Comparison of heuristics for an economic lot scheduling problem with deliberated coproduction

We built on the Economic Lot Scheduling Problem Scheduling (ELSP) literature by making some modifications in order to introduce new constraints which had not been thoroughly studied with a view to simulating specific real situations. Specifically, our aim is to propose and simulate different scheduling policies for a new ELSP variant: Deliberated Coproduction. This problem comprises a product system in an ELSP environment in which we may choose if more than one product can be produced on the machine at a given time. We expressly consider the option of coproducing two products whose demand is not substitutable. In order to draw conclusions, a simulation model and its results were developed in the article by employing modified Bomberger data which include two items that could be produced simultaneouslyPeer Reviewe

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. 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Heurísticas Dinámicas para el DCC-ELSP

[ESP] En este artículo se presentan una serie de heurísticas para la resolución del problema del DCC-ELSP (Deliberated and Controlled Coproduction Economic Lot Scheduling Problem), es decir, Problema del Programación del Lote Económico con Coproducción Deliberada y Controlada. Señalar que existe coproducción cuando un proceso productivo da como resultado más de un producto de manera simultánea (Deuermeyer y Pierskalla, 1978). Si se conoce toda la información sobre los parámetros de producción (tiempos, costes, ratios de fabricación...) se dice que la coproducción es controlada. Si se puede decidir fabricar con coproducción o independientemente cada producto se dice que la coproducción es deliberada. Se diseñan cuatro heurísticas que van a considerar unos tiempos de ciclo para cada una de las opciones productivas que son dinámicos en el tiempo y que van a ser capaces de establecer planes de fabricación que indiquen en cada periodo productivo el producto o productos a fabricar, considerando la posibilidad de coproducción, y la cantidad prevista a fabricar. Las heurísticas se van a aplicar en un entorno multí-item mixto, en el que se considera la posible coproducción deliberada y controlada de productos en parejas de dos, y la producción de manera aislada de otros productos. Se va a evaluar si las heurísticas modelan adecuadamente el fenómeno de coproducción, para ello, se simularan a partir de unos experimentos de cuyos resultados se obtendrán conclusiones.El presente trabajo se ha desarrollado gracias a la ayuda DPI2010-18243 del MICINN con el título "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", así como la beca doctoral VALi+d concedida por la Generalitat Valenciana a Julien Maheut (Ref. ACIF/2010)

Revisión de la literatura sobre la flexibilidad de decisión operacional

[ES] En este artículo se presenta una revisión de la literatura sobre la flexibilidad en la toma de decisiones operacionales en el contexto de planificación y gestión en las cadenas de suministro. Esta revisión reflexiona sobre algunas de las definiciones propuestas en la literatura sobre la flexibilidad en la planificación estratégica. Se propone una caracterización de los diferentes tipos de flexibilidad presentes en la literatura en función de las diferentes tareas de planificación existentes y de las diferentes consideraciones en el uso de los materiales.El presente trabajo se ha desarrollado gracias a la ayuda DPI2010-18243 del Ministerio de Ciencia e Innovación del Gobierno de España dentro del programa de Proyectos de Investigación Fundamental no orientada, con el título "COORDINACION DE OPERACIONES EN REDES DE SUMINISTRO/DEMANDA AJUSTADAS, RESILIENTES A LA INCERTIDUMBRE: MODELOS Y ALGORITMOS PARA LA GESTION DE LA INCERTIDUMBRE Y LA COMPLEJIDAD". Asimismo, esta investigación también ha sido financiada mediante una beca doctoral concedida por la Generalitat Valenciana de España a Julien Maheut (Ref. ACIF/2010).Maheut, J. (2011). Revisión de la literatura sobre la flexibilidad de decisión operacional. Working Papers on Operations Management. 2(1):39-48. https://doi.org/10.4995/wpom.v2i1.814SWORD394821Aissaoui, N., Haouari, M., & Hassini, E. (2007). Supplier selection and order lot sizing modeling: A review. Computers & Operations Research, 34(12), 3516-3540. doi:10.1016/j.cor.2006.01.016Akkerman, R., & van Donk, D. P. (2009). Product mix variability with correlated demand in two-stage food manufacturing with intermediate storage. International Journal of Production Economics, 121(2), 313-322. doi:10.1016/j.ijpe.2006.11.021Arunapuram, S., Mathur, K., & Solow, D. (2003). Vehicle Routing and Scheduling with Full Truckloads. Transportation Science, 37(2), 170-182. doi:10.1287/trsc.37.2.170.15248Balakrishnan, A., & Geunes, J. (2000). Requirements Planning with Substitutions: Exploiting Bill-of-Materials Flexibility in Production Planning. Manufacturing & Service Operations Management, 2(2), 166-185. doi:10.1287/msom.2.2.166.12349Bilgen, B.; Günther, H. O. (2009). Integrated production and distribution planning in the fast moving consumer goods industry: a block planning application. OR Spectrum.Calderon-Lama, J. L.; Garcia-Sabater, J. P.; Lario, F. C. (2009). Modelo para la planificación de Operaciones en Cadenas de Suministro de Productos de Innovación. DYNA Ingeniería e Industria, Vol. 84, nº. 6, pp. 517-526.Caner 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.2008.11.024Carrillo, J. E., & Franza, R. M. (2006). Investing in product development and production capabilities: The crucial linkage between time-to-market and ramp-up time. European Journal of Operational Research, 171(2), 536-556. doi:10.1016/j.ejor.2004.08.040Carvalho, J., Moreira, N., & Pires, L. (2005). Autonomous Production Systems in virtual enterprises. International Journal of Computer Integrated Manufacturing, 18(5), 357-366. doi:10.1080/09511920500081445Clement, J.; Coldrick, A.; Sari, J. (1995). Manufacturing data structures: building foundations for excellence with bills of materials and process information. Wiley.Crama, Y., Pochet, Y., & Wera, R. (2001). Production planning aproaches in the process industry. UCL, Belgium.David, F., Pierreval, H., & Caux, C. (2006). Advanced planning and scheduling systems in aluminium conversion industry. International Journal of Computer Integrated Manufacturing, 19(7), 705-715. doi:10.1080/09511920500504545De Kok, T. G., & Fransoo, J. C. (2003). Planning Supply Chain Operations: Definition and Comparison of Planning Concepts. Handbooks in Operations Research and Management Science, 597-675. doi:10.1016/s0927-0507(03)11012-2Deuermeyer, B. L., & Pierskalla, W. P. (1978). A By-Product Production System with an Alternative. Management Science, 24(13), 1373-1383. doi:10.1287/mnsc.24.13.1373Escudero, L. F. (1994). CMIT, capacitated multi-level implosion tool. European Journal of Operational Research, 76(3), 511-528. doi:10.1016/0377-2217(94)90284-4Garcia-Sabater, J. P., Maheut, J., & Garcia-Sabater, J. J. (2009a). A Capacited Material Requierements Planning Model considering Delivery Constraints, in 3rd International Conference on Industrial Engineering and Industrial Management, pp. 793-803.Garcia-Sabater, J. P., Maheut, J., & Garcia-Sabater, J. J. (2009b). A Capacited Material Requierements Planning Model considering Delivery Constraints: A Case Study from the Automotive Industry, in 39th International Conference on Computers & Industrial Engineering, pp. 378-383.Geunes, J. (2003). Solving large-scale requirements planning problems with component substitution options. Computers & Industrial Engineering, 44(3), 475-491. doi:10.1016/s0360-8352(02)00232-2Gronalt, M., Hartl, R. F., & Reimann, M. (2003). New savings based algorithms for time constrained pickup and delivery of full truckloads. European Journal of Operational Research, 151(3), 520-535. doi:10.1016/s0377-2217(02)00650-1GUPTA, S. M., & TALEB, K. N. (1994). Scheduling disassembly. International Journal of Production Research, 32(8), 1857-1866. doi:10.1080/00207549408957046Hachicha, W., Masmoudi, F., & Haddar, M. (2008). A Taguchi method application for the part routing selection in generalised group technology. International Journal of Materials and Structural Integrity, 2(4), 396. doi:10.1504/ijmsi.2008.022999Hachicha, W., Masmoudi, F., & Haddar, M. (2009). Plans d’expérience et analyse des corrélations pour la résolution du problème de formation de cellules avec gammes alternatives. Mécanique & Industries, 10(5), 337-350. doi:10.1051/meca/2009068Inderfurth, K., & Langella, I. M. (2005). Heuristics for solving disassemble-to-order problems with stochastic yields. OR Spectrum, 28(1), 73-99. doi:10.1007/s00291-005-0007-2Lang, J. C., & Domschke, W. (2008). Efficient reformulations for dynamic lot-sizing problems with product substitution. OR Spectrum, 32(2), 263-291. doi:10.1007/s00291-008-0148-1Lin, J. T., Chen, T.-L., & Lin, Y.-T. (2009). Critical material planning for TFT-LCD production industry. International Journal of Production Economics, 122(2), 639-655. doi:10.1016/j.ijpe.2009.05.027Lyon, P., Milne, R. J., Orzell, R., & Rice, R. (2001). Matching Assets with Demand in Supply-Chain Management at IBM Microelectronics. Interfaces, 31(1), 108-124. doi:10.1287/inte.31.1.108.9693Matta, A., Tomasella, M., & Valente, A. (2007). Impact of ramp-up on the optimal capacity-related reconfiguration policy. 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The role of Bill of Materials and Movements (BOMM) in the virtual enterprises environment. International Journal of Production Research, 46(4), 1163-1185. doi:10.1080/00207540600943951Ram, B., Naghshineh-Pour, M. R., & Yu†, X. (2006). Material requirements planning with flexible bills-of-material. International Journal of Production Research, 44(2), 399-415. doi:10.1080/00207540500251505Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28(5), 581-598. doi:10.1016/s0305-0483(99)00080-8Schütz, P., Tomasgard, A., & Ahmed, S. (2009). Supply chain design under uncertainty using sample average approximation and dual decomposition. European Journal of Operational Research, 199(2), 409-419. doi:10.1016/j.ejor.2008.11.040Segerstedt, A. (1996). A capacity-constrained multi-level inventory and production control problem. International Journal of Production Economics, 45(1-3), 449-461. doi:10.1016/0925-5273(96)00017-5Soman, C. A., van Donk, D. P., & Gaalman, G. J. C. (2007). Capacitated planning and scheduling for combined make-to-order and make-to-stock production in the food industry: An illustrative case study. International Journal of Production Economics, 108(1-2), 191-199. doi:10.1016/j.ijpe.2006.12.042Sousa, R., Shah, N., & Papageorgiou, L. G. (2008). Supply chain design and multilevel planning—An industrial case. Computers & Chemical Engineering, 32(11), 2643-2663. doi:10.1016/j.compchemeng.2007.09.005Spengler, T., Püchert, H., Penkuhn, T., & Rentz, O. (1997). Environmental integrated production and recycling management. European Journal of Operational Research, 97(2), 308-326. doi:10.1016/s0377-2217(96)00200-7Stadtler, H. (2005). Supply chain management and advanced planning––basics, overview and challenges. 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Enfoques para la Resolución del Problema ELSP

[ES] En este trabajo se pretende realizar una recopilación de los enfoques planteados en la literatura para la resolución del problema de Programación del Lote Económico, esto es, ELSP. Estos métodos son: Solución Independiente, Ciclo Común, Periodo Básico, Periodo Básico Extendido y Variación del Tamaño de Lote. Para cada una de las aproximaciones de solución se plantea a quien son atribuidas, el correspondiente modelo, así como una serie de referencias que lo han empleado.Este trabajo ha sido realizado gracias a la financiación de la Universidad Politécnica de Valencia, a través del proyecto PAID-05-09-4335 "Coordinación de flujos de materiales e información en sistemas distribuidos de producción".Vidal Carreras, PI. (2010). Enfoques para la Resolución del Problema ELSP. Working Papers on Operations Management. 1(2):31-43. doi:10.4995/wpom.v1i2.787SWORD314312Ballou, R. H. (2004). Logística: Administración de la cadena de suministro. Pearson Educación.Ben-Daya, M., & Hariga, M. (2000). Economic lot scheduling problem with imperfect production processes. Journal of the Operational Research Society, 51(7), 875-881. doi:10.1057/palgrave.jors.2600974Bomberger, E. E. (1966). A Dynamic Programming Approach to a Lot Size Scheduling Problem. Management Science, 12(11), 778-784. doi:10.1287/mnsc.12.11.778Brander, P.; Forsberg, R. (2004). Determination of safety stocks for cyclic schedules with stochastic demands. International Journal of Production Economics, Vol. In Press, Corrected Proof.Brander, P., Levén, E., & Segerstedt, A. (2005). Lot sizes in a capacity constrained facility—a simulation study of stationary stochastic demand. International Journal of Production Economics, 93-94, 375-386. doi:10.1016/j.ijpe.2004.06.034Carstensen, P. (1999). Das Economic Lot Scheduling Problem - Überblick und LP-basiertes Verfahren. OR Spectrum, 21(4), 429-460. doi:10.1007/s002910050097Chandrasekaran, C., Rajendran, C., Chetty, O. V. K., & Hanumanna, D. (2007). Metaheuristics for solving economic lot scheduling problems (ELSP) using time-varying lot-sizes approach. European J. of Industrial Engineering, 1(2), 152. doi:10.1504/ejie.2007.014107Davis, S. G. (1990). Scheduling Economic Lot Size Production Runs. Management Science, 36(8), 985-998. doi:10.1287/mnsc.36.8.985Delporte, C. M., & Thomas, L. J. (1977). Lot Sizing and Sequencing forNProducts on One Facility. Management Science, 23(10), 1070-1079. doi:10.1287/mnsc.23.10.1070Dobson, G. (1987). The Economic Lot-Scheduling Problem: Achieving Feasibility Using Time-Varying Lot Sizes. Operations Research, 35(5), 764-771. doi:10.1287/opre.35.5.764Doll, C. L., & Whybark, D. C. (1973). An Iterative Procedure for the Single-Machine Multi-Product Lot Scheduling Problem. Management Science, 20(1), 50-55. doi:10.1287/mnsc.20.1.50Elmaghraby, S. E. (1978). The Economic Lot Scheduling Problem (ELSP): Review and Extensions. Management Science, 24(6), 587-598. doi:10.1287/mnsc.24.6.587Erlenkotter, D. (1990). Ford Whitman Harris and the Economic Order Quantity Model. Operations Research, 38(6), 937-946. doi:10.1287/opre.38.6.937Eynan, A. (2003). The Benefits of Flexible Production Rates in the Economic Lot Scheduling Problem. IIE Transactions, 35(11), 1057-1064. doi:10.1080/07408170304400Gallego, G. (1990). Scheduling the Production of Several Items with Random Demands in a Single Facility. Management Science, 36(12), 1579-1592. doi:10.1287/mnsc.36.12.1579Gallego, G., & Moon, I. (1992). The Effect of Externalizing Setups in the Economic Lot Scheduling Problem. Operations Research, 40(3), 614-619. doi:10.1287/opre.40.3.614Gallego, G., & Roundy, R. (1992). The economic lot scheduling problem with finite backorder costs. Naval Research Logistics, 39(5), 729-739. doi:10.1002/1520-6750(199208)39:53.0.co;2-nGALLEGO, G., & SHAW, D. X. (1997). Complexity of the ELSP with general cyclic schedules. IIE Transactions, 29(2), 109-113. doi:10.1080/07408179708966318GASCON, A., LEACHMAN, R. 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A lot-sizing problem in deliberated and controlled co-production systems

We consider an uncapacitated lot sizing problem in co-production systems, in which it is possible to produce multiple items simultaneously in a single production run. Each product has a deterministic demand to be satisfied on time. The decision is to choose which items to co-produce and the amount of production throughout a predetermined planning horizon. We show that the lot sizing problem with co-production is strongly NP-Hard. Then, we develop various mixed-integer linear programming (MILP) formulation of the problem and show that LP relaxations of all MILPs are equal. We develop a separation algorithm based on a set of valid inequalities, lower bounds based on a dynamic lot-sizing relaxation of our problem and a constructive heuristic that is used to obtain an initial solution for the solver, which form the basis of our proposed Branch & Cut algorithm for the problem. We test our models and algorithms on different data sets and provide the results.WOS:000754103800001Scopus - Affiliation ID: 60105072Science Citation Index ExpandedQ2-Q3Article; Early AccessUluslararası işbirliği ile yapılan - HAYIRŞubat2022YÖK - 2021-22Aralı

Presenting an economic lot-sizing scheduling problem considering maximum permissible carbon emissions

Carbon emissions related to energy consumptions from the manufacturing industry have become a substantial part of environmental burdens. Carbon emissions related to energy consumptions from the manufacturing industry have become a substantial part of environmental burdens. This study presents carbon emission constraint into the economic lot scheduling problem to reduce carbon emissions. The aim of this research to satisfy customer demand for various items over the planning horizon, with an objective to minimize total costs, includes setup, production, rework and holding costs. In this problem, it is assumed that the production process is defective, and during the process some of the goods are produced with undesirable quality. Defective products can be sold using a rework process. This proposed model has been proven to be a nonlinear convex programming problem. Hence, the optimal solution of this proposed model can be obtained using the derivative method. Finally, a hypothetical example is solved to demonstrate the performance of the proposed exact solution algorithm

Essays on Operational Flexibilities in Production Planning under Supply and Quality Uncertainty

This dissertation investigates the use of operational flexibilities in production planning in order to mitigate the negative effects of supply and quality uncertainty. Uncertainties in supply and quality are commonly experienced among agro-businesses, and in particular, in the wine industry. The goal of the dissertation is to provide prescriptive solutions in mitigating such risks from the lives of agricultural businesses. The first essay of the dissertation examines the impact of supply and quality uncertainty on the investment decisions made by winemakers who lease vineyard space to grow their own fruit. At the end of the growing season, the winemaker receives an uncertain amount of high- and low-quality grapes, due to varying growing conditions such as adverse weather conditions, diseases and natural disasters. High-quality grapes are used in the making of a high-end (reserve) wine, and low-quality grapes are used for the production of a low-end wine. In this study, we investigate the benefits of the downward substitution flexibility, where the winemaker uses its excess high-quality grapes for the production of its low-end wine. In addition, we examine the influence of, and the interrelationships between, three forms of operational flexibilities: downward substitution, price-setting, and fruit trading flexibilities. The second essay of the dissertation investigates the use of advance selling to mitigate quality risk in wine production. This essay examines the influence of quality uncertainty on winemakers\u27 decisions regarding the allocation of its wine for retail operations. Specifically, we study what proportion of the wine should be sold through regular distribution channels versus what proportion should be sold as wine futures in advance of bottling. Due to the intricacies of the production method, the quality of wine may vary from the moment aging begins in the barrel to the time it is bottled and sold to the general public. This study examines the use of wine futures, whereby a winemaker sells its wine while it is still in the barrel in order to reduce the quality rating risk at the time of distribution. Overall, wine futures not only allow the winemaker to pass on the quality rating risk established through expert tastings to consumers but also let them bring in cash for immediate reinvestment into the next vintage

Production planning mechanisms in demand-driven wood remanufacturing industry

L'objectif principal de cette thèse est d'étudier le problème de planification de la production dans le contexte d'une demande incertaine, d’un niveau de service variable et d’approvisionnements incontrôlables dans une usine de seconde transformation du bois. Les activités de planification et de contrôle de production sont des tâches intrinsèquement complexes et difficiles pour les entreprises de seconde transformation du bois. La complexité vient de certaines caractéristiques intrinsèques de cette industrie, comme la co-production, les procédés alternatifs divergents, les systèmes de production sur commande (make-to-order), des temps de setup variables et une offre incontrôlable. La première partie de cette thèse propose une plate-forme d'optimisation/simulation permettant de prendre des décisions concernant le choix d'une politique de planification de la production, pour traiter rapidement les demandes incertaines, tout en tenant compte des caractéristiques complexes de l'industrie de la seconde transformation du bois. À cet effet, une stratégie de re-planification périodique basée sur un horizon roulant est utilisée et validée par un modèle de simulation utilisant des données réelles provenant d'un partenaire industriel. Dans la deuxième partie de cette thèse, une méthode de gestion des stocks de sécurité dynamique est proposée afin de mieux gérer le niveau de service, qui est contraint par une capacité de production limitée et à la complexité de la gestion des temps de mise en course. Nous avons ainsi développé une approche de re-planification périodique à deux phases, dans laquelle des capacités non-utilisées (dans la première phase) sont attribuées (dans la seconde phase) afin de produire certains produits jugés importants, augmentant ainsi la capacité du système à atteindre le niveau de stock de sécurité. Enfin, dans la troisième partie de la thèse, nous étudions l’impact d’un approvisionnement incontrôlable sur la planification de la production. Différents scénarios d'approvisionnement servent à identifier les seuils critiques dans les variations de l’offre. Le cadre proposé permet aux gestionnaires de comprendre l'impact de politiques d'approvisionnement proposées pour faire face aux incertitudes. Les résultats obtenus à travers les études de cas considérés montrent que les nouvelles approches proposées dans cette thèse constituent des outils pratiques et efficaces pour la planification de production du bois.The main objective of this thesis is to investigate the production planning problem in the context of uncertain demand, variable service level, and uncontrollable supply in a wood remanufacturing mill. Production planning and control activities are complex and represent difficult tasks for wood remanufacturers. The complexity comes from inherent characteristics of the industry such as divergent co-production, alternative processes, make-to-order, short customer lead times, variable setup time, and uncontrollable supply. The first part of this thesis proposes an optimization/simulation platform to make decisions about the selection of a production planning policy to deal swiftly with uncertain demands, under the complex characteristics of the wood remanufacturing industry. For this purpose, a periodic re-planning strategy based on a rolling horizon was used and validated through a simulation model using real data from an industrial partner. The computational results highlighted the significance of using the re-planning model as a practical tool for production planning under unstable demands. In the second part, a dynamic safety stock method was proposed to better manage service level, which was threatened by issues related to limited production capacity and the complexity of setup time. We developed a two-phase periodic re-planning approach whereby idle capacities were allocated to produce more important products thus increasing the realization of safety stock level. Numerical results indicated that the solution of the two-phase method was superior to the initial method in terms of backorder level as well as inventory level. Finally, we studied the impact of uncontrollable supply on demand-driven wood remanufacturing production planning through an optimization and simulation framework. Different supply scenarios were used to identify the safety threshold of supply changes. The proposed framework provided managers with a novel advanced planning approach that allowed understanding the impact of supply policies to deal with uncertainties. In general, the wood products industry offers a rich environment for dealing with uncertainties for which the literature fails to provide efficient solutions. Regarding the results that were obtained through the case studies, we believe that approaches proposed in this thesis can be considered as novel and practical tools for wood remanufacturing production planning