Solving a fuzzy multi objective model of a production–distribution system using meta-heuristic based approaches

This paper studies a multi-objective production–distribution system. The objectives are to minimize total costs and maximize the reliability of transportations system. Each transportation system is assumed to be of unique reliability. In the real world, some parameters may be of vagueness; therefore, some tools such as fuzzy logic is applied to tackle with. The problem is formulated using a mixed integer programming model. Commercial software can optimally solve small sized instances. We propose two novel HEURISTICS called ranking genetic algorithm (RGA) and concessive variable neighborhood search (CVNS) in order to solve the large sized instances. RGA utilizes various crossover operators and compares their performances so that better crossover operators are used during the solution process. CVNS applies several neighborhood search structures with a novel learning procedure. The heuristics can recognize which neighborhood structure performs well and applies those more than the others. The results indicated that RGA is of higher performance.

[1]  Manoj Kumar Tiwari,et al.  Two-echelon fuzzy stochastic supply chain for the manufacturer–buyer integrated production–inventory system , 2016, J. Intell. Manuf..

[2]  R. H. Myers,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[3]  Manish Bachlaus,et al.  Designing an integrated multi-echelon agile supply chain network: a hybrid taguchi-particle swarm optimization approach , 2008, J. Intell. Manuf..

[4]  Wally Klatch.  Managing the Supply Chain for Liquids , 2005 .

[5]  Mahdi Bashiri,et al.  A NEW APPROACH TO TACTICAL AND STRATEGIC PLANNING IN PRODUCTION– DISTRIBUTION NETWORKS , 2012 .

[6]  Govindan Kannan,et al.  A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling , 2010 .

[7]  Belén Melián-Batista,et al.  The Parallel Variable Neighborhood Search for the p-Median Problem , 2002, J. Heuristics.

[8]  Reay-Chen Wang,et al.  Aggregate production planning with multiple objectives in a fuzzy environment , 2001, Eur. J. Oper. Res..

[9]  Shu-Chu Liu,et al.  Variable neighborhood search for the inventory routing and scheduling problem in a supply chain , 2012, Expert Syst. Appl..

[10]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11]  El-Houssaine Aghezzaf,et al.  A matheuristic for aggregate production–distribution planning with mould sharing , 2013 .

[12]  Yankui Liu,et al.  Robust optimization of supply chain network design in fuzzy decision system , 2016, J. Intell. Manuf..

[13]  Mostafa Hajiaghaei-Keshteli,et al.  Genetic algorithms for coordinated scheduling of production and air transportation , 2010, Expert Syst. Appl..

[14]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[15]  R. J. Kuo,et al.  A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem – A case study on supply chain model , 2011 .

[16]  Teodor Gabriel Crainic,et al.  A cooperative parallel meta-heuristic for the vehicle routing problem with time windows , 2005, Comput. Oper. Res..

[17]  Bülent Çatay,et al.  Strategic level three-stage production distribution planning with capacity expansion , 2006, Comput. Ind. Eng..

[18]  Hans-Otto Günther,et al.  Multi-objective integrated production and distribution planning of perishable products , 2012 .

[19]  Reha Uzsoy,et al.  A genetic algorithm for a single product network design model with lead time and safety stock considerations , 2009, Eur. J. Oper. Res..

[20]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[21]  Fariborz Jolai,et al.  A fuzzy goal programming and meta heuristic algorithms for solving integrated production: distribution planning problem , 2011, Central Eur. J. Oper. Res..

[22]  G. Raidl,et al.  A Variable Neighborhood Search for the Periodic Vehicle Routing Problem with Time Windows , 2008 .

[23]  Guanrong Chen,et al.  Fuzzy Control Systems , 2009, Encyclopedia of Artificial Intelligence.

[24]  S. Afshin Mansouri,et al.  A Multi-Objective Genetic Algorithm for mixed-model sequencing on JIT assembly lines , 2005, Eur. J. Oper. Res..

[25]  Olli Bräysy,et al.  A Reactive Variable Neighborhood Search for the Vehicle-Routing Problem with Time Windows , 2003, INFORMS J. Comput..

[26]  Miguel A. Lejeune,et al.  Production , Manufacturing and Logistics A variable neighborhood decomposition search method for supply chain management planning problems , 2006 .

[27]  K. Rameshkumar,et al.  Application of particle swarm intelligence algorithms in supply chain network architecture optimization , 2012, Expert Syst. Appl..

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

[29]  Kikuo Fujita,et al.  Mathematical model for simultaneous design of module commonalization and supply chain configuration toward global product family , 2013, J. Intell. Manuf..

[30]  Irem Ozkarahan,et al.  COLLABORATIVE PRODUCTION-DISTRIBUTION PLANNING IN SUPPLY CHAIN: A FUZZY GOAL PROGRAMMING APPROACH , 2008 .

[31]  Abalfazl Zareei,et al.  Parallel machines scheduling to minimize job tardiness and machine deteriorating cost with deteriorating jobs , 2010 .

[32]  Tien-Fu Liang,et al.  Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain , 2008, Comput. Ind. Eng..

[33]  Pierre Hansen,et al.  Variable neighborhood search , 1997, Eur. J. Oper. Res..

[34]  Rafik A. Aliev,et al.  Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management , 2007, Inf. Sci..

[35]  Ümit Sami Sakalli.  A note on "fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain" , 2010, Comput. Ind. Eng..

[36]  Ruey-Shun Chen,et al.  A hybrid genetic algorithm approach on multi-objective of assembly planning problem , 2002 .

[37]  Pierre Hansen,et al.  Solving the p‐Center problem with Tabu Search and Variable Neighborhood Search , 2000, Networks.

[38]  Susmita Bandyopadhyay,et al.  Applying modified NSGA-II for bi-objective supply chain problem , 2013, J. Intell. Manuf..

[39]  Z. H. Che,et al.  A particle swarm optimization algorithm for solving unbalanced supply chain planning problems , 2012, Appl. Soft Comput..

[40]  Mostafa Zandieh,et al.  An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness , 2009, Expert Syst. Appl..

[41]  André I. Khuri,et al.  Response surface methodology , 2010 .

[42]  Mitsuo Gen,et al.  Hybrid genetic algorithm for multi-time period production/distribution planning , 2005, Comput. Ind. Eng..

[43]  J. Gattorna,et al.  Managing the Supply Chain , 1996 .

[44]  Guanrong Chen,et al.  Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems , 2000 .

[45]  Mohammad Ali Soukhakian,et al.  A hybrid fuzzy goal programming approach with different goal priorities to aggregate production planning , 2009, Comput. Ind. Eng..

[46]  Mostafa Zandieh,et al.  A study on integrating sequence dependent setup time flexible flow lines and preventive maintenance scheduling , 2009, J. Intell. Manuf..

[47]  A. Kazemi,et al.  A multi-agent system to solve the production–distribution planning problem for a supply chain: a genetic algorithm approach , 2009 .

[48]  Rakesh Nagi,et al.  A review of integrated analysis of production-distribution systems , 1999 .

[49]  H. Zimmerman.  Using fuzzy sets in operational research , 1983 .

[50]  Mir-Bahador Aryanezhad,et al.  A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty , 2011 .

[51]  Shaligram Pokharel,et al.  A two objective model for decision making in a supply chain , 2008 .

[52]  Daeki Kim,et al.  An optimal policy for a single-vendor single-buyer integrated production–distribution model with both deteriorating and defective items , 2014 .

[53]  N. Jawahar,et al.  A genetic algorithm for the two-stage supply chain distribution problem associated with a fixed charge , 2009, Eur. J. Oper. Res..

[54]  Gwo-Hshiung Tzeng,et al.  Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS , 2004, Eur. J. Oper. Res..

[55]  L. X. Cui,et al.  Joint optimization of production planning and supplier selection incorporating customer flexibility: an improved genetic approach , 2016, J. Intell. Manuf..