Document Type : Research Paper

Authors

1 Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran.

2 Nottingham Business School, Nottingham Trent University, Nottingham NG1 4FQ, UK.

Abstract

This paper deals with the modeling of the Food Bank (FB) network in the conditions of uncertainty in the demand of charities and the capacity of donating food. The importance of creating a FB network, along with providing quality food, led to consider the two objective functions of minimizing the costs of the total FB network and maximizing the minimum freshness of the food basket. The simultaneous optimization of the above two objective functions is aimed at making correct routing-inventory and allocation decisions. In this paper, food items in food baskets with high shelf-life and low shelf-life are considered. The results of solving the sample problems by combining the operators of two Genetic Algorithm (GA) and Salp Swarm Algorithm (SSA) showed that with the increase in the freshness of the food baskets, the costs of the FB network have increased. Also, the sensitivity analysis showed that the increase in uncertainty in the network leads to an increase in the cost of FB network and a decrease in the freshness of the food basket. The comparison of the results between the algorithms also showed that the efficiency of HGSSA is much higher than GA and SSA and the problem solving time by these methods is extremely lower. The use of HGSSA has increased the rate of achieving effective solutions by 14.06%.

Keywords

Main Subjects

[1]     Tarasuk, V., Dachner, N., & Loopstra, R. (2014). Food banks, welfare, and food insecurity in Canada. British food journal, 116(9), 1405–1417.
[2]     Hodges, R. J., Buzby, J. C., & Bennett, B. (2011). Postharvest losses and waste in developed and less developed countries: opportunities to improve resource use. The journal of agricultural science, 149(S1), 37–45.
[3]     Godfray, H. C. J., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Nisbett, N., ... & Whiteley, R. (2010). The future of the global food system. Philosophical transactions of the royal society B: biological sciences365(1554), 2769-2777.
[4]     Food and Agriculture Organization of the United Nations. (2011). Food loss reduction strategy. Rome: FAO. https://www.fao.org/fileadmin/user_upload/ags/publications/brochure_phl_low.pdf
[5]     Kantor, L. S., Lipton, K., Manchester, A., & Oliveira, V. (1997). Estimating and addressing America’s food losses. Food review/national food review, 20(1), 2–12.
[6]     Mena, C., Adenso-Diaz, B., & Yurt, O. (2011). The causes of food waste in the supplier--retailer interface: Evidences from the UK and Spain. Resources, conservation and recycling, 55(6), 648–658.
[7]     Ali, A. Y., & Ayele, A. (2019). Contribution of quality tools for reducing food waste in university canteen. Journal of applied research on industrial engineering, 6(2), 125–130.
[8]     Ali, A. Y., Hassen, J. M., & Wendim, G. G. (2019). Forecasting as a framework for reducing food waste in Ethiopian university canteens. Journal of applied research on industrial engineering, 6(4), 374–380.
[9]     Starkey, L. J., Gray-Donald, K., & Kuhnlein, H. V. (1999). Nutrient intake of food bank users is related to frequency of food bank use, household size, smoking, education and country of birth. The journal of nutrition, 129(4), 883–889.
[10]   González-Torre, P. L., & Coque, J. (2016). How is a food bank managed? Different profiles in Spain. Agriculture and human values, 33, 89–100.
[11]   Tavakkoli-Moghaddam, R., Ghahremani-Nahr, J., Samadi Parviznejad, P., Nozari, H., & Najafi, E. (2022). Application of internet of things in the food supply chain: a literature review. Journal of applied research on industrial engineering, 9(4), 475–492.
[12]   Ghahremani-Nahr, J., Ghaderi, A., & Kian, R. (2023). A food bank network design examining food nutritional value and freshness: A multi objective robust fuzzy model. Expert systems with applications, 215, 119272. https://www.sciencedirect.com/science/article/pii/S0957417422022904
[13]   Nozari, H., Ghahremani-Nahr, J., & Szmelter-Jarosz, A. (2023). A multi-stage stochastic inventory management model for transport companies including several different transport modes. International journal of management science and engineering management, 18(2), 134–144.
[14]   Berner, M., & O’Brien, K. (2004). The shifting pattern of food security support: Food stamp and food bank usage in North Carolina. Nonprofit and voluntary sector quarterly, 33(4), 655–672.
[15]   Ghahremani-Nahr, J., Nozari, H., & Najafi, S. E. (2020). Design a green closed loop supply chain network by considering discount under uncertainty. Journal of applied research on industrial engineering, 7(3), 238–266.
[16]   Wetherill, M. S., White, K. C., & Seligman, H. K. (2019). Nutrition-focused food banking in the United States: a qualitative study of healthy food distribution initiatives. Journal of the academy of nutrition and dietetics, 119(10), 1653–1665.
[17]   Delpish, R., Jiang, S., Davis, L., & Odubela, K. (2019). A visual analytics approach to combat confirmation bias for a local food bank [presentation]. Advances in human error, reliability, resilience, and performance: proceedings of the ahfe 2018 international conference on human error, reliability, resilience, and performance (pp. 13–23). https://link.springer.com/chapter/10.1007/978-3-319-94391-6_2
[18]   Yang, Q. (2018). Optimal allocation algorithm for sequential resource allocation in the context of food banks operations. https://ecommons.cornell.edu/handle/1813/59691
[19]   Crama, Y., Rezaei, M., Savelsbergh, M., & Woensel, T. Van. (2018). Stochastic inventory routing for perishable products. Transportation science, 52(3), 526–546.
[20]   Huang, X., Yang, S., & Wang, Z. (2021). Optimal pricing and replenishment policy for perishable food supply chain under inflation. Computers & industrial engineering, 158, 107433. https://doi.org/10.1016/j.cie.2021.107433
[21]   Martins, C. L., Melo, M. T., & Pato, M. V. (2019). Redesigning a food bank supply chain network in a triple bottom line context. International journal of production economics, 214, 234–247.
[22]   Holmes, E., Fowokan, A., Seto, D., Lear, S. A., & Black, J. L. (2019). Examining food insecurity among food bank members in Greater Vancouver. Journal of hunger & environmental nutrition, 14(1–2), 141–154.
[23]   Onggo, B. S., Panadero, J., Corlu, C. G., & Juan, A. A. (2019). Agri-food supply chains with stochastic demands: A multi-period inventory routing problem with perishable products. Simulation modelling practice and theory, 97, 101970. https://www.sciencedirect.com/science/article/pii/S1569190X19301030
[24]   Dai, Z., Gao, K., & Giri, B. C. (2020). A hybrid heuristic algorithm for cyclic inventory-routing problem with perishable products in VMI supply chain. Expert systems with applications, 153, 113322. https://doi.org/10.1016/j.eswa.2020.113322
[25]   Violi, A., Laganá, D., & Paradiso, R. (2020). The inventory routing problem under uncertainty with perishable products: an application in the agri-food supply chain. Soft computing, 24(18), 13725–13740.
[26]   Alkaabneh, F., Diabat, A., & Gao, H. O. (2021). A unified framework for efficient, effective, and fair resource allocation by food banks using an approximate dynamic programming approach. Omega, 100, 102300. https://doi.org/10.1016/j.omega.2020.102300
[27]   Mandal, J., Mitra, R., Gupta, V. K., Subramanian, N., Kayikci, Y., & Tiwari, M. K. (2021). Optimal allocation of near-expiry food in a retailer-foodbank supply network with economic and environmental considerations: An aggregator’s perspective. Journal of cleaner production, 318, 128481. https://doi.org/10.1016/j.jclepro.2021.128481
[28]   Arabsheybani, A., & Arshadi Khasmeh, A. (2021). Robust and resilient supply chain network design considering risks in food industry: Flavour industry in Iran. International journal of management science and engineering management, 16(3), 197–208.
[29]   Orjuela Castro, J. A., Orejuela-Cabrera, J. P., & Adarme-Jaimes, W. (2021). Logistics network configuration for seasonal perishable food supply chains. Journal of industrial engineering and management (JIEM), 14(2), 135–151.
[30]   Kaviyani-Charati, M., Ameli, M., Souraki, F. H., & Jabbarzadeh, A. (2022). Sustainable network design for a non-profit food bank supply chain with a heterogeneous fleet under uncertainty. Computers & industrial engineering, 171, 108442. https://doi.org/10.1016/j.cie.2022.108442
[31]   Jin, X., Ezeonwu, M., Ayad, A., & Bowman, K. (2022). Using a Food Bank as a Platform for Educating Communities during the COVID-19 Pandemic. Journal of community health nursing, 39(1), 50–57.
[32]   Sosenko, F., Bramley, G., & Bhattacharjee, A. (2022). Understanding the post-2010 increase in food bank use in England: new quasi-experimental analysis of the role of welfare policy. BMC public health, 22(1), 1363. https://link.springer.com/article/10.1186/s12889-022-13738-0
[33]   Li, J., & Song, Z. (2022). Dynamic impacts of external uncertainties on the stability of the food supply chain: Evidence from China. Foods, 11(17), 2552. https://doi.org/10.3390/foods11172552
[34]   Perdana, T., Chaerani, D., & Achmad, A. L. H. (2022). Supporting data for the integrated agent-based modelling and robust optimization on food supply network design in COVID-19 pandemic. Data in brief, 40, 107809. https://www.sciencedirect.com/science/article/pii/S235234092200021X
[35]   Orjuela-Castro, J. A., Orejuela-Cabrera, J. P., & Adarme-Jaimes, W. (2022). Multi-objective model for perishable food logistics networks design considering availability and access. OPSEARCH, 59(4), 1244–1270.
[36]   Abbas, H., Zhao, L., Faiz, N., Ullah, H., Gong, J., & Jiang, W. (2022). One belt one road influence on perishable food supply chain robustness. Environment, development and sustainability, 1–17.
[37]   Kayikci, Y., Demir, S., Mangla, S. K., Subramanian, N., & Koc, B. (2022). Data-driven optimal dynamic pricing strategy for reducing perishable food waste at retailers. Journal of cleaner production, 344, 131068. https://doi.org/10.1016/j.jclepro.2022.131068
[38]   Abbasian, M., Sazvar, Z., & Mohammadisiahroudi, M. (2023). A hybrid optimization method to design a sustainable resilient supply chain in a perishable food industry. Environmental science and pollution research, 30(3), 6080–6103. DOI:10.1007/s11356-022-22115-8
[39]   Krishnan, R., Arshinder, K., & Agarwal, R. (2022). Robust optimization of sustainable food supply chain network considering food waste valorization and supply uncertainty. Computers & industrial engineering, 171, 108499.
[40]   Akbarzadeh Sarabi, M., Ghaffari, M., & Torabi, S. A. (2022). A multi-objective sustainable supply chain network design problem for perishable foods. Journal of industrial and systems engineering, 14(3), 84–108.
[41]   Gómez-Pantoja, J. Á., Salazar-Aguilar, M. A., & González-Velarde, J. L. (2021). The food bank resource allocation problem. Top, 29, 266–286.
[42]   Orgut, I. S., Ivy, J., Uzsoy, R., & Wilson, J. R. (2016). Modeling for the equitable and effective distribution of donated food under capacity constraints. IIE transactions, 48(3), 252–266.
[43]   Ortuño, J. C., & Padilla, A. G. (2017). Assembly of customized food pantries in a food bank by fuzzy optimization. Journal of industrial engineering and management, 10(4), 663–686.
[44]   Bocewicz, G., Janardhanan, M. N., Krenczyk, D., & Banaszak, Z. (2017). Traffic flow routing and scheduling in a food supply network. Industrial management & data systems, 117(9), 1972–1994.
[45]   Aras, N., & Bilge, Ü. (2018). Robust supply chain network design with multi-products for a company in the food sector. Applied mathematical modelling, 60, 526–539.
[46]   Schneider, K., & Nurre, S. G. (2019). A multi-criteria vehicle routing approach to improve the compliance audit schedule for food banks. Omega, 84, 127–140.
[47]   Li, H., Li, D., & Jiang, D. (2021). Optimising the configuration of food supply chains. International journal of production research, 59(12), 3722–3746.
[48]   Castañón, R., Campos, F. A., Doménech Martínez, S., & Villar, J. (2020). The food bank of madrid: A linear model for optimal nutrition. International journal of environmental research and public health17(21), 8097. https://www.mdpi.com/1660-4601/17/21/8097
[49]   Marthak, Y. V. (2020). Characterizing and planning for key logistic obstacles in food banks operations after hurricane events (Master Thesis, Texas State University). https://digital.library.txst.edu/items/5e3f231b-5997-4f42-85d5-a899cf68c563.
[50]   Burgess, P. R., & Sunmola, F. T. (2021). Prioritising requirements of informational short food supply chain platforms using a fuzzy approach. Procedia computer science, 180, 852–861.
[51]   Solina, V., & Mirabelli, G. (2021). Integrated production-distribution scheduling with energy considerations for efficient food supply chains. Procedia computer science, 180, 797–806.
[52]   Güner, G. G., & Utku, D. H. (2020). An optimization approach for a fresh food supply chain: An application for the orange supply chain design in Turkey. Düzce üniversitesi bilim ve teknoloji dergisi, 9(4), 1563–1569.
[53]   Kazancoglu, Y., Ekinci, E., Mangla, S. K., Sezer, M. D., & Kayikci, Y. (2021). Performance evaluation of reverse logistics in food supply chains in a circular economy using system dynamics. Business strategy and the environment, 30(1), 71–91.
[54]   Taghikhah, F., Voinov, A., Shukla, N., Filatova, T., & Anufriev, M. (2021). Integrated modeling of extended agro-food supply chains: A systems approach. European journal of operational research, 288(3), 852–868.
[55]   Gholami-Zanjani, S. M., Klibi, W., Jabalameli, M. S., & Pishvaee, M. S. (2021). The design of resilient food supply chain networks prone to epidemic disruptions. International journal of production economics, 233, 108001. https://doi.org/10.1016/j.ijpe.2020.108001
[56]   Kothamasu, M., Pérez, E., & Mediavilla, F. A. M. (2021). A stochastic programming model for food bank disaster relief operations considering transportation capacity limitations. IIE annual conference. proceedings (pp. 25–30). Institute of Industrial and Systems Engineers (IISE).
[57]   Jiménez, M., Arenas, M., Bilbao, A., & Rodrı, M. V. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European journal of operational research177(3), 1599-1609.
[58]   Abualigah, L., Shehab, M., Alshinwan, M., & Alabool, H. (2020). Salp swarm algorithm: a comprehensive survey. Neural computing and applications, 32, 11195–11215.
[59]   Aljarah, I., Habib, M., Faris, H., Al-Madi, N., Heidari, A. A., Mafarja, M., … Mirjalili, S. (2020). A dynamic locality multi-objective salp swarm algorithm for feature selection. Computers & industrial engineering, 147, 106628. https://www.sciencedirect.com/science/article/pii/S0360835220303624
[60]   Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in engineering software, 114, 163–191.