Optimization Model for Fresh Fruit Supply Chains: Case-Study of Dragon Fruit in Vietnam
Abstract
:1. Introduction
- The influence of traditional trade practices—there are many intermediate nodes involved in the network, making the food supply chain longer and more complex than in other developed countries.
- The high cost of storage after harvesting and transportation—this is due to the tropical climate with high temperature and humidity.
- The continued use of low paid labor. Though labor is cheap, there is a high workforce turnaround. The workforce shortages are acute at the beginning and end of the harvest season when labor demand is high due to competition. During these periods, workers often change employers for better pay.
- The poor availability of information within the value chain from growers to collectors/traders, wholesalers, retailers, and supermarkets about the harvest, preliminary processing, packing, labeling, preserving, and transportation.
- The inability of farmers to set produce prices—farmers play the most important role in the food supply chain but most of them are small, with little influence on price. They must sell their products at prices determined by traders due to lack of market information and experience.
- Due to the lack of long-term orientation at the macro-level of management, farmers target profits based on market demand. In the dragon fruit case, this may imply cutting existing varieties of the fruit and changing over to other varieties, based on anticipated demand. Since dragon fruit is a perennial plant, the impact of these decisions can last several years.
- Product development is still nascent;
- Market price fluctuations;
- Chinese imports are subject to price and currency exchange risks;
- High competition with other exporting countries (such as Thailand, Malaysia, etc.) driving down value despite increased export volumes;
- Export has been increasing both in volume and value but the increase in value has been declining.
2. Dragon Fruit Plantation Characteristics
2.1. Fruit Distribution Context
2.2. Methodology
3. Linear Programming Optimization Model
3.1. Hypotheses and Assumptions
- The facilities (farms) are already operational since the model does not deal with network decisions.
- The time horizon for tactical planning is 10 years and two harvesting seasons (rainy season and dry season) for each year are considered.
- Fruit trees are cut down when they are 10 years of age or earlier, if the model chooses to (e.g., when the future prices of other crops are much higher than the planted crop). It is assumed that cutting down or replanting decisions are only carried out in season 1.
- The distribution of yield, demand, and market prices are represented by their expected values.
- Storage is not allowed for fresh dragon fruit. However, the fruit may be used for by-products such as dry snacks and wine.
- The amounts of each crop to cut and plant are decision variables which cannot exceed the maximum amount of land that is already determined.
- Decision variables of the model are the new plantation and truncating areas of each crop, and the amount of fruit sent to customers (traders, wholesalers, and by-product producers) in every year.
- Other decisions include the quantity of fruit to sell to customers and the amount of labor (fixed and part-time) is required to cover all the activities in the model.
3.2. Objective Function
3.3. Constraints
- -
- Land availability
- -
- Water restriction
- -
- Lighting restrictions
- -
- Minimum plantation size
- -
- Yield
- -
- Plantation age class balance
- -
- Labor constraints
4. Case Study
4.1. Baseline Scenario
4.2. Changes to Price of Crop 2
4.3. Changing Demands
4.3.1. Crop 3 Demand Increases Four-Fold
4.3.2. All Crop Demands Increasing by a Fixed Percentage
4.4. Crop 3 Selling Price with a Probability Factor
4.5. Land Restriction
4.6. Influence of the Initial Plantation Conditions
4.6.1. Initial Plantation with Only One Kind of Crop
4.6.2. The Initial Crop Allocation with Different Crop 1 Ages
4.7. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Nomenclature
DC | Distribution Centers |
DF | Dragon fruit |
FFSC | Fresh Fruit Supply Chain |
FSC | Fruit Supply Chain |
GLPK | GNU Linear Programming Kit |
GUSEK | GLPK Under Scite Extended Kit |
LP | Linear Programming |
MIP | Mixed Integer Programming |
VIED | The Vietnam International Education Development |
Appendix A
t | Time periods |
k | Age classes in the plantation, each representing a two-year period |
s | Harvesting season (1 for wet, 2 for dry) |
j | Different species of dragon fruit |
i | Traders |
m | Wholesale markets (WM) |
b | By-product |
L | Amount of land available |
Water required per hectare for crop j of age class k in season s | |
Lighting required per hectare for crop j in season s | |
Ws | Water restriction in season |
Vs | Lighting restriction in season |
Minimum planting area per crop j in period t | |
Yield in kgs per hectare of crop j belonging to age class k in season s | |
Price per kg of crop j for trader i in season s of period t | |
Price per kg of crop j for wholesaler m in season s of period t | |
Price per kg of byproducts (e.g., wine) in period t | |
Demand of trader i for crop j in season s of period t | |
Demand of wholesale market m for crop j in season s of period t | |
Demand for byproducts (e.g., wine) in period t | |
Number of workers needed to plant one hectare | |
Number of workers needed to harvest one hectare | |
Number of workers needed to cut one hectare | |
M | Maximum number of fixed workers in a period |
N | Maximum number of part-time workers in a period |
Initial area of crop j of age class k |
Cost per hectare of planting in period t | |
Cost per hectare of harvesting in period t | |
Cost per hectare of cut in period t | |
Cost per kg of processing (e.g. wine) | |
Cost of fixed workers per period | |
Labor cost of part-time workers per period | |
Penalty for not meeting demand per kg of crop j for trader i in season s of period t | |
Cost of required water per hectare for crop j of age class k in season s | |
Cost of required light per hectare for crop j in season s |
Plantation area of crop j in period t of age class k | |
Quantity of crop j shipped to trader i in season s of period t | |
Quantity of crop j under shipped to trader i in season s of period t | |
Quantity of crop j shipped to WM m in season s of period t | |
Quantity of crop j harvested for by-products (e.g. wine) in season s of period t | |
Number of fixed workers in t | |
Part-time workers hired in period t | |
Area of crop j planted in period t | |
Area of crop j of age class k cut optionally in period t | |
Area of crop j of age class k = 10 that must be cut in period t | |
Area of crop j of age class k cut in period t in total |
Appendix B
Scenario | Situation (Sub-Scenario or Case) | Limit of Planting Area for Each Crop | Descriptions |
---|---|---|---|
Baseline scenario | No limit for each crop | Demands and prices unchanged within 10 years | |
Changes in price of Crop 2 | 1 | No limit for each crop | The price increasing gradually within 10 years |
2 | No limit for each crop | The price decreasing gradually within 10 years | |
Changes in demands | 1 | No limit for each crop | Demand of Crop 3 increasing 4 times |
2 | No limit for each crop | Demands of all crops increasing 20% | |
3 | No limit for each crop | Demands of all crops increasing 40% | |
4 | No limit for each crop | Demands of all crops increasing 80% | |
Crop 3 selling price with probability factor | 1 | No limit for each crop | 0.2 for $1, 0.2 for $5, and 0.6 for $10 |
2 | No limit for each crop | 0.2 for $1, 0.6 for $5, and 0.2 for $10 | |
3 | No limit for each crop | 0.6 for $1, 0.2 for $5, and 0.2 for $10 | |
Land restriction | 50% for Crop 1, 35% for Crop 2, 15% for Crop 3 | Demands and prices unchanged within 10 years | |
Influence of initial land | Crop 1 | No limit for each crop | All initial land used for Crop 1. Demands and prices unchanged within 10 years |
Crop 2 | No limit for each crop | All initial land used for Crop 2. Demands and prices unchanged within 10 years | |
Crop 3 | No limit for each crop | All initial land used for Crop 2. Demands and prices unchanged within 10 years | |
Crop 1—Age 1 | No limit for each crop | All initial land used for Crop 1 at age 1. Demands and prices unchanged within 10 years | |
Crop 1—Age 3 | No limit for each crop | All initial land used for Crop 1 at age 3. Demands and prices unchanged within 10 years | |
Crop 1—Age 5 | No limit for each crop | All initial land used for Crop 1 at age 5. Demands and prices unchanged within 10 years |
References
- Ahumada, O.; Villalobos, J.R. Application of planning models in the agri-food supply chain: A review. Eur. J. Oper. Res. 2009, 196, 1–20. [Google Scholar] [CrossRef]
- Hamer, P.J. A decision support system for the provision of planting plans for Brussels sprouts. Comput. Electron. Agric. 1994, 11, 97–115. [Google Scholar] [CrossRef]
- Maia, L.O.A.; Lago, R.A.; Qassim, R.Y. Selection of postharvest technology routes by mixed-integer linear programming. Int. J. Prod. Econ. 1997, 49, 85–90. [Google Scholar] [CrossRef]
- Ferrer, J.C.; Mac Cawley, A.; Maturana, S.; Toloza, S.; Vera, J. An optimization approach for scheduling wine grape harvest operations. Int. J. Prod. Econ. 2007, 112, 985–999. [Google Scholar] [CrossRef]
- Widodo, K.H.; Nagasawa, H.; Morizawa, K.; Ota, M. A periodical flowering–harvesting model for delivering agricultural fresh products. Eur. J. Oper. Res. 2006, 170, 24–43. [Google Scholar] [CrossRef]
- Caixeta-Filho, J.V. Orange harvesting scheduling management: A case study. J. Oper. Res. Soc. 2006, 57, 637–642. [Google Scholar] [CrossRef]
- Itoh, T.; Ishii, H.; Nanseki, T. A model of crop planning under uncertainty in agricultural management. Int. J. Prod. Econ. 2003, 81, 555–558. [Google Scholar] [CrossRef]
- Ten Berge, H.; Van Ittersum, M.; Rossing, W.; Van de Ven, G.; Schans, J. Farming options for The Netherlands explored by multi-objective modelling. Eur. J. Agron. 2000, 13, 263–277. [Google Scholar] [CrossRef]
- Rantala, J. Optimizing the supply chain strategy of a multi-unit Finnish nursery company. Silva Fenn. 2004, 38, 203–215. [Google Scholar] [CrossRef] [Green Version]
- Catalá, L.P.; Durand, G.A.; Blanco, A.M.; Bandoni, J.A. Mathematical model for strategic planning optimization in the pome fruit industry. Agric. Syst. 2013, 115, 63–71. [Google Scholar] [CrossRef]
- Rocco, C.D.; Morabito, R. Production and logistics planning in the tomato processing industry: A conceptual scheme and mathematical model. Comput. Electron. Agric. 2016, 127, 763–774. [Google Scholar] [CrossRef]
- Grillo, H.; Alemany, M.; Ortiz, A.; Fuertes-Miquel, V. Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Appl. Math. Model. 2017, 49, 255–278. [Google Scholar] [CrossRef]
- Ahumada, O.; Villalobos, J.R. Operational model for planning the harvest and distribution of perishable agricultural products. Int. J. Prod. Econ. 2011, 133, 677–687. [Google Scholar] [CrossRef]
- Ahumada, O.; Villalobos, J.R. A tactical model for planning the production and distribution of fresh produce. Ann. Oper. Res. 2011, 190, 339–358. [Google Scholar] [CrossRef]
- Amorim, P.; Günther, H.O.; Almada-Lobo, B. Multi-objective integrated production and distribution planning of perishable products. Int. J. Prod. Econ. 2012, 138, 89–101. [Google Scholar] [CrossRef]
- Masini, G.L.; Blanco, A.M.; Petracci, N.; Bandoni, J.A. Supply chain tactical optimization in the fruit industry. Process Syst. Eng. Supply Chain Optim. 2007, 4, 121–172. [Google Scholar]
- Jena, S.D.; Poggi, M. Harvest planning in the Brazilian sugar cane industry via mixed integer programming. Eur. J. Oper. Res. 2013, 230, 374–384. [Google Scholar] [CrossRef]
- Willis, C.; Hanlon, W. Temporal Model for Long-Run Orchard Decisions. Can. J. Agric. Econ. 1976, 24, 17–28. [Google Scholar] [CrossRef]
- González-Araya, M.C.; Soto-Silva, W.E.; Espejo, L.G.A. Harvest Planning in Apple Orchards Using an Optimization Model. In Handbook of Operations Research in Agriculture and the Agri-Food Industry; Springer: New York, NY, USA, 2015; pp. 79–105. [Google Scholar]
- Starbird, S.A. Optimal loading sequences for fresh-apple storage facilities. J. Oper. Res. Soc. 1998, 39, 911–917. [Google Scholar] [CrossRef]
- Hester, S.M.; Cacho, O. Modelling apple orchard systems. Agric. Syst. 2003, 77, 137–154. [Google Scholar] [CrossRef] [Green Version]
- Blanco, A.; Masini, G.; Petracci, N.; Bandoni, J. Operations management of a packaging plant in the fruit industry. J. Food Eng. 2005, 70, 299–307. [Google Scholar] [CrossRef]
- Nadal-Roig, E.; Plà-Aragonés, L.M. Optimal Transport Planning for the Supply to a Fruit Logistic Centre. In Handbook of Operations Research in Agriculture and the Agri-Food Industry; Springer: New York, NY, USA, 2015; pp. 163–177. [Google Scholar]
- Cittadini, E.D.; Lubbers, M.; de Ridder, N.; Van Keulen, H.; Claassen, G. Exploring options for farm-level strategic and tactical decision-making in fruit production systems of South Patagonia, Argentina. Agric. Syst. 2008, 98, 189–198. [Google Scholar] [CrossRef]
- Arnaout, J.P.M.; Maatouk, M. Optimization of quality and operational costs through improved scheduling of harvest operations. Int. Trans. Oper. Res. 2010, 17, 595–605. [Google Scholar] [CrossRef]
- Vitoriano, B.; Ortuño, M.T.; Recio, B.; Rubio, F.; Alonso-Ayuso, A. Two alternative models for farm management: Discrete versus continuous time horizon. Eur. J. Oper. Res. 2003, 144, 613–628. [Google Scholar] [CrossRef]
- Van Der Vorst, J.G.; Tromp, S.O.; van der Zee, D.J. Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics. Int. J. Prod. Res. 2009, 47, 6611–6631. [Google Scholar] [CrossRef]
- Banaeian, N.; Omid, M.; Ahmadi, H. Greenhouse strawberry production in Iran, efficient or inefficient in energy. Energy Effic. 2012, 5, 201–209. [Google Scholar] [CrossRef]
- Blackburn, J.; Scudder, G. Supply chain strategies for perishable products: The case of fresh produce. Prod. Oper. Manag. 2009, 18, 129–137. [Google Scholar] [CrossRef]
- Miller, W.; Leung, L.; Azhar, T.; Sargent, S. Fuzzy production planning model for fresh tomato packing. Int. J. Prod. Econ. 1997, 53, 227–238. [Google Scholar] [CrossRef]
- Ahumada, O.; Villalobos, J.R.; Mason, A.N. Tactical planning of the production and distribution of fresh agricultural products under uncertainty. Agric. Syst. 2012, 112, 17–26. [Google Scholar] [CrossRef]
- Nielsen, I. Plant Resources of Tropical Africa 2: Vegetables Grubben, G.J.H., Denton, O.A., Eds. Nord. J. Bot. 2004, 23, 298. [Google Scholar] [CrossRef]
- Lambert, G.F.; Lasserre, A.A.A.; Ackerman, M.M.; Sánchez, C.G.M.; Rivera, B.O.I.; Azzaro-Pantel, C. An expert system for predicting orchard yield and fruit quality and its impact on the Persian lime supply chain. Eng. Appl. Artif. Intel. 2014, 33, 21–30. [Google Scholar] [CrossRef]
- Defraeye, T.; Nicolai, B.; Kirkman, W.; Moore, S.; van Niekerk, S.; Verboven, P.; Cronjé, P. Integral performance evaluation of the fresh-produce cold chain: A case study for ambient loading of citrus in refrigerated containers. Postharvest Biol. Technol. 2016, 112, 1–13. [Google Scholar] [CrossRef]
- Wu, W.; Defraeye, T. Identifying heterogeneities in cooling and quality evolution for a pallet of packed fresh fruit by using virtual cold chains. Appl. Therm. Eng. 2018, 133, 407–417. [Google Scholar] [CrossRef]
- Raut, R. Improvement in the Food Losses in Fruits and Vegetable Supply Chain-A Perspective of Cold Third-Party Logistics Approach. Oper. Res. Perspect. 2019, 6, 100117. [Google Scholar] [CrossRef]
- Wu, W.; Beretta, C.; Cronje, P.; Hellweg, S.; Defraeye, T. Environmental trade-offs in fresh-fruit cold chains by combining virtual cold chains with life cycle assessment. Appl. Energy 2019, 254, 113586. [Google Scholar] [CrossRef]
- Zhao, X.; Xia, M.; Wei, X.; Xu, C.; Luo, Z.; Mao, L. Consolidated cold and modified atmosphere package system for fresh strawberry supply chains. LWT 2019, 109, 207–215. [Google Scholar] [CrossRef]
- Luong, N.T.L.; Nguyen, M.C. Increasing Market Access of Selected Tropical Fruit Through Value Chain Improvements in Vietnam; Food and Fertilizer Technology Center for the Asian and Pacific Region: Taipei, Taiwan, 2015; Available online: http://www.fftc.agnet.org/library.php?func=view&style=type&id=20150805092541 (accessed on 9 July 2017).
- AXIS Research. Report on Value Chain of Dragon Fruit of Binh Thuan Province. 2009. Report No.9. Retrieved from Information Center for Agriculture and Rural development, Vietnam (Agroinfo). Available online: http://agro.gov.vn/images/2007/05/Dragon_Fruit_in_BT(E).pdf (accessed on 21 May 2015).
- Hai, D.H. Vietnamese Dragonfruit. The Edible Plants in Vietnam. Available online: https://www.edibleplantsinvietnam.com/vietnamese-dragon-fruit-thanh-long.html (accessed on 9 July 2017).
- Miller, T.C. Hierarchical Operations and Supply Chain Planning; Springer-Verlag: London, UK, 2002; ISBN 978-1-4471-1110-8. [Google Scholar]
- Kiet, Mr.; Cuoc, Mr.; Manh, Mr.; Binh Thuan, Vietnam. Personal communication, 2017.
- Birge, J.R.; Louveaux, F. Introduction and Examples. In Introduction to Stochastic Programming, 2nd ed.; Springer: New York, NY, USA, 2011; ISBN 978-1-4614-0237-4. [Google Scholar]
- Darby-Dowman, K.; Barker, S.; Audsley, E.; Parsons, D. A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture. J. Oper. Res. Soc. 2000, 51, 83–89. [Google Scholar] [CrossRef]
- Kazaz, B. Production planning under yield and demand uncertainty with yield-dependent cost and price. Manuf. Serv. Oper. Manag. 2004, 6, 209–224. [Google Scholar] [CrossRef]
- Ben-Tal, A.; Ghaoui, L.E.; Nemirovski, A. Robust optimization. In Applied Mathematics; Princeton University Press: Princeton, NJ, USA, 2009; ISBN 978-0-691-14368-2. [Google Scholar]
Average Yield | Average Demand | Average Price | |
---|---|---|---|
Crop 1 | 15 | 30 | 0.5 US$ |
Crop 2 | 14 | 30 | 1.5 US$ |
Crop 3 | 5 | 5 | 5 US$ |
Scenario | Sub-Scenario | Limit on Plantation Area for Each Crop | Profit |
---|---|---|---|
Baseline scenario | No limit for each crop | USD 12,576,086.80 | |
Changes in price of Crop 2 | 1 | No limit for each crop | USD 20,089,622.07 |
2 | No limit for each crop | USD 20,116,237.86 | |
Changes in demands | 1 | No limit for each crop | USD 16,815,478.39 |
2 | No limit for each crop | USD 13,932,145.96 | |
3 | No limit for each crop | USD 15,166,044.33 | |
4 | No limit for each crop | USD 15,740,093.18 | |
Crop 3 selling price with probability factor | 1 | No limit for each crop | USD 14,438,789.39 |
2 | No limit for each crop | USD 12,745,440.97 | |
3 | No limit for each crop | USD 11,415,607.18 | |
Land restriction | 50% for Crop 1, 35% for Crop 2, 15% for Crop 3 | USD 9,963,130.32 | |
Influence of initial land | Crop 1 | No limit for each crop | USD 10,921,697.80 |
Crop 2 | No limit for each crop | USD 12,182,762.60 | |
Crop 3 | No limit for each crop | USD 11,630,711.50 | |
Crop 1—Age 1 | No limit for each crop | USD 10,763,932.06 | |
Crop 1—Age 3 | No limit for each crop | USD 10,907,367.04 | |
Crop 1—Age 5 | No limit for each crop | USD 10,879,376.28 |
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Nguyen, T.-D.; Venkatadri, U.; Nguyen-Quang, T.; Diallo, C.; Adams, M. Optimization Model for Fresh Fruit Supply Chains: Case-Study of Dragon Fruit in Vietnam. AgriEngineering 2020, 2, 1-26. https://doi.org/10.3390/agriengineering2010001
Nguyen T-D, Venkatadri U, Nguyen-Quang T, Diallo C, Adams M. Optimization Model for Fresh Fruit Supply Chains: Case-Study of Dragon Fruit in Vietnam. AgriEngineering. 2020; 2(1):1-26. https://doi.org/10.3390/agriengineering2010001
Chicago/Turabian StyleNguyen, Tri-Dung, Uday Venkatadri, Tri Nguyen-Quang, Claver Diallo, and Michelle Adams. 2020. "Optimization Model for Fresh Fruit Supply Chains: Case-Study of Dragon Fruit in Vietnam" AgriEngineering 2, no. 1: 1-26. https://doi.org/10.3390/agriengineering2010001