Fuzzy multi-objective optimisation for master planning in a ceramic supply chain

This is an Accepted Manuscript of an article published in International Journal of Production Research on 2012, available online: http://www.tandfonline.com/10.1080/00207543.2011.588267.In this paper, we consider the master planning problem for a centralised replenishment, production and distribution ceramic tile supply chain. A fuzzy multi-objective linear programming (FMOLP) approach is presented which considers the maximisation of the fuzzy gross margin, the minimisation of the fuzzy idle time and the minimisation of the fuzzy backorder quantities. By using an interactive solution methodology to convert this FMOLP model into an auxiliary crisp single-objective linear model, a preferred compromise solution is obtained. For illustration purposes, an example based on modifications of real-world industrial problems is used.This research has been carried out in the framework of a project funded by the Science and Technology Ministry of the Spanish Government, entitled 'Project of reinforcement of the competitiveness of the Spanish managerial fabric through the logistics as a strategic factor in a global environment' (Ref. PSE-370000-2008-8).Peidro Payá, D.; Mula, J.; Alemany Díaz, MDM.; Lario Esteban, FC. (2012). Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. International Journal of Production Research. 50(11):3011-3020. https://doi.org/10.1080/00207543.2011.588267S301130205011Alemany, M.M.E.et al., 2010. Mathematical programming model for centralized master planning in ceramic tile supply chains.International Journal of Production Research, 48 (17), 5053–5074Beamon, B. M. (1998). Supply chain design and analysis: International Journal of Production Economics, 55(3), 281-294. doi:10.1016/s0925-5273(98)00079-6Chen, C.-L., & Lee, W.-C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28(6-7), 1131-1144. doi:10.1016/j.compchemeng.2003.09.014Chern, C.-C., & Hsieh, J.-S. (2007). A heuristic algorithm for master planning that satisfies multiple objectives. Computers & Operations Research, 34(11), 3491-3513. doi:10.1016/j.cor.2006.02.022Kreipl, S., & Pinedo, M. (2009). Planning and Scheduling in Supply Chains: An Overview of Issues in Practice. Production and Operations Management, 13(1), 77-92. doi:10.1111/j.1937-5956.2004.tb00146.xLai, Y.-J., & Hwang, C.-L. (1993). Possibilistic linear programming for managing interest rate risk. Fuzzy Sets and Systems, 54(2), 135-146. doi:10.1016/0165-0114(93)90271-iLi, X., Zhang, B., & Li, H. (2006). Computing efficient solutions to fuzzy multiple objective linear programming problems. Fuzzy Sets and Systems, 157(10), 1328-1332. doi:10.1016/j.fss.2005.12.003Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390. doi:10.1016/j.ejor.2009.09.008Mula, J., Peidro, D., and Poler, R., 2010b. The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand.International Journal of Production Economics, In pressPark *, Y. B. (2005). An integrated approach for production and distribution planning in supply chain management. International Journal of Production Research, 43(6), 1205-1224. doi:10.1080/00207540412331327718Peidro, D., Mula, J., Poler, R., & Lario, F.-C. (2008). Quantitative models for supply chain planning under uncertainty: a review. The International Journal of Advanced Manufacturing Technology, 43(3-4), 400-420. doi:10.1007/s00170-008-1715-yPeidro, D., Mula, J., Poler, R., & Verdegay, J.-L. (2009). Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160(18), 2640-2657. doi:10.1016/j.fss.2009.02.021Selim, H., Araz, C., & Ozkarahan, I. (2008). Collaborative production–distribution planning in supply chain: A fuzzy goal programming approach. Transportation Research Part E: Logistics and Transportation Review, 44(3), 396-419. doi:10.1016/j.tre.2006.11.001Selim, H., & Ozkarahan, I. (2006). A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 36(3-4), 401-418. doi:10.1007/s00170-006-0842-6Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214. doi:10.1016/j.fss.2007.08.010Haehling von Lanzenauer, C., & Pilz-Glombik, K. (2002). Coordinating supply chain decisions: an optimization model. OR Spectrum, 24(1), 59-78. doi:10.1007/s291-002-8200-3Zimmermann, H.-J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55. doi:10.1016/0165-0114(78)90031-

Possibilistic compositions and state functions: application to the order promising process for perishables

"This is an Author's Accepted Manuscript of an article published in Grillo, H., M.M.E. Alemany, A. Ortiz, and B. De Baets. 2019. Possibilistic Compositions and State Functions: Application to the Order Promising Process for Perishables. International Journal of Production Research 57 (22). Informa UK Limited: 7006 31. doi:10.1080/00207543.2019.1574039, available online at: https://www.tandfonline.com/doi/full/10.1080/00207543.2019.1574039"[EN] In this paper, we propose the concepts of the composition of possibilistic variables and state functions. While in conventional compositional data analysis, the interdependent components of a deterministic vector must add up to a specific quantity, we consider such components as possibilistic variables. The concept of state function is intended to describe the state of a dynamic variable over time. If a state function is used to model decay in time, it is called the ageing function. We present a practical implementation of our concepts through the development of a model for a supply chain planning problem, specifically the order promising process for perishables. We use the composition of possibilistic variables to model the existence of different non-homogeneous products in a lot (sub-lots with lack of homogeneity in the product), and the ageing function to establish a shelf life-based pricing policy. To maintain a reasonable complexity and computational efficiency, we propose the procedure to obtain an equivalent interval representation based on alpha -cuts, allowing to include both concepts by means of linear mathematical programming. Practical experiments were conducted based on data of a Spanish supply chain dedicated to pack and distribute oranges and tangerines. The results validated the functionality of both, the compositions of possibilistic variables and ageing functions, showing also a very good performance in terms of the interpretation of a real problem with a good computational performance.We would also thank Dr. José De Jesús Arias García for useful discussions during the development of this work. This research has been supported by the Ministry of Science, Technology and Telecommunications, government of Costa Rica (MICITT), through the Program of Innovation and Human Capital for Competitiveness (PINN) (contract number PED-019-2015-1). We acknowledge the partial support of the project 691249, RUCAPS: Enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems , funded by the European Union s research and innovation programme under the H2020 Marie Skłodowska-Curie Actions.Grillo-Espinoza, H.; Alemany Díaz, MDM.; Ortiz Bas, Á.; De Baets, B. (2019). Possibilistic compositions and state functions: application to the order promising process for perishables. International Journal of Production Research. 57(22):7006-7031. https://doi.org/10.1080/00207543.2019.1574039S700670315722Grillo, H., M. Alemany, and A. Ortiz. 2016b. Modelling Pricing Policy Based on Shelf-Life of Non-Homogeneous Available-To-Promise in Fruit Supply Chains, 608–617. doi:10.1007/978-3-319-45390-3_52Steglich, M., and T. Schleiff. 2010. “CMPL: Coliop Mathematical Programming Language.” Technische Hochschule Wildau. doi:10.15771/978-3-00-031701-9

Modelling Pricing Policy Based on Shelf-Life of Non Homogeneous Available-To-Promise in Fruit Supply Chains

[EN] Fruit Supply Chains (SCs) are influenced by uncontrollable natural factors causing heterogeneity in their products, as regards certain attributes that are relevant to customers and vary over time because of the shelf-life. As a consequence customers should be served not only with the required quantity and due date as usual, but also with the quality, freshness and homogeneity specified in their orders. The order promising process (OPP) is based on the uncommitted availability of homogeneous product quantities in planned lots (ATP) that are uncertain. Therefore, there is a risk of not being reliable in the commitments because of discrepancies between the real and planned homogeneous quantities. Furthermore, due to the shelf-life (SL), serving customers with the freshest product introduce the risk of increasing waste because of the aging process. To efficiently manage these risks, this work proposes a mathematical model for handling the heterogeneous ATP in fruit SCs and a pricing policy based on the product SL in the moment of delivery. In order to illustrate the application of the modelling approach, a short numerical example is introduced. The example evidences a conflictive situation when optimizing the assignation of homogeneous ATP between serving orders with fresh and more valuable product, what could lead to increase the risk of having waste because of expiration, and consequently, more costs and less profit.This research has been supported by the Ministry of Science, Technology and Telecommunications, government of Costa Rica (MICITT), through the program of innovation and human capital for competitiveness (PINN) (PED-019-2015-1).Grillo-Espinoza, H.; Alemany Díaz, MDM.; Ortiz Bas, Á. (2016). Modelling Pricing Policy Based on Shelf-Life of Non Homogeneous Available-To-Promise in Fruit Supply Chains. IFIP Advances in Information and Communication Technology. 480:608-617. https://doi.org/10.1007/978-3-319-45390-3_52S608617480Alarcon, F., Alemany, M.M.E., Lario, F.C., Oltra, R.F.: The lack of homogeneity in the product (LHP) in the ceramic tile industry and its impact on the reallocation of inventories. Boletin Soc. Espanola Ceram. Vidr. 50, 49–57 (2011). doi: 10.3989/cyv.072011Alemany, M.M.E., Grillo, H., Ortiz, A., Fuertes-Miquel, V.S.: A fuzzy model for shortage planning under uncertainty due to lack of homogeneity in planned production lots. Appl. Math. Model. (2015). doi: 10.1016/j.apm.2014.12.057Alemany, M.M.E., Lario, F.-C., Ortiz, A., Gomez, F.: Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: an illustration of a ceramic case. Appl. Math. Model. 37, 3380–3398 (2013). doi: 10.1016/j.apm.2012.07.022Blanco, A.M., Masini, G., Petracci, N., Bandoni, J.A.: Operations management of a packaging plant in the fruit industry. J. Food Eng. 70, 299–307 (2005). doi: 10.1016/j.jfoodeng.2004.05.075Grillo, H., Alemany, M.M.E., Ortiz, A.: A review of mathematical models for supporting the order promising process under Lack of Homogeneity in Product and other sources of uncertainty. Comput. Ind. Eng. 91, 239–261 (2016)Kilic, O.A., van Donk, D.P., Wijngaard, J., Tarim, S.A.: Order acceptance in food processing systems with random raw material requirements. Spectrum 32, 905–925 (2010). doi: 10.1007/s00291-010-0213-4Lin, J.T., Hong, I.H., Wu, C.H., Wang, K.S.: A model for batch available-to-promise in order fulfillment processes for TFT-LCD production chains. Comput. Ind. Eng. 59, 720–729 (2010). doi: 10.1016/j.cie.2010.07.026Maihami, R., Karimi, B.: Optimizing the pricing and replenishment policy for non-instantaneous deteriorating items with stochastic demand and promotional efforts. Comput. Oper. Res. 51, 302–312 (2014). doi: 10.1016/j.cor.2014.05.022Mundi, M.I., Alemany, M.M.E., Poler, R., Fuertes-Miquel, V.S.: Fuzzy sets to model master production effectively in Make to Stock companies with Lack of Homogeneity in the Product. Fuzzy Sets Syst. 293, 95–112 (2016). http://dx.doi.org/10.1016/j.fss.2015.06.009Tsao, Y.-C., Sheen, G.-J.: Dynamic pricing, promotion and replenishment policies for a deteriorating item under permissible delay in payments. Part Spec. Issue Top. Real-Time Supply Chain Manag. 35, 3562–3580 (2008). doi: 10.1016/j.cor.2007.01.02

A multi-objective model for inventory and planned production reassignment to committed orders with homogeneity requirements

[EN] Certain industries are characterized by obtaining non-homogeneous units of the same product. However, customers require homogeneity in some attributes between units of the same and different products requesting in their orders. To commit such orders, an estimation of the homogeneous product to be obtained can be used. Unfortunately, estimations of homogenous product quantities can differ considerably from real distributions. This fact could entail the impossibility of accomplishing the delivery of customer orders in the terms previously committed. To solve this, we propose a multi-objective mathematical programming model to reallocate already available homogeneous products in stock and planned production to committed orders. The main contributions of this model are the consideration of the homogeneity requirement between units of different lines of the same order, the allowance of partial deliveries of order lines, and the specification of some relevant attributes of products to accomplish with the customer homogeneity requirement. Different hypotheses are proved through experiments and statistical analyses applied to a ceramic tile company. The epsilon-constraint method is used to obtain an implementable solution for the company. The weighted sum method is used when proving other hypotheses that offer some managerial insights to companies.This work was supported by the Program of Formation of University Professors (FPU) of the Spanish Ministry of Education, Culture and Sport (FPU15/03595), and by the Spanish Ministry of Economy and Competitiveness Project DPI2011-23597.Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á.; Peidro Payá, D. (2018). A multi-objective model for inventory and planned production reassignment to committed orders with homogeneity requirements. Computers & Industrial Engineering. 124:180-194. https://doi.org/10.1016/j.cie.2018.07.025S18019412

Structural elements of coordination mechanisms in collaborative planning processes and their assessment through maturity models: Application to a ceramic tile company

Maturity is defined as a measure to evaluate the capabilities of an organization in regards to a certain discipline. The Collaborative Planning Process is a very complex process and Coordination mechanisms are especially relevant in this field to align the plans of the supply chain members. The objective of this paper is to develop a maturity model and a methodology to perform assessment for the Structural Elements of Coordination Mechanisms in the Collaborative Planning Process. Structural elements are specified in order to characterize coordination mechanisms in a collaborative planning context and they have been defined as key areas to be assessed by the maturity model. The identified structural elements are: number of decision-makers, collaboration level, interdependence relationships nature, interdepen-dence relationships type, number of coordination mechanisms, information exchanged, information processing, decision sequence characteristics and stopping criteria. Structural elements are assessed using the scheme of five levels: Initial, Repeatable, Defined, Managed and Optimized. This proposal has been applied to a ceramic tile company and the results are also reported.Cuenca, L.; Boza Garcia, A.; Alemany Díaz, MDM.; Trienekens, JJ. (2013). Structural elements of coordination mechanisms in collaborative planning processes and their assessment through maturity models: Application to a ceramic tile company. Computers in Industry. 64(8):898-911. doi:10.1016/j.compind.2013.06.019S89891164

Conceptual framework for the characterization of the order promising process in a collaborative selling network context

In recent years, the creation of supply chains (SCs) and selling networks (SNs) has offered firms new business opportunities based on collaboration and cooperation. One of these opportunities consists in a better response to dynamic market needs by marketing products/services packages (PS-Ps) with greater added value for the client. The aim of this work is to define a conceptual framework to support the characterization, design, and/or management of one of the key processes in client satisfaction and operational efficiency: the order promising process (OPP) in the context of collaborative selling network (CSN), which markets PS-Ps. To attain this objective, a research method based initially on the search for studies was followed to help define a conceptual framework for OPP characterization, and also the elements and structure it should have. On the basis of this initial search, a structure for a conceptual framework for the characterization of the OPP is proposed and specific studies that assist in determining and defining the contents of such a conceptual framework are analyzed. With the proposed conceptual framework, the members of a CSN can easily and systematically identify and define all the aspects that influence the OPP in a collaborative context, and may design the process in such a way that client satisfaction and SN efficiency are maximized. Furthermore, the framework structure and contents may be useful to analyze existing literature from a scientific point of view, thus enabling the identification of future lines of research.Order promising process (OPP) Conceptual framework Collaborative selling network (CSN) Product/services package (PS-P) Supply chain (SC)

Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: An illustration of a ceramic case

Lack of homogeneity in the product (LHP) appears in some production processes which incorporate raw materials that originate directly from nature and/or production processes with operations that confer heterogeneity to the characteristics of the outputs obtained, even when the inputs used are homogeneous. Poor LHP management may have a very negative impact on the customer service level and on the supply chains’ operation costs, especially when the customer needs to be served with homogeneous units of one same product. One of the key processes for suitable LHP management is the order-promising process. This work presents a mathematical programming order-promising model for make-to-stock environments with LHP. The model considers two objectives placed within a single objective by the weighted sum method. For the purpose of testing the validity of the proposed model and to evaluate the characteristics of the solutions obtained in different scenarios, numerical experiments based on realistic data from a ceramic tile company have been conducted. The results show that better results are obtained for the defined performance measures if multiple objectives are considered when promising orders than the single objective of maximizing profits. Furthermore, the superiority of the results obtained from the proposed model, if compared with current company practice, proves the model’s utility.This research has been carried out in the framework of the project funded by the Spanish Ministry of Science and Technology, entitled "Personalizacion en Masa y Cadenas de Suministro Inteligentes, con Productos y Procesos Complejos (PER-MACASI)" Ref. DPI 2008-06788-C02-01, and the project funded by the Spanish Ministry of Economy and Competitiveness (Ref. DPI2011-23597) and the Polytechnic University of Valencia (Ref. PAID-06-11/1840) entitled "Methods and models for operations planning and order management in supply chains characterized by uncertainty in production due to the lack of product uniformity" (PLANGES-FHP)".Alemany Díaz, MDM.; Lario Esteban, FC.; Ortiz Bas, Á.; 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. https://doi.org/10.1016/j.apm.2012.07.022S3380339837