ABSTRACTS, PAPERS PRESENTED, ANNUAL MEETING, SAEA, TULSA, OKLAHOMA, FEBRUARY 1993

Teaching/Communication/Extension/Profession,

An information adaptive system study report and development plan

The purpose of the information adaptive system (IAS) study was to determine how some selected Earth resource applications may be processed onboard a spacecraft and to provide a detailed preliminary IAS design for these applications. Detailed investigations of a number of applications were conducted with regard to IAS and three were selected for further analysis. Areas of future research and development include algorithmic specifications, system design specifications, and IAS recommended time lines

Gaining a competitive advantage through market research into home buyer preferences

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1992.Includes bibliographical references (leaves 126-129).by Arthur Paul Boytinck and Maaria Indermuehle Olander.M.S

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Integrated asset management system for performance-based road maintenance contracts

Performance-based maintenance contracts (PBMC) for highways are increasingly becoming an attractive mechanism for transferring activities traditionally undertaken by the public sector to private entities. Increased financial pressures on governments, demands for improved service levels by highway users, and the operational efficiencies offered by the private sector, all create a strong business case for PBMC. In order to enable government road agencies and private sector investors to engage in the use of PBMC, there is a need for quantitative tools that allow both entities to 1) Properly structure the PBMC in terms of risk allocation, 2) Develop appropriate levels for service level penalties and incentives in the contract, 3) Determine appropriate targets for highway level of service, and 4) Determine the most cost-effective set of road maintenance and rehabilitation (M&R) activities to be undertaken throughout the duration of the contract. This research developed a GIS-based Integrated Highway Asset Management System (IHAMS), which extends typical functionality of traditional pavement management systems to cover specific contractual requirements of PBMC. The system allows the analysis of both network-level and project-level asset management decisions. Defect-specific pavement deterioration models are developed using multivariate regression. Stochastic network-level deterioration models are developed using markov chains. Life cycle costing models are developed to cover specific financial obligations in PBMC like penalties and incentives, in addition to traditional M&R expenditure. A GA-based optimization modules is used to trade-off various decision scenarios that are beneficial to both road maintenance contracts and road agencies. A case study for the Cairo-Ismalliyah desert highway is used to demonstrate the capability of the system

Optimal Decision Making for Capacitated Reverse Logistics Networks with Quality Variations

Increasing concerns about the environmental impact of production, product take-back laws and dwindling natural resources have heightened the need to address the impact of disposing end-of-life (EOL) products. To cope this challenge, manufacturers have integrated reverse logistics into their supply chain or chosen to outsource product recovery activities to third party firms. The uncertain quality of returns as well as uncertainty in return flow limit the effectiveness of planning, control and monitoring of reverse logistics networks. In addition, there are different recovery routes for each returned product such as reuse, repair, disassembling, remanufacturing and recycling. To determine the most profitable option for EOL product management, remanufacturers must consider the quality of returns and other limitations such as inventory size, demand and quantity of returns. The work in this dissertation addresses these pertinent aspects using two models that have been motivated by two remanufacturing facilities whereby there are uncertainties in the quality and quantity of return and capacitated inventories. In the first case, a disposition decision making model is developed for a remanufacturing process in which the inventory capacity of recoverable returns is limited and where there\u27s a constant demand to be met, for remanufactured products that meet a minimum quality threshold. It is assumed that the quality of returns is uncertain and remanufacturing cost is dependent on the quality grade. In this model, remanufacturing takes place when there is demand for remanufactured products. Accepted returns that meet the minimum quality threshold undergo the remanufacturing processes, and any unacceptable returns are salvaged. A continuous time Markov chain (CTMC) is presented as the modeling approach. The Matrix-Geometric solution methodology is applied to evaluate several key performance metrics for this system, to result in the optimal disposition policy. The numerical study shows an intricate trade-off between the acceptable quality threshold value and the recoverable product inventory capacity. Particularly, there are periodic system starvation whenever there is a mis-match between these two system metrics. In addition, the sensitivity analysis indicates that changes to the demand rate for remanufactured products necessitates the need to re-evaluate the existing system configuration. In the second case, a general framework is presented for a third party remanufacturer, where the remanufacturer has the alternative of salvaging EOL products and supplying parts to external suppliers, or remanufacture the disassembled parts to \u27as new\u27 conditions. The remanufacturing processes of reusable products and parts is studied in the context of other process variables such as the cost and demand of remanufactured products and parts. The goal of this model is to determine the return quality thresholds for a multi-product, multi-period remanufacturing setting. The problem is formulated as a mixed integer non-linear programming (MINLP) problem, which involves a discretization technique that turns the problem turns into a quadratic mixed integer programming (QMIP) problem. Finally, a numerical analysis using a personal computer (PC) remanufacturing facility data is used to test the extent to which the minimum acceptance quality threshold is dependent on the inventory level capacities of the EOL product management sites, varying operational costs and the upper bound of disposal rate

Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

[EN] Lack of homogeneity in the product (LHP) appears in some production processes that confer heterogeneity in the characteristics of the products obtained. Supply chains with this issue have to classify the product in different homogeneous subsets, whose quantity is uncertain during the production planning process. This paper proposes a generic framework for reviewing in a unified way the literature about production planning models dealing with LHP uncertainty. This analysis allows the identification of similarities among sectors to transfer solutions between them and gaps existing in the literature for further research. The results of the review show: (1) sectors affected by LHP inherent uncertainty, (2) the inherent LHP uncertainty types modelled, and (3) the approaches for modelling LHP uncertainty most widely employed. Finally, we suggest a conceptual model reflecting the aspects to be considered when modelling the production planning in sectors with LHP in an uncertain environment.This research was initiated within the framework of the project funded by the Ministerio de Economía y Competitividad [Ref. DPI2011-23597] entitled ‘Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity’ (PLANGES-FHP) already finished. After, the project leading to this application has received funding from the European Union’s research and innovation programme under the H2020 Marie Skłodowska-Curie Actions with the grant agreement No 691249, Project entitled ’Enhancing and implementing Knowledge based ICT solutions within high Riskand Uncertain Conditions for Agriculture Production Systems’ (RUC-APS).Mundi, I.; Alemany Díaz, MDM.; Poler, R.; Fuertes-Miquel, VS. (2019). Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model. International Journal of Production Research. 57(15-16):5239-5283. https://doi.org/10.1080/00207543.2019.1566665S523952835715-16Ahumada, O., Rene Villalobos, J., & Nicholas Mason, A. (2012). Tactical planning of the production and distribution of fresh agricultural products under uncertainty. Agricultural Systems, 112, 17-26. doi:10.1016/j.agsy.2012.06.002Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 196(1), 1-20. doi:10.1016/j.ejor.2008.02.014Alarcón, F., Alemany, M. M. E., Lario, F. C., & Oltra, R. F. (2011). La falta de homogeneidad del producto (FHP) en las empresas cerámicas y su impacto en la reasignación del inventario. Boletín de la Sociedad Española de Cerámica y Vidrio, 50(1), 49-58. doi:10.3989/cyv.072011Albornoz, V. M., M. González-Araya, M. C. Gripe, and S. V. Rodrıguez. 2014. “A Mixed Integer Linear Program for Operational Planning in a Meat Packing Plant.” Accessed January 15, 2015. http://www.researchgate.net/profile/Victor_Albornoz/publication/268687089_A_Mixed_Integer_Linear_Program_for_Operational_Planning_in_a_Meat_Packing_Plant/links/547382bf0cf29afed60f55c7.pdf.José Alem, D., & Morabito, R. (2012). Production planning in furniture settings via robust optimization. Computers & Operations Research, 39(2), 139-150. doi:10.1016/j.cor.2011.02.022Alemany, M. M. E., Lario, F.-C., Ortiz, A., & Gómez, F. (2013). Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: An illustration of a ceramic case. Applied Mathematical Modelling, 37(5), 3380-3398. doi:10.1016/j.apm.2012.07.022Alemany, M., Ortiz, A., & Fuertes-Miquel, V. S. (2018). A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company. Computers & Industrial Engineering, 122, 219-234. doi:10.1016/j.cie.2018.05.040Alfalla-Luque, R., Medina-Lopez, C., & Dey, P. K. (2012). Supply chain integration framework using literature review. Production Planning & Control, 24(8-9), 800-817. doi:10.1080/09537287.2012.666870Al-Othman, W. B. E., Lababidi, H. M. S., Alatiqi, I. M., & Al-Shayji, K. (2008). Supply chain optimization of petroleum organization under uncertainty in market demands and prices. European Journal of Operational Research, 189(3), 822-840. doi:10.1016/j.ejor.2006.06.081Al-Shammari, A., & Ba-Shammakh, M. S. (2011). Uncertainty Analysis for Refinery Production Planning. Industrial & Engineering Chemistry Research, 50(11), 7065-7072. doi:10.1021/ie200313rAmaro, A. C. S., & Barbosa-Póvoa, A. P. F. D. (2009). The effect of uncertainty on the optimal closed-loop supply chain planning under different partnerships structure. Computers & Chemical Engineering, 33(12), 2144-2158. doi:10.1016/j.compchemeng.2009.06.003ARAS, N., BOYACI, T., & VERTER, V. (2004). The effect of categorizing returned products in remanufacturing. IIE Transactions, 36(4), 319-331. doi:10.1080/07408170490279561Aydin, R., Kwong, C. K., Geda, M. W., & Okudan Kremer, G. E. (2017). Determining the optimal quantity and quality levels of used product returns for remanufacturing under multi-period and uncertain quality of returns. The International Journal of Advanced Manufacturing Technology, 94(9-12), 4401-4414. doi:10.1007/s00170-017-1141-0Bakhrankova, K., Midthun, K. T., & Uggen, K. T. (2014). Stochastic optimization of operational production planning for fisheries. Fisheries Research, 157, 147-153. doi:10.1016/j.fishres.2014.03.018Banasik, A., Kanellopoulos, A., Claassen, G. D. H., Bloemhof-Ruwaard, J. M., & van der Vorst, J. G. A. J. (2017). Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain. International Journal of Production Economics, 183, 409-420. doi:10.1016/j.ijpe.2016.08.012Beaudoin, D., LeBel, L., & Frayret, J.-M. (2007). Tactical supply chain planning in the forest products industry through optimization and scenario-based analysis. Canadian Journal of Forest Research, 37(1), 128-140. doi:10.1139/x06-223Begen, M. A., & Puterman, M. L. (2003). Development Of A Catch Allocation Tool Design For Production Planning At Js Mcmillan Fisheries. INFOR: Information Systems and Operational Research, 41(3), 235-244. doi:10.1080/03155986.2003.11732678Benedito, E., & Corominas, A. (2010). Optimal manufacturing and remanufacturing capacities of systems with reverse logistics and deterministic uniform demand. Journal of Industrial Engineering and Management, 3(1). doi:10.3926/jiem.2010.v3n1.p33-53Bertrand, J. 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Risk Management in the Oil Supply Chain: A CVaR Approach. Industrial & Engineering Chemistry Research, 49(7), 3286-3294. doi:10.1021/ie901265nChakraborty, M., & Chandra, M. K. (2005). Multicriteria decision making for optimal blending for beneficiation of coal: a fuzzy programming approach. Omega, 33(5), 413-418. doi:10.1016/j.omega.2004.07.005LUO, C., & RONG, G. (2009). A Strategy for the Integration of Production Planning and Scheduling in Refineries under Uncertainty. Chinese Journal of Chemical Engineering, 17(1), 113-127. doi:10.1016/s1004-9541(09)60042-2Davoli, G., Gallo, S., Collins, M., & Melloni, R. (2011). A stochastic simulation approach for production scheduling and investment planning in the tile industry. International Journal of Engineering, Science and Technology, 2(9). doi:10.4314/ijest.v2i9.64006Denizel, M., Ferguson, M., & Souza, G. (2010). Multiperiod Remanufacturing Planning With Uncertain Quality of Inputs. IEEE Transactions on Engineering Management, 57(3), 394-404. doi:10.1109/tem.2009.2024506Dong, M., Lu, S., & Han, S. (2011). Production Planning for Hybrid Remanufacturing and Manufacturing System with Component Recovery. Advances in Electrical Engineering and Electrical Machines, 511-518. doi:10.1007/978-3-642-25905-0_66Dubois, D., Fargier, H., & Fortemps, P. (2003). Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge. European Journal of Operational Research, 147(2), 231-252. doi:10.1016/s0377-2217(02)00558-1DUENYAS, I., & TSAI, C.-Y. (2000). Control of a manufacturing system with random product yield and downward substitutability. IIE Transactions, 32(9), 785-795. doi:10.1080/07408170008967438Esteso, A., Alemany, M. M. E., & Ortiz, A. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research, 56(13), 4418-4446. doi:10.1080/00207543.2018.1447706French, M. L., & LaForge, R. L. (2005). Closed-loop supply chains in process industries: An empirical study of producer re-use issues. Journal of Operations Management, 24(3), 271-286. doi:10.1016/j.jom.2004.07.012Gallo, M., R. Grisi, G. Guizzi, and E. Romano. 2009. “A Comparison of Production Policies in Remanufacturing Systems,” Proceedings of the 8th WSEAS International Conference on System Science and Simulation in Engineering, ICOSSSE ‘09, pp. 334.Goodfellow, R., & Dimitrakopoulos, R. (2017). Simultaneous Stochastic Optimization of Mining Complexes and Mineral Value Chains. Mathematical Geosciences, 49(3), 341-360. doi:10.1007/s11004-017-9680-3Graves, S. C. (2010). Uncertainty and Production Planning. Planning Production and Inventories in the Extended Enterprise, 83-101. doi:10.1007/978-1-4419-6485-4_5Grillo, H., Alemany, M. M. E., Ortiz, A., & Fuertes-Miquel, V. S. (2017). Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Applied Mathematical Modelling, 49, 255-278. doi:10.1016/j.apm.2017.04.037Guan, Z., & Philpott, A. B. (2011). A multistage stochastic programming model for the New Zealand dairy industry. International Journal of Production Economics, 134(2), 289-299. doi:10.1016/j.ijpe.2009.11.003Guide, V. D. R. (2000). Production planning and control for remanufacturing: industry practice and research needs. Journal of Operations Management, 18(4), 467-483. doi:10.1016/s0272-6963(00)00034-6Gupta, V., & Grossmann, I. E. (2011). Solution strategies for multistage stochastic programming with endogenous uncertainties. Computers & Chemical Engineering, 35(11), 2235-2247. doi:10.1016/j.compchemeng.2010.11.013Gupta, S., and Z. Nan. 2006. “‘Multiperiod Planning of Refinery Operations Under Market Uncertainty,’ AIChE Annual Meeting.” Conference Proceedings.Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk – Definition, measure and modeling. Omega, 52, 119-132. doi:10.1016/j.omega.2014.10.004Heydari, J., & Ghasemi, M. (2018). A revenue sharing contract for reverse supply chain coordination under stochastic quality of returned products and uncertain remanufacturing capacity. Journal of Cleaner Production, 197, 607-615. doi:10.1016/j.jclepro.2018.06.206Hovelaque, V., Duvaleix-Tréguer, S., & Cordier, J. (2009). Effects of constrained supply and price contracts on agricultural cooperatives. European Journal of Operational Research, 199(3), 769-780. doi:10.1016/j.ejor.2008.08.005Hsieh, S., & Chiang, C.-C. (2001). Manufacturing-to-Sale Planning Model for Fuel Oil Production. The International Journal of Advanced Manufacturing Technology, 18(4), 303-311. doi:10.1007/s001700170070Igarashi, M., de Boer, L., & Fet, A. M. (2013). What is required for greener supplier selection? A literature review and conceptual model development. Journal of Purchasing and Supply Management, 19(4), 247-263. doi:10.1016/j.pursup.2013.06.001Jamshidi, M., & Osanloo, M. (2019). Reliability analysis of production schedule in multi-element deposits under grade-tonnage uncertainty with multi-destinations for the run of mine material. International Journal of Mining Science and Technology, 29(3), 483-489. doi:10.1016/j.ijmst.2018.04.016Jin, X., Hu, S. J., Ni, J., & Xiao, G. (2013). Assembly Strategies for Remanufacturing Systems With Variable Quality Returns. IEEE Transactions on Automation Science and Engineering, 10(1), 76-85. doi:10.1109/tase.2012.2217741Jindal, A., & Sangwan, K. S. (2016). Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors. Annals of Operations Research, 257(1-2), 95-120. doi:10.1007/s10479-016-2219-zJohnson, P., G. Evatt, P. Duck, and S. Howell. 2010. “The Derivation and Impact of an Optimal Cut-off Grade Regime Upon Mine Valuations,” Proceedings of the World Congress on Engineering 2010 Vol I.Junior, M. L., & Filho, M. G. (2011). Production planning and control for remanufacturing: literature review and analysis. Production Planning & Control, 23(6), 419-435. doi:10.1080/09537287.2011.561815Kamrad, B., & Ernst, R. (2001). An Economic Model for Evaluating Mining and Manufacturing Ventures with Output Yield Uncertainty. Operations Research, 49(5), 690-699. doi:10.1287/opre.49.5.690.10610Kannegiesser, M., Günther, H.-O., van Beek, P., Grunow, M., & Habla, C. (2008). Value chain management for commodities: a case study from the chemical industry. OR Spectrum, 31(1), 63-93. doi:10.1007/s00291-008-0124-9Karabuk, S. (2008). Production planning under uncertainty in textile manufacturing. Journal of the Operational Research Society, 59(4), 510-520. doi:10.1057/palgrave.jors.2602370Khor, C. S., Elkamel, A., & Douglas, P. L. (2008). Stochastic Refinery Planning with Risk Management. Petroleum Science and Technology, 26(14), 1726-1740. doi:10.1080/10916460701287813Kumral, M. (2004). Genetic algorithms for optimization of a mine system under uncertainty. Production Planning & Control, 15(1), 34-41. doi:10.1080/09537280310001654844Lalmazloumian, M., and K. Y. Wong. 2012. “A Review of Modelling Approaches for Supply Chain Planning Under Uncertainty,” Service Systems and Service Management (ICSSSM), 2012 9th International Conference on, pp. 197.Leiras, A., Ribas, G., Hamacher, S., & Elkamel, A. (2013). Tactical and Operational Planning of Multirefinery Networks under Uncertainty: An Iterative Integration Approach. 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Integrated capacitated lot sizing and scheduling problems in a flexible flow line

The lot sizing and scheduling problem in a Flexible Flow Line (FFL) has extensive real-world applications in many industries. An FFL consists of several production stages in series with parallel machines at each stage. The decisions to be taken are the determination of production quantities (lots), machine assignments and production sequences (schedules) on each machine at each stage in an FFL. Lot sizing and scheduling problems are closely interrelated. Solving them separately and then coordinating their interdependencies is often ineffective. However due to their complexity, there is a lack of mathematical modelling and solution procedures in the literature to combine and jointly solve them.Up to now most research has been focused on combining lotsizing and scheduling for the single machine configuration, and research on other configurations like FFL is sparse. This thesis presents several mathematical models with practical assumptions and appropriate algorithms, along with experimental test problems, for simultaneously lotsizing and scheduling in FFL. This problem, called the ‘General Lot sizing and Scheduling Problem in a Flexible Flow Line’ (GLSP-FFL). The objective is to satisfy varying demand over a finite planning horizon with minimal inventory, backorder and production setup costs. The problem is complex as any product can be processed on any machine, but these have different processing rates and sequence-dependent setup times & costs. As a result, even finding a feasible solution of large problems in reasonable time is impossible. Therefore the heuristic solution procedure named Adaptive Simulated Annealing (ASA), with four well-designed initial solutions, is designed to solve GLSP-FFL.A further original contribution of this study is to design linear mixed-integer programming (MILP) formulations for this problem, incorporating all necessary features of setup carryovers, setup overlapping, non-triangular setup while allowing multiple lot production per periods, lot splitting and sequencing through ATSP-adaption based on a variety of subtour elimination