Quadratic Approximation of the Newsvendor Problem with Imperfect Quality

The paper presents a newsvendor problem in a fuzzy environment by introducing product quality as a fuzzy variable, and product demand as a probability distribution in an economic and supply chain management environment. In order to determine the optimal order quantity, a methodology is developed where the solution is achieved using a fuzzy ranking method combined with a quadratic programming problem approximation. Numerical examples are provided and compared in both situations, namely fuzzy and crisp. The results of these numerical examples show that the decision maker has to order a higher quantity when product quality is a fuzzy variable. The model can be useful for real world problems when historical data are not available

How Yield Process Misspecification Affects the Solution of Disassemble-to-order Problems

Random yields from production are often present in manufacturing systems and there are several ways that this can be modeled. In disassembly planning, the yield uncertainty in harvesting parts from cores can be modeled as either stochastically proportional or binomial, two of these alternatives. A statistical analysis of data from engine remanufacturing of a major car producer fails to provide conclusive evidence on which kind of yield randomness might prevail. In order to gain insight into the importance of this yield assumption, the impact of possible yield misspecification on the solution of the disassemble-to-order problem is investigated. Our results show that the penalty for misspecifying the yield method can be substantial, and provide insight on when the penalty would likely be problematic. The results also indicate that in the absence of conclusive information on which alternative should be chosen, presuming binomial yields generally leads to lower cost penalties and therefore preferable results

Long-term stimulation and recording with a penetrating microelectrode array in cat sciatic nerve

Journal ArticleWe studied the consequences of long-term implantation of a penetrating microelectrode array in peripheral nerve over the time course of 4-6 mo. Electrode arrays without lead wires were implanted to test the ability of different containment systems to protect the array and nerve during contractions of surrounding muscles. Treadmill walking was monitored and the animals showed no functional deficits as a result of implantation. In a different set of experiments, electrodes with lead wires were implanted for up to 7 mo and the animals were tested at 2-4 week intervals at which time stimulation thresholds and recorded sensory activity were monitored for every electrode. It was shown that surgical technique highly affected the long-term stimulation results. Results between measurement sessions were compared, and in the best case, the stimulation properties stabilized in 80% of the electrodes over the course of the experiment (162 days). The recorded sensory signals, however, were not stable over time. A histological analysis performed on all implanted tissues indicated that the morphology and fiber density of the nerve around the electrodes were normal

Coproducción: Una revisión de la literatura

[ES] El objetivo del presente artículo es analizar la literatura existente en el entorno de la coproducción. De acuerdo con Deuermeyer y Pierskalla (1978), es posible afirmar que existe coproducción cuando un proceso productivo da como resultado más de un producto de manera simultánea. La coproducción aparece en ambientes de alta y baja tecnología de producción. La coproducción, suele ocurrir en entornos de producción en los que algunos procesos no se conocen/comprenden perfectamente y/o no están totalmente bajo control (coproducción incontrolada). Sin embargo, tal y como, se ha podido constatar en la realidad industrial, en ocasiones el proceso de coproducción, si se conoce/comprende perfectamente (coproducción controlada). La coproducción puede ser un fenómeno intrínseco al propio proceso productivo (coproducción no deliberada). Aunque en ocasiones puede ser escogida por el gestor del proceso (coproducción deliberada). Así, resulta interesante clasificar la literatura respecto a estas variables, pues hasta la fecha no se había realizado, proporcionando al lector una visión clara de la literatura existente en torno a la coproducción.Este trabajo ha sido realizado gracias a la financiación del Ministerio de Ciencia e Innovación a través del proyecto CORSARI MAGIC: Coordinación de operaciones en redes de suministro/demanda ajustadas, resilientes a la incertidumbre: modelos y algoritmos para la gestión de la incertidumbre y la complejidad, DPI: 2010-18243.Vidal Carreras, PI. (2011). Coproducción: Una revisión de la literatura. Working Papers on Operations Management. 2(1):11-17. doi:10.4995/wpom.v2i1.810SWORD111721BITRAN, G. B., & LEONG, T.-Y. (1995). Co-production of substitutable products. Production Planning & Control, 6(1), 13-25. doi:10.1080/09537289508930249Bitran, G. R., & Dasu, S. (1992). Ordering Policies in an environment of Stochastic Yields and Substitutable Demands. Operations Research, 40(5), 999-1017. doi:10.1287/opre.40.5.999Bitran, G. R., & Gilbert, S. M. (1994). Co-Production Processes with Random Yields in the Semiconductor Industry. Operations Research, 42(3), 476-491. doi:10.1287/opre.42.3.476Bitran, G. R., & Leong, T.-Y. (1992). Deterministic Approximations to Co-Production Problems with Service Constraints and Random Yields. Management Science, 38(5), 724-742. doi:10.1287/mnsc.38.5.724Bitran, G. R., & Yanasse, H. H. (1984). Deterministic Approximations to Stochastic Production Problems. Operations Research, 32(5), 999-1018. doi:10.1287/opre.32.5.999Bravo, D., Rodríguez, E., & Medina, M. (2009). Nisin and lacticin 481 coproduction by Lactococcus lactis strains isolated from raw ewes’ milk. Journal of Dairy Science, 92(10), 4805-4811. doi:10.3168/jds.2009-2237Deuermeyer, B. L. (1979). A Multi-Type Production System for Perishable Inventories. Operations Research, 27(5), 935-943. doi:10.1287/opre.27.5.935Deuermeyer, B. L., & Pierskalla, W. P. (1978). A By-Product Production System with an Alternative. Management Science, 24(13), 1373-1383. doi:10.1287/mnsc.24.13.1373DUENYAS, I., & TSAI, C.-Y. (2000). Control of a manufacturing system with random product yield and downward substitutability. IIE Transactions, 32(9), 785-795. doi:10.1080/07408170008967438Evans, R. V. (1969). Inventory control of by-products. Naval Research Logistics Quarterly, 16(1), 85-92. doi:10.1002/nav.3800160107Garcia-Sabater, J. P.; Vidal-Carreras, P. I. (2010). Programación de producción en los proveedores del automóvil. Revista Virtual Pro, Vol. 104, p. 23.García-Sabater, J. P., Vidal-Carreras, P.I., & García-Sabater, J. J. (2005). Estudio de la Problemática de Programación de la Producción en el sector del Automóvil. Aplicación a una red de fabricación, in VIII Congreso de Ingeniería de Organización.Gerchak, Y., & Grosfeld-Nir, A. (1999). International Journal of Flexible Manufacturing Systems, 11(4), 371-377. doi:10.1023/a:1008131213614GERCHAK, Y., TRIPATHY, A., & WANG, K. (1996). Co-production models with random functionality yields. IIE Transactions, 28(5), 391-403. doi:10.1080/07408179608966286Grosfeld-Nir, A., & Gerchak, Y. (2004). Multiple Lotsizing in Production to Order with Random Yields: Review of Recent Advances. Annals of Operations Research, 126(1-4), 43-69. doi:10.1023/b:anor.0000012275.01260.f5LISBONA, P., & ROMEO, L. (2008). Enhanced coal gasification heated by unmixed combustion integrated with an hybrid system of SOFC/GT. International Journal of Hydrogen Energy, 33(20), 5755-5764. doi:10.1016/j.ijhydene.2008.06.031Mcgillivray, R., & Silver, E. (1978). Some Concepts For Inventory Control Under Substitutable Demand*. INFOR: Information Systems and Operational Research, 16(1), 47-63. doi:10.1080/03155986.1978.11731687Nahmias, S., & Moinzadeh, K. (1997). Lot Sizing with Randomly Graded Yields. Operations Research, 45(6), 974-989. doi:10.1287/opre.45.6.974Nielsen, D. R., Yoon, S.-H., Yuan, C. J., & Prather, K. L. J. (2010). Metabolic engineering of acetoin and meso-2, 3-butanediol biosynthesis in E. coli. Biotechnology Journal, 5(3), 274-284. doi:10.1002/biot.200900279Öner, S., & Bilgiç, T. (2008). Economic lot scheduling with uncontrolled co-production. European Journal of Operational Research, 188(3), 793-810. doi:10.1016/j.ejor.2007.05.016Ou, J., & Wein, L. M. (1995). Dynamic Scheduling of a Production/Inventory System with By-Products and Random Yield. Management Science, 41(6), 1000-1017. doi:10.1287/mnsc.41.6.1000Caner Taşkın, Z., & Tamer Ünal, A. (2009). Tactical level planning in float glass manufacturing with co-production, random yields and substitutable products. European Journal of Operational Research, 199(1), 252-261. doi:10.1016/j.ejor.2008.11.024Tomlin, B., & Wang, Y. (2008). Pricing and Operational Recourse in Coproduction Systems. Management Science, 54(3), 522-537. doi:10.1287/mnsc.1070.0807Vidal-Carreras, P. I., & Garcia-Sabater, J. P. (2009). Comparison of heuristics for an economic lot scheduling problem with deliberated coproduction. Journal of Industrial Engineering and Management, 2(3). doi:10.3926/jiem.2009.v2n3.p437-463Yano, C. A., & Lee, H. L. (1995). Lot Sizing with Random Yields: A Review. Operations Research, 43(2), 311-334. doi:10.1287/opre.43.2.31

Multi-period, multi-product production planning in an uncertain manufacturing environment

Les travaux de cette thèse portent sur la planification de la production multi-produits, multi-périodes avec des incertitudes de la qualité de la matière première et de la demande. Un modèle de programmation stochastique à deux étapes avec recours est tout d'abord proposé pour la prise en compte de la non-homogénéité de la matière première, et par conséquent, de l'aspect aléatoire des rendements de processus. Ces derniers sont modélisés sous forme de scénarios décrits par une distribution de probabilité stationnaire. La méthodologie adoptée est basée sur la méthode d'approximation par moyenne d'échantillonnage. L'approche est appliquée pour planifier la production dans une unité de sciage de bois et le modèle stochastique est validé par simulation de Monte Carlo. Les résultats numériques obtenus dans le cas d'une scierie de capacité moyenne montrent la viabilité de notre modèle stochastique, en comparaison au modèle équivalent déterministe. Ensuite, pour répondre aux préoccupations du preneur de décision en matière de robustesse, nous proposons deux modèles d'optimisation robuste utilisant chacun une mesure de variabilité du niveau de service différente. Un cadre de décision est développé pour choisir parmi les deux modèles d'optimisation robuste, en tenant compte du niveau du risque jugé acceptable quand à la variabilité du niveau de service. La supériorité de l'approche d'optimisation robuste, par rapport à la programmation stochastique, est confirmée dans le cas d'une usine de sciage de bois. Finalement, nous proposons un modèle de programmation stochastique qui tient compte à la fois du caractère aléatoire de la demande et du rendement. L'incertitude de la demande est modélisée par un processus stochastique dynamique qui est représenté par un arbre de scénarios. Des scénarios de rendement sont ensuite intégrés dans chaque noeud de l'arbre de scénarios de la demande, constituant ainsi un arbre hybride de scénarios. Nous proposons un modèle de programmation stochastique multi-étapes qui utilise un recours complet pour les scénarios de la demande et un recours simple pour les scénarios du rendement. Ce modèle est également appliqué au cas industriel d'une scierie et les résultats numériques obtenus montrent la supériorité du modèle stochastique multi- étapes, en comparaison avec le modèle équivalent déterministe et le modèle stochastique à deux étapes

Multisupplier procurement under uncertainty in industrial fishing environments

viii, 51 leaves : ill. ; 28 cm.Includes abstract.Includes bibliographical references (leaves 44-51).In this paper we address the issue of multi supplier sourcing as a tool for hedging against supply yield uncertainty. Our work was motivated by the problems in the fishing industry whereby fish processing firms are constantly faced with the problems of random supply yields. We formulated a mathematical programming model that can be used to determine the quantities to be ordered from two or more suppliers so as to minimize annual expected procurement cost while attempting to satisfy demand requirements and operating constraints. The cost included are purchasing cost, inventory related cost and ordering cost. We assume that at the beginning of a planning horizon comprised of 12 periods a firm enters into minimum contractual agreement with two suppliers, and in return each supplier offers a discounted price schedule. In our numerical analysis we solved the model for both the 2-supplier case and the single supplier case and compared the cost of using a single supplier versus two suppliers under varying levels of yield variability. We compared deterministic solutions for the single and two-supplier case and use Monte Carlo simulation to assess the robustness of the solutions under varying levels of yield uncertainty. Results show that as the variability of the yield rate increases it becomes cost effective to use two suppliers as a means for hedging against uncertainty. We compared the results from our model to that of a heuristic procedure proposed by Parlar and Wang, an alternative approach for solving the 2-supplier inventory problem. The results indicated that our model provides superior solutions to that of the heuristic procedure