References
- Aitchison, J. 1982. “The Statistical Analysis of Compositional Data.” Journal of the Royal Statistical Society. Series B (Methodological) 44 (2): 139–177. doi:0035-9246/82/44139BaseDatosRef.00 doi: 10.1111/j.2517-6161.1982.tb01195.x
- Aitchison, J., and J. J. Egozcue. 2005. “Compositional Data Analysis: Where are We and Where Should We be Heading?” Mathematical Geology 37 (7): 829–850. doi:10.1007/s11004-005-7383-7
- Alemany, M. M. E., H. Grillo, A. Ortiz, and V. S. Fuertes-Miquel. 2015. “A Fuzzy Model for Shortage Planning under Uncertainty Due to Lack of Homogeneity in Planned Production Lots.” Applied Mathematical Modelling 39 (15): 4463–4481. doi:10.1016/j.apm.2014.12.057
- Alemany, M. M. E., F. C. Lario, A. Ortiz, and F. Gomez. 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.2012.07.022
- Ball, M. O., C.-Y. Chen, and Z.-Y. Zhao. 2004. Available to Promise, 447–483. Boston, MA: Springer US. doi:10.1007/978-1-4020-7953-5_11
- Dubois, D., H. Fargier, and P. Fortemps. 2003. “Fuzzy Scheduling: Modelling Flexible Constraints vs. Coping with Incomplete Knowledge.” Fuzzy Sets in Scheduling and Planning 147 (2): 231–252. doi:10.1016/S0377-2217(02)00558-1
- Dubois, D., and H. Prade. 1988. Possibility Theory: An Approach to Computerized Processing of Uncertainty. Springer Science & Business Media. doi:10.1007/978-1-4684-5287-7.
- Entrup, M. L., H. O. Gunther, P. Van Beek, M. Grunow, and T. Seiler. 2005. “Mixed-Integer Linear Programming Approaches to Shelf-Life-Integrated Planning and Scheduling in Yoghurt Production.” International Journal of Production Research 43 (23): 5071–5100. doi:10.1080/00207540500161068
- Grillo, H., M. M. E. Alemany, and A. Ortiz. 2016a. “A Review of Mathematical Models for Supporting the Order Promising Process under Lack of Homogeneity in Product and Other Sources of Uncertainty.” Computers & Industrial Engineering 91: 239–261. doi:10.1016/j.cie.2015.11.013
- Grillo, 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_52
- Grillo, H., M. Alemany, A. Ortiz, and V. Fuertes-Miquel. 2017a. “Mathematical Modelling of the Order-Promising Process for Fruit Supply Chains Considering the Perishability and Subtypes of Products.” Applied Mathematical Modelling 49: 255–278. ISSN 0307-904X. doi:10.1016/j.apm.2017.04.037
- Grillo, H., M. M. E. Alemany, A. Ortiz, and J. Mula. 2017b. “A Fuzzy Order Promising Model with Non-Uniform Finished Goods.” International Journal of Fuzzy Systems 1–22. doi:10.1007/s40815-017-0317-y
- Grillo, H., D. Peidro, M. M. E. Alemany, and J. Mula. 2015. “Application of Particle Swarm Optimisation with Backward Calculation to Solve a Fuzzy Multi-Objective Supply Chain Master Planning Model.” International Journal of Bio-Inspired Computation 7 (3): 157–169. doi:10.1504/IJBIC.2015.069557
- Haijema, R. 2013. “A New Class of Stock-Level Dependent Ordering Policies for Perishables with a Short Maximum Shelf Life.” International Journal of Production Economics 143 (2): 434–439. doi:10.1016/j.ijpe.2011.05.021
- Kilic, O. A., R. Akkerman, D. P. van Donk, and M. Grunow. 2013. “Intermediate Product Selection and Blending in the Food Processing Industry.” International Journal of Production Research 51 (1): 26–42. doi:10.1080/00207543.2011.640955
- Mula, J., D. Peidro, M. Dí?az-Madroñero, and E. Vicens. 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.2009.09.008
- Nureize, A., and J. Watada. 2010. “A Fuzzy Regression Approach to a Hierarchical Evaluation Model for Oil Palm Fruit Grading.” Fuzzy Optimization and Decision Making 9 (1): 105–122. doi:10.1007/s10700-010-9072-3
- Pawlowsky-Glahn, V., and A. Buccianti. 2011. Compositional Data Analysis: Theory and Applications. Chichester: John Wiley & Sons. ISBN 0-470-71135-3.
- Pawlowsky-Glahn, V., and J. J. Egozcue. 2016. “Spatial Analysis of Compositional Data: A Historical Review.” Journal of Geochemical Exploration 164: 28–32. doi:10.1016/j.gexplo.2015.12.010
- Peidro, D., J. Mula, and R. Poler. 2010. “Fuzzy Linear Programming for Supply Chain Planning under Uncertainty.” International Journal of Information Technology & Decision Making 09 (03): 373–392. doi:10.1142/S0219622010003865
- Peidro, D., J. Mula, R. Poler, and J.-L. Verdegay. 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.2009.02.021
- Puccetti, G., and R. Wang. 2015. “Detecting Complete and Joint Mixability.” Journal of Computational and Applied Mathematics 280: 174–187. doi:10.1016/j.cam.2014.11.050
- Slotnick, S. A. 2011. “Optimal and Heuristic Lead-Time Quotation for an Integrated Steel Mill with a Minimum Batch Size.” European Journal of Operational Research 210 (3): 527–536. doi:10.1016/j.ejor.2010.09.031
- Steglich, M., and T. Schleiff. 2010. “CMPL: Coliop Mathematical Programming Language.” Technische Hochschule Wildau. doi:10.15771/978-3-00-031701-9.
- Wang, B., and R. Wang. 2016. “Joint Mixability.” Mathematics of Operations Research 41 (3): 808–826. doi:10.1287/moor.2015.0755
- Zadeh, L. 1978. “Fuzzy Sets as a Basis for a Theory of Possibility.” Fuzzy Sets and Systems 1 (1): 3–28. doi:10.1016/0165-0114(78)90029-5
- Zeng, W., Y. Shi, and H. Li. 2006. “Representation Theorem of Interval-Valued Fuzzy Set.” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14 (03): 259–269. doi:10.1142/S0218488506003996