogels, where some parameters are difficult to obtain experimentally, is considered as a case study. Reaction yield and swelling degree are predicted as a function of reaction conditions (acrylamide concentration, initiator concentration, crosslinking agent concentration, temperature, reaction time, and amount of inclusion polymer, which could be starch, poly(vinyl alcohol) or gelatin). The experimental results contain 175 data. Artificial neural networks are obtained in optimal form with biologically inspired algorithm; the optimization being perform at two level: structural and parametric. Feedforward neural networks with one or two hidden layers and no more than 25 neurons in intermediate layers were obtained with values of correlation coefficient in the validation phase over 0.90. The best results were obtained with TLBO algorithm, correlation coefficient being 0.94 for an MLP(6:9:20:2) – a feedforward neural network with two hidden layers and 9 and 20, respectively, intermediate neurons. Good results obtained prove the efficiency of the optimization algorithms. More than the good results, what is important in this approach is the simulation methodology, including neural networks and optimization biologically inspired algorithms, which provide satisfactory results. In addition, the methodology developed in this approach is general and has flexibility so that it can be easily adapted to other processes in association with different types of models.

Keywords: artificial neural networks, human behaviors of learning and cooperation, human competitive behavior, optimization algorithms

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Authors: Gamal Abd El-Nasser A. Said, El-Sayed M. El-Horbaty

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Container handling problems at container terminals are NP-hard problems. This paper presents an approach using discrete-event simulation modeling to optimize solution for storage space allocation problem, taking into account all various interrelated container terminal handling activities. The proposed approach is applied on a real case study data of container terminal at Alexandria port. The computational results show the effectiveness of the proposed model for optimization of storage space allocation in container terminal where 54% reduction in containers handling time in port is achieved.

Keywords: container terminal, discrete-event simulation, optimization, storage space allocation

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Authors: A. Boudjemai, A. Zafrane, R. Hocine

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Honeycomb sandwich panels are increasingly used in the construction of space vehicles because of their outstanding strength, stiffness and light weight properties. However, the use of honeycomb sandwich plates comes with difficulties in the design process as a result of the large number of design variables involved, including composite material design, shape and geometry. Hence, this work deals with the presentation of an optimal design of hexagonal honeycomb sandwich structures subjected to space environment. The optimization process is performed using a set of algorithms including the gravitational search algorithm (GSA). Numerical results are obtained and presented for a set of algorithms. The results obtained by the GSA algorithm are much better compared to other algorithms used in this study.

Keywords: optimization, gravitational search algorithm, genetic algorithm, honeycomb plate

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Authors: Badr M. Alshammari, T. Guesmi

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In recent years, the combined economic and emission power dispatch is one of the main problems of electrical power system. It aims to schedule the power generation of generators in order to minimize cost production and emission of harmful gases caused by fossil-fueled thermal units such as CO, CO₂, NO_x, and SO₂. To solve this complicated multi-objective problem, an improved version of the particle swarm optimization technique that includes non-dominated sorting concept has been proposed. Valve point loading effects and system losses have been considered. The three-unit and ten-unit benchmark systems have been used to show the effectiveness of the suggested optimization technique for solving this kind of nonconvex problem. The simulation results have been compared with those obtained using genetic algorithm based method. Comparison results show that the proposed approach can provide a higher quality solution with better performance.

Keywords: power dispatch, valve point loading effects, multiobjective optimization, Pareto solutions

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Authors: M. Boudjerda, B. Touaibia, M. K. Mihoubi

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In Algeria agricultural irrigation is the primary water consuming sector followed by the domestic and industrial sectors. Economic development in the last decade has weighed heavily on water resources which are relatively limited and gradually decreasing to the detriment of agriculture. The research presented in this paper focuses on the optimization of irrigation water demand. Dynamic Programming-Neural Network (DPNN) method is applied to investigate reservoir optimization. The optimal operation rule is formulated to minimize the gap between water release and water irrigation demand. As a case study, Foum El-Gherza dam’s reservoir system in south of Algeria has been selected to examine our proposed optimization model. The application of DPNN method allowed increasing the satisfaction rate (SR) from 12.32% to 55%. In addition, the operation rule generated showed more reliable and resilience operation for the examined case study.

Keywords: water management, agricultural demand, dam and reservoir operation, Foum el-Gherza dam, dynamic programming, artificial neural network

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Authors: Abdul Rawuf Hussein, Rusnani Abdul Kadir, Mona Adlina Binti Adanan

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Guidance program has been an essential approach in helping professions from many institutions of learning as well as communities, organizations, and clinical settings. Although the term varies depending on the approaches, objectives, and theories, the core and central element is typically developmental in nature. In this conceptual paper, the researcher will review literature on the concept of group guidance, its impact on students’ and individual’s development, developing a guidance module and proposing a synthesised framework for group guidance program.

Keywords: concept, framework, group guidance, module development

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Authors: Mustafa Sengul, Enes Sahin, Sertac Arslan

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Pumpjets are one of the marine propulsion systems frequently used in underwater vehicles nowadays. The reasons for frequent use of pumpjet as a propulsion system are that it has higher relative efficiency at high speeds, better cavitation, and acoustic performance than its rivals. Pumpjets are composed of rotor, stator, and duct, and there are two different types of pumpjet configurations depending on the desired hydrodynamic characteristic, which are with accelerating and decelerating duct. Pumpjet with an accelerating channel is used at cargo ships where it works at low speeds and high loading conditions. The working principle of this type of pumpjet is to maximize the thrust by reducing the pressure of the fluid through the channel and throwing the fluid out from the channel with high momentum. On the other hand, for decelerating ducted pumpjets, the main consideration is to prevent the occurrence of the cavitation phenomenon by increasing the pressure of the fluid about the rotor region. By postponing the cavitation, acoustic noise naturally falls down, so decelerating ducted systems are used at noise-sensitive vehicle systems where acoustic performance is vital. Therefore, duct design becomes a crucial step during pumpjet design. This study, it is aimed to optimize the duct geometry of a decelerating ducted pumpjet for a highly speed underwater vehicle by using proper optimization tools. The target output of this optimization process is to obtain a duct design that maximizes fluid pressure around the rotor region to prevent from cavitation and minimizes drag force. There are two main optimization techniques that could be utilized for this process which are parameter-based optimization and gradient-based optimization. While parameter-based algorithm offers more major changes in interested geometry, which makes user to get close desired geometry, gradient-based algorithm deals with minor local changes in geometry. In parameter-based optimization, the geometry should be parameterized first. Then, by defining upper and lower limits for these parameters, design space is created. Finally, by proper optimization code and analysis, optimum geometry is obtained from this design space. For this duct optimization study, a commercial codedparameter-based optimization algorithm is used. To parameterize the geometry, duct is represented with b-spline curves and control points. These control points have x and y coordinates limits. By regarding these limits, design space is generated.

Keywords: pumpjet, decelerating duct design, optimization, underwater vehicles, cavitation, drag minimization

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Authors: Stojan Kravanja, Andrej Ivanič, Tomaž Žula

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This contribution focuses on structural optimization in civil engineering using mixed integer non-linear programming (MINLP). MINLP is characterized as a versatile method that can handle both continuous and discrete optimization variables simultaneously. Continuous variables are used to optimize parameters such as dimensions, stresses, masses, or costs, while discrete variables represent binary decisions to determine the presence or absence of structural elements within a structure while also calculating discrete materials and standard sections. The optimization process is divided into three main steps. First, a mechanical superstructure with a variety of different topology-, material- and dimensional alternatives. Next, a MINLP model is formulated to encapsulate the optimization problem. Finally, an optimal solution is searched in the direction of the defined objective function while respecting the structural constraints. The economic or mass objective function of the material and labor costs of a structure is subjected to the constraints known from structural analysis. These constraints include equations for the calculation of internal forces and deflections, as well as equations for the dimensioning of structural components (in accordance with the Eurocode standards). Given the complex, non-convex and highly non-linear nature of optimization problems in civil engineering, the Modified Outer-Approximation/Equality-Relaxation (OA/ER) algorithm is applied. This algorithm alternately solves subproblems of non-linear programming (NLP) and main problems of mixed-integer linear programming (MILP), in this way gradually refines the solution space up to the optimal solution. The NLP corresponds to the continuous optimization of parameters (with fixed topology, discrete materials and standard dimensions, all determined in the previous MILP), while the MILP involves a global approximation to the superstructure of alternatives, where a new topology, materials, standard dimensions are determined. The optimization of a convex problem is stopped when the MILP solution becomes better than the best NLP solution. Otherwise, it is terminated when the NLP solution can no longer be improved. While the OA/ER algorithm, like all other algorithms, does not guarantee global optimality due to the presence of non-convex functions, various modifications, including convexity tests, are implemented in OA/ER to mitigate these difficulties. The effectiveness of the proposed MINLP approach is demonstrated by its application to various structural optimization tasks, such as mass optimization of steel buildings, cost optimization of timber halls, composite floor systems, etc. Special optimization models have been developed for the optimization of these structures. The MINLP optimizations, facilitated by the user-friendly software package MIPSYN, provide insights into a mass or cost-optimal solutions, optimal structural topologies, optimal material and standard cross-section choices, confirming MINLP as a valuable method for the optimization of structures in civil engineering.

Keywords: MINLP, mixed-integer non-linear programming, optimization, structures

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Authors: Emre Ipekci Cetin, Ebru Tarcan Icigen

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Personnel selection process is considered as one of the most important and most difficult issues in human resources management. At the stage of personnel selection, the applicants are handled according to certain criteria, the candidates are dealt with, and efforts are made to select the most appropriate candidate. However, this process can be more complicated in terms of the managers who will carry out the staff selection process. Candidates should be evaluated according to different criteria such as work experience, education, foreign language level etc. It is crucial that a rational selection process is carried out by considering all the criteria in an integrated structure. In this study, the problem of choosing the front office manager of a 5 star accommodation enterprise operating in Antalya is addressed by using multi-criteria decision-making methods. In this context, SWARA (Step-wise weight assessment ratio analysis) and MOORA (Multi-Objective Optimization on the basis of ratio analysis) methods, which have relatively few applications when compared with other methods, have been used together. Firstly SWARA method was used to calculate the weights of the criteria and subcriteria that were determined by the business. After the weights of the criteria were obtained, the MOORA method was used to rank the candidates using the ratio system and the reference point approach. Recruitment processes differ from sector to sector, from operation to operation. There are a number of criteria that must be taken into consideration by businesses in accordance with the structure of each sector. It is of utmost importance that all candidates are evaluated objectively in the framework of these criteria, after these criteria have been carefully selected in the selection of suitable candidates for employment. In the study, staff selection process was handled by using SWARA and MOORA methods together.

Keywords: accommodation establishments, human resource management, multi-objective optimization on the basis of ratio analysis, multi-criteria decision making, step-wise weight assessment ratio analysis

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Authors: Flur R. Ismagilov, Irek Kh. Khayrullin, Vyacheslav E. Vavilov, Ruslan D. Karimov, Anton S. Gorbunov, Danis R. Farrakhov

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The paper presents another structural scheme of high-temperature starter-generator with external rotor to be installed on High Pressure Shaft (HPS) of aircraft engines (AE) to implement More Electrical Engine concept. The basic materials to make this starter-generator (SG) were selected and justified. Multi-criteria optimization of the developed structural scheme was performed using a genetic algorithm and Pareto method. The optimum (in Pareto terms) active length and thickness of permanent magnets of SG were selected as a result of the optimization. Using the dimensions obtained, allowed to reduce the weight of the designed SG by 10 kg relative to a base option at constant thermal loads. Multidisciplinary computer simulation was performed on the basis of the optimum geometric dimensions, which proved performance efficiency of the design. We further plan to make a full-scale sample of SG of HPS and publish the results of its experimental research.

Keywords: high-temperature starter-generator, more electrical engine, multi-criteria optimization, permanent magnet

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Authors: M. Gulam Kibria, Shourav Ahmed, Kais Zaman

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In system analysis, the information on the uncertain input variables cause uncertainty in the system responses. Different probabilistic approaches for uncertainty representation and propagation in such cases exist in the literature. Different uncertainty representation approaches result in different outputs. Some of the approaches might result in a better estimation of system response than the other approaches. The NASA Langley Multidisciplinary Uncertainty Quantification Challenge (MUQC) has posed challenges about uncertainty quantification. Subproblem A, the uncertainty characterization subproblem, of the challenge posed is addressed in this study. In this subproblem, the challenge is to gather knowledge about unknown model inputs which have inherent aleatory and epistemic uncertainties in them with responses (output) of the given computational model. We use two different methodologies to approach the problem. In the first methodology we use sampling-based uncertainty propagation with first order error analysis. In the other approach we place emphasis on the use of Percentile-Based Optimization (PBO). The NASA Langley MUQC’s subproblem A is developed in such a way that both aleatory and epistemic uncertainties need to be managed. The challenge problem classifies each uncertain parameter as belonging to one the following three types: (i) An aleatory uncertainty modeled as a random variable. It has a fixed functional form and known coefficients. This uncertainty cannot be reduced. (ii) An epistemic uncertainty modeled as a fixed but poorly known physical quantity that lies within a given interval. This uncertainty is reducible. (iii) A parameter might be aleatory but sufficient data might not be available to adequately model it as a single random variable. For example, the parameters of a normal variable, e.g., the mean and standard deviation, might not be precisely known but could be assumed to lie within some intervals. It results in a distributional p-box having the physical parameter with an aleatory uncertainty, but the parameters prescribing its mathematical model are subjected to epistemic uncertainties. Each of the parameters of the random variable is an unknown element of a known interval. This uncertainty is reducible. From the study, it is observed that due to practical limitations or computational expense, the sampling is not exhaustive in sampling-based methodology. That is why the sampling-based methodology has high probability of underestimating the output bounds. Therefore, an optimization-based strategy to convert uncertainty described by interval data into a probabilistic framework is necessary. This is achieved in this study by using PBO.

Keywords: aleatory uncertainty, epistemic uncertainty, first order error analysis, uncertainty quantification, percentile-based optimization

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Authors: Nadezhda Kvatashidze

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The International Accounting Standards Board updated the conceptual framework for financial reporting. The main reason behind it is to resolve the tasks of the accounting, which are caused by the market development and business-transactions of a new economic content. Also, the investors call for higher transparency of information and responsibility for the results in order to make a more accurate risk assessment and forecast. All these make it necessary to further develop the conceptual framework for financial reporting so that the users get useful information. The market development and certain shortcomings of the conceptual framework revealed in practice require its reconsideration and finding new solutions. Some issues and concepts, such as disclosure and supply of information, its qualitative characteristics, assessment, and measurement uncertainty had to be supplemented and perfected. The criteria of recognition of certain elements (assets and liabilities) of reporting had to be updated, too and all this is set out in the updated edition of the conceptual framework for financial reporting, a comprehensive collection of concepts underlying preparation of the financial statement. The main objective of conceptual framework revision is to improve financial reporting and development of clear concepts package. This will support International Accounting Standards Board (IASB) to set common “Approach & Reflection” for similar transactions on the basis of mutually accepted concepts. As a result, companies will be able to develop coherent accounting policies for those transactions or events that are occurred from particular deals to which no standard is used or when standard allows choice of accounting policy.

Keywords: conceptual framework, measurement basis, measurement uncertainty, neutrality, prudence, stewardship

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Authors: Choonoh Lee, Seyeon Park, Dongyun Kang, Jaehyeong Choi, Soojee Kim, Younggeun Kim

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Market Kurly is the first South Korean online grocery retailer that guarantees same-day, overnight shipping. More than 1.6 million customers place an average of 4.7 million orders and add 3 to 14 products into a cart per month. The company has sold almost 30,000 kinds of various products in the past 6 months, including food items, cosmetics, kitchenware, toys for kids/pets, and even flowers. The company is operating and expanding multiple dry, cold, and frozen fulfillment centers in order to store and ship these products. Due to the scale and complexity of the fulfillment, pick-pack-ship processes are planned and operated in batches, and thus, the planning that decides the batch of the customers’ orders is a critical factor in overall productivity. This paper introduces a metaheuristic optimization method that reduces the complexity of batch processing in a fulfillment center. The method is an iterative genetic algorithm with heuristic creation and evolution strategies; it aims to group similar orders into pick-pack-ship batches to minimize the total number of distinct products. With a well-designed approach to create initial genes, the method produces streamlined plans, up to 13.5% less complex than the actual plans carried out in the company’s fulfillment centers in the previous months. Furthermore, our digital-twin simulations show that the optimized plans can reduce 3% of operation time for packing, which is the most complex and time-consuming task in the process. The optimization method implements a multithreading design on the Spring framework to support the company’s warehouse management systems in near real-time, finding a solution for 4,000 orders within 5 to 7 seconds on an AWS c5.2xlarge instance.

Keywords: fulfillment planning, genetic algorithm, online grocery retail, optimization

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Authors: Yohanes Kristianto

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The objective of this paper is to compose a sustainable supply chain that integrates product, process and networks design. An integrated life cycle assessment and techno-economic optimization is proposed that might deliver more economically feasible operations, minimizes environmental impacts and maximizes social contributions. Closed loop economy of the supply chain is achieved by reusing waste to be raw material of final products. Societal benefit is given by the supply chain by absorbing waste as source of raw material and opening new work opportunities. A case study of ethanol supply chain from rice straws is considered. The modeling results show that optimization within the scope of LCA is capable of minimizing both CO₂ emissions and energy and utility consumptions and thus enhancing raw materials utilization. Furthermore, the supply chain is capable of contributing to local economy through jobs creation. While the model is quite comprehensive, the future research recommendation on energy integration and global sustainability is proposed.

Keywords: life cycle assessment, techno-economic optimization, sustainable supply chains, closed loop economy

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Authors: Mohammed Almohammedali, David Edgar, Duncan Peter

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The aim of this exploratory research is to further understand how organisations can evaluate their activities, which generate knowledge creation, to meet changing stakeholder expectations. A Scale of Knowledge (SoK) Framework is proposed which links knowledge management and organisational activities to changing stakeholder expectations. The framework was informed by the knowledge management literature, as well as empirical work conducted via a single case study of a multi-site hospital organisation in Saudi Arabia. Eight in-depth semi-structured interviews were conducted with managers from across the organisation regarding current and future stakeholder expectations, organisational strategy/activities and knowledge management. Data were analysed using thematic analysis and a hierarchical value map technique to identify activities that can produce further knowledge and consequently impact on how stakeholder expectations are met. The SoK Framework developed may be useful to practitioners as an analytical aid to determine if current organisational activities produce organisational knowledge which helps them meet (increasingly higher levels of) stakeholder expectations. The limitations of the research and avenues for future development of the proposed framework are discussed.

Keywords: knowledge creation, knowledge management, organisational knowledge, analytical aid, stakeholders

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Authors: Mahmoud Samkan

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In this paper, the design and assessment of new coordination between Power System Stabilizers (PSSs) and Static Var Compensator (SVC) in a multimachine power system via statistical method are proposed. The coordinated design problem of PSSs and SVC over a wide range of loading conditions is handled as an optimization problem. The Bacterial Swarming Optimization (BSO), which synergistically couples the Bacterial Foraging (BF) with the Particle Swarm Optimization (PSO), is employed to seek for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is enhanced. To compare the capability of PSS and SVC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the BSO based coordinated controller gives robust damping performance over wide range of operating conditions and large disturbance in compare to optimized PSS controller based on BSO (BSOPSS) and optimized SVC controller based on BSO (BSOSVC). Moreover, a statistical T test is executed to validate the robustness of coordinated controller versus uncoordinated one.

Keywords: SVC, PSSs, multimachine power system, coordinated design, bacteria swarm optimization, statistical assessment

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Authors: N. Santatriniaina, J. Deseure, T. Q. Nguyen, H. Fontaine, C. Beitia, L. Rakotomanana

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Nowadays, with the increasing of the wafer's size and the decreasing of critical size of integrated circuit manufacturing in modern high-tech, microelectronics industry needs a maximum attention to challenge the contamination control. The move to 300 mm is accompanied by the use of Front Opening Unified Pods for wafer and his storage. In these pods an airborne cross contamination may occur between wafers and the pods. A predictive approach using modeling and computational methods is very powerful method to understand and qualify the AMCs cross contamination processes. This work investigates the required numerical tools which are employed in order to study the AMCs cross-contamination transfer phenomena between wafers and FOUPs. Numerical optimization and finite element formulation in transient analysis were established. Analytical solution of one dimensional problem was developed and the calibration process of physical constants was performed. The least square distance between the model (analytical 1D solution) and the experimental data are minimized. The behavior of the AMCs intransient analysis was determined. The model framework preserves the classical forms of the diffusion and convection-diffusion equations and yields to consistent form of the Fick's law. The adsorption process and the surface roughness effect were also traduced as a boundary condition using the switch condition Dirichlet to Neumann and the interface condition. The methodology is applied, first using the optimization methods with analytical solution to define physical constants, and second using finite element method including adsorption kinetic and the switch of Dirichlet to Neumann condition.

Keywords: AMCs, FOUP, cross-contamination, adsorption, diffusion, numerical analysis, wafers, Dirichlet to Neumann, finite elements methods, Fick’s law, optimization

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Authors: K.Narasimhulu, Y. Pydi Setty

Abstract:

The objective of this present study is the optimization of process parameters in biosorption of Ni(II) and Cd(II) ions by Pseudomonas putida using Response Surface Methodology in a Packed bed bioreactor. The experimental data were also tested with theoretical models to find the best fit model. The present paper elucidates RSM as an efficient approach for predictive model building and optimization of Ni(II) and Cd(II) ions using Pseudomonas putida. In packed bed biosorption studies, comparison of the breakthrough curves of Ni(II) and Cd(II) for Agar immobilized and PAA immobilized Pseudomonas putida at optimum conditions of flow rate of 300 mL/h, initial metal ion concentration of 100 mg/L and bed height of 20 cm with weight of biosorbent of 12 g, it was found that the Agar immobilized Pseudomonas putida showed maximum percent biosorption and bed saturation occurred at 20 minutes. Optimization results of Ni(II) and Cd(II) by Pseudomonas putida from the Design Expert software were obtained as bed height of 19.93 cm, initial metal ion concentration of 103.85 mg/L, and flow rate of 310.57 mL/h. The percent biosorption of Ni(II) and Cd(II) is 87.2% and 88.2% respectively. The predicted optimized parameters are in agreement with the experimental results.

Keywords: packed bed bioreactor, response surface mthodology, pseudomonas putida, biosorption, waste water

Procedia PDF Downloads 440

Authors: Nesreddine Djafar Henni, Nassim Djedoui, Rachid Chebili

Abstract:

Recent decades have witnessed significant efforts being made to optimize different types of structures and components. The concept of cost optimization in reinforced concrete structures, which aims at minimizing financial resources while ensuring maximum building safety, comprises multiple materials, and the objective function for their optimal design is derived from the construction cost of the steel as well as concrete that significantly contribute to the overall weight of reinforced concrete (RC) structures. To achieve this objective, this work has been devoted to optimizing the structural design of 3D RC frame buildings which integrates, for the first time, the Algerian regulations. Three different test examples were investigated to assess the efficiency of our work in optimizing RC frame buildings. The hybrid GWOPSO algorithm is used, and 30000 generations are made. The cost of the building is reduced by iteration each time. Concrete and reinforcement bars are used in the building cost. As a result, the cost of a reinforced concrete structure is reduced by 30% compared with the initial design. This result means that the 3D cost-design optimization of the framed structure is successfully achieved.

Keywords: optimization, automation, API, Malab, RC structures

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Authors: P. Parthiban, Sonu Rajak, R. Dhanalakshmi

Abstract:

This paper presents an Ant Colony Optimization (ACO) approach, combined with a Speed Optimization Algorithm (SOA) to solve the Vehicle Routing Problem (VRP) with environmental considerations, which is well known as Pollution-Routing Problem (PRP). It consists of routing a number of vehicles to serve a set of customers, and determining fuel consumption, driver wages and their speed on each route segment, while respecting the capacity constraints and time windows. Since VRP is NP-hard problem, so PRP also a NP-hard problem, which requires metaheuristics to solve this type of problems. The proposed solution method consists of two stages. Stage one is to solve a Vehicle Routing Problem with Time Window (VRPTW) using ACO and in the second stage, a SOA is run on the resulting VRPTW solution. Given a vehicle route, the SOA consists of finding the optimal speed on each arc of the route to minimize an objective function comprising fuel consumption costs and driver wages. The proposed algorithm tested on benchmark problem, the preliminary results show that the proposed algorithm can provide good solutions within reasonable computational time.

Keywords: ant colony optimization, CO2 emissions, speed optimization, vehicle routing

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Authors: Sneha Thakur, Sanjeev Karmakar

Abstract:

This paper proposed hybrid Particle Swarm Optimization (PSO) – Long-Short Term Memory (LSTM) model for groundwater level prediction. The evaluation of the performance is realized using the parameters: root mean square error (RMSE) and mean absolute error (MAE). Ground water level forecasting will be very effective for planning water harvesting. Proper calculation of water level forecasting can overcome the problem of drought and flood to some extent. The objective of this work is to develop a ground water level forecasting model using deep learning technique integrated with optimization technique PSO by applying 29 years data of Chhattisgarh state, In-dia. It is important to find the precise forecasting in case of ground water level so that various water resource planning and water harvesting can be managed effectively.

Keywords: long short-term memory, particle swarm optimization, prediction, deep learning, groundwater level

Procedia PDF Downloads 59