Decision Support for Allocating Farmed Fish to Customer Orders Using a bi-objective Optimization Model

• Autorzy: Sunniva Haukvik Knudseth , Even Molland , Lars Magnus Hvattum , Johan Oppen
• Języki publikacji: EN

Abstrakty

EN

Aquaculture is an important industry in certain coastal areas. Focusing on the farming of salmon and trout, an operational planning problem arises with the goal of allocating a supply of fish to the demand t hat is expressed t hrough c ustomer orders. This paper provides a conceptual model of such a planning problem and defines a corresponding bi-objective mathematical programming model. The problem is novel with respect to the structure of fish transport and the rules for satisfying customer orders with respect to fish size, quality, certification, and health status. Computational experiments have been conducted to gain further insight into the use of the provided model to provide support for planners who are involved in operational decision-making. The results indicated that the bi-objective optimization model can be useful in situations where a supply is insufficient to cover all of the demand within a given planning horizo(original abstract)

Słowa kluczowe

EN

Supply chain; Fish; Theory of decision support

PL

Łańcuch dostaw; Ryby; Teoria wspomagania decyzji

• Czasopismo: Decision Making in Manufacturing and Services
• Rocznik: 2022
• Tom: 16
• Strony: 67--90

Twórcy

autor

Sunniva Haukvik Knudseth

• Molde University College

autor

Even Molland

• Molde University College

autor

Lars Magnus Hvattum

• Molde University College

autor

Johan Oppen

• Molde University College

Bibliografia

• Abdollahi Saadatlu E., Barzinpour F. & Yaghoubi S. (2022). A sustainable model for municipal solid waste system considering global warming potential impact: A case study. Computers & Industrial Engineering, 169, art. no. 108127. doi: 10.1016/j.cie.2022.108127.
• Abedi A. & Zhu W. (2017). An optimisation model for purchase, production and distribution in fish supply chain - A case study. International Journal of Production Research, 55(12), pp. 3451-3464. doi: 10.1080/00207543.2016.1242800.
• Ahumada O. & Villalobos J.R. (2009). Application of planning models in the agrifood supply chain: A review. European Journal of Operational Research, 196(1), pp. 1-20. doi: 10.1016/j.ejor.2008.02.014.
• Ahumada O. & Villalobos J.R. (2011). Operational model for planning the harvest and distribution of perishable agricultural products. International Journal of Production Economics, 133(2), pp. 677-687. doi: 10.1016/j.ijpe.2011.05.015.
• Amorim P., Günther H.-O. & Almada-Lobo B. (2012). Multi-objective integrated production and distribution planning of perishable products. International Journal of Production Economics, 138(1), pp. 89-101. doi: 10.1016/j.ijpe.2012.03.005.
• Chowdhury M. & Tan P. (2004). A multi-objective decision-making framework for transportation investments. Journal of the Transportation Research Forum, 43(1), pp. 91-104. doi: 10.22004/ag.econ.206723.
• Ebrahimi S. & Bagheri E. (2022). Optimizing profit and reliability using a bi-objective mathematical model for oil and gas supply chain under disruption risks. Computers & Industrial Engineering, 163(100), art. no. 107849. doi: 10.1016/j.cie.2021.107849.
• Fan Z., Li S. & Gao Z. (2019). Multiobjective sustainable order allocation problem optimization with improved genetic algorithm using priority encoding. Mathematical Problems in Engineering, 2019, art. no. 8218709. doi: 10.1155/2019/8218709.
• Fasihi M., Tavakkoli-Moghaddam R., Najafi S. & Hajiaghaei M. (2021a). Optimizing a bi-objective multi-period fish closed-loop supply chain network design by three multi-objective meta-heuristic algorithms. Scientia Iranica. doi: 10.24200/sci.2021. 57930.5477.
• Fasihi M., Tavakkoli-Moghaddam R., Najafi S. & Hajiaghaei-Keshteli M. (2021b). Developing a bi-objective mathematical model to design the fish closed-loop supply chain. International Journal of Engineering, 34(5), pp. 1257-1268. doi: 10.5829/ ije.2021.34.05b.19.
• Ghasemi P., Khalili-Damghani K., Hafezalkotob A. & Raissi S. (2019). Uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation model for earthquake evacuation planning. Applied Mathematics and Computation, 350(100), pp. 105-132. doi: 10.1016/j.amc.2018.12.061.
• Gholizadeh H., Jahani H., Abareshi A. & Goh M. (2021). Sustainable closed-loop supply chain for dairy industry with robust and heuristic optimization. Computers & Industrial Engineering, 157, art. no. 107324. doi: 10.1016/j.cie.2021.107324.
• Hwang C.-L. & Masud A.S.M. (1979). Multiple Objective Decision Making. A Stateof-the-Art Survey. Springer Verlag. doi: 10.1007/978-3-642-45511-7.
• Jia R., Liu Y. & Bai X. (2020). Sustainable supplier selection and order allocation: Distributionally robust goal programming model and tractable approximation. Computers & Industrial Engineering, 140, art. no. 106267. doi: 10.1016/j.cie.2020.106267.
• Kaviani M.A., Peykam A., Khan S.A., Brahimi N. & Niknam R. (2020). A new weighted fuzzy programming model for supplier selection and order allocation in the food industry. Journal of Modelling in Management, 15(2), pp. 381-406. doi: 10.1108/JM2-11-2018-0191.
• Kiwa Norway (2021). Mattrygghet og akvakultur [in Norwegian]. https://www.kiwa. com/no/no/vaare-tjenester/sertifisering/mattrygghet-og-akvakultur/ [10.05.2021].
• Koldborg Jensen T., Nielsen J., Larsen E.P. & Clausen J. (2010). The fish industry - toward supply chain modeling. Journal of Aquatic Food Product Technology, 19(3-4), pp. 214-226. doi: 10.1080/10498850.2010.508964.
• Mavrotas G. (2009). Effective implementation of the ε-constraint method in MultiObjective Mathematical Programming problems. Applied Mathematics and Computation, 213(2), pp. 455-465. doi: 10.1016/j.amc.2009.03.037.
• Moheb-Alizadeh H. & Handfield R. (2019). Sustainable supplier selection and order allocation: A novel multi-objective programming model with a hybrid solution approach. Computers & Industrial Engineering, 129, pp. 192-209. doi: 10.1016/j.cie. 2019.01.011.
• Mosallanezhad B., Hajiaghaei-Keshteli M. & Triki C. (2021). Shrimp closed-loop supply chain network design. Soft Computing, 25(11), pp. 7399-7422. doi: 10.1007/ s00500-021-05698-1.
• Musavi M. & Bozorgi-Amiri A. (2017). A Multi-objective sustainable hub locationscheduling problem for perishable food supply chain. Computers & Industrial Engineering, 113(100), pp. 766-778. doi: 10.1016/j.cie.2017.07.039.
• Norwegian Food Safety Authority (2020). Fish health. https://www.mattilsynet.no/ fisk_og_akvakultur/fiskehelse/ [10.05.2021].
• NOU (2019). Skattlegging av havbruksvirksomhet. Taxation of aquaculture activities, Finansdepartement.
• Rabbani M., Amirhossein Sadati S. & Farrokhi-Asl H. (2020). Incorporating location routing model and decision making techniques in industrial waste management: Application in the automotive industry. Computers & Industrial Engineering, 148, art. no. 106692. doi: 10.1016/j.cie.2020.106692.
• Seitz A., Grunow M. & Akkerman R. (2020). Data driven supply allocation to individual customers considering forecast bias. International Journal of Production Economics, 227, art. no. 107683. doi: 10.1016/j.ijpe.2020.107683.
• Sharma R. & Darbari J.D. (2021). Integrated optimization model for sustainable supplier selection and order allocation in food supply chain. In: A. Tiwari, K. Ahuja, A. Yadav, J.C. Bansal, K. Deep & A.K. Nagar (Eds.). Soft Computing for Problem Solving, Springer, pp. 557-572.
• Wang S. & Ma S. (2018). Efficient methods for a bi-objective nursing home location and allocation problem: A case study. Applied Soft Computing, 65, pp. 280-291. doi: 10.1016/j.asoc.2018.01.014.
• Zhang Z., Guo C., Ruan W., Wang W. & Zhou P. (2022). An intelligent stochastic optimization approach for stochastic order allocation problems with high-dimensional order uncertainties. Computers & Industrial Engineering, 167, art. no. 108008. doi: 10.1016/j.cie.2022.108008

Identyfikatory

DOI

10.7494/dmms.2022.16.5019