Energy-oriented bi-objective optimisation for a multi-module reconfigurable manufacturing system

This paper investigates a multi-module reconfigurable manufacturing system for multi-product manufacturing. The system consists of a rotary table and multiple machining modules (turrets and spindles). The production plan of the system is divided into the system design phase and the manufacturing phase, where the installation cost and the energy consumption cost correspond to the two phases, respectively. A mixed-integer programming model for a more general problem is presented. The objectives are to minimise the total cost and minimise the cycle time simultaneously. To solve the optimisation problem, the ε-constraint method is adopted to obtain the Pareto front for small size problems. Since the ε-constraint method is time consuming when problem size increases, we develop a multi-objective simulated annealing algorithm for practical size problems. To demonstrate the efficiency of the proposed algorithm, we compare it with a classic non-dominated sorting genetic algorithm. Experimental results demonstrate the efficiency of the multi-objective simulated annealing algorithm in terms of solution quality and computation time.

[1]  Carin Rösiö,et al.  Towards a generic design method for reconfigurable manufacturing systems: Analysis and synthesis of current design methods and evaluation of supportive tools , 2017 .

[2]  Abderrahmane Bensmaine,et al.  A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment , 2013, Comput. Ind. Eng..

[3]  Alexandre Dolgui,et al.  Combinatorial techniques to optimally customize an automated production line with rotary transfer and turrets , 2014 .

[4]  Ying Liu,et al.  A categorical framework of manufacturing for industry 4.0 and beyond , 2016 .

[5]  Thomas R. Stidsen,et al.  A Branch and Bound Algorithm for a Class of Biobjective Mixed Integer Programs , 2014, Manag. Sci..

[6]  Andrzej Jaszkiewicz,et al.  Proper balance between search towards and along Pareto front: biobjective TSP case study , 2017, Ann. Oper. Res..

[7]  Alexandre Dolgui,et al.  Optimal Design of Rotary Transfer Machines with Turrets , 2012 .

[8]  Madhu Jain,et al.  Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS , 2012 .

[9]  Alexander Verl,et al.  Making existing production systems Industry 4.0-ready , 2015, Prod. Eng..

[10]  Nadia Bhuiyan,et al.  A new model of sustainable product development process for making trade-offs , 2018 .

[11]  Mohammed Dahane,et al.  Hybrid Heuristic to Minimize Machine’s Unavailability Impact on Reconfigurable Manufacturing System Using Reconfigurable Process Plan , 2016 .

[12]  Ujjwal Maulik,et al.  A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA , 2008, IEEE Transactions on Evolutionary Computation.

[13]  L. Lasdon,et al.  On a bicriterion formation of the problems of integrated system identification and system optimization , 1971 .

[14]  Chung Yee Lee,et al.  Bi-objective optimization for the container terminal integrated planning , 2016 .

[15]  Manoj Kumar Tiwari,et al.  Towards green automated production line with rotary transfer and turrets: a multi-objective approach using a binary scatter tabu search procedure , 2016, Int. J. Comput. Integr. Manuf..

[16]  Dinesh Kumar,et al.  Optimum configuration selection in Reconfigurable Manufacturing System involving multiple part families , 2014 .

[17]  Carlos A. Coello Coello,et al.  Solving Multiobjective Optimization Problems Using an Artificial Immune System , 2005, Genetic Programming and Evolvable Machines.

[18]  Mohammed Dahane,et al.  Modularity assessment in reconfigurable manufacturing system (RMS) design: an Archived Multi-Objective Simulated Annealing-based approach , 2018 .

[19]  Jianping Dou,et al.  Graph theory-based approach to optimize single-product flow-line configurations of RMS , 2009 .

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

[21]  Marco Laumanns,et al.  An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method , 2006, Eur. J. Oper. Res..

[22]  F. Musharavati.  RECONFIGURABLE MANUFACTURING SYSTEMS , 2010 .

[23]  Yoram Koren,et al.  Design of reconfigurable manufacturing systems , 2010 .

[24]  D.-H. Lee,et al.  Iterative algorithms for part grouping and loading in cellular reconfigurable manufacturing systems , 2012, J. Oper. Res. Soc..

[25]  Sebastián Lozano,et al.  Cell design and multi-period machine loading in cellular reconfigurable manufacturing systems with alternative routing , 2017, Int. J. Prod. Res..

[26]  Angelo Oreste Andrisano,et al.  A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems , 2014, The International Journal of Advanced Manufacturing Technology.

[27]  Zhang Yingjie,et al.  Energy efficiency techniques in machining process: a review , 2014 .

[28]  Donald H. Liles,et al.  A computer based production scheduling architecture suitable for driving a reconfigurable manufacturing system , 1990 .

[29]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[30]  Xianzhong Dai,et al.  A GA-based approach for optimizing single-part flow-line configurations of RMS , 2011, J. Intell. Manuf..

[31]  Alexandre Dolgui,et al.  Decision support for design of reconfigurable rotary machining systems for family part production , 2017 .

[32]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[33]  Xianzhong Dai,et al.  Optimisation for multi-part flow-line configuration of reconfigurable manufacturing system using GA , 2010 .