IDEAS home Printed from https://ideas.repec.org/a/apb/japsss/2019p21-29.html
   My bibliography  Save this article

Pareto-based algorithm for adaptive aggregate production and distribution planning in shrimp agroindustry supply chain

Author

Listed:
  • L. Herlina

    (Faculty of Agricultural Technology, IPB University, Bogor Agricultural University, Bogor, Indonesia)

  • Machfud

    (Faculty of Agricultural Technology, IPB University, Bogor Agricultural University, Bogor, Indonesia)

  • E. Anggraeni

    (Faculty of Agricultural Technology, IPB University, Bogor Agricultural University, Bogor, Indonesia)

  • Sukardi

    (Faculty of Agricultural Technology, IPB University, Bogor Agricultural University, Bogor, Indonesia)

Abstract

In the global supply chain, the integration of production and distribution is one of the important activities that must be carried out. This also applies to the shrimp agroindustry supply chain. The shrimp agroindustry is one of the agro-food industries that deals with processing raw shrimp into various frozen shrimp products. The demand for frozen shrimp products is very diverse, while the supply of raw shrimp consists of various sizes and has perishable properties. To fulfill consumer demand, aggregate production planning must be made adaptively. Adaptive means being able to improve aggregate planning due to changes in demand. Integration of adaptive aggregate production and distribution planning will result in better planning. Based on this, we developed an adaptive aggregate production and distribution model for the shrimp agroindustry supply chain. Non-dominated Sorting Genetic Algorithm II (NSGA-II) which is a pareto-based algorithm is used to solve the problem. The aim is to minimize total costs and maximize service levels. The sample problem from the shrimp agroindustry in East Java is used to show the efficiency of the proposed algorithm.

Suggested Citation

  • L. Herlina & Machfud & E. Anggraeni & Sukardi, 2019. "Pareto-based algorithm for adaptive aggregate production and distribution planning in shrimp agroindustry supply chain," Journal of Applied and Physical Sciences, Prof. Vakhrushev Alexander, vol. 5(1), pages 21-29.
  • Handle: RePEc:apb:japsss:2019:p:21-29
    DOI: 10.20474/japs-5.1.3
    as

    Download full text from publisher

    File URL: https://tafpublications.com/platform/Articles/full-japs5.1.3.php
    Download Restriction: no

    File URL: https://tafpublications.com/gip_content/paper/Japs-5.1.3.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.20474/japs-5.1.3?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
    ---><---

    References listed on IDEAS

    as
    1. Ahmad Najim Noorzad & Takuro Sato, 2017. "Multi-Criteria Fuzzy-Based Handover Decision System for Heterogeneous Wireless Networks," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 3(4), pages 159-168.
    2. Rihab Khemiri & Khaoula Elbedoui-Maktouf & Bernard Grabot & Belhassen Zouari, 2017. "A fuzzy multi-criteria decision making approach for managing performance and risk in integrated procurement-production planning," Post-Print hal-01758604, HAL.
    3. Arash Nobari & AmirSaman Khierkhah & Vahid Hajipour, 2018. "A Pareto-based approach to optimise aggregate production planning problem considering reliable supplier selection," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 29(1), pages 59-84.
    4. Savkovic B. & Kovac P. & Mankova I. & Gostimirovic M. & Rokosz K. & Rodic D., 2017. "Surface roughness modeling of semi solid aluminum milling by fuzzy logic," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 3(2), pages 34-46.
    5. Wei, Wenchao & Guimarães, Luis & Amorim, Pedro & Almada-Lobo, Bernardo, 2017. "Tactical production and distribution planning with dependency issues on the production process," Omega, Elsevier, vol. 67(C), pages 99-114.
    6. Ahumada, Omar & Rene Villalobos, J. & Nicholas Mason, A., 2012. "Tactical planning of the production and distribution of fresh agricultural products under uncertainty," Agricultural Systems, Elsevier, vol. 112(C), pages 17-26.
    7. Rihab Khemiri & Khaoula Elbedoui-Maktouf & Bernard Grabot & Belhassen Zouari, 2017. "A fuzzy multi-criteria decision-making approach for managing performance and risk in integrated procurement–production planning," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5305-5329, September.
    8. Banasik, Aleksander & Kanellopoulos, Argyris & Claassen, G.D.H. & Bloemhof-Ruwaard, Jacqueline M. & van der Vorst, Jack G.A.J., 2017. "Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 409-420.
    Full references (including those not matched with items on IDEAS)

    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. Rukundo Jean D'amour & Mukamuhirwa Floride & Nsigaye Alfred, 2020. "Effect of organic, inorganic fertilizers and their combination on vegetative growth and production of common bush beans RWR2245 variety in Rwanda," Journal of Applied and Physical Sciences, Prof. Vakhrushev Alexander, vol. 6(1), pages 18-24.
    2. Pereira, Daniel Filipe & Oliveira, José Fernando & Carravilla, Maria Antónia, 2020. "Tactical sales and operations planning: A holistic framework and a literature review of decision-making models," International Journal of Production Economics, Elsevier, vol. 228(C).
    3. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    4. Meng, Lin & Lv, Wangyong & Yuan, George Xianzhi & Wang, Huiqi, 2023. "The dynamic risk profiles and management strategies in supply chain coopetition under altruistic preference," International Review of Financial Analysis, Elsevier, vol. 90(C).
    5. Iuliana Marin & Nicolae Goga & Razvan-Constantin Stanciu, 2019. "Web application for self-diagnosis and drug recommendation based on user symptoms," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 5(2), pages 62-71.
    6. M. Vulic´ & M. Pavlovic´ & D. Tadic & A. Aleksic & A. Tomovic, 2018. "Ranking of recycling technologies metal components of end of life vehicles by using modified electre," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 4(4), pages 143-148.
    7. Yunita & Mariana Syamsudin & Wendhi Yuniarto & Freska Rolansa, 2019. "Dynamic Energy Conservation Based on Room Characteristics at Polytechnic State of Pontianak," Journal of ICT, Design, Engineering and Technological Science, Juhriyansyah Dalle, vol. 3(2), pages 39-45.
    8. Tuğçe Taşkıner & Bilge Bilgen, 2021. "Optimization Models for Harvest and Production Planning in Agri-Food Supply Chain: A Systematic Review," Logistics, MDPI, vol. 5(3), pages 1-27, August.
    9. Ramos, Francisco López & Batres, Rafael & De-la-Cruz-Márquez, Cynthia Griselle & Anzures, Melina López, 2023. "Optimization models for nopal crop planning with land usage expansion and government subsidy," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    10. Saeedeh Anvari & Metin Turkay, 2017. "The facility location problem from the perspective of triple bottom line accounting of sustainability," International Journal of Production Research, Taylor & Francis Journals, vol. 55(21), pages 6266-6287, November.
    11. Britz, Wolfgang & Ciaian, Pavel & Gocht, Alexander & Kanellopoulos, Argyris & Kremmydas, Dimitrios & Müller, Marc & Petsakos, Athanasios & Reidsma, Pytrik, 2021. "A design for a generic and modular bio-economic farm model," Agricultural Systems, Elsevier, vol. 191(C).
    12. Jawad, Hussam & Jaber, Mohamad Y. & Nuwayhid, Rida Y., 2018. "Improving supply chain sustainability using exergy analysis," European Journal of Operational Research, Elsevier, vol. 269(1), pages 258-271.
    13. Ana Esteso & M. M. E. Alemany & Angel Ortiz & Shaofeng Liu, 2022. "Optimization model to support sustainable crop planning for reducing unfairness among farmers," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(3), pages 1101-1127, September.
    14. Luo, Na & Olsen, Tava & Liu, Yanping & Zhang, Abraham, 2022. "Reducing food loss and waste in supply chain operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    15. Fatemeh Keshavarz-Ghorbani & Seyed Hamid Reza Pasandideh, 2022. "Modeling and optimizing an agro-supply chain considering different quality grades and storage systems for fresh products: a Benders decomposition solution approach," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 21-50, August.
    16. Farzad Tahriri & Ali Azadeh, 2018. "Lean Traffic Control (LTC) for Emergency Vehicles Applied in Developing Countries: Tehran Transport Planning," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 4(2), pages 57-63.
    17. Leah A. Alindayo & Jonathan C. Maglasang, 2018. "Wireless sensor network development: Targeting and control system for semi-ballistic vehicle for rapid and precise search and rescue applications," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 4(4), pages 149-161.
    18. Dong Hee Suh & Charles B. Moss, 2021. "Examining the Input and Output Linkages in Agricultural Production Systems," Agriculture, MDPI, vol. 11(1), pages 1-13, January.
    19. Soto-Silva, Wladimir E. & Nadal-Roig, Esteve & González-Araya, Marcela C. & Pla-Aragones, Lluis M., 2016. "Operational research models applied to the fresh fruit supply chain," European Journal of Operational Research, Elsevier, vol. 251(2), pages 345-355.
    20. Van Engeland, Jens & Beliën, Jeroen & De Boeck, Liesje & De Jaeger, Simon, 2020. "Literature review: Strategic network optimization models in waste reverse supply chains," Omega, Elsevier, vol. 91(C).

    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:apb:japsss:2019:p:21-29. 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: Prof. Vakhrushev Alexander (email available below). General contact details of provider: https://tafpublications.com/platform/published_papers/11 .

    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.