How to cite this paper
Ren, M & Wang, G. (2024). A hybrid genetic algorithm with variable neighborhood search for batch dispersion problem to improve traceability.International Journal of Industrial Engineering Computations , 15(1), 41-58.
Refrences
Astill, J., Dara, R. A., Campbell, M., Farber, J. M., Fraser, E. D., Sharif, S., & Yada, R. Y. (2019). Transparency in food supply chains: A review of enabling technology solutions. Trends in Food Science & Technology, 91, 240-247. https://doi.org/10.1016/j.tifs.2019.07.024
Aung, M. M., & Chang, Y. S. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food Control, 39, 172–184. https://doi.org/10.1016/j.foodcont.2013.11.007
Badia-Melis, R., Mishra, P., & Ruiz-García, L. (2015). Food traceability: New trends and recent advances. A review. Food Control, 57, 393–401. https://doi.org/10.1016/j.foodcont.2015.05.005
Chen, T., Ding, K., & Hao, S. (2019). Batch-based traceability for pork: A mobile solution with 2D barcode technology. Food Control, 107. https://doi.org/10.1016/j.foodcont.2019.106770
Comba, L., Belforte, G., Dabbene, F., & Gay, P. (2013). Methods for traceability in food production processes involving bulk products. Biosystems Engineering, 116(1), 51–63. https://doi.org/10.1016/j.biosystemseng.2013.06.006
Dabbene, F., & Gay, P. (2011). Food traceability systems: Performance evaluation and optimization. Computers and Electronics in Agriculture, 75(1), 139–146. https://doi.org/10.1016/j.biosystemseng.2013.09.006
Dabbene, F., Gay, P., & Tortia, C. (2014). Traceability issues in food supply chain management: A review. Biosystems Engineering, 120, 65–80. https://doi.org/10.1016/j.biosystemseng.2013.09.006
Deng, W., Zhao, H., Zou, L., Li, G., Yang, X., & Wu, D. (2017). A novel collaborative optimization algorithm in solving complex optimization problems. Soft Computing, 21(15), 4387–4398. https://doi.org/10.1007/s00500-016-2071-8
Dupuy, C., Botta-Genoulaz, V., & Guinet, A. (2005). Batch dispersion model to optimise traceability in food industry. Journal of Food Engineering, 70(3), 333–339. https://doi.org/10.1016/j.jfoodeng.2004.05.074
Fan, B., Qian, J., Wu, X., Du, X., Li, W., Ji, Z., & Xin, X. (2019). Improving continuous traceability of food stuff by using barcode-RFID bidirectional transformation equipment: Two field experiments. Food Control, 98, 449–456. https://doi.org/10.1016/j.foodcont.2018.12.002
Friedman, M. (1940). A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings. The Annals of Mathematical Statistics, 11(1), 86–92. https://doi.org/10.1214/aoms/1177731944
Gao, K. Z., Suganthan, P. N., Pan, Q. K., Chua, T. J., Chong, C. S., & Cai, T. X. (2016). An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time. Expert Systems with Applications, 65, 52–67. https://doi.org/10.1016/j.eswa.2016.07.046
Garg, H. (2016). A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation, 274, 292–305. https://doi.org/10.1016/j.amc.2015.11.001
ISO (EN ISO 8402.1995, Point 3.16). (1995). [ISO European Standard (1995)].
Katoch, S., Chauhan, S. S., & Kumar, V. (2021a). A review on genetic algorithm: Past, present, and future. Multimedia Tools and Applications, 80(5), 8091–8126. https://doi.org/10.1007/s11042-020-10139-6
Katoch, S., Chauhan, S. S., & Kumar, V. (2021b). A review on genetic algorithm: Past, present, and future. Multimedia Tools and Applications, 80(5), 8091–8126. https://doi.org/10.1007/s11042-020-10139-6
Khalilpourazari, S., & Khalilpourazary, S. (2019). An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Computing, 23(5), 1699–1722. https://doi.org/10.1007/s00500-017-2894-y
Kong, M., Pei, J., Liu, X., Lai, P.-C., & Pardalos, P. M. (2020). Green manufacturing: Order acceptance and scheduling subject to the budgets of energy consumption and machine launch. Journal of Cleaner Production, 248, 119300. https://doi.org/10.1016/j.jclepro.2019.119300
Li, M., Qian, J.-P., Yang, X.-T., Sun, C.-H., & Ji, Z.-T. (2010). A PDA-based record-keeping and decision-support system for traceability in cucumber production. Computers and Electronics in Agriculture, 70(1), 69–77. https://doi.org/10.1016/j.compag.2009.09.009
Liang, K., Chen, X., He, R., Li, J., Okinda, C., Han, D., & Shen, M. (2019). Development and parameter optimization of automatic separation and identification equipment for grain tracing systems based on grain tracers with QR codes. Computers and Electronics in Agriculture, 162, 709–718. https://doi.org/10.1016/j.compag.2019.04.039
Liang, K., Thomasson, J. A., Lee, K.-M., Shen, M., Ge, Y., & Herrman, T. J. (2012). Printing data matrix code on food-grade tracers for grain traceability. Biosystems Engineering, 113(4), 395–401. https://doi.org/10.1016/j.biosystemseng.2012.09.012
Luvisi, A., Panattoni, A., Bandinelli, R., Rinaldelli, E., Pagano, M., & Triolo, E. (2012). Ultra-High Frequency transponders in grapevine: A tool for traceability of plants and treatments in viticulture. Biosystems Engineering, 113(2), 129–139. https://doi.org/10.1016/j.biosystemseng.2012.06.015
Maity, M., Tolooie, A., Sinha, A. K., & Tiwari, M. K. (2021). Stochastic batch dispersion model to optimize traceability and enhance transparency using Blockchain. Computers & Industrial Engineering, 154, 107134. https://doi.org/10.1016/j.cie.2021.107134
Moe, T. (1998). Perspectives on traceability in food manufacture—ScienceDirect. Trends in Food Science & Technology, 9(5), 211–214. https://doi.org/10.1016/S0924-2244(98)00037-5
Mokarram, M. J., Niknam, T., Aghaei, J., Shafie-khah, M., & Catalão, J. P. S. (2019). Hybrid Optimization Algorithm to Solve the Nonconvex Multiarea Economic Dispatch Problem. IEEE Systems Journal, 13(3), 3400–3409. https://doi.org/10.1109/JSYST.2018.2889988
Pierini, G. D., Fernandes, D. D. S., Diniz, P. H. G. D., de Araújo, M. C. U., Di Nezio, M. S., & Centurión, M. E. (2016). A digital image-based traceability tool of the geographical origins of Argentine propolis. Microchemical Journal, 128, 62–67. https://doi.org/10.1016/j.microc.2016.04.015
Qian, J., Dai, B., Wang, B., Zha, Y., & Song, Q. (2022). Traceability in food processing: Problems, methods, and performance evaluations—a review. Critical Reviews in Food Science and Nutrition, 62(3), 679–692. https://doi.org/10.1080/10408398.2020.1825925
Qian, J., Fan, B., & Wu, X. (2017). Cbmprehensive and quantifiable granularity: A novel model to measure agro-food traceability. Food Control, 74, 98–106. https://doi.org/10.1016/j.foodcont.2016.11.034
Qian, J., Ruiz-Garcia, L., Fan, B., Robla Villalba, J. I., McCarthy, U., Zhang, B., Yu, Q., & Wu, W. (2020). Food traceability system from governmental, corporate, and consumer perspectives in the European Union and China: A comparative review. Trends in Food Science & Technology, 99, 402–412. https://doi.org/10.1016/j.tifs.2020.03.025
Sardina, M. T., Tortorici, L., Mastrangelo, S., Di Gerlando, R., Tolone, M., & Portolano, B. (2015). Application of microsatellite markers as potential tools for traceability of Girgentana goat breed dairy products. Food Research International, 74, 115–122. https://doi.org/10.1016/j.foodres.2015.04.038
Slowik, A., & Kwasnicka, H. (2020). Evolutionary algorithms and their applications to engineering problems. Neural Computing and Applications, 32(16), 12363–12379. https://doi.org/10.1007/s00521-020-04832-8
Tian, Y., Cheng, R., Zhang, X., & Jin, Y. (2017). PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization. IEEE Computational Intelligence Magazine, 12, 73–87. https://doi.org/10.1109/MCI.2017.2742868
Van der Spiegel, M., Sterrenburg, P., Haasnoot, W., & van der Fels-Klerx, H. J. (2013). Towards a decision support system for control of multiple food safety hazards in raw milk production. Trends in Food Science & Technology, 34(2), 137–145. https://doi.org/10.1016/j.tifs.2013.10.001
Wang, W., Tian, G., Zhang, H., Li, Z., & Zhang, L. (2023). A hybrid genetic algorithm with multiple decoding methods for energy-aware remanufacturing system scheduling problem. Robotics and Computer-Integrated Manufacturing, 81, 102509. https://doi.org/10.1016/j.rcim.2022.102509
Yang, X., Qian, J., Li, J., Ji, Z., Fan, B., Xing, B., & Li, W. (2016). A real-time agro-food authentication and supervision system on a novel code for improving traceability credibility. Food Control, 66, 17–26. https://doi.org/10.1016/j.foodcont.2016.01.032
Ye, J. (2014). Similarity measures between interval neutrosophic sets and their applications in multicriteria decision-making. Journal of Intelligent & Fuzzy Systems, 26(1), 165–172. https://doi.org/10.3233/IFS-120724
Zhang, Z., Ding, S., & Jia, W. (2019). A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems. Engineering Applications of Artificial Intelligence, 85, 254–268. https://doi.org/10.1016/j.engappai.2019.06.017
Zhao, H., Wang, F., & Yang, Q. (2020). Origin traceability of peanut kernels based on multi‐element fingerprinting combined with multivariate data analysis. Journal of the Science of Food and Agriculture, 100(10), 4040–4048. https://doi.org/10.1002/jsfa.10449
Aung, M. M., & Chang, Y. S. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food Control, 39, 172–184. https://doi.org/10.1016/j.foodcont.2013.11.007
Badia-Melis, R., Mishra, P., & Ruiz-García, L. (2015). Food traceability: New trends and recent advances. A review. Food Control, 57, 393–401. https://doi.org/10.1016/j.foodcont.2015.05.005
Chen, T., Ding, K., & Hao, S. (2019). Batch-based traceability for pork: A mobile solution with 2D barcode technology. Food Control, 107. https://doi.org/10.1016/j.foodcont.2019.106770
Comba, L., Belforte, G., Dabbene, F., & Gay, P. (2013). Methods for traceability in food production processes involving bulk products. Biosystems Engineering, 116(1), 51–63. https://doi.org/10.1016/j.biosystemseng.2013.06.006
Dabbene, F., & Gay, P. (2011). Food traceability systems: Performance evaluation and optimization. Computers and Electronics in Agriculture, 75(1), 139–146. https://doi.org/10.1016/j.biosystemseng.2013.09.006
Dabbene, F., Gay, P., & Tortia, C. (2014). Traceability issues in food supply chain management: A review. Biosystems Engineering, 120, 65–80. https://doi.org/10.1016/j.biosystemseng.2013.09.006
Deng, W., Zhao, H., Zou, L., Li, G., Yang, X., & Wu, D. (2017). A novel collaborative optimization algorithm in solving complex optimization problems. Soft Computing, 21(15), 4387–4398. https://doi.org/10.1007/s00500-016-2071-8
Dupuy, C., Botta-Genoulaz, V., & Guinet, A. (2005). Batch dispersion model to optimise traceability in food industry. Journal of Food Engineering, 70(3), 333–339. https://doi.org/10.1016/j.jfoodeng.2004.05.074
Fan, B., Qian, J., Wu, X., Du, X., Li, W., Ji, Z., & Xin, X. (2019). Improving continuous traceability of food stuff by using barcode-RFID bidirectional transformation equipment: Two field experiments. Food Control, 98, 449–456. https://doi.org/10.1016/j.foodcont.2018.12.002
Friedman, M. (1940). A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings. The Annals of Mathematical Statistics, 11(1), 86–92. https://doi.org/10.1214/aoms/1177731944
Gao, K. Z., Suganthan, P. N., Pan, Q. K., Chua, T. J., Chong, C. S., & Cai, T. X. (2016). An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time. Expert Systems with Applications, 65, 52–67. https://doi.org/10.1016/j.eswa.2016.07.046
Garg, H. (2016). A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation, 274, 292–305. https://doi.org/10.1016/j.amc.2015.11.001
ISO (EN ISO 8402.1995, Point 3.16). (1995). [ISO European Standard (1995)].
Katoch, S., Chauhan, S. S., & Kumar, V. (2021a). A review on genetic algorithm: Past, present, and future. Multimedia Tools and Applications, 80(5), 8091–8126. https://doi.org/10.1007/s11042-020-10139-6
Katoch, S., Chauhan, S. S., & Kumar, V. (2021b). A review on genetic algorithm: Past, present, and future. Multimedia Tools and Applications, 80(5), 8091–8126. https://doi.org/10.1007/s11042-020-10139-6
Khalilpourazari, S., & Khalilpourazary, S. (2019). An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Computing, 23(5), 1699–1722. https://doi.org/10.1007/s00500-017-2894-y
Kong, M., Pei, J., Liu, X., Lai, P.-C., & Pardalos, P. M. (2020). Green manufacturing: Order acceptance and scheduling subject to the budgets of energy consumption and machine launch. Journal of Cleaner Production, 248, 119300. https://doi.org/10.1016/j.jclepro.2019.119300
Li, M., Qian, J.-P., Yang, X.-T., Sun, C.-H., & Ji, Z.-T. (2010). A PDA-based record-keeping and decision-support system for traceability in cucumber production. Computers and Electronics in Agriculture, 70(1), 69–77. https://doi.org/10.1016/j.compag.2009.09.009
Liang, K., Chen, X., He, R., Li, J., Okinda, C., Han, D., & Shen, M. (2019). Development and parameter optimization of automatic separation and identification equipment for grain tracing systems based on grain tracers with QR codes. Computers and Electronics in Agriculture, 162, 709–718. https://doi.org/10.1016/j.compag.2019.04.039
Liang, K., Thomasson, J. A., Lee, K.-M., Shen, M., Ge, Y., & Herrman, T. J. (2012). Printing data matrix code on food-grade tracers for grain traceability. Biosystems Engineering, 113(4), 395–401. https://doi.org/10.1016/j.biosystemseng.2012.09.012
Luvisi, A., Panattoni, A., Bandinelli, R., Rinaldelli, E., Pagano, M., & Triolo, E. (2012). Ultra-High Frequency transponders in grapevine: A tool for traceability of plants and treatments in viticulture. Biosystems Engineering, 113(2), 129–139. https://doi.org/10.1016/j.biosystemseng.2012.06.015
Maity, M., Tolooie, A., Sinha, A. K., & Tiwari, M. K. (2021). Stochastic batch dispersion model to optimize traceability and enhance transparency using Blockchain. Computers & Industrial Engineering, 154, 107134. https://doi.org/10.1016/j.cie.2021.107134
Moe, T. (1998). Perspectives on traceability in food manufacture—ScienceDirect. Trends in Food Science & Technology, 9(5), 211–214. https://doi.org/10.1016/S0924-2244(98)00037-5
Mokarram, M. J., Niknam, T., Aghaei, J., Shafie-khah, M., & Catalão, J. P. S. (2019). Hybrid Optimization Algorithm to Solve the Nonconvex Multiarea Economic Dispatch Problem. IEEE Systems Journal, 13(3), 3400–3409. https://doi.org/10.1109/JSYST.2018.2889988
Pierini, G. D., Fernandes, D. D. S., Diniz, P. H. G. D., de Araújo, M. C. U., Di Nezio, M. S., & Centurión, M. E. (2016). A digital image-based traceability tool of the geographical origins of Argentine propolis. Microchemical Journal, 128, 62–67. https://doi.org/10.1016/j.microc.2016.04.015
Qian, J., Dai, B., Wang, B., Zha, Y., & Song, Q. (2022). Traceability in food processing: Problems, methods, and performance evaluations—a review. Critical Reviews in Food Science and Nutrition, 62(3), 679–692. https://doi.org/10.1080/10408398.2020.1825925
Qian, J., Fan, B., & Wu, X. (2017). Cbmprehensive and quantifiable granularity: A novel model to measure agro-food traceability. Food Control, 74, 98–106. https://doi.org/10.1016/j.foodcont.2016.11.034
Qian, J., Ruiz-Garcia, L., Fan, B., Robla Villalba, J. I., McCarthy, U., Zhang, B., Yu, Q., & Wu, W. (2020). Food traceability system from governmental, corporate, and consumer perspectives in the European Union and China: A comparative review. Trends in Food Science & Technology, 99, 402–412. https://doi.org/10.1016/j.tifs.2020.03.025
Sardina, M. T., Tortorici, L., Mastrangelo, S., Di Gerlando, R., Tolone, M., & Portolano, B. (2015). Application of microsatellite markers as potential tools for traceability of Girgentana goat breed dairy products. Food Research International, 74, 115–122. https://doi.org/10.1016/j.foodres.2015.04.038
Slowik, A., & Kwasnicka, H. (2020). Evolutionary algorithms and their applications to engineering problems. Neural Computing and Applications, 32(16), 12363–12379. https://doi.org/10.1007/s00521-020-04832-8
Tian, Y., Cheng, R., Zhang, X., & Jin, Y. (2017). PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization. IEEE Computational Intelligence Magazine, 12, 73–87. https://doi.org/10.1109/MCI.2017.2742868
Van der Spiegel, M., Sterrenburg, P., Haasnoot, W., & van der Fels-Klerx, H. J. (2013). Towards a decision support system for control of multiple food safety hazards in raw milk production. Trends in Food Science & Technology, 34(2), 137–145. https://doi.org/10.1016/j.tifs.2013.10.001
Wang, W., Tian, G., Zhang, H., Li, Z., & Zhang, L. (2023). A hybrid genetic algorithm with multiple decoding methods for energy-aware remanufacturing system scheduling problem. Robotics and Computer-Integrated Manufacturing, 81, 102509. https://doi.org/10.1016/j.rcim.2022.102509
Yang, X., Qian, J., Li, J., Ji, Z., Fan, B., Xing, B., & Li, W. (2016). A real-time agro-food authentication and supervision system on a novel code for improving traceability credibility. Food Control, 66, 17–26. https://doi.org/10.1016/j.foodcont.2016.01.032
Ye, J. (2014). Similarity measures between interval neutrosophic sets and their applications in multicriteria decision-making. Journal of Intelligent & Fuzzy Systems, 26(1), 165–172. https://doi.org/10.3233/IFS-120724
Zhang, Z., Ding, S., & Jia, W. (2019). A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems. Engineering Applications of Artificial Intelligence, 85, 254–268. https://doi.org/10.1016/j.engappai.2019.06.017
Zhao, H., Wang, F., & Yang, Q. (2020). Origin traceability of peanut kernels based on multi‐element fingerprinting combined with multivariate data analysis. Journal of the Science of Food and Agriculture, 100(10), 4040–4048. https://doi.org/10.1002/jsfa.10449