Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels

A novel strategic model for designing the MDSCN is proposed.Multiple objectives of the MDSCN are designed for sustainable development.A novel MOABC algorithm is introduced with unique features.Both the model and the method are validated through experimentation. The emergence of Omni-channel has affected the practical design of the supply chain network (SCN) with the purpose of providing better products and services for customers. In contrast to the conventional SCN, a new strategic model for designing SCN with multiple distribution channels (MDCSCN) is introduced in this research. The MDCSCN model benefits customers by providing direct products and services from available facilities instead of the conventional flow of products and services. Sustainable objectives, i.e., reducing economic cost, enlarging customer coverage and weakening environmental influences, are involved in designing the MDCSN. A modified multi-objective artificial bee colony (MOABC) algorithm is introduced to solve the MDCSCN model, which integrates the priority-based encoding mechanism, the Pareto optimality and the swarm intelligence of the bee colony. The effect of the MDCSCN model are examined and validated through numerical experiment. The MDCSCN model is innovative and pioneering as it meets the latest requirements and outperforms the conventional SCN. More importantly, it builds the foundation for an intelligent customer order assignment system. The effectiveness and efficiency of the MOABC algorithm is evaluated in comparison with the other popular multi-objective meta-heuristic algorithm with promising results.

[1]  Rafael S. Parpinelli,et al.  New inspirations in swarm intelligence: a survey , 2011, Int. J. Bio Inspired Comput..

[2]  R. Narasimhan,et al.  Supplier integration—Finding an optimal configuration , 2006 .

[3]  Stephan M. Wagner,et al.  Modeling carbon footprints across the supply chain , 2010 .

[4]  W. H. Ip,et al.  Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem , 2014 .

[5]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[6]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[7]  Zuo-Jun Max Shen,et al.  A profit-maximizing supply chain network design model with demand choice flexibility , 2006, Oper. Res. Lett..

[8]  H. Chan,et al.  Green marketing and its impact on supply chain management in industrial markets , 2012 .

[9]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[10]  V K Vishwa,et al.  Environmental integrated closed loop logistics model: An artificial bee colony approach , 2010, 2010 8th International Conference on Supply Chain Management and Information.

[11]  J. Linton,et al.  Sustainable supply chains: An introduction , 2007 .

[12]  Shuzhu Zhang,et al.  Swarm intelligence applied in green logistics: A literature review , 2015, Eng. Appl. Artif. Intell..

[13]  Patrick Siarry,et al.  Three new metrics to measure the convergence of metaheuristics towards the Pareto frontier and the aesthetic of a set of solutions in biobjective optimization , 2005, Comput. Oper. Res..

[14]  Amit Sachan,et al.  Review of supply chain management and logistics research , 2005 .

[15]  J.A.E.E. van Nunen,et al.  Designing and Evaluating Sustainable Logistics Networks , 2006 .

[16]  R. Piplani,et al.  Redesigning closed-loop service network at a computer manufacturer: A case study , 2008 .

[17]  Saman Hassanzadeh Amin,et al.  An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach , 2012, Expert Syst. Appl..

[18]  Ehsan Nikbakhsh,et al.  A parallel variable neighborhood search for the multi-objective sustainable post-sales network design problem , 2013 .

[19]  Rajesh Piplani,et al.  Sustainable supply chain management , 2008 .

[20]  M. Gen,et al.  Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach , 2002 .

[21]  C. Searcy,et al.  A literature review and a case study of sustainable supply chains with a focus on metrics , 2012 .

[22]  B. Beamon Supply chain design and analysis:: Models and methods , 1998 .

[23]  Mitsuo Gen,et al.  A genetic algorithm for two-stage transportation problem using priority-based encoding , 2006, OR Spectr..

[24]  Halit Üster,et al.  A closed-loop supply chain network design problem with integrated forward and reverse channel decisions , 2010 .

[25]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

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

[27]  Reza Akbari,et al.  A multi-objective artificial bee colony algorithm , 2012, Swarm Evol. Comput..

[28]  Mark Fleischer,et al.  The measure of pareto optima: Applications to multi-objective metaheuristics , 2003 .

[29]  Andrés L. Medaglia,et al.  Solution methods for the bi-objective (cost-coverage) unconstrained facility location problem with an illustrative example , 2006, Ann. Oper. Res..

[30]  Efstratios N. Pistikopoulos,et al.  Environmentally conscious long-range planning and design of supply chain networks , 2005 .

[31]  Quan-Ke Pan,et al.  Flexible job shop scheduling problems by a hybrid artificial bee colony algorithm , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[32]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[33]  Kannan Govindan,et al.  Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food , 2014 .

[34]  Xiaowei Xu,et al.  Multi-criteria decision making approaches for supplier evaluation and selection: A literature review , 2010, Eur. J. Oper. Res..

[35]  N. C. Hiremath,et al.  Multi objective outbound logistics network design for a manufacturing supply chain , 2013, J. Intell. Manuf..

[36]  Felix T.S. Chan,et al.  Integrating environmental criteria into the supplier selection process , 2003 .

[37]  Jean-Sébastien Tancrez,et al.  A location-inventory model for large three-level supply chains , 2012 .

[38]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[39]  Reynaldo Cruz-Rivera,et al.  Production , Manufacturing and Logistics Reverse logistics network design for the collection of End-of-Life Vehicles in Mexico , 2009 .

[40]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[41]  Xiaodong Li,et al.  Swarm Intelligence in Optimization , 2008, Swarm Intelligence.

[42]  I-Lin Wang,et al.  Fast Heuristics for Designing Integrated E-Waste Reverse Logistics Networks , 2007, IEEE Transactions on Electronics Packaging Manufacturing.