Metaheuristics for a bi-objective green vendor managed inventory problem in a two-echelon supply chain network

Document Type : Article

Authors

1 Department of Industrial Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran

2 - Department of Industrial Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran. - Departamento de Ingeniera Industrial, Tecnologico de Monterrey, Puebla Campus, 72453, Mexico

3 Department of Electrical Engineering, Ecole de Technologie Superieure, University of Quebec, Montreal, Canada

4 - School of Mechanical Engineering, Shandong University, Jinan, 250061, China - Key Laboratory of High Efficiency and Clean Mechanical Manufacture (Ministry of Education), Shandong University, Jinan 250061, China - National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China

Abstract

A bi-objective non-linear optimization model with the goal of maximizing the profit of inventory and minimizing the carbon emissions of transportation, simultaneously, is developed. Another contribution of this work is to propose three capable metaheuristics to solve it optimality in large-scale samples. In this regard, the Non-dominated Sorting Genetic Algorithm (NSGA-II) as a well-known method as well as Multi-Objective of Keshtel Algorithm (MOKA) and Multi-Objective of Red Deer Algorithm (MORDA) are firstly applied in this research area. The results of metaheuristics are checked by the ε-constraint method in a set of small-scale samples as compared with the results of literature. Finally, the outputs confirm that the allowed shortage situation along with the lack of cost reduction shows a greater amount of shipping and orders. As such, the performance of MORDA is approved in comparison with MOKA and NSGA-II through different criteria.

Keywords

References

  • References:

    • Tian, G., Ren, Y., and Feng, Y., et al., “Modeling and planning for dual-objective selective disassembly using AND/OR graph and discrete artificial bee colony.” IEEE Transactions on Industrial Informatics, 15(4), pp. 2456-2468 (2018).
    • Abdi, A., Abdi, A., and Fathollahi-Fard, A. M., et al., “A set of calibrated metaheuristics to address a closed-loop supply chain network design problem under uncertainty.” International Journal of Systems Science: Operations & Logistics, pp. 1-18 (2019).
    • Tian, G., Zhang, H., and Feng, Y., et al., “Operation patterns analysis of automotive components remanufacturing industry development in China.” Journal of cleaner production, 164, pp. 1363-1375 (2017).
    • Zhang, H., Peng, Y., and Hou, L., et al., “A hybrid multi-objective optimization approach for energy-absorbing structures in train collisions.” Information Sciences, 481, pp. 491-506 (2019).
    • Tyan, J., and Wee, H. M., “Vendor managed inventory: a survey of the Taiwanese grocery industry.” Journal of Purchasing and Supply Management, 9(1), pp. 11-18 (2003).
    • Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., and Tian, G., “An adaptive Lagrangian relaxation-based algorithm for a coordinated water supply and wastewater collection network design problem.” Information Sciences. 512, pp. 1335-1359 (2020).
    • Liu, X., Tian, G., Fathollahi-Fard, A. M., et al., “Evaluation of ship’s green degree using a novel hybrid approach combining group fuzzy entropy and cloud technique for the order of preference by similarity to the ideal solution theory”, Clean Technologies and Environmental Policy, 22, pp. 493-512 (2020).
    • Fathollahi-Fard, A. M., Govindan, K., Hajiaghaei-Keshteli, M., et al., “A green home health care supply chain: New modified simulated annealing algorithms”, Journal of Cleaner Production, 139, pp. 118200 (2019).
    • Tian, G., Hao, N., Zhou, M., et al., “Fuzzy grey Coquet integral for evaluation of multicriteria decision making problems with interactive and qualitative indices”. IEEE Transactions on Systems, Man, and Cybernetics: Systems. doi: 10.1109/TSMC.2019.2906635 (2019).
    • Wang, W. T., Wee, H. M., and Tsao, H. S. J., “Revisiting the note on supply chain integration in vendor-managed inventory”. Decision Support Systems, 48(2), pp. 419-420 (2010).
    • Weraikat, D., Zanjani, M. K., and Lehoux, N., “Improving sustainability in a two-level pharmaceutical supply chain through Vendor-Managed Inventory system”, Operations Research for Health Care, 21, pp. 44-55 (2019).
    • Devika, K., Jafarian, A., and Nourbakhsh, V., “Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques”,European Journal of Operational Research, 235(3), pp. 594-615 (2014).
    • Hajiaghaei-Keshteli, M., and Sajadifar, S. M., “Deriving the cost function for a class of three-echelon inventory system with N-retailers and one-for-one ordering policy”, The International Journal of Advanced Manufacturing Technology, 50(1-4), pp. 343-351 (2010).
    • Hajiaghaei-Keshteli, M., Sajadifar, S. M., and Haji, R. “Determination of the economical policy of a three-echelon inventory system with (R, Q) ordering policy and information sharing”, The International Journal of Advanced Manufacturing Technology, 55(5-8), pp. 831-841 (2011).
    • Waller, M., Johnson, M. E., and Davis, T., “Vendor-managed inventory in the retail supply chain”,Journal of business logistics, 20(1), pp. 183-192 (1999).
    • Darwish, M. A., and Odah, O. M. “Vendor managed inventory model for single-vendor multi-retailer supply chains.”European Journal of Operational Research, 204(3), pp. 473-484 (2010).
    • Kaasgari, M. A., Imani, D. M., and Mahmoodjanloo, M., “Optimizing a vendor managed inventory (VMI) supply chain for perishable products by considering discount: Two calibrated meta-heuristic algorithms”,Computers & Industrial Engineering, 103, pp. 227-241, (2017).
    • Kırılmaz, O., and Erol, S. “A proactive approach to supply chain risk management: Shifting orders among suppliers to mitigate the supply side risks”, Journal of Purchasing and Supply Management, 23(1), pp. 54-65 (2017).
    • Angulo, A., Nachtmann, H., and Waller, M. A., “Supply chain information sharing in a vendor managed inventory partnership”,Journal of business logistics, 25(1), pp. 101-120 (2004).
    • Yao, Y., Evers, P. T., and Dresner, M. E., “Supply chain integration in vendor-managed inventory”,Decision support systems, 43(2), pp. 663-674 (2007).
    • Kwak, C., Choi, J. S., and Kim, C. O., et al., “Situation reactive approach to Vendor Managed Inventory problem”,Expert Systems with Applications, 36(5), pp. 9039-9045 (2009).
    • Tyan, J., and Wee, H. M., “Vendor managed inventory: a survey of the Taiwanese grocery industry”.Journal of Purchasing and Supply Management, 9(1), pp. 11-18 (2003).
    • Dong, Y., and Xu, K. “A supply chain model of vendor managed inventory”,Transportation research part E: logistics and transportation review, 38(2), pp. 75-95 (2002).
    • Diabat, A., “Hybrid algorithm for a vendor managed inventory system in a two-echelon supply chain”,European Journal of Operational Research, 238(1), pp. 114-121 (2014).
    • Nachiappan, S. P., and Jawahar, N., “A genetic algorithm for optimal operating parameters of VMI system in a two-echelon supply chain”,European Journal of Operational Research, 182(3), pp. 1433-1452 (2007).
    • Sadeghi, J., Sadeghi, S., and Niaki, S. T. A., “A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: An NSGA-II with tuned parameters”,Computers & Operations Research, 41, pp. 53-64 (2014).
    • Nia, A. R., Far, M. H., and Niaki, S. T. A., “A hybrid genetic and imperialist competitive algorithm for green vendor managed inventory of multi-item multi-constraint EOQ model under shortage”,Applied Soft Computing, 30, pp. 353-364 (2015).
    • Park, Y. B., Yoo, J. S., and Park, H. S., “A genetic algorithm for the vendor-managed inventory routing problem with lost sales”,Expert Systems with Applications, 53, pp. 149-159 (2016).
    • Lee, J. Y., Cho, R. K., and Paik, S. K., “Supply chain coordination in vendor-managed inventory systems with stockout-cost sharing under limited storage capacity”,European Journal of Operational Research, 248(1), pp. 95-106 (2016).
    • Khan, M., Jaber, M. Y., and Zanoni, S., et al., “Vendor managed inventory with consignment stock agreement for a supply chain with defective items”,Applied Mathematical Modelling, 40(15), pp. 7102-7114 (2016).
    • Pasandideh, S. H. R., Niaki, S. T. A., and Nia, A. R.., “A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model”,Expert Systems with Applications, 38(3), pp. 2708-2716 (2011).
    • Nia, A. R., Far, M. H., and Niaki, S. T. A., “A fuzzy vendor managed inventory of multi-item economic order quantity model under shortage: An ant colony optimization algorithm”,International Journal of Production Economics, 155, pp. 259-271 (2014).
    • Hariga, M., Gumus, M., and Daghfous, A., “Storage constrained vendor managed inventory models with unequal shipment frequencies”,Omega, 48, pp. 94-106 (2014).
    • Taleizadeh, A. A., Noori-daryan, M., and Cárdenas-Barrón, L. E., “Joint optimization of price, replenishment frequency, replenishment cycle and production rate in vendor managed inventory system with deteriorating items”,International Journal of Production Economics, 159, pp. 285-295 (2015).
    • Beklari, A., Nikabadi, M. S., and Farsijani, H., et al., “A Hybrid Algorithm for Solving Vendors Managed Inventory (VMI) Model with the Goal of Maximizing Inventory Turnover in Producer Warehouse”, Industrial Engineering & Management Systems, 17(3), pp. 570-587 (2018).
    • Safaeian, M., Fathollahi-Fard, A. M., and Tian, G., et al., “A multi-objective supplier selection and order allocation through incremental discount in a fuzzy environment”, Journal of Intelligent & Fuzzy Systems, 37(1), pp. 1435-1455 (2019).
    • Dai, Z., Gao, K., and Giri, B. C. “A hybrid heuristic algorithm for cyclic inventory-routing problem with perishable products in VMI supply chain”, Expert Systems with Applications, 214, pp. 113322 (2020).
    • Wolpert, D. H., and Macready, W. G., “No free lunch theorems for optimization”, IEEE transactions on evolutionary computation, 1(1), pp. 67-82 (1997).
    • Golmohamadi, S., Tavakkoli-Moghaddam, R., and Hajiaghaei-Keshteli, M., “Solving a fuzzy fixed charge solid transportation problem using batch transferring by new approaches in meta-heuristic”,Electronic Notes in Discrete Mathematics, 58, pp. 143-150 (2017).
    • Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., and Mirjalili, S., “A set of efficient heuristics for a home healthcare problem”, Neural Computing and Applications, 32(10), pp. 6185-6205 (2020).
    • Bahadori-Chinibelagh, S., Fathollahi-Fard, A. M., and Hajiaghaei-Keshteli, M., “Two Constructive Algorithms to Address a Multi-Depot Home Healthcare Routing Problem”, IETE Journal of Research, pp. 1-7 (2019).
    • Fathollahi-Fard, A. M., Ranjbar-Bourani, M., and Cheikhrouhou, N., et al., “Novel modifications of social engineering optimizer to solve a truck scheduling problem in a cross-docking system”, Computers & Industrial Engineering, 137, pp. 106103 (2019).
    • Holland, J. H., “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence”, University of Michigan Press, Michigan, Ann Arbor (1975).
    • Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T., “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II”, In International Conference on Parallel Problem Solving From Nature(pp. 849-858). Springer, Berlin, Heidelberg, (2000, September).
    • Hajiaghaei-Keshteli, M., and Aminnayeri, M., “Keshtel Algorithm (KA); a new optimization algorithm inspired by Keshtels’ feeding”, InProceeding in IEEE Conference on Industrial Engineering and Management Systems, 2249-2253 (2013).
    • Fard, A. M. F., and Hajiaghaei-Keshteli, M., “Red deer algorithm (RDA); a new optimization algorithm inspired by red deer's mating” InInternational Conference on Industrial Engineering, IEEE., 12, 331-342 (2016).
    • Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M. and Tavakkoli-Moghaddam, R., “Red deer algorithm (RDA): a new nature-inspired meta-heuristic”, Soft Computing, 1007/s00500-020-04812-z (2020).
    • Nasiri, E., Afshari, A. J., and Hajiaghaei-Keshteli, M., “Addressing the freight consolidation and containerization by recent and hybridized meta-heuristic algorithms”, International Journal of Engineering-Transactions C: Aspects,30(3), pp. 403-412.
    • Fathollahi-Fard, A. M., Azari, M. N., and Hajiaghaei-Keshteli, M., “An Improved Red Deer Algorithm to Address a Direct Current Brushless Motor Design Problem”, Scientia Iranica, pp., 1-25 (2019).
    • Fathollahi-Fard, A. M., Ahmadi, A., and Sajadieh, M. S. “An Efficient Modified Red Deer Algorithm to Solve a Truck Scheduling Problem Considering Time Windows and Deadline for Trucks’ Departure”, Evolutionary Computation in Scheduling, 6, pp. 137-167 (2020).
    • Haimes, Y. Y., Ladson, L. S., and Wismer, D. A., “Bicriterion formulation of problems of integrated system identification and system optimization”,IEEE Transactions on Systems Man and Cybernetics, (3), pp. 296-308 (1971).
    • Mayers, R. H., Montgomery, D. C. and Anderson-Cook, C. M., “Response surface methodology: process and product optimization using designed experiments”, John Wiley & Sons Inc (2009).
Scientia Iranica
Volume 29, Issue 2
Transactions on Industrial Engineering (E)
March and April 2022
Pages 816-837

Files

Share

How to cite

Statistics