Relevant bibliographies by topics / Cost-to-Serve Optimization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Cost-to-Serve Optimization'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Cost-to-Serve Optimization"

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Researcher. "OPTIMIZING GLOBAL COST-TO-SERVE THROUGH ROBOTICS AND DIGITAL SMART FACTORIES: A MULTI-PLANT MANUFACTURING MODEL." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 501–15. https://doi.org/10.5281/zenodo.14003462.

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This article examines the optimization of global cost-to-serve in manufacturing through the implementation of robotics and digital smart factories. In the context of Industry 4.0, we present a comprehensive framework for multinational manufacturers to leverage advanced technologies across their global plant networks. Our research, based on extensive literature review and empirical data from multiple countries, addresses key considerations including technology selection, implementation strategies, workforce development, and network optimization. The article reveals significant benefits, includi
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Lörincz, András, and Barnabás Póczos. "Cost Component Analysis." International Journal of Neural Systems 13, no. 03 (2003): 183–92. http://dx.doi.org/10.1142/s0129065703001558.

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In optimizations the dimension of the problem may severely, sometimes exponentially increase optimization time. Parametric function approximatiors (FAPPs) have been suggested to overcome this problem. Here, a novel FAPP, cost component analysis (CCA) is described. In CCA, the search space is resampled according to the Boltzmann distribution generated by the energy landscape. That is, CCA converts the optimization problem to density estimation. Structure of the induced density is searched by independent component analysis (ICA). The advantage of CCA is that each independent ICA component can be
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Wang, Yantao, Jiashuai Li, Yujie Yuan, and Chun Sing Lai. "Joint Optimization of Cost and Scheduling for Urban Air Mobility Operation Based on Safety Concerns and Time-Varying Demand." Aerospace 11, no. 10 (2024): 861. http://dx.doi.org/10.3390/aerospace11100861.

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As the value and importance of urban air mobility (UAM) are being recognized, there is growing attention towards UAM. To ensure that urban air traffic can serve passengers to the greatest extent while ensuring safety and generating revenue, there is an urgent need for a transportation scheduling plan based on safety considerations. The region of Beijing–Tianjin–Hebei was selected as the case study in this research. A real-time demand transportation scheduling model for a single day was constructed, with the total service population and total cost as objective functions, and safety intervals, e
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Xu, Wenxiu, and Shuang Liu. "Research on Optimization of Enterprise Logistics Management Mode based on Cost Control." Frontiers in Humanities and Social Sciences 3, no. 8 (2023): 86–91. http://dx.doi.org/10.54691/fhss.v3i8.5542.

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The competition among enterprises is fierce, not only in performance, price and quality, but also in high-quality customer service. The rationality of logistics system determines the reliability of services and products provided by enterprises, and the level of customer service directly affects the level of logistics costs. Reasonable planning of enterprise cost can reduce enterprise's resource energy consumption to a certain extent, thus improving enterprise's economic benefits. By analyzing the influencing factors of enterprise logistics cost and the principles of logistics cost control, thi
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Zhang, De Jiang, Na Na Dong, and Xiao Mei Lin. "Contour Extraction of Cerebrovascular Based on Maximum Optimization Cost." Advanced Materials Research 204-210 (February 2011): 1415–18. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1415.

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By studying the conventional algorithm of contour extraction, a new method of contour extraction in blood vessel of brain is proposed based on the MOC maximum optimization cost. First of all, the theory computes the gray differential of the image by conventional differential method to build the cost space. Then, by using dynamic programming theory, the maximum optimization cost curve in the space is extracted to serve as the specific cerebrovascular profile. The experiments show that this method ensures high efficiency in extracting cerebrovascular contour and a high accuracy in positioning ce
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Sumarti, Novriana, Ferdyanto Chandra, and Jeremy Minardi. "Optimization of Personnel Cost in Aircrew Assignment Problem using a Simple Fuzzy Logic Approach." MENDEL 23, no. 1 (2017): 133–40. http://dx.doi.org/10.13164/mendel.2017.1.133.

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In aviation industries, the aircrew assignment problem is one of the most important factors in total operational cost optimization. This problem will be solved in two steps: flight pairing and aircrew scheduling. The constraints to be satisfied in flight pairing include having the same airport for first departure and final destination, and the limitations of flying time, duty time and transit time. The optimization process results in optimal flight pairings that minimize the number of personnel needed to serve a flight schedule over a given period of time. Further optimization is needed to obt
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Xu, Bowei, Xiaoyan Liu, Yongsheng Yang, Junjun Li, and Octavian Postolache. "Optimization for a Multi-Constraint Truck Appointment System Considering Morning and Evening Peak Congestion." Sustainability 13, no. 3 (2021): 1181. http://dx.doi.org/10.3390/su13031181.

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Gate and yard congestion is a typical type of container port congestion, which prevents trucks from traveling freely and has become the bottleneck that constrains the port productivity. In addition, urban traffic increases the uncertainty of the truck arrival time and additional congestion costs. More and more container terminals are adopting a truck appointment system (TAS), which tries to manage the truck arrivals evenly all day long. Extending the existing research, this work considers morning and evening peak congestion and proposes a novel approach for multi-constraint TAS intended to ser
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Sissodia, Rajeshwari, ManMohan Singh Rauthan, and Varun Barthwal. "A Multi-Objective Optimization Scheduling Method Based on the Genetic Algorithm in Cloud Computing." International Journal of Cloud Applications and Computing 12, no. 1 (2022): 1–21. http://dx.doi.org/10.4018/ijcac.305217.

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For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing. This paper propose a resource cost model that defines the demand of tasks on resources with more details. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan, wall clock time , execution time and the costs as constraints of the optimization problem. This paper proposed a multi-objective improved genetic algorithm (MOIGA) t
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Kushwaha, Arvinda, and Mohd Amjad. "A Particle Swarm Optimization Based Load Scheduling Algorithm in Cloud Platform for Wireless Sensor Networks." Scalable Computing: Practice and Experience 20, no. 1 (2019): 71–82. http://dx.doi.org/10.12694/scpe.v20i1.1464.

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Integration of wireless sensor network into cloud computing is a growing paradigm that supports a massive amount of applications in cloud computing, optimization of resources required in the machines. This integration requires the optimization of resources to efficiently complete the different tasks in the devices at cloud platform. This optimization can be done using load scheduling algorithms. These algorithms reduce overload and achieve higher throughput by maximizing the machine utilization concerning cost stabilization. There are lots of methods like First Come First Serve, Min-Min, Parti
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Sommer, Jörg, Elias A. Doumith, and Andreas Reifert. "Cost-Based Topology Optimization of Embedded Ethernet Networks." International Journal of Embedded and Real-Time Communication Systems 2, no. 1 (2011): 1–22. http://dx.doi.org/10.4018/jertcs.2011010101.

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During past decades, Ethernet progressively became the most widely used Local Area Network (LAN) technology. Apart from LAN installations, Ethernet also became attractive for other application areas such as industrial control, automotive, and avionics. In traditional LAN design, the objective is to minimize the network deployment cost. However, in embedded networks, additional constraints and ambient conditions add to the complexity of the problem. In this paper, the authors propose a Simulated Annealing (SA) algorithm to optimize the physical topology of an embedded Ethernet network. The vari
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Dissertations / Theses on the topic "Cost-to-Serve Optimization"

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Ceia, João Pedro Oliveira. "Cost-to-serve customers’ optimization: macro project Unilever – Jerónimo Martins." Master's thesis, 2014. http://hdl.handle.net/10362/15050.

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The objective of this work project is to analyse and discuss the importance of the “Cost to Serve” as a differentiation key factor, by accessing cost to serve customers of a Portuguese subsidiary of a multinational company, which is operating in the sector of fast moving consumer goods (FMCG) – Unilever – Jerónimo Martins (UJM). I will also suggest and quantify key proposals to decrease costs and increase customers’ value. Hence, the scope of this work project is focused on logistics and distribution processes of the company supply chain.<br>NSBE - UNL
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Book chapters on the topic "Cost-to-Serve Optimization"

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Bushell, J., M. Ghazali, and A. E. Hartavi. "Short Review: Optimization Formulations for Enhancing Electric and Hybrid Electric Powertrain Performance." In Lecture Notes in Mobility. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89444-2_76.

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Abstract This paper presents a concise review of objective function formulations employed for optimizing the sizing of powertrain components in electric and hybrid electric powertrains, within the scope of the EU-funded Horizon Europe ESCALATE project. The objective is to analyze the techniques used to achieve improved performance and efficiency for powertrains. Efficient utilization of available energy sources, improved range, reduced GHG emissions, and overall system performance are crucial goals. Objective function formulations serve as essential tools for achieving these objectives. A broad spectrum of optimization techniques is employed in the objective function formulations for electric and hybrid electric vehicles (EVs/HEVs). These include multiple objective functions which simultaneously optimize conflicting/complementary goals, allowing for trade-offs and Pareto-optimal solutions. The review explores the key parameters considered during the optimization process, with a focus on the sizing of electric powertrain components. The goal is to identify the optimal combination of these variables to achieve an optimal powertrain design that maximizes energy efficiency while minimizing cost and environmental impact. Furthermore, the review delves into the challenges and prospects of objective function formulations in the context of electric and hybrid electric powertrains, including the potential to focus on sustainability of designs which has not previously been researched.
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Habyarimana, Ephrem. "Future Vision, Summary and Outlook." In Big Data in Bioeconomy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_21.

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AbstractThe DataBio’s agriculture pilots were carried out through a multi-actor whole-farm management approach using information technology, satellite positioning and remote sensing data as well as Internet of Things technology. The goal was to optimize the returns on inputs while reducing environmental impacts and streamlining the CAP monitoring. Novel knowledge was delivered for a more sustainable agriculture in line with the FAO call to achieve global food security and eliminate malnutrition for the more than nine billion people by 2050. The findings from the pilots shed light on the potential of digital agriculture to solve Europe’s concern of the declining workforce in the farming industry as the implemented technologies would help run farms with less workforce and manual labor. The pilot applications of big data technologies included autonomous machinery, mapping of yield, variable rate of applying agricultural inputs, input optimization, crop performance and in-season yields prediction as well as the genomic prediction and selection method allowing to cut cost and duration of cultivar development. The pilots showed their potential to transform agriculture, and the improved predictive analytics is expected to play a fundamental role in the production environment. As AI models are retrained with more data, the decision support systems become more accurate and serve the farmer better, leading to faster adoption. Adoption is further stimulated by cooperation between farmers to share investment costs and technological platforms allowing farmers to benchmark among themselves and across cropping season.
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Dehankar, Pooja, and Susanta Das. "New Frontiers in Machine Learning Optimization." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7352-1.ch018.

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Machine learning (ML) optimization techniques serve as essential for training models to achieve high performance in a diverse areas. This chapter offers a thorough summary of machine learning optimization techniques. This chapter analysis the development of optimization techniques over time. A number of common optimization constraints are also discussed. Developing a model that works effectively and provides accurate predictions in a certain set of instances is the main objective of ML. We require ML optimization to accomplish that. The practice of modifying hyper parameters with an optimization technique to minimize the cost function is called ML optimization. Because the cost function indicates the difference between the actual value of the estimated parameter and the value predicted by the model, it is crucial to reduce it. This chapter will provide a general explanation of the workings and drawbacks of the ML optimization strategies. Numerous advancements in machine learning optimization techniques have been put forth in this chapter.
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Braekers, Kris, and Gerrit K. Janssens. "A Fuzzy Measure of Vulnerability for the Optimization of Vehicle Routing Problems With Time Windows." In Optimization Techniques for Problem Solving in Uncertainty. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5091-4.ch007.

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In a vehicle routing problem (VRP) with time windows, the start of service needs to take place within the customer time window. Due to uncertainty on travel times, vehicles might arrive late at a customer's site. A VRP is mostly solved to minimize a total cost criterion (travel time, travel distance, fixed and variable vehicle costs). But the dispatcher might also take into consideration the risk of non-conformance with the service agreement to start service within the time window. Therefore, a measure of risk, called “vulnerability of a solution,” is developed to serve as a second criterion. This chapter develops such a measure based on a distance metric and investigates its strengths and weaknesses.
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Sharma, Shivam, and Vipin. "Optimization of Process Parameters of TIG Welded AA6082 Aluminum Alloy for Higher Strength Using Taguchi Method." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220800.

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In modern industrial applications like automobile manufacturing, shipbuilding, and aircraft construction. Aluminum alloy is employed to obtain a cost favorable, corrosion-resistant, and weight optimized body which provides a high stiffness. This paper aims to find the influence of Process parameters like welding current, Filler rod diameter, and Gas flow rate on tensile strength of TIG welded AA6082 alloy using ER 4047 filler wire and argon as a shielding gas. Tensile properties of welded specimen were determined as per ASTM E8 Standard. The process parameters of the TIG welded butt joint of AA6082 plates were optimized using the Taguchi method. L9 orthogonal array of Taguchi design of experiments was taken to perform experiments using MINITAB software to find the parameter which provides the best ultimate tensile strength for butt welded joint of aluminum 6082 alloy. The maximum value of tensile strength comes out to be 184 MPa. Residual stress of welded specimen was also investigated at optimized process 1parameter. The purpose of this document is to serve as a resource for selecting right parameters for welding of AA6082 to save time and cost.
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Esonye, Chizoo, Okechukwu Donminic Onukwuli, Akuzuo Uwaoma Ofoefule, Cyril Sunday Ume, and Nkiruka Jacintha Ogbodo. "Sea Almond as a Promising Feedstock for Green Diesel: Statistical Optimization and Power Rate Law Based Chemical Kinetics of Its Consecutive Irreversible Methanolysis." In Prunus. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93880.

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For successful industrial scale-up and effective cost analysis of transesterification process, presentation of complimentary research data from process optimization using statistical design techniques, chemical kinetics and thermodynamics are essential. Full factorial central composite design (FFCCD) was applied for the statistical optimization of base methanolysis of sea almond (Terminalia catappa) seed oil using response surface methodology (RSM) coupled with desirability function analysis on quadratic model. Reaction time had the most significant impact on the biodiesel yield. Optimum conditions for biodiesel yield of 93.09 wt% validated at 92.58 wt% were 50.03°C, 2.04 wt% catalyst concentration, 58.5 min and 4.66 methanol/oil molar ratio with overall desirability of 1.00. Ascertained fuel properties of the FAME were in compliance with international limits. GC–MS, FTIR and NMR characterizations confirmed unsaturation and good cold-flow qualities of the biodiesel. Based on power rate law, second-order kinetic model out-performed first-order kinetic model. Rate constants of the triglyceride (TG), diglycerides (DG) and monoglycerides (MG) hydrolysis were in the range of 0.00838–0.0409 wt%/min while activation energies were 12.76, 15.83 and 22.43 kcal/mol respectively. TG hydrolysis to DG was the rate determining step. The optimal conditions have minimal error and would serve as a springboard for industrial scale-up of biodiesel production from T. catappa seed oil.
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Sundar, Sasikala, and Sheetal Nitin. "Emerging Trends of Graphene-Based Nanomaterials as Digital Materials for Data Generation in Industry 4.0." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5380-6.ch013.

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Industry 4.0 represented the ongoing transformation of manufacturing and industry through the integration of digital technologies, automation, and data exchange. Key technologies driving Industry 4.0 include the internet of things (IoT), artificial intelligence (AI), big data analytics, robotics, and additive manufacturing (3D printing). This revolution aims to create “smart factories” that are more efficient, flexible, and interconnected, enabling the optimization of production processes and the customization of products to meet individual customer needs. In this chapter, the authors have analysed the conception, challenges, and the future scope of Industry 4.0 by the role of grapheme-based 2D material-enhanced composites, which can serve as a bridge between real-world and virtual environments. An overview of the use of graphene-based smart embedded sensors in real-time structural health monitoring is presented, along with their application at different stages of the manufacturing processes for composites. They have also addressed the technical challenges associated with integrating digital space with graphene-based sensing networks. Moreover, a summary provides the importance of graphene-based devices and structures are integrated with related technologies like artificial intelligence, machine learning, sensors, energy storage devices, and blockchain technology. Owing to its advantageous characteristics and the synergistic benefits that arise from combining its various properties, graphene is hailed for several applications. Graphene is already being used in a wide range of commercial applications, and more are still being developed. Graphene is also anticipated to have an impact on industrial automation shortly. Absolutely, the transition towards automation in manufacturing and industrial processes, often referred to as Industry 4.0, brings about numerous benefits like cost efficiency, automation, system monitoring, early warning systems, and optimization with minimal human input.
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Hussain, T., and M. Mahesh. "Hybrid Solar-Wind Energy Systems for Decentralized Power Generation." In Solar Energy Systems and Smart Electrical Grids for Sustainable Renewable Energy. RADemics Research Institute, 2025. https://doi.org/10.71443/9789349552517-03.

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Hybrid solar-wind energy systems have emerged as a transformative solution for decentralized power generation, offering enhanced energy reliability, sustainability, and efficiency. These systems leverage the complementary nature of solar and wind resources to mitigate intermittency issues and optimize power generation in diverse environmental conditions. However, several technical, economic, and operational challenges hinder their widespread adoption, including system design complexities, energy storage limitations, and maintenance requirements. Advanced control strategies, predictive maintenance techniques, and smart grid integration play a crucial role in ensuring the optimal performance and longevity of hybrid energy systems. Decentralized energy management frameworks are essential for enhancing energy accessibility in rural and off-grid regions, fostering energy independence and resilience. Recent advancements in long-duration energy storage technologies, artificial intelligence-driven predictive analytics, and blockchain-enabled peer-to-peer energy trading have further revolutionized hybrid energy systems, improving their efficiency and economic viability. This book chapter explores the fundamentals, system optimization techniques, and cost-benefit analysis of hybrid solar-wind energy systems while addressing key reliability concerns and emerging technological trends. The discussion provides insights into innovative strategies for reducing system downtime, improving energy storage efficiency, and enhancing integration with smart grids for decentralized power generation. The findings presented in this chapter serve as a comprehensive reference for researchers, policymakers, and industry professionals aiming to advance the development and deployment of hybrid renewable energy solutions.
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Michael Shehan Kurera, Mihidukulasuriya Jude, Dineth Pramuditha Nagahawatta, and You-Jin Jeon. "Structure of Fucoidan and Isolation Techniques." In The Role of Seaweeds in Blue Bioeconomy. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815223644124010007.

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Fucoidans exhibit various biological activities, including immunomodulation, anti-cancer, and pathogenic inhibition. This approach can be used to isolate numerous natural resources and various applications in the pharmaceutical, food, and cosmetic industries. Fucoidan surrounds sulfated L-fucose as a vital monosaccharide and small amounts of mannose, galactose, glucose, xylose, arabinose, uronic acid, and glucuronic acid. Structural analysis revealed that purified fucoidan consists of a carbohydrate chain composed mainly of (1→3)-linked or (1→4)-linked Lfucose residues, with sulfate groups at C-2 and C-4 positions. Fucose residues at C-3 or C-4 serve as branch sites for galactose residues with 1–4)-linkages. Low molecular weight fucoidan, medium molecular weight fucoidan, and high molecular weight fucoidan are the three different types of fucoidan based on molecular weights. The structure of fucoidan determines its bioactivity and its economic and commercial value depending on the species, geographical location, and harvest season. Therefore, shortened phases, low temperatures, and low acidity are used in the extraction technique to determine the distinct structures of fucoidans. In industrial manufacturing, the extraction techniques must be environmentally friendly and cost-effective. In this chapter, classical extraction and purification procedures such as hot water, acidic or alkaline extractions, and chromatographic techniques are discussed and detailed. Microwave-assisted extraction (MAE), ultrasound-assisted extraction (UAE), and enzyme-assisted extraction (EAE) are innovative techniques for fucoidan extraction. Optimization strategies for pH, temperature, pretreatment conditions, extraction time, and yield parameters also discussed in detail.
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Sulc, Bohumil, and David Klimanek. "Evolutionary Algorithms in Supervision of Error-Free Control." In Soft Computing Applications for Database Technologies. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-814-7.ch003.

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Evolutionary algorithms are well known as optimization techniques, suitable for solving various kinds of problems (Ruano, 2005). The new application of evolutionary algorithms represents their use in the detection of biased control loop functions caused by controlled variable sensor discredibility (Klimanek, Sulc, 2005). Sensor discredibility occurs when a sensor transmitting values of the controlled variable provides inexact information, however the information is not absolutely faulty yet. The use of discredible sensors in control circuits may cause the real values of controlled variables to exceed the range of tolerated differences, whereas zero control error is being displayed. However, this is not the only negative consequence. Sometimes, sensor discredibility is accompanied with undesirable and hardly recognizable side effects. Most typical is an increase of harmful emission production in the case of combustion control, (Sulc, Klimanek, 2005). We have found that evolutionary algorithms are useful tools for solving the particular problem of finding a software-based way (so called software redundancy) of sensor discredibility detection. Software redundancy is a more economical way than the usual hardware redundancy, which is otherwise necessary in control loop protection against this small, invisible control error occurrence. New results from a long-term tracking residuum trends show that credibility loss can be forecasted. Operators can be warned in advance that the sensor measuring the controlled variable needs to be exchanged. This need can be effectively reflected in maintenance plans. Namely, the standard genetic algorithm and the simulated annealing algorithm have been successfully applied and tested to minimize the given cost function. By means of these algorithms, a newly developed method is able to detect controlled variable sensor discredibility. When applied to combustion processes, production of harmful emissions can be kept within accepted limits. The application of the used evolutionary algorithms inclusive terminology transfer in this application area can serve as an explanatory case study to help readers gain a better understanding of the how the evolutionary algorithms operate.
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Conference papers on the topic "Cost-to-Serve Optimization"

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Yang, Bo. "Minimizing Localized Corrosion via New Chemical Treatments and Performance Based Treatment Optimization and Control." In CORROSION 1999. NACE International, 1999. https://doi.org/10.5006/c1999-99307.

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Abstract Localized corrosion such as pitting, crevice and/or underdeposit corrosion is the main limiting factor in determining the service life of equipment (such as carbon steel heat exchangers) in industrial water systems. Because of the complexity and dynamic nature of industrial operations, as well as lack of effective monitoring tools, little systematic understanding on the impact of system conditions and process variations on localized corrosion and the inhibition performance profiles of the chemical treatments have been available. As a result, premature failure of equipment occurs, in s
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Smith, Daniel Gregory, Anthony Joseph Palandro, and Ujwol Parajuli. "Artificial Intelligence Utilization for Ship Design Optimization." In SNAME Maritime Convention. SNAME, 2024. http://dx.doi.org/10.5957/smc-2024-023.

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Rapid developments in technology and Artificial Intelligence (AI) serve as primary sources of utilization for improvements of the ship design processes. Structured initially by ship inputs and constraints, Artificial Intelligence algorithms hold the potential to generate and optimize machinery arrangements to reduce size, weight, power, and cost (SWAP-C) impacts. Integration of AI capabilities into ship design in capacities such as machinery arrangements offer overall design process time improvements and increase ship production capabilities.
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Thurston, Deborah L., James V. Carnahan, and Tiefu Liu. "Optimization of Design Utility." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0045.

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Abstract Design optimization is often carried out with respect to a single objective, for example, weight or manufacturing cost. Weight, cost, quality and robustness are each important considerations in design evaluation. However, when the design process is completed, a design is evaluated with respect to its performance in all of these areas, and possibly others, in addition to mechanical design considerations. The process of design should be driven from the very beginning by consideration of how the design artifact will ultimately be evaluated. We present a method for design analysis whose b
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Zhou, Shengtao, Frank Lemmer, Wei Yu, Po Wen Cheng, Chao Li, and Yiqing Xiao. "Optimization of the Dynamic Response of Semi-Submersibles: Influence of the Mooring System." In ASME 2019 2nd International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/iowtc2019-7553.

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Abstract The design and manufacturing cost of substructures is a major component of the total expenditure for a floating wind project. Applying optimization techniques to hull shape designs has become an effective way to reduce the life-cycle cost of a floating wind system. The mooring system is regarded as the component with the highest risk, mainly due to the poor accessibility. This paper extends the previous work by investigating the influences of the mooring design on the optimization process of a semisubmersible substructure. Two optimization loops are set up. In the first loop, only the
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Sharp, Chris, and Bryony DuPont. "Wave Energy Converter Array Optimization: A Review of Current Work and Preliminary Results of a Genetic Algorithm Approach Introducing Cost Factors." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46553.

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Currently, ocean wave energy is a novel means of electricity generation that is projected to potentially serve as a primary energy source in coastal areas. However, for wave energy converters (WECs) to be applicable on a scale that allows for grid implementation, these devices will need to be placed in close relative proximity to each other. From what’s been learned in the wind industry of the U.S., the placement of these devices will require optimization considering both cost and power. However, current research regarding optimized WEC layouts only considers the power produced. This work expl
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Chaiwiboonpol, P., I. Sa-Nguanwong, S. Katanyoowongcharoen, et al. "Remarkable Step Toward 4th Generation of Wellhead Platform Design Optimization." In SPE Conference at Oman Petroleum & Energy Show. SPE, 2025. https://doi.org/10.2118/224822-ms.

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Developing marginal fields in Thailand is challenging due to small reservoirs and aging fields. Economic development requires extensive optimization in drilling, well servicing, maintenance, operational expenses, and Wellhead Platform (WHP) costs, which are crucial for feasibility. Rising inflation and increased demand for renewable energy (boosting demand in transportation and installation market) have led to higher WHP costs, making field development unviable without intervention. PTTEP is working to reduce these costs to enhance net present value by reassessing contracting strategies, explo
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Pramanik, N., U. Roy, R. Sudarsan, R. D. Sriram, and K. W. Lyons. "Synthesis of Geometric Tolerances of a Gearbox Using a Deviation-Based Tolerance Synthesis Scheme." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/cie-48192.

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In this paper a step-by-step procedure for carrying out synthesis of geometric tolerances of a planetary gearbox using a deviation-based tolerance synthesis (TS) scheme has been presented. The TS scheme uses an optimization for minimizing total cost of manufacturing, subject to constraints for assemblability of parts and constraints for functional requirements. The TS work is carried out in several stages. First the gearbox with its nominal dimensions is converted into a model that could serve as input to the synthesis module. Cost functions and constraints equations are then generated using d
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Machin, Jon. "From Subsurface to Riser: An Integrated Approach to Subsea Surveillance." In ASME 2010 29th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/omae2010-20792.

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The high reliability performance of a subsea surveillance system, from subsurface to riser, is of the utmost importance for maximizing production availability. In designing such a surveillance system, there are a multitude of considerations that need to be addressed. These have traditionally focused on safe and cost effective production control system availability. However, they are now being extended to also address enablers for secondary recovery, production optimization, and increased recovery activities. This paper addresses the idea that latest-generation surveillance systems must operate
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9

Sharafi, Masoud, Hamid Afshari, Tarek Y. ElMekkawy, Andrei V. Sleptchenko, and Qingjin Peng. "Stochastic Optimization of the Repair Shops Location Problem Using Particle Swarm Optimization Algorithm." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46605.

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The optimization of facility location decisions is critical for the success of a supply chain in a market since it can contribute to long-term performance of the supply chain. In the last two decades, the number of research in this field has been growing to address more realistic problems such as incorporating uncertainties in repair time and demand. In this paper, a particle swarm optimization algorithm (PSO) is employed to locate repair shops in a stochastic environment. The problem aim is to decide about the location and the capacity of local repair shops as well as identifying the capacity
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Valliyatt, Minas Poyil, Sarafudheen M. Tharayil, Abdulilah A. Razhi, Shouq AlQarni, and Leen AlThubaiti. "AI-Driven Optimization of Refinery Configurations: A Novel Approach to Predicting Production Outputs and Capacities." In SPE Annual Technical Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/220241-ms.

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Abstract In the context of conceptualization and operation of refineries, achieving an optimal configuration emerges as a critical endeavor. Our proposed methodology transcends conventional approaches by seamlessly integrating domain expertise, empirical insights, and cutting-edge artificial intelligence (AI). Our approach holistically captures the intricate linkages between different refinery process facilities (also referred as process units or modules in later part of this paper) to make their best use in ever changing market conditions. Key aspects include robust predictive modeling for in
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Reports on the topic "Cost-to-Serve Optimization"

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Chandra, Shailesh, Timothy Thai, Vivek Mishra, and Princeton Wong. Evaluating Innovative Financing Mechanisms for the California High-Speed Rail Project. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2047.

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Millions of dollars are involved in high-speed rail (HSR) infrastructure construction and maintenance. Large-scale projects like HSR require funding from a variety of avenues beyond those available through public monies. Although HSR serves the general public’s mobility needs, any funds (whether State or Federal) flowing from the public exchequer usually undergo strict review and scrutiny. Funds from public agencies are always limited, making such traditional financing mechanisms unsustainable for fulfilling HSR’s long-term operational and maintenance cost needs—on top of initial costs involve
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Amirav, Aviv, and Steven Lehotay. Fast Analysis of Pesticide Residues in Agricultural Products. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7695851.bard.

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The overall theme of this project was to increase the speed of analysis for monitoring pesticide residues in food. Traditionally, analytical methods for multiple pesticides are time-consuming, expensive, laborious, wasteful, and ineffective to meet critical needs related to food safety. Faster and better methods were needed to provide more cost-effective detection of chemical contaminants, and thus provide a variety of benefits to agriculture. This overarching goal to speed and improve pesticide analysis was successfully accomplished even beyond what was originally proposed by the investigator
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