An improved artificial bee colony for facility location allocation problem of end-of-life vehicles recovery network

Abstract Reverse logistics is indispensable for resources reuse and circular economy, and a reverse logistics network optimization problem for end-of-life vehicles is studied frequently. Recent researches have focused on the material flow for different end-of-life vehicles. However, the primary question for an end-of-life vehicles recovery network is to determine optimal network nodes. To account for it, we considered a facility location allocation problem of end-of-life vehicles recovery network, and established a mathematical model to solve it. The model is used to achieve the minimization of cost for deciding optimal locations of end-of-life vehicles recovery network. The facility location allocation problem is a non-deterministic polynomial complete problem proved with increase in the number of candidate locations. This type of problem usually handled by a metaheuristics. Therefore, we proposed a valid novel approach based on artificial bee colony to solve the problem. Artificial bee colony is an optimization method that imitates bee behavior. Also, the proposed algorithm is applied to two different scale real-life cases, and some comparisons with several presented algorithms are presented to illustrate the effectiveness of the presented method.

[1]  Wei Deng Solvang,et al.  A general reverse logistics network design model for product reuse and recycling with environmental considerations , 2016 .

[2]  B. Yu,et al.  Optimization of a regional distribution center location for parts of end-of-life vehicles , 2018, Simul..

[3]  Ioannis Minis,et al.  A new model for designing sustainable supply chain networks and its application to a global manufacturer , 2017 .

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

[5]  Bronisław Gołębiewski,et al.  Modelling of the location of vehicle recycling facilities: A case study in Poland , 2013 .

[6]  Quan-Ke Pan,et al.  A discrete teaching-learning-based optimisation algorithm for realistic flowshop rescheduling problems , 2015, Eng. Appl. Artif. Intell..

[7]  Seda Yanik Ugurlu,et al.  A Novel Approach for Multi-Period Reverse Logistics Network Design under High Uncertainty , 2017, Int. J. Comput. Intell. Syst..

[8]  Quan-Ke Pan,et al.  An Improved Artificial Bee Colony Algorithm for Solving Hybrid Flexible Flowshop With Dynamic Operation Skipping , 2016, IEEE Transactions on Cybernetics.

[9]  Quan-Ke Pan,et al.  A Hybrid Fruit Fly Optimization Algorithm for the Realistic Hybrid Flowshop Rescheduling Problem in Steelmaking Systems , 2016, IEEE Transactions on Automation Science and Engineering.

[10]  M. Fatih Tasgetiren,et al.  A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities , 2014 .

[11]  MengChu Zhou,et al.  AHP, Gray Correlation, and TOPSIS Combined Approach to Green Performance Evaluation of Design Alternatives , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[12]  Cristiano Hora de Oliveira Fontes,et al.  Sustainable and renewable energy supply chain: A system dynamics overview , 2018 .

[13]  Yuyan Han,et al.  Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions , 2018 .

[14]  K. Winans,et al.  The history and current applications of the circular economy concept , 2017 .

[15]  Rajesh K. Singh,et al.  A literature review and perspectives in reverse logistics , 2015 .

[16]  William Sarache,et al.  Redesign of a sustainable reverse supply chain under uncertainty: A case study , 2017 .

[17]  Feng Xia,et al.  Vehicular Social Networks: A survey , 2018, Pervasive Mob. Comput..

[18]  Qishan Zhang,et al.  A Discrete Artificial Bee Colony Algorithm for the Reverse Logistics Location and Routing Problem , 2017, Int. J. Inf. Technol. Decis. Mak..

[19]  Kuan Yew Wong,et al.  Development of key performance measures for the automobile green supply chain , 2011 .

[20]  Zhiwu Li,et al.  Operation patterns analysis of automotive components remanufacturing industry development in China , 2017 .

[21]  Feng Xia,et al.  Mobility Dataset Generation for Vehicular Social Networks Based on Floating Car Data , 2018, IEEE Transactions on Vehicular Technology.

[22]  Thomas Poulsen,et al.  Is the supply chain ready for the green transformation? The case of offshore wind logistics , 2017 .

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

[24]  Geraldo Robson Mateus,et al.  A capacitated plant location model for Reverse Logistics Activities , 2017 .

[25]  Ali Diabat,et al.  A Genetic Algorithm for Reverse Logistics network design: A case study from the GCC , 2017 .

[26]  Nursel Öztürk,et al.  Network modeling for reverse flows of end-of-life vehicles. , 2015, Waste management.

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

[28]  Wei Cai,et al.  An energy-consumption model for establishing energy-consumption allowance of a workpiece in a machining system , 2016 .

[29]  Diane Riopel,et al.  A reverse logistics decisions conceptual framework , 2011, Comput. Ind. Eng..

[30]  Feng You,et al.  Study on Self-Tuning Tyre Friction Control for Developing Main-Servo Loop Integrated Chassis Control System , 2017, IEEE Access.

[31]  Mingyuan Chen,et al.  Solving reverse logistics vehicle routing problems with time windows , 2013 .

[32]  Vedat Verter,et al.  Multi-period reverse logistics network design , 2012, Eur. J. Oper. Res..

[33]  William Sarache,et al.  A decisional simulation-optimization framework for sustainable facility location of a biodiesel plant in Colombia , 2017 .

[34]  Alessia Amighini,et al.  China and India in the international fragmentation of automobile production , 2012 .

[35]  Kannan Govindan,et al.  A fuzzy multi-objective optimization model for sustainable reverse logistics network design , 2016 .

[36]  Guangdong Tian,et al.  Technology innovation system and its integrated structure for automotive components remanufacturing industry development in China , 2014 .

[37]  Arshinder Kaur,et al.  Price optimization of multi-stage remanufacturing in a closed loop supply chain , 2018, Journal of Cleaner Production.

[38]  Sajan T. John,et al.  Multi-period reverse logistics network design with emission cost , 2017 .

[39]  Mir Saman Pishvaee,et al.  Reverse logistics network design using simulated annealing , 2010 .

[40]  Guangdong Tian,et al.  Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method , 2018 .

[41]  MengChu Zhou,et al.  Disassembly Sequence Planning Considering Fuzzy Component Quality and Varying Operational Cost , 2018, IEEE Transactions on Automation Science and Engineering.

[42]  Ullah Saif,et al.  Close loop supply chain network problem with uncertainty in demand and returned products: Genetic artificial bee colony algorithm approach , 2017 .

[43]  Ali Husseinzadeh Kashan,et al.  Designing a Reverse Logistics Network for End-of-Life Vehicles Recovery , 2010 .

[44]  Fausto Freire,et al.  A review of fleet-based life-cycle approaches focusing on energy and environmental impacts of vehicles , 2017 .

[45]  Guangdong Tian,et al.  Fuzzy random cost–profit tradeoff location model for a vehicle inspection station with regional constraints , 2016 .

[46]  Huiyuan Xiong,et al.  Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation , 2018, Complex..

[47]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[48]  Ali Diabat,et al.  A hybrid genetic algorithm based heuristic for an integrated supply chain problem , 2016 .

[49]  Quan-Ke Pan,et al.  Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm , 2014, Knowl. Based Syst..

[50]  Saeed Mansour,et al.  To develop a third-party reverse logistics network for end-of-life vehicles in Iran , 2013 .

[51]  Mohammad Mahdi Paydar,et al.  A robust optimization model for the design of a cardboard closed-loop supply chain , 2017 .