Optimizing lot sizing model for perishable bread products using genetic algorithm

Hayati Mukti Asih; Raden Achmad Chairdino  Leuveano; Dhimas Arief  Dharmawan

doi:10.30656/jsmi.v7i2.7172

Hayati Mukti Asih Universitas Ahmad Dahlan, Yogyakarta http://orcid.org/0000-0002-1163-6560
Raden Achmad Chairdino Leuveano Universitas Pembangunan Nasional "Veteran" Yogyakarta https://orcid.org/0000-0001-9980-9966
Dhimas Arief Dharmawan Universitas Pembangunan Nasional "Veteran" Yogyakarta

https://doi.org/10.30656/jsmi.v7i2.7172

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Keywords: genetic algorithm, inventory, Lot sizing, Perishable product

Abstract

This research addresses order planning challenges related to perishable products, using bread products as a case study. The problem is how to efficiently manage the various bread products ordered by diverse customers, which requires distributors to determine the optimal number of products to order from suppliers. This study aims to formulate the problem as a lot-sizing model, considering various factors, including customer demand, inventory constraints, ordering capacity, return rate, and defect rate, to achieve a near or optimal solution, Therefore determining the optimal order quantity to reduce the total ordering cost becomes a challenge in this study. However, most lot sizing problems are combinatorial and difficult to solve. Thus, this study uses the Genetic Algorithm (GA) as the main method to solve the lot sizing model and determine the optimal number of bread products to order. With GA, experiments have been conducted by combining the values of population, crossover, mutation, and generation parameters to maximize the feasibility value that represents the minimal total cost. The results obtained from the application of GA demonstrate its effectiveness in generating near or optimal solutions while also showing fast computational performance. By utilizing GA, distributors can effectively minimize wastage arising from expired or perishable products while simultaneously meeting customer demand more efficiently. As such, this research makes a significant contribution to the development of more effective and intelligent decision-making strategies in the domain of perishable products in bread distribution.

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References

R. A. C. Leuveano, F. A. Bin Jafar, C. Saleh, and M. R. Bin Muhamad, ‘Incorporating Transportation Cost into Joint Economic Lot Size For Single Vendor-Buyer’, J. Softw., vol. 9, no. 5, pp. 1313–1323, May 2014, doi: https://doi.org/10.4304/jsw.9.5.1313-1323.

H. Irwan et al., ‘A Review of Integration ModelOF Lot-Sizing-Scheduling Problem’, Malaysian Constr. Res. J., vol. 17, no. 3, pp. 160–174, 2023, [Online]. Available: https://www.cream.my/usr/product.aspx?pgid=88&id=58&lang=en.

I. Tiseo, ‘Annual household food waste produced in selected countries worldwide as of 2020’, Statista, 2023. https://www.statista.com/statistics/933083/food-waste-of-selected-countries/#:~:text=China%20and%20India%20produce%20more,metric%20tons%20every%20year,%20respectively.

A. Y. Yalciner, ‘Determination of the Cost-Effective Lot-Sizing Technique for Perishable Goods: A Case Study’, Int. J. Manag. Adm., vol. 5, no. 9, pp. 33–46, 2021, [Online]. Available: https://dergipark.org.tr/en/pub/ijma/issue/60472/867955.

R. As’ad, M. Hariga, and A. Shamayleh, ‘Sustainable dynamic lot sizing models for cold products under carbon cap policy’, Comput. Ind. Eng., vol. 149, no. January, p. 106800, Nov. 2020, doi: https://doi.org/10.1016/j.cie.2020.106800.

J. A. Muckstadt and A. Sapra, Principles of Inventory Management. New York, NY: Springer New York, 2010, doi: https://doi.org/10.1007/978-0-387-68948-7.

Z. Hammoudan, ‘Production and Delivery Integrated Scheduling Problems in Multi-Transporter Multi-Customer Supply Chain with Costs Considerations’, Université de Technologie de Belfort-Montbeliard, 2018, [Online]. Available: https://theses.hal.science/tel-01864063/document.

M. Kırcı, I. Biçer, and R. W. Seifert, ‘Optimal replenishment cycle for perishable items facing demand uncertainty in a two-echelon inventory system’, Int. J. Prod. Res., vol. 57, no. 4, pp. 1250–1264, Feb. 2019, doi: https://doi.org/10.1080/00207543.2018.1504244.

V. Polotski, A. Gharbi, and J. P. Kenne, ‘Production control in manufacturing systems with perishable products under periodic demand’, J. Manuf. Syst., vol. 63, pp. 288–303, 2022, doi: https://doi.org/10.1016/j.jmsy.2022.03.013.

M. Dehghani, B. Abbasi, and F. Oliveira, ‘Proactive transshipment in the blood supply chain: A stochastic programming approach’, Omega, vol. 98, p. 102112, 2021, doi: https://doi.org/10.1016/j.omega.2019.102112.

B. Adenso-Díaz, S. Lozano, and A. Palacio, ‘Effects of dynamic pricing of perishable products on revenue and waste’, Appl. Math. Model., vol. 45, pp. 148–164, May 2017, doi: https://doi.org/10.1016/j.apm.2016.12.024.

O. Kaya and H. Bayer, ‘Pricing and lot-sizing decisions for perishable products when demand changes by freshness’, J. Ind. Manag. Optim., vol. 17, no. 6, p. 3113, 2021, doi: https://doi.org/10.3934/jimo.2020110.

R. Li, J.-T. Teng, and C.-T. Chang, ‘Lot-sizing and pricing decisions for perishable products under three-echelon supply chains when demand depends on price and stock-age’, Ann. Oper. Res., vol. 307, no. 1–2, pp. 303–328, Dec. 2021, doi: https://doi.org/10.1007/s10479-021-04272-0.

L. Feng, Y.-L. Chan, and L. E. Cárdenas-Barrón, ‘Pricing and lot-sizing polices for perishable goods when the demand depends on selling price, displayed stocks, and expiration date’, Int. J. Prod. Econ., vol. 185, pp. 11–20, Mar. 2017, doi: https://doi.org/10.1016/j.ijpe.2016.12.017.

J. C. Chen, Y.-Y. Chen, and Y. Liang, ‘Application of a genetic algorithm in solving the capacity allocation problem with machine dedication in the photolithography area’, J. Manuf. Syst., vol. 41, pp. 165–177, Oct. 2016, doi: https://doi.org/10.1016/j.jmsy.2016.08.010.

M. N. A. Rahman, R. A. C. Leuveano, F. A. Bin Jafar, C. Saleh, and B. M. Deros, ‘Total cost reduction using a genetic algorithm for multi-vendor and single manufacturer’, Int. J. Math. Model. Methods Appl. Sci., vol. 9, no. January 2023, pp. 566–575, 2015, [Online]. Available: https://www.naun.org/main/NAUN/ijmmas/2015/b302001-407.pdf.

H. Guner Goren, S. Tunali, and R. Jans, ‘A review of applications of genetic algorithms in lot sizing’, J. Intell. Manuf., vol. 21, no. 4, pp. 575–590, Aug. 2010, doi: https://doi.org/10.1007/s10845-008-0205-2.

S. H. R. Pasandideh, S. T. A. Niaki, and A. R. Nia, ‘A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model’, Expert Syst. Appl., vol. 38, no. 3, pp. 2708–2716, Mar. 2011, doi: https://doi.org/10.1016/j.eswa.2010.08.060.

A. Azadeh, S. Elahi, M. H. Farahani, and B. Nasirian, ‘A genetic algorithm-Taguchi based approach to inventory routing problem of a single perishable product with transshipment’, Comput. Ind. Eng., vol. 104, no. February 2017, pp. 124–133, Feb. 2017, doi: https://doi.org/10.1016/j.cie.2016.12.019.

S. Wang, J. Hui, B. Zhu, and Y. Liu, ‘Adaptive Genetic Algorithm Based on Fuzzy Reasoning for the Multilevel Capacitated Lot-Sizing Problem with Energy Consumption in Synchronizer Production’, Sustainability, vol. 14, no. 9, p. 5072, Apr. 2022, doi: https://doi.org/10.3390/su14095072.

S. S. Kurade and R. Latpate, ‘Demand and deterioration of items per unit time inventory models with shortages using genetic algorithm’, J. Manag. Anal., vol. 8, no. 3, pp. 502–529, Jul. 2021, doi: https://doi.org/10.1080/23270012.2020.1829113.

S. Panda, S. Saha, and M. Basu, ‘Optimal pricing and lot-sizing for perishable inventory with price and time dependent ramp-type demand’, Int. J. Syst. Sci., vol. 44, no. 1, pp. 127–138, Jan. 2013, doi: https://doi.org/10.1080/00207721.2011.598956.

A. Rahman and H. M. Asih, ‘Optimizing shipping routes to minimize cost using particle swarm optimization’, Int. J. Ind. Optim., vol. 1, no. 1, pp. 53–60, Feb. 2020, doi: https://doi.org/10.12928/ijio.v1i1.1605.

A. Moghaddas and S. M. H. Hosseini, ‘Short-term scheduling of hybrid thermal, pumped-storage, and wind plants using firefly optimization algorithm’, Int. J. Ind. Optim., vol. 3, no. 2, pp. 80–97, Sep. 2022, doi: https://doi.org/10.12928/ijio.v3i2.5994.

T. Nguyen, H.-N. Dinh, V.-T. Nguyen, B. S. Do, T. T. Nguyen, and B.-L. Do, ‘Ecpoc: an evolutionary computation-based proof ofcriteriaconsensus protocol’, Int. J. Ind. Optim., vol. 3, no. 2, pp. 98–109, 2022, doi: https://doi.org/10.12928/ijio.v3i2.6049.

R. Perez-Rodriguez, ‘An estimation of distribution algorithm for combinatorial optimization problems’, Int. J. Ind. Optim., vol. 3, no. 1, pp. 47–67, Feb. 2022, doi: https://doi.org/10.12928/ijio.v3i1.5862.

M. N. Khasanah and H. M. Asih, ‘Developing Simulation Optimization Model to Minimize Total Inventory Cost under Uncertain Demand’, Proc. Second Asia Pacific Int. Conf. Ind. Eng. Oper. Manag., pp. 1998–2007, 2021, [Online]. Available: http://ieomsociety.org/proceedings/2021indonesia/377.pdf.

A. Hassan, C. Saleh, B. Md Deros, M. N. Ab Rahman, R. A. C. Leuveano, and A. Adiyoga, ‘Parameter Optimization of VMI System in a Manufacturer and Multi Retailer Using Genetic Algorithm’, Adv. Mater. Res., vol. 1115, no. January 2023, pp. 622–626, Jul. 2015, doi: https://doi.org/10.4028/www.scientific.net/AMR.1115.622.

C. K. Eng and H. M. Asih, ‘An integrated robust optimization model of capacity planning under demand uncertainty in electronic industry’, Int. J. Mech. Mechatronics Eng., vol. 15, no. 3, pp. 88–96, 2015, [Online]. Available: https://www.ijens.org/IJMMEVol15Issue03.html.

H. M. Asih and K. E. Chong, ‘An Integrated of Robust Optimization and TOPSIS model for Capacity Planning under Demand Uncertainty’, 2015, [Online]. Available: https://www.researchgate.net/profile/Kuan-Chong/publication/283352474.

I. Slama, O. Ben-Ammar, A. Dolgui, and F. Masmoudi, ‘Genetic algorithm and Monte Carlo simulation for a stochastic capacitated disassembly lot-sizing problem under random lead times’, Comput. Ind. Eng., vol. 159, p. 107468, Sep. 2021, doi: https://doi.org/10.1016/j.cie.2021.107468.

B. Le Badezet, F. Larroche, O. Bellenguez, and G. Massonnet, ‘A Genetic Algorithm for a Capacitated Lot-Sizing Problem with Lost Sales, Overtimes and Safety Stock Constraints’, Commun. Comput. Inf. Sci., vol. 1541 CCIS, pp. 170–181, 2022, doi: https://doi.org/10.1007/978-3-030-94216-8_13.

M. Liu, H. Tang, F. Chu, F. Zheng, and C. Chu, ‘Joint optimization of lot-sizing and pricing with backlogging’, Comput. Ind. Eng., vol. 167, p. 107979, 2022, doi: https://doi.org/10.1016/j.cie.2022.107979.

M. Darwish, ‘Lot-Sizing And Scheduling Optimization using Genetic Algorithm’, University of Skovde, 2018, [Online]. Available: https://www.diva-portal.org/smash/get/diva2:1323785/FULLTEXT01.pdf.

M. Vahdani, Z. Sazvar, and K. Govindan, ‘An integrated economic disposal and lot-sizing problem for perishable inventories with batch production and corrupt stock-dependent holding cost’, Ann. Oper. Res., vol. 315, no. 2, pp. 2135–2167, Aug. 2022, doi: https://doi.org/10.1007/s10479-021-04110-3.

H. M. Asih, R. A. C. Leuveano, A. Rahman, and M. Faishal, 'Traveling Salesman Problem with Prioritisation for Perishable Products in Yogyakarta, Indonesia', J. Adv. Manuf. Technol., vol. 16, no.3, pp. 15-27, 2022, [Online]. Available: https://jamt.utem.edu.my/jamt/article/view/6405/3995.

R. A. C. Leuveano, F. A. B. Jafar, & M. R. B. Muhamad. 'Incorporating transportation costs into integrated inventory model for single supplier and single purchaser'. Adv. Sci. Letters, vol. 20, no. 1, pp. 290-293, 2014, doi: https://doi.org/10.1166/asl.2014.5262.

S. Li, J. Zhang, and W. Tang, ‘Joint dynamic pricing and inventory control policy for a stochastic inventory system with perishable products’, Int. J. Prod. Res., vol. 53, no. 10, pp. 2937–2950, May 2015, doi: https://doi.org/10.1080/00207543.2014.961206.

T. Singh and H. Pattanayak, ‘An EOQ inventory model for deteriorating items with linear demand, salvage value and partial backlogging’, Int. J. Appl. Eng. Res., vol. 11, no. 9, pp. 6479–6484, 2016, [Online]. Available: http://www.ripublication.com/ijaer16/ijaerv11n9_70.pdf.

L. He, H. Gao, X. Zhang, Q. Wang, and C. Hu, ‘Optimal Replenishment for Perishable Products with Inventory-Dependent Demand and Backlogging under Continuous and Discrete Progressive Payments’, Sustainability, vol. 10, no. 10, p. 3723, Oct. 2018, doi: https://doi.org/10.3390/su10103723.

Optimizing lot sizing model for perishable bread products using genetic algorithm

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