Abstract
This paper addresses the cost minimization problem of an integrated production-inventory model which has optimized by analytical method and evolutionary algorithm. We have formulated our model for items that deteriorate with respect to time and follow Weibull distribution. For controlling deterioration rate, we have used preservation technology. Further, we assumed that ordering cost is lot size dependent. Classical optimization methods demonstrate a number of difficulties when faced with complex problems. Moreover, most of the classical optimization methods do not have the global perspective and often get converged to a locally optimum solution. Genetic algorithm (GA) is an adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics. In this model, we optimized our model by gradient-based analytical method and GA in integrated as well as independent scenario. Numerical example is carried out. Sensitivity of different inventory parameters is carried out. The results of the proposed model help researchers to think about optimizing their complex problems using different evolutionary search algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alejo-Reyes, A., Mendoza, A., & Olivares-Benitez, E. (2021). A heuristic method for the supplier selection and order quantity allocation problem. Applied Mathematical Modelling, 90, 1130–1142.
Bakker, M., Reizebos, J., & Teunter, R. H. (2012). Review of inventory system with deterioration since 2001. European Journal of Operational Research, 221(2), 275–284.
Banerjee, A. (1986). A joint economic-lot-size model for purchaser and vendor. Decision Sciences, 17(3), 292–311.
Chang, Y. (2013). The effect of preservation technology investment on a non instantaneous deteriorating inventory model. Omega, 41, 872–880.
Chung, K. J., & Cardenas-Barron, L. E. (2013). The simplified solution procedure for deteriorating items under stock-dependent demand and two-level trade-credit in the supply chain management. Applied Mathematical Modelling, 37(7), 4653–4660.
Chung, K. J., Cardenas-Barron, L. E., & Ting, P. S. (2014). An inventory model with non-instantaneous receipt and exponentially deteriorating items for an integrated three layer supply chain system under two levels of trade credit. International Journal of Production Economics, 155, 310–317.
Dave, U., & Patel, L. K. (1981). (T, Si) policy inventory model for deteriorating items with time proposal demand. Journal of Operational Research Society, 32, 137–142.
Donaldson, W. A. (1977). Inventory replenishment policy for a linear trend in demand—An analytical solution. Operation Research Quarterly, 28(3), 663–670.
Ghare, P. M., & Schrader, G. P. (1963). A model for an exponentially decaying inventory. Journal of Industrial Engineering, 14(5), 238–243.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley.
Goren, G. H., Tunali, S., & Jans, R. (2008). A review of applications of genetic algorithms in lot sizing. Journal of Intelligent Manufacturing.
Goyal, S. K. (1976). An integrated inventory model for a single supplier-single customer problem. International Journal of Production Research, 15(1), 107–111.
Mahapatra, A. S., Sarkar, B., Mahapatra, M. S., Soni, H. N., & Mazumder, S. K. (2019). Development of a fuzzy economic order quantity model of deteriorating items with promotional effort and learning in fuzziness with a finite time horizon. Inventions, 4, 36.
Manna, S. K., & Chaudhuri, K. S. (2001). An economic order quantity model for deteriorating items with time-dependent deterioration rate, demand rate, unit production cost and shortages. International Journal of Production Research, 32(8), 1003–1009.
Mishra, P., & Talati, I. (2015). A genetic algorithm approach for an inventory model when ordering cost is lot size dependent. International Journal of Latest Technology in Engineering, Management & Applied Science, 4(2), 92–97.
Mishra, P., & Talati, I. (2018). Quantity discount for integrated supply chain model with back order and controllable deterioration rate. Yogoslav Journal of Operation Research, 28(3), 355–369.
Mishra, V. K. (2013). Deteriorating inventory model using preservation technology with salvage value and shortages. APEM Journal, 8(3), 185–192.
Mishra, V. K., & Singh, L. S. (2011). Deteriorating inventory model for time dependent demand and holding cost with partial backlogging. International Journal of Management Science and Engineering Management, 6(4), 267–271.
Mishra, V. K., Singh, L. S., & Kumar, R. (2013). An inventory model for deteriorating items with time dependent demand and time varying holding cost under partial backlogging. International Journal of Industrial Engineering, 9(4), 1–5.
Murata, T., Ishibuchi, H., & Tanaka, H. (1996). Multi objective algorithm and its applications to flow shop scheduling. Computers & Industrial Engineering, 30(4), 954–968.
Narmadha, S., Selladurai, V., & Sathish, G. (2010). Multiproduct inventory optimization using uniform crossover genetic algorithm. IJCSIS, 7(1), 170–179.
Philip, G. C. (1974). A generalized EOQ model for items with Weibull distribution. AIIE Transactions, 6(2), 159–162.
Philip, G. C., & Covert, R. P. (1973). An EOQ model for items with Weibull distribution deterioration. AIIE Transactions, 5(4), 323–326.
Radhakrishnan, P., Prasad, V. M., & Jeyanthi, N. (2009). Inventory optimization in supply chain management using genetic algorithm. International Journal of Computer Science and Network Security, 9(1), 33–40.
Radhakrishnan, P., Prasad, V. M., & Jeyanthi, N. (2010). Design of genetic algorithm based supply chain inventory optimization with lead time. International Journal of Computer Science and Network Security, 10(3), 1820–1826.
Shah, N. H. (2015). Manufacturer-retailer inventory model for deteriorating items with price-sensitive credit-linked demand under two-level trade credit financing and profit sharing contract. Cogent Engineering, 2(1).
Shah, N. H., Shah, D. B., & Patel, D. G. (2015). Optimal pricing and ordering policies for inventory system with two-level trade credits under price-sensitive trended demand. International Journal of Applied and Computational Mathematics, 1(1), 101–110.
Singh, S., & Rathore, H. (2015). Optimal payment policy with preservation technology investment and shortages under trade credit. Indian Journal of Science and Technology, 8(S7), 203–212.
Talati, I., & Mishra, P. (2019). Optimal production integrated inventory model with quadratic demand for deteriorating items under inflation using genetic algorithm. Investigacion Operacional, 40(3).
Wee, H. M., & Wang, W. T. (1999). A variable production scheduling policy for deteriorating items with time varying demand. Computers & Operations Research, 26(3), 237–254.
Woarawichai, C., Kuruvit, K., & Vashirawongpinyo, P. (2012). Applying genetic algorithms for inventory lot-sizing problem with supplier selection under storage capacity. Constraints, 9(1), 18–23.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Talati, I., Mishra, P., Shaikh, A. (2021). An Analytic and Genetic Algorithm Approach to Optimize Integrated Production-Inventory Model Under Time-Varying Demand. In: Shah, N.H., Mittal, M. (eds) Soft Computing in Inventory Management. Inventory Optimization. Springer, Singapore. https://doi.org/10.1007/978-981-16-2156-7_8
Download citation
DOI: https://doi.org/10.1007/978-981-16-2156-7_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2155-0
Online ISBN: 978-981-16-2156-7
eBook Packages: Business and ManagementBusiness and Management (R0)