Modeling an agent-based cooperative dynamic behavior in an uncertain context of SME's sustainable supply chain

by drawing upon the concept of competitiveness and the dynamism of the environment, a knowledge base model is developed to design the behavior of autonomous actors, having different objectives, within a group of cluster named Small and Medium Enterprises' (SMEs‘) located in different geographical area. The actors within these SMEs' must collaborate for the achievement of the cluster's objective. This later is influenced by sustainable responsibilities and should be ameliorate over an achievable set of constraints and parameters. In this work, a mechanism for monitoring and evaluation is needed to assess the SME's cluster performance. For that, we design a multiagent cooperative dynamic behavior as a dynamic multi-objective optimization (DMOP) problem and resort a modified dynamic multi-objective evolutionary method (M-DMOE) to choose a common solution for all actors, and then, the cluster's preferences are operated to determine the most suitable one (posterior).

[1]  S. G. Ponnambalam,et al.  Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence , 2018, Comput. Oper. Res..

[2]  Qingfu Zhang,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 RM-MEDA: A Regularity Model-Based Multiobjective Estimation of , 2022 .

[3]  Ben Hua,et al.  Supply chain optimization of continuous process industries with sustainability considerations , 2000 .

[4]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[5]  Ali H. Diabat Hybrid algorithm for a vendor managed inventory system in a two-echelon supply chain , 2014, Eur. J. Oper. Res..

[6]  Changhe Li,et al.  A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments , 2012, IEEE Transactions on Evolutionary Computation.

[7]  Behnam Fahimnia,et al.  Supply chain planning for a multinational enterprise: a performance analysis case study , 2013 .

[8]  Lamjed Ben Said,et al.  Multi-objective Optimization with Dynamic Constraints and Objectives: New Challenges for Evolutionary Algorithms , 2015, GECCO.

[9]  Djamel Khadraoui,et al.  Dynamic carpooling mobility services based on secure multi-agent platform , 2012, 2012 Global Information Infrastructure and Networking Symposium (GIIS).

[10]  Seyyed M. T. Fatemi Ghomi,et al.  Production , Manufacturing and Logistics A hybrid genetic algorithm for the finite horizon economic lot and delivery scheduling in supply chains , 2006 .

[11]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[12]  Jye-Chyi Lu,et al.  Designing sustainable supply chain networks under uncertain environments: Fuzzy multi-objective programming , 2018 .

[13]  Hlynur Stefansson,et al.  Procedure for reducing the risk of delayed deliveries in make-to-order production , 2009 .

[14]  Bin Li,et al.  Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment , 2009, 2009 IEEE Congress on Evolutionary Computation.

[15]  Kalyanmoy Deb,et al.  Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling , 2007, EMO.

[16]  Reza Ramezanian,et al.  An efficient hybrid genetic algorithm for multi-product competitive supply chain network design with price-dependent demand , 2018, Appl. Soft Comput..

[17]  Seyed Hessameddin Zegordi,et al.  A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain , 2010, Comput. Ind. Eng..

[18]  David J. Israel,et al.  Plans and resource‐bounded practical reasoning , 1988, Comput. Intell..

[19]  Lamjed Ben Said,et al.  A Multiple Reference Point-based evolutionary algorithm for dynamic multi-objective optimization with undetectable changes , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[20]  L. Ben Said,et al.  Fuzzy Multi-Agent approach for monitoring SMEs sustainable SC under uncertainty , 2019, CENTERIS/ProjMAN/HCist.

[21]  Erik D. Goodman,et al.  A differential prediction model for evolutionary dynamic multiobjective optimization , 2018, GECCO.

[22]  Changhe Li,et al.  Dynamic multi-objective evolutionary algorithms for single-objective optimization , 2017, Appl. Soft Comput..

[23]  Xiao-Bing Hu,et al.  Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach , 2009 .

[24]  D. Rogers,et al.  A framework of sustainable supply chain management: moving toward new theory , 2008 .

[25]  Andries Petrus Engelbrecht,et al.  Solving dynamic multi-objective problems with vector evaluated particle swarm optimisation , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[26]  Ali Azadeh,et al.  Evolutionary multi-objective optimization of environmental indicators of integrated crude oil supply chain under uncertainty , 2017 .

[27]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

[28]  Stefan Seuring,et al.  From a literature review to a conceptual framework for sustainable supply chain management , 2008 .