Abstract
Food supply has become one of the most important problems and challenges that the world faces nowadays due to the tremendous population growth and resource depletion around the world. In developing countries, where this problem is more noticeable, governments have implemented assistance programs to ensure food supply to disenfranchised people. However, these programs do not guarantee individual food security; besides, the local economic development is not promoted. In this paper, an optimization formulation for the strategic planning of food supply networks in disenfranchised communities is proposed, which includes the use and exchange of local resources between different communities to improve the local economy and satisfy specific nutritional needs according to age and gender, taking into account that the government should coordinate these activities. Since the strategic planning involves multiple interests and priorities, a multi-stakeholder optimization formulation to get trade-off solutions, useful for decision makers, is considered. A case study from Mexico involving 14 of the poorest communities from the State of Michoacán is presented. The results indicate that it is possible to meet nutritional needs of all the considered communities using the local resources and enhancing the local economy, avoiding this way government dependence. An initial inversion of MMUS$27.7 to supply 5 × 106 t of vegetable products and 6552 t of animal food is needed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- L :
-
Type of processed food (1 = cheese, 2 = bred, 3 = chicken meat, 4 = beef, 5 = pork meat, 6 = ham, 7 = milk, 8 = protein, 9 = tortillas, 10 = sugar)
- Nut:
-
Nutrient package (oils, fruits, vegetables, dairy products, beans and meat)
- Pa:
-
Type of animal product (1 = meat, 2 = milk, 3 = eggs)
- R :
-
Type of raw material (1 = beans, 2 = corn, 3 = green tomato, 4 = red tomato, 5 = sorghum, 6 = oats, 7 = chili, 8 = avocado, 9 = pumpkin, 10 = mango, 11 = onion, 12 = wheat and 13 = sugar cane)
- Ra:
-
Type of animal (1 = chicken, 2 = caw, 3 = pig)
- T :
-
Period of time considered, one period takes 1 week (periods 1–52)
- \( {A}_{r,m1,t}^{used} \) :
-
Land used for agriculture (km2)
- \( {A}_{r,m1,t}^{new} \) :
-
New area designated to cultivation (km2)
- \( {A}_{ra,m1,t}^{used- animal- food} \) :
-
Area used for animal feed (km2)
- \( {Af}_{ra,m1,t}^{usedfood} \) :
-
Tonnes of animal food used to feed (tonnes)
- \( {Af}_{ra,m1,t}^{cultivatedfood} \) :
-
Tonnes of animal food cultivated, residues from the crops (tonnes)
- \( {Af}_{ra,m1,t}^{purchasedfood} \) :
-
Tonnes of animal food purchased from other sites (tonnes)
- \( {AF}_{ra,m1,t}^{cultivatedfood- animal} \) :
-
Amount of cultivated animal food (tonnes)
- \( {AF}_{ra,m1,t}^{purchased- animal- food} \) :
-
Amount of purchased animal food (tonnes)
- \( {C}_{ra,m1,t}^{animal- purchase} \) :
-
Generated cost by the animal purchase (USD$)
- \( {C}_{r,m1,t}^{vegetable- production} \) :
-
Total production cost of the vegetal raw material (USD$)
- \( {C}_{r,m3,t}^{vegetable- purchase} \) :
-
Generated cost by purchasing vegetables (USD$)
- \( {C}_{ra,m1,t}^{purchased- food- animal} \) :
-
Purchased animal food cost (USD$)
- \( {C}_{\mathrm{pa},m3,t}^{animal- product- purchase} \) :
-
Purchased animal product cost (USD$)
- \( {C}_{ra,m1,t}^{cultivatedfood- animal} \) :
-
Production animal food cost (USD$)
- \( {C}_{pa,m1,t}^{animal- production} \) :
-
Production cost of animal food (USD$)
- \( {C}_{pa,t}^{trans- ap- prod- hub} \) :
-
Cost by the animal product transportation to the hub (USD$)
- \( {C}_{pa,t}^{trans- ap- prod- hubtomarket} \) :
-
Cost by the animal product transportation to the market from hub (USD$)
- \( {C}_{pa,t}^{trans- ap- prod- market} \) :
-
Cost by the animal product transportation to the market from harvesting sites (USD$)
- \( {C}_{r,t}^{Trans- vegetabletohub} \) :
-
Cost by vegetable transportation to the hub (USD$)
- \( {C}_{r,t}^{Trans- vegetable- product- market} \) :
-
Cost by vegetable transportation to the hub (USD$)
- \( {C}_{\mathrm{r},t}^{Trans- vegetable- hub- market} \) :
-
Cost by vegetable transportation from the hub to the market (USD$)
- \( {C}_{pa,m2,t}^{storage- ap} \) :
-
Cost by the animal product storage (USD$)
- \( {C}_{r,m2,t}^{storage- vegetable} \) :
-
Cost by the vegetable product storage (USD$)
- \( {C}_{pa,m2,t}^{ap- sales} \) :
-
Earnings by animal product sales (USD$)
- \( {C}_{r,m2,t}^{vegetable- sales} \) :
-
Earnings by the vegetable product sales (USD$)
- D nut, m3, t :
-
Nutritional demand (tonne of each food group)
- Dissatisfaction :
-
Level of dissatisfaction in each solution
- \( {F}_{pa,m1,t}^{\mathrm{animal}-\mathrm{product}} \) :
-
Animal food produced (tonnes)
- \( {F}_{\mathrm{pa},m3,t}^{animalproduct-\mathrm{markets}} \) :
-
Animal product sent to the markets (tonnes)
- \( {F}_{pa,m1,m2,t}^{animal- produc- hub} \) :
-
Animals sent to the hub (tonnes)
- \( {F}_{pa,m2,m3,t}^{animal- hub- market} \) :
-
Animals sent to the market from the hub (tonnes)
- \( {F}_{pa,m1,m3,t}^{animalproduct- market} \) :
-
Animals sent directly to the market (tonnes)
- \( {F}_{\mathrm{pa},m2,t}^{animalproduct- hub- sale} \) :
-
Animals sold (tonnes)
- \( {F}_{pa,m3,t}^{\mathrm{animalproduct}- purchase} \) :
-
Animal products purchased (tonnes)
- \( {F}_{r,m1,t}^{vegetable} \) :
-
Total tonnes of vegetables produced (tonnes)
- \( {F}_{r,m1,m2,t}^{vegetable- hub} \) :
-
Vegetables sent to the hubs (tonnes)
- \( {F}_{r,m3,t}^{vegetable- market} \) :
-
Flow rate of vegetables in the markets (tonnes)
- \( {F}_{r,m3,t}^{vegetable- purchase} \) :
-
Vegetable products purchased from other producers (tonnes)
- \( {F}_{r,m2,m3,t}^{vegetable- hub- market} \) :
-
Vegetables sent from the hub to the market (tonnes)
- \( {F}_{r,m1,m3,t}^{vegetable- product- market} \) :
-
Vegetable products sent from the harvesting site to the market (tonnes)
- \( {F}_{r,m2,t}^{vegetable- hub- sale} \) :
-
Vegetables sold in the market (tonnes)
- NA ra, m1, t :
-
Number of animals produced in each site (number)
- \( {Na}_{ra,m1,t}^{purchased} \) :
-
Number of bought animals from other municipality (animals)
- \( {Na}_{ra,m1,t}^{sale} \) :
-
Number of sold animals to other municipalities (animals)
- RM r, m1, tvegetal :
-
Tonnes of vegetable raw material (tonnes)
- \( {S}_{\mathrm{pa},m2,t}^{animalproduct- stored} \) :
-
Animal product stored in the hub (tonnes)
- \( {S}_{pa,m2,t-1}^{animalproduct- stored} \) :
-
Animal product stored in the hub in the previous time period (tonnes)
- \( {S}_{r,m2,t}^{vegetable- stored} \) :
-
Vegetables stored (tonnes)
- \( {S}_{r,m2,t-1}^{vegetable- stored} \) :
-
Vegetables stored in previous period of time (tonnes)
- TACM m1 :
-
Total annual cost by municipality (USD$)
- TAC :
-
Total annual cost (USD$)
- TAC LB :
-
Lower bound of the total annual cost by municipality
- TAC UB :
-
Upper bound of the total annual cost by municipality
- \( {A}_{ra,m1,t}^{usedanimalfood} \) :
-
Area used to feed the animals (km2)
- \( {A}_{r,m1,t}^{Current} \) :
-
Current area designated to harvesting each crop (km2)
- \( {A}_{r,m1,t}^{Total- Vegetable} \) :
-
Limit for the used area in each municipality (km2)
- \( {A}_{ra,m1,t}^{Total- animal- food} \) :
-
Available area for the animal feed (km2)
- GE nut :
-
Nutrient group (tonnes)
- Population m3, t :
-
Inhabitants (number)
- \( {\beta}_{r,m1,t}^{harvesting\kern0.17em sites} \) :
-
Yield factor of each cultivated crop (t/km)
- \( {\theta}_{ra,m1,t}^{YieldPA} \) :
-
Yield factor of produced animals (animals/t food)
- \( {\xi}_{nut,\mathrm{pa}}^{nut- ap} \) :
-
Amount of needed animal nutrients (tonnes)
- \( {\xi}_{nut,r}^{nut- vegetable} \) :
-
Amount of needed vegetable nutrients (tonnes)
- \( {\phi}_{ra,r,m1,t}^{residePA} \) :
-
Yield of raw material residues (tonnes/t)
- λ pa, ra, m1, t :
-
Yield factor of produced animals (tonnes/amount of animal)
- \( {\sigma}_{ra,m1,t}^{animal- purchased} \) :
-
Unit animal purchase cost (USD$/animal)
- \( {\sigma}_{pa,m1,t}^{animal- production} \) :
-
Price of animal production (USD$/animal)
- \( {\sigma}_{r,m1,t}^{vegetable- production} \) :
-
Unit vegetable production cost (USD$/t)
- \( {\sigma}_{pa,m1,m2,t}^{Trans- ap- prod- hub} \) :
-
Animal product transportation cost from the harvesting sites to the hub (USD$/t)
- \( {\sigma}_{pa,m2,m3,t}^{Trans- ap- prod- hubtomarket} \) :
-
Animal product transportation cost from the hub to the market (USD$/t)
- \( {\sigma}_{pa,m1,m3,t}^{Trans- ap- prod- market} \) :
-
Animal product transportation cost from the harvesting site to the market (USD$/t)
- \( {\sigma}_{r,m1,m2,t}^{transp- veg- hub} \) :
-
Vegetable transportation cost from the harvesting sites to the hub (USD$/t)
- \( {\sigma}_{r,m1,m3,t}^{transp- veg- product- market} \) :
-
Vegetable transportation cost from the harvesting sites to the market (USD$/t)
- \( {\sigma}_{ra,m1,t}^{cultivatedfood- animal} \) :
-
Unit animal food cost (USD$/t)
- \( {\sigma}_{ra,m1,t}^{purchased- food- animal} \) :
-
Unit animal purchase food cost (USD$/t)
- \( {\sigma}_{pa,m2,t}^{ap- stored} \) :
-
Price for the animal product storage (USD$/t)
- \( {\sigma}_{r,m2,t}^{vegetable- stored} \) :
-
Price for the vegetable product storage (USD$/t)
- \( {\sigma}_{pa,m2,t}^{ap- sale} \) :
-
Unit price for animal product sales (USD$/t)
- \( {\sigma}_{r,m2,t}^{vegetable- sale} \) :
-
Unit price for vegetable sales (USD$/t)
References
Allaoui H, Guo Y, Choudhary A, Bloemhof J (2018) Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach. Comput Oper Res 89(1):369–384. https://doi.org/10.1016/j.cor.2016.10.012
Arouna A, Lokossou JC, Wopereis MCS, Bruce-Oliver S, Roy-Macauley H (2017) Contribution of improved rice varieties to poverty reduction and food security in sub-Saharan Africa. Glob Food Sec 14(1):54–60. https://doi.org/10.1016/j.gfs.2017.03.001
Babbitt CW (2017) Foundations of sustainable food waste solutions: innovation, evaluation and standardization. Clean Techn Environ Policy 19(5):1255–1256. https://doi.org/10.1007/s10098-017-1364-7
Bakalis S, Knoerzer K, Fryer P (2015) Modeling food processing operations. Woodhead Publishing, Sawton, Cambridge
Banasik A, Kanellopoulos A, Claassen GDH, Bloemhof-Ruwaard JM, Van der Vorst JGAJ (2017) Closing loops in agricultural supply chains using multi-objective optimization: a case study of an industrial mushroom supply chain. Int J Prod Econ 183(1:409–420. https://doi.org/10.1016/j.ijpe.2016.08.012
Bourges H, Casanueva E, Rosado JL (2009) Intake recommendations for the Mexican population. Medica Panamericana, Mexico City
Brooke A, Kendrick D, Meeruas A, Raman R (2017) GAMS-Language guide. Washington DC, GAMS Development Corp
Clift R (2003) Metrics for supply chain sustainability. Clean Techn Environ Policy 5(3–4):240–247. https://doi.org/10.1007/s10098-003-0220-0
Dowling AW, Ruiz-Mercado G, Zavala VM (2016) A framework for multi-stakeholder decision-making and conflict resolution. Comput Chem Eng 90(1):136–150. https://doi.org/10.1016/j.compchemeng.2016.03.034
Ehrgott M (2005) Multicriteria optimization. Springer, Berlin Heidelberg, New York
Fuentes-Cortés LF, Dowling AW, Rubio-Maya C, Zavala VM, Ponce-Ortega JM (2016) Integrated design and control of multigeneration system for building complexes. Energy 116(1:1403–1416. https://doi.org/10.1016/j.energy.2016.05.093
FND “National Financing for Rural, Forestry and Fisheries Development”, 2017. Economic Annual Report https://www.gob.mx/fnd#documentos (Accessed August 2016)
Gao J, You F (2015) Shale gas supply chain design and operations toward better economic and life cycle environmental performance: MINLP model and global optimization algorithm. ACS Sustainable Chemistry and Engineering. 3(7):1282–1291. https://doi.org/10.1021/acssuschemeng.5b00122
Huang J, Yang G (2017) Understanding recent challenges and new food policy in China. Global Food Security. 12(1):119–126. https://doi.org/10.1016/j.gfs.2016.10.002
INEGI “National Institute of Statistic and Geography”, 2017. Mexico in figures www.beta.inegi.org.mx (Accessed August 2017)
Irabien A, Darton RC (2015) Energy-water-food nexus in the Spanish greenhouse tomato production. Clean Techn Environ Policy 18(5):1307–1316. https://doi.org/10.1007/s10098-015-1076-9
Islam S, Ponnambalam SG, Loong-Lam H (2016) A novel framework for analyzing the green value of food supply chain based on life cycle assessment. Clean Techn Environ Policy 19(1):93–103. https://doi.org/10.1007/s10098-016-1192-1
Karimi A, Rahim KA (2015) Classification of external stakeholders pressures in green supply chain management. Procedia Environ Sci 30(1):27–32. https://doi.org/10.1016/j.proenv.2015.10.005
Limleamthong P, Guillén-Gosálbez G (2017) Rigorous analysis of Pareto fronts in sustainability studies based on bilevel optimization: application to the redesign of the UK electricity mix. J Clean Prod 164(1):1602–1613. https://doi.org/10.1016/j.jclepro.2017.06.134
Logist F, Vallerio M, Houska B, Diehl M, Van-Impe J (2012) Multi-objective optimal control of chemical processes using ACADO toolkit. Computers & Chemical Engineering. 37(1): 191–199. doi:https://doi.org/10.1016/j.compchemeng.2011.11.002
López-Díaz DC, Lira-Barragán LF, Rubio-Castro E, Serna-González M, El-Halwagi MM, Ponce-Ortega JM (2017) Optimization of biofuels production via a water-energy-food nexus framework. Clean Techn Environ Policy. https://doi.org/10.1007/s10098-017-1395-0
Martínez-Guido SI, González-Campos JB, El-Halwagi MM, Ponce-Ortega JM (2017) Sustainable optimization of food networks in disenfranchised communities. ACS Sustainable Chemistry and Engineering. 5(10):8895–8907. https://doi.org/10.1021/acssuschemeng.7b01703
Mexican Health Ministry. (2006) Official Mexican Norm 043-SSA2–2005 www.promocion.salud.gob.mx/dgps/descargas1/programas/2_norma_oficial_mexicana_nom_043_SSA2_2005.pdf (Accessed August 2017)
Miran-Ackerman MA, Azzaro-Pantel C, Aguilar-Lasserre AAA (2017) Green supply chain network design framework for the processed food industry: application to the orange juice agrofood cluster. Comput Ind Eng 109:369–389. https://doi.org/10.1016/j.cie.2017.04.031
Mogale DG, Dolgui A, Kandhway R, Kumar SK, Kumar-Tiwari MA (2017) Multi-period inventory transportation model for tactical planning of food grain supply chain. Comput Ind Eng 110(1):379–394. https://doi.org/10.1016/j.cie.2017.06.008
Mottet A, de-Haan C, Falcucci A, Tempio G, Opio C, Gerber P (2017) Livestock: on our plates or eating at our table? A new analysis of the feed/food debate. Global Food Security 14(1):1–8. https://doi.org/10.1016/j.gfs.2017.01.001
Pérez-Escamilla R, Shamah-Levy T, Candel J (2017) Food security governance in Latin America: principles and the way forward. Global Food Security 14(1):68–72. https://doi.org/10.1016/j.gfs.2017.07.001
Pierre J, Guerchet M, Pilleron S, Fayemendy P, Mouanga AM, Mbelesso P, Preux PM, Desport JC (2017) Undernutrition and obesity among elderly people living in two cities of developing countries: prevalence and associated factors in the EDAC study. Clinical Nutrition ESPEN 21(1):40–50. https://doi.org/10.1016/j.clnesp.2017.05.007
Quiroz-Ramírez JJ, Sánchez-Ramírez E, Segovia-Hernandez JG, Hernández S, Ponce-Ortega JM (2017) Optimal selection of feedstock for biobutanol production considering economic and environmental aspects. ACS Sustainable Chemistry and Engineering 5(5):4018–4030. https://doi.org/10.1021/acssuschemeng.7b00015
Rong A, Akkerman R, Grunow M (2011) An optimization approach for managing fresh food quality through the supply chain. Int J Prod Econ 131(1):421–429. https://doi.org/10.1016/j.ijpe.2009.11.026
Salim HK, Padfield R, Tin-Lee C, Syayuti K, Papargyropoulou E, Tham MH (2017) An investigation of the drivers, barriers, and incentives for environmental management systems in the Malaysian food and beverage industry. Clean Techn Environ Policy. https://doi.org/10.1007/s10098-017-1436-8
de Sánchez-Bautista A, F, Sántibañez-Aguilar JE, You F, Ponce-Ortega JM (2017) Optimal design of energy system involving pollution trading through forest plantations. ACS Sustain Chem Eng 5(3):2585–2604. https://doi.org/10.1021/acssuschemeng.6b02928
Sántibañez-Aguilar JE, Ponce-Ortega JM, González-Campos JB, Serna-González M, El-Halwagi MM (2013) Synthesis of distributed biorefinering networks for the value-added processing of water hyacinth. ACS Sustain Chem Eng 1(2):284–305. https://doi.org/10.1021/sc300137a
Shamah-Levy T, Mundo-Rosas V, Flores-de la Vega MM, Luiselli-Fernández C (2017) Food security governance in Mexico: how can it be improved? Global Food Security 14(1):73–78. https://doi.org/10.1016/j.gfs.2017.05.004
Song C, Zhuang J (2017) Modeling government-manufacturer-farmer game for food supply chain risk management. Food Control 78(1):443–455. https://doi.org/10.1016/j.foodcont.2017.02.047
The Global Hunger Index (GHI). (2016) GHI scores by country, from highest to lowest hunger levels, available on: http://ghi.ifpri.org/ (Access August 2017)
UNDP (United Nations Development Programme. (2016a). Human Development Index (HDI). www.mx.undp.org/content/mexico/es/home/library/poverty/informe-nacional-sobre-desarrollo-humano-mexico-2016.html (Access August 2017)
UNDP “United Nations Development Programme”. (2016b). Technical notes: Calculating the human development indices-graphical presentation http://hdr.undp.org/sites/default/files/hdr2016_technical_notes_0.pdf (Access August 2017)
UNICEF “United Nations Children’s Fund”. (2017). Progress for children. https://www.unicef.org/progressforchildren/2006n4/index_introduction.html (accessed July 2017)
Vilar-Compte M, Gaitán-Rossi P, Pérez-Escamilla R (2017) Food insecurity measurement among older adults: implications for policy and food security governance. Global Food Security 14(1):87–95. https://doi.org/10.1016/j.gfs.2017.05.003
Wang J, Li Y, Huang J, Yan T, Sun T (2017) Growing water scarcity, food security and government responses in China. Global Food Security. 14(1):9–17. https://doi.org/10.1002/fes3.48
World Food Programme (WFP). (2017) Zero Hunger www1.wfp.org/zero-hunger (Access August 2017)
Zavala VM (2012) Real-time optimization strategies for building systems. Ind Eng Chem Res 52(9):3137–3150. https://doi.org/10.1021/ie3008727
Zavala VM, Flores-Tlacuahuac A (2012) Stability of multiobjective predictive control: a utopia-tracking approach. Automatica 48(10):2627–2263. https://doi.org/10.1016/j.automatica.2012.06.066
Zilberman D, Lu L, Reardon T (2017) Innovation-induced food supply chain design. Food Policy. https://doi.org/10.1016/j.foodpol.2017.03.010
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
On behalf of all authors, the corresponding author sates that there is no conflict of interest.
Rights and permissions
About this article
Cite this article
Martínez-Guido, S.I., González-Campos, J.B. & Ponce-Ortega, J.M. A Multi-Stakeholder Optimization of Food Supply Chains: an Undernourishment Reduction Strategy. Process Integr Optim Sustain 2, 239–257 (2018). https://doi.org/10.1007/s41660-018-0039-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41660-018-0039-0