IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v44y2022i3d10.1007_s10878-022-00891-w.html
   My bibliography  Save this article

A sustainable supply chain network considering lot sizing with quantity discounts under disruption risks: centralized and decentralized models

Author

Listed:
  • Parisa Rafigh

    (South Tehran Branch, Islamic Azad University)

  • Ali Akbar Akbari

    (South Tehran Branch, Islamic Azad University)

  • Hadi Mohammadi Bidhandi

    (South Tehran Branch, Islamic Azad University)

  • Ali Husseinzadeh Kashan

    (South Tehran Branch, Islamic Azad University
    Tarbiat Modares University)

Abstract

This study proposes a framework for the main parties of a sustainable supply chain network considering lot-sizing impact with quantity discounts under disruption risk among the first studies. The proposed problem differs from most studies considering supplier selection and order allocation in this area. First, regarding the concept of the triple bottom line, total cost, environmental emissions, and job opportunities are considered to cover the criteria of sustainability. Second, the application of this supply chain network is transformer production. Third, applying an economic order quantity model lets our model have a smart inventory plan to control the uncertainties. Most significantly, we present both centralized and decentralized optimization models to cope with the considered problem. The proposed centralized model focuses on pricing and inventory decisions of a supply chain network with a focus on supplier selection and order allocation parts. This model is formulated by a scenario-based stochastic mixed-integer non-linear programming approach. Our second model focuses on the competition of suppliers based on the price of products with regard to sustainability. In this regard, a Stackelberg game model is developed. Based on this comparison, we can see that the sum of the costs for both levels is lower than the cost without the bi-level approach. However, the computational time for the bi-level approach is more than for the centralized model. This means that the proposed optimization model can better solve our problem to achieve a better solution than the centralized optimization model. However, obtaining this better answer also requires more processing time. To address both optimization models, a hybrid bio-inspired metaheuristic as the hybrid of imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) is utilized. The proposed algorithm is compared with its individuals. All employed optimizers have been tuned by the Taguchi method and validated by an exact solver in small sizes. Numerical results show that striking similarities are observed between the results of the algorithms, but the standard deviations of PSO and ICA–PSO show better behavior. Furthermore, while PSO consumes less time among the metaheuristics, the proposed hybrid metaheuristic named ICA–PSO shows more time computations in all small instances. Finally, the provided results confirm the efficiency and the performance of the proposed framework and the proposed hybrid metaheuristic algorithm.

Suggested Citation

  • Parisa Rafigh & Ali Akbar Akbari & Hadi Mohammadi Bidhandi & Ali Husseinzadeh Kashan, 2022. "A sustainable supply chain network considering lot sizing with quantity discounts under disruption risks: centralized and decentralized models," Journal of Combinatorial Optimization, Springer, vol. 44(3), pages 1387-1432, October.
    • Handle: RePEc:spr:jcomop:v:44:y:2022:i:3:d:10.1007_s10878-022-00891-w
    DOI: 10.1007/s10878-022-00891-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-022-00891-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10878-022-00891-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Erfan Babaee Tirkolaee & Zahra Dashtian & Gerhard-Wilhelm Weber & Hana Tomaskova & Mehdi Soltani & Nasim Sadat Mousavi, 2021. "An Integrated Decision-Making Approach for Green Supplier Selection in an Agri-Food Supply Chain: Threshold of Robustness Worthiness," Mathematics, MDPI, vol. 9(11), pages 1-30, June.
    2. Venegas, Bárbara B. & Ventura, José A., 2018. "A two-stage supply chain coordination mechanism considering price sensitive demand and quantity discounts," European Journal of Operational Research, Elsevier, vol. 264(2), pages 524-533.
    3. Castellano, D. & Glock, C. H., 2021. "The average-cost formulation of lot sizing models and inventory carrying charges: a technical note," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 127109, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Christian Scheller & Kerstin Schmidt & Thomas Stefan Spengler, 2021. "Decentralized master production and recycling scheduling of lithium-ion batteries: a techno-economic optimization model," Journal of Business Economics, Springer, vol. 91(2), pages 253-282, March.
    5. Yu, Haijun & Dai, Hongliang & Tian, Guangdong & Wu, Benben & Xie, Yinghao & Zhu, Ying & Zhang, Tongzhu & Fathollahi-Fard, Amir Mohammad & He, Qi & Tang, Hong, 2021. "Key technology and application analysis of quick coding for recovery of retired energy vehicle battery," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    6. Davide Castellano & Christoph H. Glock, 2021. "The average-cost formulation of lot sizing models and inventory carrying charges: a technical note," Operations Management Research, Springer, vol. 14(1), pages 194-201, June.
    7. Shuting Li & Xiangfeng Chen, 2019. "The role of supplier collaboration and risk management capabilities in managing product complexity," Operations Management Research, Springer, vol. 12(3), pages 146-158, December.
    8. Snyder, Lawrence V. & Daskin, Mark S., 2006. "A random-key genetic algorithm for the generalized traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 174(1), pages 38-53, October.
    9. Mehdi Soltanifar & Hamid Sharafi, 2022. "A modified DEA cross efficiency method with negative data and its application in supplier selection," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 265-296, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qingsheng Zhu & Kai Gao & Jia-Bao Liu, 2023. "Cloud model for new energy vehicle supply chain management based on growth expectation," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-23, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Debabrata Das & Sameer Kumar & Nirmal Baran Hui & Vipul Jain & Charu Chandra, 2023. "Pricing and revenue-based outsourcing strategies in a multi-echelon lot-sizing model under insufficient production capacity," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(6), pages 514-530, December.
    2. Bernardino, Raquel & Paias, Ana, 2018. "Solving the family traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 267(2), pages 453-466.
    3. Alev Ozer Torgaloz & Mehmet Fatih Acar & Cemil Kuzey, 2023. "The effects of organizational learning culture and decentralization upon supply chain collaboration: analysis of covid-19 period," Operations Management Research, Springer, vol. 16(1), pages 511-530, March.
    4. Wang, Moran & Guo, Xiaolong & Wang, Shouyang, 2022. "Financial hedging in two-stage sustainable commodity supply chains," European Journal of Operational Research, Elsevier, vol. 303(2), pages 803-818.
    5. Liu, Chang-Yi & Wang, Hui & Tang, Juan & Chang, Ching-Ter & Liu, Zhi, 2021. "Optimal recovery model in a used batteries closed-loop supply chain considering uncertain residual capacity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    6. Wang, Mengmeng & Liu, Kang & Dutta, Shanta & Alessi, Daniel S. & Rinklebe, Jörg & Ok, Yong Sik & Tsang, Daniel C.W., 2022. "Recycling of lithium iron phosphate batteries: Status, technologies, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    7. Katerina Fotova Čiković & Ivana Martinčević & Joško Lozić, 2022. "Application of Data Envelopment Analysis (DEA) in the Selection of Sustainable Suppliers: A Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(11), pages 1-30, May.
    8. Fu, Yuqiang & Yuan, Tao & Zhu, Xiaoyan, 2019. "Importance-measure based methods for component reassignment problem of degrading components," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    9. He Huang & Liwei Zhong & Ting Shen & Huixin Wang, 2022. "Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3778-3791, December.
    10. Xiongyong Zhou & Madeleine Pullman & Zhiduan Xu, 2022. "The impact of food supply chain traceability on sustainability performance," Operations Management Research, Springer, vol. 15(1), pages 93-115, June.
    11. Qiu, Xuan & Lee, Chung-Yee, 2019. "Quantity discount pricing for rail transport in a dry port system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 563-580.
    12. Qingsheng Zhu & Kai Gao & Jia-Bao Liu, 2023. "Cloud model for new energy vehicle supply chain management based on growth expectation," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-23, July.
    13. Raunaq Srivastav & Pritee Ray, 2020. "Contracts Choice in Retailer-led Supply Chain," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 19(1), pages 77-90, June.
    14. Mohammad Reza Gholamian & Mahdi Ebrahimzadeh-Afruzi, 2021. "Credit and discount incentive options for two-level supply chain coordination, under uncertain price-dependent demand," Operational Research, Springer, vol. 21(4), pages 2283-2307, December.
    15. Johari, Maryam & Hosseini-Motlagh, Seyyed-Mahdi & Rasti-Barzoki, Morteza, 2019. "An evolutionary game theoretic model for analyzing pricing strategy and socially concerned behavior of manufacturers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 506-525.
    16. Li, Yuming & Wang, Tingyu & Li, Xinxi & Zhang, Guoqing & Chen, Kai & Yang, Wensheng, 2022. "Experimental investigation on thermal management system with flame retardant flexible phase change material for retired battery module," Applied Energy, Elsevier, vol. 327(C).
    17. Seyed Jafar Sadjadi & Hashem Asadi & Ramin Sadeghian & Hadi Sahebi, 2018. "Retailer Stackelberg game in a supply chain with pricing and service decisions and simple price discount contract," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-24, April.
    18. Arab Momeni, Mojtaba & Bagheri, Mehdi, 2022. "Shared warehouse as an inter-supply chain cooperation strategy to reduce the time-dependent deterioration costs," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    19. Solomon Feleke & Balamurali Pydi & Raavi Satish & Degarege Anteneh & Kareem M. AboRas & Hossam Kotb & Mohammed Alharbi & Mohamed Abuagreb, 2023. "DE-Based Design of an Intelligent and Conventional Hybrid Control System with IPFC for AGC of Interconnected Power System," Sustainability, MDPI, vol. 15(7), pages 1-23, March.
    20. Dontas, Michael & Sideris, Georgios & Manousakis, Eleftherios G. & Zachariadis, Emmanouil E., 2023. "An adaptive memory matheuristic for the set orienteering problem," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1010-1023.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v:44:y:2022:i:3:d:10.1007_s10878-022-00891-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.