Optimizing a sustainable inventory-routing problem in tomato agri-chain considering postharvest biological behavior

Journal of Industrial Engineering International

دوره 17، شماره 2، تیر 2021، صفحه 45-60   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.30495/jiei.2021.1927213.1113

نویسندگان

Shima Shirzadi¹؛ Vahidreza Ghezavati^{* 2}؛ Reza Tavakkoli-Moghaddam³؛ Sadoullah Ebrahimnejad⁴

¹School of Industrial Engineering, South Tehran Branch, Islamic Azad university, Tehran, Iran

²School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

³University of Tehran

⁴School of Industrial Engineering, Islamic Azad university, Karaj Branch, Karaj, Iran

چکیده

A new mixed-integer multi-objective mathematical model is developed to optimize sustainable inventory-routing decisions in products agri-chain. The first aim is to optimize the network total revenue besides noticing logistics decisions related to the distribution and collection of perishable products. Also, the economical, social and environmental factors have been integrated in the proposed model. The first objective function considers some traditional terms and novel issues (e.g., postharvest biological behavior of agricultural products), which is related to deviation from ideal quality (customer's dissatisfaction) and the costs of expired products. Because old products have significant environmental impacts and require recycling, the reverse logistics framework is used to collect and bring products back to recycling. A function is applied to compute the level of deviation from suitable maturity and customer's dissatisfaction costs. A numerical example is analyzed to indicate the model's applicability by applying the ε-constraint methodology to show the opposite pattern between the two objectives. Results show that a lower level of accidents leads to lower revenue or higher costs of the supply chain. Remarking the NP-hardness of the presented model, two multi-objective meta-heuristic algorithms, namely the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Dragonfly Algorithm (MODA) are used to explore near-optimal solutions for medium and large-sized problems. Results show a better performance of the NSGA-II. Furthermore, the sensitivity analysis is presented and explained in four parts to show the trend of the proposed model by fluctuations on important parameters.

کلیدواژه‌ها

Fresh agricultural products؛ Sustainable development؛ Fair pricing؛ Postharvest biological behavior؛ Inventory routing problem

مراجع

[1] [USDA] United States standards for grades of fresh tomatoes. (1991) USDA Agricultural Marketing Service, Washington, DC.

[2] Abdoli, B., Mirhassani, S. A., & Hooshmand, F. (2017) Model and algorithm for bi-feul vehicle routing problem to reduce GHG emissions. Environmental Science and Pollution Research, 24, pp.
21610-21624,.

[3] Ahumada, O., Villalobos, JR. (2011) A tactical model for planning the production and distribution of fresh produce Annual Operations Research, 190(1), 339-358,.

[4] Bertazzi, L., Coelho. L.C., Maio. A., & Lagana. D. (2019) A metaheuristic algorithm for the multi-depot inventory routing problem. Transportation Research part E, 122,. 524-544.

[5] Bottani, E., Vignali, G., Mosna, D., & Montanari, R. (2019) Economic and environmental assessment of different reverse logistics scenarios for food waste recovery, Sustainable Production and Consumption, 20, 289-303.

[6] Calvin, L., Cook, R.L., Dimitri, C., Glaser, L., Handi, C., & Thomsbury, S. (2001) US fresh fruit and vegetable marketing: emerging trade practices, trends, and issues. US Department of Agriculture,
Economic Research Service.

[7] Chankong, V., Haimes, Y.Y. (1983) Multi-objective Decision Making: Theory and Methodology, North-Holland, New York.
[8] Cohon, J. L., (1978) Multi-objective Programming and Planning, Academic Press, New York,.
[9] Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) A fast elitist multi-objective genetic algorithm: NSGA-II, IEEE Transaction on Evolutionary Computation,. 6( 2), 182-197.
[10] Ding, S., Chen. C,. Xin. B., & Pardalos. P.M. (2018) A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches, Applied Soft Computing, 63, 249-267.
[11] Garey, M. R., Johnson, D. (1979) Computers and intractability: A guide to the theory of NP-completeness, New York, USA: W.H. Freeman & Co Ltd,.
[12] Gary, C., Tchamitchian, M. (2001) Modelling and management of fruit production: the case of optimizing distribution of perishable products considering postharvest biological behavior in tomatoes, food process modeling, CRC Press, Boca Raton.
[13] Ghezavati, V. R., Hooshyar, S., & Tavakkoli-Moghaddam, R. (2017) A Benders’ decomposition algorithm for optimizing distribution of perishable products considering postharvest biological behavior in agri-food supply chain: a case study of tomato. Central European Journal of Operations Research,25(1), 29-54.
[14] Hertog, M. L., Lammertyn, J., Desmet, M., Scheerlinck, N., & Nicolai, B. M. (2004) The impact of biological variation on postharvest behavior of tomato fruit. Postharvest Biology and Technology, 34( 3). 271-284.
[15] Hosseini-Motlagh, S. M., Samani, M. R. G., & Saadi, F. A. (2021) Strategic optimization of wheat supply chain network under uncertainty: a real case study. Operational Research, 21, 1487–1527.
[16] Jafari, M., Bayati, M.H., (2017) Using dragonfly algorithm for optimization of orthotropic infinite plates with a quasi-triangular cut- out. European Journal of Mechanics A/Solids, 66,. 1-14.
[17] Kuhnle, A., Lanza, G. (2019) Investigation of closed-loop supply chains with product refurbishment as integrated location-inventory problem. Production Engineering, 13(3-4), 293-303.
[18] Miller, C.E., Tucker, A.W., & Zemlin. R.A. (1960) Integer programming formulations and travelling salesman problems, Journal of Association for Computing Machinery,.7 , 326-329.
[19] Lutke Entrup, M., Gunther, HO., VanBeek, P., Grunow, M., & Seiler, T. (2005) Mixed-Integer linear programming approaches to shelf-life- integrated planning and scheduling in yoghurt production. International Journal of Production Research, 43(3), 5071-5100.
[20] McLaughlin, E. W., Green, G. M., & Park, K. (1999) Changing distribution patterns in the US fresh produce industry: Mid/Late-70s to Mid/Late-90s. Department of agricultural, Resource, and
Managerial Economics, College of Agriculture and Life Science, Cornell University.
[21] Mirjalili, S. (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Computing and applications, 27, 1053-1073.
[22] Minihan, E. S., Wu, Z., (2014) Capturing the macroeconomic impact of technology-based greenhouse gas mitigation in agriculture: a computable general equilibrium approach. Operational Research, 14(2),189-203.
[23] Mousavi, S., M., Sadeghi, J., Akhavan Niaki, S., T., & Tavana, M., (2016) A bi-objective inventory optimization model under inflation and discount using tuned Pareto-based algorithms: NSGA-II, NRGA and MOPSO. Applied Soft Computing, 43, 57-72,.
[24] Onggo, B. S., Panadero, J., Gurlo, 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.
[25] Osvald, A., Stirn, LZ., (2008) A vehicle routing algorithm for the distribution of fresh vegetables and similar perishable food., Journal of Food Engineering, 85(2), 285-295,.
[26] Quimby, A., Maycock, G., Palmer, C., & Buttress, S. (1999) The factors that influence a driver’s choice of speed: a questionnaire study, Transport Research Laboratory TRL, Crowthorn, Berkshire,.
[27] Rau, H., Budiman, S. D., & Widyadana, G. A. (2018) Optimization of the multi-objective cyclical inventory routing problem using discrete multi-swarm PSO method. Transportation Research Part E: Logistics and Transportation Review, 120, 51-75.
[28] Saltveit, ME., (2005) Fruit ripening and fruit quality. Crop Production Science Hortic, 13-145
[29] Song, C., Zhuang, J. (2017) Modeling a government-Manufacturer- Farmer game for food supply chain risk management. Food Control, 78, 443-455.
[30] Shirzadi, S., Tavakkoli-Moghaddam, R., Kia, R., & Mohammadi, M. (2017) A multi-objective imperialist competitive algorithm for integrating intra-cell layout and processing route reliability in a cellular manufacturing system. International Journal of Computer Integrated Manufacturing, 30 (8), 839-855.
[31] Tavakkoli-Moghaddam, R., Azarkish, M., & Sadeghnejad, A. (2011) A new hybrid multi objective Pareto archive PSO algorithm for a bi- objective job shop scheduling problem, Expert systems with applications, 38(9), 10812-10821
[32] Tavana, M., Abtahi, A. R., Di Caprio, D., Hashemi, R., & Yousefi- Zenouz, R. (2018) An integrated location-inventory-routing humanitarian supply chain network with pre-and post-disaster management considerations. Socio-Economic Planning Sciences, 64, 21-37.
[33] Thorp, JH., Rogers, DC. (2014) Thorp and Covich's freshwater invertebrates: ecology and general biology. Elsevier, Amsterdam.
[34] Tijsken, LMM., Evelo, RG. (1994) Modeling color of tomatoes during postharvest storage, Postharvest Biological Technology, 4(1), 85-98.
[35] Tijskens, LMM., Polderdijk, JJ., (1996) A generic model for keeping quality of vegetable produce during storage and distribution, Agricultural System, 51(4), 431-452.
[36] Utomo, D. S., Onggo, B. S., & Eldridge, S. (2018) Applications of agent-based modelling and simulation in the ari-food supply chains, European Journal of Operational Research, 269, 794-805.
[37] Yan, B., Chen, Z., & Li, H., (2019) Evaluation of agri-product supply chain competitiveness based on extension theory. Operational Research, 1-28.