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Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art

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Abstract

Supply chain network designing and programming is a momentous issue that many practitioners have focused on and contributed numerous novelties for this prompt. This paper puts forward a fuzzy multi-agent system according to which compatible with the decision makers’ interests and environmental survey, identifies the parameters of the mathematical model. An embedded optimization party including evolutionary-based optimizer intelligent agents, obtains non-dominated potential solutions. The output of these optimizer agents during the calibration process is an underpinning for evaluating the performance of the party. The system makes the policy of optimization complying with the results evaluation as well as the decision makers’ elaborated desires. Afterwards, in step with this policy, it sets a pool from obtained Pareto Fronts and aggregates them to extract a set of the best individuals. It interactively represents this set to the decision makers and catches their desired circumstance amongst these optional solutions. Proposing the network graph and program—which its generic morphography is determined—for decision makers is contrived as the system last stage. The main competencies of this system could be contemplated regarding the facts that it interactively fulfills the decision makers’ utilities relying on its robustness in optimization, self-tuning, training loop, ambient intelligence and consciousness toward the changes in environment.

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Notes

  1. Decision maker.

  2. Multi-agent system.

  3. Fuzzy multi-agent system.

  4. Small and medium-sized enterprises.

  5. Decision support system.

  6. Chain environment survey.

  7. Computer-human interaction center.

  8. Relative deviation index.

  9. Signal-to-noise.

  10. Membership functions.

  11. Center of gravity.

  12. Distribution center.

  13. Foundation for intelligent physical agents.

  14. Plan-commit-execute.

  15. Entity relationship diagram.

  16. Balanced supply chain network.

  17. Ordered weighting averaging.

References

  • Al Jadaan, O., Rajamani, L., & Rao, C. (2008). Non-dominated ranked genetic algorithm for solving multiobjective optimization problems. in NRGA. Journal of Theoretical and Applied Information chnology. Citeseer.

  • Alavidoost, M. H., Tarimoradi, M., & Zarandi, M. H. F. (2015a). Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks. Journal of Intelligent Manufacturing, 2015, 1–18.

    Google Scholar 

  • Alavidoost, M. H., Tarimoradi, M., & Zarandi, M. H. F. (2015b). Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems. Applied Soft Computing, 34, 655–677.

    Article  Google Scholar 

  • Alavidoost, M., Zarandi, M. F., Tarimoradi, M., & Nemati, Y. (2014). Modified genetic algorithm for simple straight and U-shaped assembly line balancing with fuzzy processing times. Journal of Intelligent Manufacturing, 6, 1–24.

  • Aliev, R. A., et al. (2007). Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management. Information Sciences, 177(20), 4241–4255.

    Article  Google Scholar 

  • Amid, A., Ghodsypour, S., & O’Brien, C. (2006). Fuzzy multiobjective linear model for supplier selection in a supply chain. International Journal of Production Economics, 104(2), 394–407.

    Article  Google Scholar 

  • Ballou, R.H. (1968). Dynamic warehouse location analysis. Journal of Marketing Research, 271–276.

  • Barbuceanu, M., & Fox, M.S. (1995). The architecture of an agent based infrastructure for agile manufacturing. In Proceedings of IJCAI.

  • Baumgaertel, H., & John, U. (2003). Combining agent-based supply net simulation and constraint technology for highly efficient simulation of supply networks using APS systems. In Simulation conference, 2003. Proceedings of the 2003 Winter. IEEE.

  • Becker, M., et al. (2006). Agent-based and discrete event simulation of autonomous logistic processes. In W. Borutzky, Orsoni, A. & Zobel, R. (Eds.), Proceedings of (pp. 566–571).

  • Bikakis, A., & Caire, P. (2015). Computing coalitions in multiagent systems: A contextual reasoning approach. In N. Bulling (Ed.), Multi-agent systems (pp. 85–100). New York: Springer.

    Google Scholar 

  • Brintrup, A. (2010). Behaviour adaptation in the multi-agent, multi-objective and multi-role supply chain. Computers in Industry, 61(7), 636–645.

    Article  Google Scholar 

  • Bruekner, S., et al. (2003). Agent models of supply network dynamics: Analysis, design and operation. The Practice of Supply Chain Management: Where Theory and Application converge, Harrison, Lee and Neal Eds.

  • Cárdenas-Barrón, L. E., & Treviño-Garza, G. (2014). An optimal solution to a three echelon supply chain network with multi-product and multi-period. Applied Mathematical Modelling, 38(5–6), 1911–1918.

    Article  Google Scholar 

  • Cardona-Valdés, Y., Álvarez, A., & Pacheco, J. (2014). Metaheuristic procedure for a bi-objective supply chain design problem with uncertainty. Transportation Research Part B: Methodological, 60, 66–84.

    Article  Google Scholar 

  • Chaturvedi, A., Armstrong, B., & Chaturvedi, R. (2014). Securing the food supply chain: understanding complex interdependence through agent-based simulation. Health and Technology, 4(2), 159–169.

    Article  Google Scholar 

  • Chen, L.-W. (2002). Apparatus for providing a reverse star schema data model. Google Patents.

  • Chen, C.-L., & Lee, W.-C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28(6), 1131–1144.

    Article  Google Scholar 

  • Chen, C.-T., Lin, C.-T., & Huang, S.-F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2), 289–301.

    Article  Google Scholar 

  • Chinh, N.Q., et al. (2013). An agent-based simulation to quantify and analyze bullwhip effects in supply chains. In IEEE international conference on systems, man, and cybernetics (SMC), 2013. IEEE.

  • Corne, D.W., et al. (2001). PESA-II: Region-based selection in evolutionary multiobjective optimization. In Proceedings of the genetic and evolutionary computation conference (GECCO’2001). Citeseer.

  • Czechowski, K., & Vuduc, R. (2013). A theoretical framework for algorithm-architecture co-design. In 2013 IEEE 27th international symposium on parallel & distributed processing (IPDPS).

  • Deb, K., et al. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on, 6(2), 182–197.

    Article  Google Scholar 

  • Earnshaw, R. (2001). Frontiers of human-centered computing, online communities and virtual environments. New York: Springer.

    Book  Google Scholar 

  • Emerson, D., & Piramuthu, S. (2004). Agent-based framework for dynamic supply chain configuration. In Proceedings of the 37th annual Hawaii international conference on system sciences, 2004. IEEE.

  • Fazlollahi, B. (2002). Soft computing agent-based distributed intelligent systems. Amsterdam: IOS Press.

    Google Scholar 

  • Fazzolari, M., et al. (2013). A review of the application of multiobjective evolutionary fuzzy systems: Current status and further directions. Fuzzy Systems, IEEE Transactions on, 21(1), 45–65.

    Article  Google Scholar 

  • Feng, Y. (2000). Gaussian fuzzy random variables. Fuzzy Sets and Systems, 111(3), 325–330.

    Article  Google Scholar 

  • Ferber, J. (1999). Multi-agent systems: An introduction to distributed artificial intelligence (Vol. 1). Reading: Addison-Wesley Reading.

    Google Scholar 

  • Fernández, A., et al. (2015). Revisiting evolutionary fuzzy systems: Taxonomy, applications, new trends and challenges. Knowledge-Based Systems, 80, 109–121.

    Article  Google Scholar 

  • Finin, T., Labrou, Y., & Mayfield, J. (1995). KQML as an agent communication language.

  • Forget, P., et al. (2008). Performance analysis of multi-behaviour agents for supply chain planning.

  • Fox, M. S., Chionglo, J. F., & Barbuceanu, M. (1993). The integrated supply chain management system. Citeseer.

  • Gamasaee, R., & Zarandi, M.F. (2013). A fuzzy hybrid intelligent agent system for mitigating demand amplification in supply chain of steel manufacturing. In IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), 2013 joint. IEEE.

  • Garcia, D.J., & You, F. (2015). Supply chain design and optimization: Challenges and opportunities. Computers & Chemical Engineering.

  • Gebennini, E., Gamberini, R., & Manzini, R. (2009). An integrated production-distribution model for the dynamic location and allocation problem with safety stock optimization. International Journal of Production Economics, 122(1), 286–304.

    Article  Google Scholar 

  • Hadavandi, E., et al. (2011). Tourist arrival forecasting by evolutionary fuzzy systems. Tourism Management, 32(5), 1196–1203.

    Article  Google Scholar 

  • Henesey, L., Davidsson, P., & Persson, J. A. (2009). Agent based simulation architecture for evaluating operational policies in transshipping containers. Autonomous Agents and Multi-Agent Systems, 18(2), 220–238.

    Article  Google Scholar 

  • Hernández, J. E., et al. (2014). Collaborative planning in multi-tier supply chains supported by a negotiation-based mechanism and multi-agent system. Group Decision and Negotiation, 23(2), 235–269.

    Article  Google Scholar 

  • Husseini, Z.M., Zarandi, M., & Husseini, S. (2015). Trust evaluation for buyer-supplier relationship concerning fuzzy approach. In Fuzzy information processing society (NAFIPS) held jointly with 2015 5th world conference on soft computing (WConSC), 2015 annual conference of the North American. IEEE.

  • Inmon, W. (1997). What is a data mart. D2K: Data to Knowledge, 18.

  • Ishibuchi, H., Masuda, H., & Nojima, Y. (2014). Selecting a small number of non-dominated solutions to be presented to the decision maker. In 2014 IEEE international conference on systems, man and cybernetics (SMC).

  • Jana, D. K., Maity, K., & Roy, T. K. (2013). A three-layer supply chain integrated production-inventory model under permissible delay in payments in uncertain environments. Journal of Uncertainty Analysis and Applications, 1(1), 1–17.

    Article  Google Scholar 

  • Jarrah, M., & Zhang, J. (2015). Trusted mediator agents to better manage complex and competitive supply chains. In Proceedings of the 2015 international conference on autonomous agents and multiagent systems. International Foundation for Autonomous Agents and Multiagent Systems.

  • Kaihara, T. (2003). Multi-agent based supply chain modelling with dynamic environment. International Journal of Production Economics, 85(2), 263–269.

    Article  Google Scholar 

  • Karageorgos, A., et al. (2003). Agent-based optimisation of logistics and production planning. Engineering Applications of Artificial Intelligence, 16(4), 335–348.

    Article  Google Scholar 

  • Kasprzyk, A., et al. (2004). EnsMart: A generic system for fast and flexible access to biological data. Genome Research, 14(1), 160–169.

    Article  Google Scholar 

  • Kaufmann, A., & Gupta, M. M. (1991). Introduction to fuzzy arithmetic: Theory and applications. Arden Shakespeare.

  • Kavka, C. (2010). Evolutionary fuzzy systems. In C. Sammut & G. Webb (Eds.), Encyclopedia of machine learning (pp. 357–362). New York: Springer.

    Google Scholar 

  • Klusch, M. (2001). Information agent technology for the internet: A survey. Data & Knowledge Engineering, 36(3), 337–372.

    Article  Google Scholar 

  • Knowles, J., & Corne, D. (2002). On metrics for comparing nondominated sets. In Proceedings of the 2002 congress on evolutionary computation, 2002. CEC’02. IEEE.

  • Kumari, S., et al. (2015). A multi-agent architecture for outsourcing SMEs manufacturing supply chain. Robotics and Computer-Integrated Manufacturing.

  • Li, W., & Li, C. (2008). Transshipment policy research of multi-location inventory system based on multi-agent system. In IEEE international conference on automation and logistics, 2008. ICAL 2008, IEEE.

  • Li, Y., et al. (2014). Calibration of the agent-based continuous double auction stock market by scaling analysis. Information Sciences, 256, 46–56.

    Article  Google Scholar 

  • Liang, T.-F. (2008). Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain. Computers & Industrial Engineering, 55(3), 676–694.

    Article  Google Scholar 

  • Liang, W.-Y., & Huang, C.-C. (2006). Agent-based demand forecast in multi-echelon supply chain. Decision support systems, 42(1), 390–407.

    Article  Google Scholar 

  • Long, Q. (2014). Distributed supply chain network modelling and simulation: integration of agent-based distributed simulation and improved SCOR model. International Journal of Production Research, 52(23), 6899–6917.

    Article  Google Scholar 

  • Long, Q., & Zhang, W. (2014). An integrated framework for agent based inventory-production-transportation modeling and distributed simulation of supply chains. Information Sciences, 277, 567–581.

    Article  Google Scholar 

  • Mamdani, E. H. (1976). Advances in the linguistic synthesis of fuzzy controllers. International Journal of Man-Machine Studies, 8(6), 669–678.

    Article  Google Scholar 

  • Mamdani, E. H. (1977). Application of fuzzy logic to approximate reasoning using linguistic synthesis. Computers, IEEE Transactions on, 100(12), 1182–1191.

    Article  Google Scholar 

  • Manna, A. K., Dey, J. K., & Mondal, S. K. (2014). Three-layer supply chain in an imperfect production inventory model with two storage facilities under fuzzy rough environment. Journal of Uncertainty Analysis and Applications, 2(1), 17.

    Article  Google Scholar 

  • Mohebbi, S., & Shafaei, R. (2012). e-Supply network coordination: The design of intelligent agents for buyer-supplier dynamic negotiations. Journal of Intelligent Manufacturing, 23(3), 375–391.

    Article  Google Scholar 

  • Montgomery, D. C. (2008). Design and analysis of experiments. New York: Wiley.

    Google Scholar 

  • Mosahar, T., Fazel Zarandi, M. H., & Türksen, I. B. (2014). Hybrid intelligent agent-based internal analysis architecture for CRM strategy planning. International Journal of Intelligent Systems and Applications, 6, 1–20.

  • Moyaux, T., Chaib-draa, B., & D’Amours, S. (2003). Multi-agent coordination based on tokens: Reduction of the bullwhip effect in a forest supply chain. In Proceedings of the second international joint conference on autonomous agents and multiagent systems. ACM.

  • Naderi, B., et al. (2009). An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness. Expert Systems with Applications, 36(6), 9625–9633.

    Article  Google Scholar 

  • Nozick, L. K., & Turnquist, M. A. (2001). Inventory, transportation, service quality and the location of distribution centers. European Journal of Operational Research, 129(2), 362–371.

    Article  Google Scholar 

  • Olfati-Saber, R., Fax, A., & Murray, R. M. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95(1), 215–233.

    Article  Google Scholar 

  • O’Neil, P.E., O’Neil, E.J., & Chen, X. (2007). The star schema benchmark (SSB). Pat.

  • Padgham, L., & Winikoff, M. (2005). Prometheus: A practical agent-oriented methodology. Agent-Oriented Methodologies, 107–135

  • Paksoy, T., Pehlivan, N. Y., & Özceylan, E. (2012). Application of fuzzy optimization to a supply chain network design: A case study of an edible vegetable oils manufacturer. Applied Mathematical Modelling, 36(6), 2762–2776.

    Article  Google Scholar 

  • Pasandideh, S. H. R., Niaki, S. T. A., & Asadi, K. (2015). Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA. Information Sciences, 292(C), 57–74.

    Article  Google Scholar 

  • Peidro, D., et al. (2009). Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy sets and Systems, 160(18), 2640–2657.

    Article  Google Scholar 

  • Priyan, S., Palanivel, M., & Uthayakumar, R. (2015). Two-echelon production-inventory system with fuzzy production rate and promotional effort dependent demand. Journal of Management Analytics, 2(1), 72–92.

    Article  Google Scholar 

  • Procyk, T. J., & Mamdani, E. H. (1979). A linguistic self-organizing process controller. Automatica, 15(1), 15–30.

    Article  Google Scholar 

  • Rabiee, M., Zandieh, M., & Ramezani, P. (2012). Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches. International Journal of Production Research, 50(24), 7327–7342.

    Article  Google Scholar 

  • Roorda, M. J., et al. (2010). A conceptual framework for agent-based modelling of logistics services. Transportation Research Part E: Logistics and Transportation Review, 46(1), 18–31.

    Article  Google Scholar 

  • Ruiz, R., Maroto, C., & Alcaraz, J. (2006). Two new robust genetic algorithms for the flowshop scheduling problem. Omega, 34(5), 461–476.

    Article  Google Scholar 

  • Sadeh, N. M., et al. (2001). MASCOT: An agent-based architecture for dynamic supply chain creation and coordination in the internet economy. Production Planning & Control, 12(3), 212–223.

    Article  Google Scholar 

  • Santa-Eulalia, L. A., et al. (2011). Integrated methodological frameworks for modelling agent-based advanced supply chain planning systems: a systematic literature review. Journal of Industrial Engineering and Management, 4(4), 624–668.

    Article  Google Scholar 

  • Sauvageau, G., & Frayret, J.-M. (2015). Waste paper procurement optimization: An agent-based simulation approach. European Journal of Operational Research, 242(3), 987–998.

    Article  Google Scholar 

  • Schuldt, A. (2012). Multiagent coordination enabling autonomous logistics. KI-Künstliche Intelligenz, 26(1), 91–94.

    Article  Google Scholar 

  • Shakeri, B., et al. (2015). Fuzzy clustering rule-based expert system for stock price movement prediction. In Fuzzy information processing society (NAFIPS) held jointly with 2015 5th world conference on soft computing (WConSC), 2015 annual conference of the North American. IEEE.

  • Shaw, K., et al. (2012). Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Systems with Applications, 39(9), 8182–8192.

    Article  Google Scholar 

  • Sheikhalishahi, M., et al. (2013). A hybrid GA-PSO approach for reliability optimization in redundancy allocation problem. The International Journal of Advanced Manufacturing Technology, 68(1–4), 317–338.

    Article  Google Scholar 

  • Shen, Z.-J. M., Coullard, C., & Daskin, M. S. (2003). A joint location-inventory model. Transportation Science, 37(1), 40–55.

    Article  Google Scholar 

  • Shi, Y., Eberhart, R., & Chen, Y. (1999). Implementation of evolutionary fuzzy systems. Fuzzy Systems, IEEE Transactions on, 7(2), 109–119.

    Article  Google Scholar 

  • Sinha, A. K., et al. (2011). Agent oriented petroleum supply chain coordination: Co-evolutionary Particle Swarm optimization based approach. Expert Systems with Applications, 38(5), 6132–6145.

    Article  Google Scholar 

  • Sivakumar, N., Vivekan, K., & Kanimozhi, M. (2013). Testing in prometheus methodology-plan oriented approach. International Journal of Emerging Technology and Advanced Engineering, 3, 657–667.

    Google Scholar 

  • Sridharan, R. (1995). The capacitated plant location problem. European Journal of Operational Research, 87(2), 203–213.

    Article  Google Scholar 

  • Sycara, K. (1999). In-context information management through adaptive collaboration of intelligent agents. In Intelligent information agents (pp. 78-99). Springer.

  • Taguchi, G. (1986). Introduction to quality engineering: designing quality into products and processes.

  • Talukdar, S., et al. (1998). Asynchronous teams: Cooperation schemes for autonomous agents. Journal of Heuristics, 4(4), 295–321.

    Article  Google Scholar 

  • Tan, K. C., Lee, T. H., & Khor, E. F. (2002). Evolutionary algorithms for multi-objective optimization: performance assessments and comparisons. Artificial Intelligence Review, 17(4), 251–290.

    Article  Google Scholar 

  • Tarimoradi, M., Alavidoost, M., & Zarandi, M. F. (2015). Comparative Corrigendum note on papers Fuzzy adaptive GA for multi-objective assembly line balancing continued Modified GA for different types of assembly line balancing with fuzzy processing times: differences and similarities. Applied Soft Computing, 35, 786–788.

    Article  Google Scholar 

  • Team, S. M. (2000). MAESTRO: Conductor of multimedia analysis technologies. Communications of the ACM, 43(2), 57–63.

    Article  Google Scholar 

  • Van Landeghem, H., & Vanmaele, H. (2002). Robust planning: A new paradigm for demand chain planning. Journal of Operations Management, 20(6), 769–783.

    Article  Google Scholar 

  • Verdicchio, M., & Colombetti, M. (2001). Commitments for agent-based supply chain management. ACM SIGecom Exchanges, 3(1), 13–23.

    Article  Google Scholar 

  • Weininger, A. (2002). Efficient execution of joins in a star schema. In Proceedings of the 2002 ACM SIGMOD international conference on management of data (pp. 542-545) ACM: Madison, Wisconsin.

  • Wisner, J., Tan, K.-C., & Leong, G. (2015). Principles of supply chain management: a balanced approach. Cengage Learning.

  • Wu, T.-F., et al. (2013). Supply chain risk management: An agent-based simulation to study the impact of retail stockouts. Engineering Management, IEEE Transactions on, 60(4), 676–686.

    Article  Google Scholar 

  • Xue, X., et al. (2005). An agent-based framework for supply chain coordination in construction. Automation in Construction, 14(3), 413–430.

    Article  Google Scholar 

  • Yager, R. R. (1996). Quantifier guided aggregation using OWA operators. International Journal of Intelligent Systems, 11(1), 49–73.

    Article  Google Scholar 

  • Yu, J. C., Lin, Y.-S., & Wang, K.-J. (2013). Coordination-based inventory management for deteriorating items in a two-echelon supply chain with profit sharing. International Journal of Systems Science, 44(9), 1587–1601.

    Article  Google Scholar 

  • Zaman, H., & Shakouri, G.H. (2010). A combined 2-dimensional fuzzy regression model to study effect of climate change on the electricity consumption in Iran. In 2010 1st international conference on energy, power and control (EPC-IQ). IEEE.

  • Zaman, H., & Shakouri, G.H. (2010). A simple nonlinear mathematical model for wind turbine power maximization with cost constraints. In 2010 1st international conference on energy, power and control (EPC-IQ). IEEE.

  • Zarandi, M., et al. (2015). Fuzzy approximate reasoning toward Multi-Objective optimization policy: Deployment for supply chain programming. In Fuzzy information processing society (NAFIPS) held jointly with 2015 5th world conference on soft computing (WConSC), 2015 annual conference of the North American. IEEE.

  • Zarandi, M., et al. (2015). Fuzzy Comparison dashboard for multi-objective evolutionary applications: An implementation in supply chain planning. In Fuzzy information processing society (NAFIPS) held jointly with 2015 5th world conference on Soft computing (WConSC), 2015 annual conference of the North American. IEEE.

  • Zarandi, M., et al. (2015). Fuzzy intelligent agent-based expert system to keep information systems aligned with the strategy plans: A novel approach toward SISP. In Fuzzy information processing society (NAFIPS) held jointly with 2015 5th world conference on soft computing (WConSC), 2015 annual conference of the North American. IEEE.

  • Zarandi, M. F., et al. (2008). A fuzzy intelligent information agent architecture for supply chains. Scientia Iranica, 15(5), 623–636.

    Google Scholar 

  • Zarandi, M. H. F., & Ahmadpour, P. (2009). Fuzzy agent-based expert system for steel making process. Expert Systems with Applications, 36(5), 9539–9547.

    Article  Google Scholar 

  • Zarandi, M. H. F., Pourakbar, M., & Turksen, I. B. (2008). A Fuzzy agent-based model for reduction of bullwhip effect in supply chain systems. Expert Systems with Applications, 34(3), 1680–1691.

    Article  Google Scholar 

  • Zegordi, S. H., Abadi, I. N. K., & Nia, M. A. B. (2010). A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain. Computers & Industrial Engineering, 58(3), 373–381.

    Article  Google Scholar 

  • Zhang, H. (2015). An agent-based simulation model for supply chain collaborative technological innovation diffusion. International Journal of Simulation Modelling (IJSIMM), 14(2).

  • Zhang, L., & Levinson, D. (2004). Agent-based approach to travel demand modeling: Exploratory analysis. Transportation Research Record: Journal of the Transportation Research Board, 1898, 28–36.

    Article  Google Scholar 

  • Zhu, K., Ludema, M., & van der Heijden, R.E. (2000). Air cargo transport by multi-agent based planning. In Proceedings of the 33rd annual Hawaii international conference on system sciences, 2000, IEEE.

  • Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. Evolutionary Computation, IEEE Transactions on, 3(4), 257–271.

    Article  Google Scholar 

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Acknowledgments

The authors express their gratitude to the editor and anonymous reviewers for their time and also for the constructive and valuable comments on the prior version of the paper. Taking care of the comments significantly improved the representation.

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Authors and Affiliations

  1. Department of Industrial Engineering, Computational Intelligent Systems Laboratory, Amirkabir University of Technology, 424 Hafez Ave, P.O. Box 15875-4413, Tehran, Iran

    Mosahar Tarimoradi & M. H. Fazel Zarandi

  2. Knowledge Intelligent Systems Laboratory, University of Toronto, Toronto, Canada

    M. H. Fazel Zarandi & I. B. Turksan

  3. Group for Research in Decision Analysis (GERAD), HEC Montréal, Montreal, Canada

    Hosain Zaman

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  1. Mosahar Tarimoradi
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Tarimoradi, M., Zarandi, M.H.F., Zaman, H. et al. Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art. J Intell Manuf 28, 1551–1579 (2017). https://doi.org/10.1007/s10845-015-1170-1

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