Design of energy efficient RAL system using evolutionary algorithms

Purpose – Manufacturing industries these days gives importance to reduce the energy consumption due to the increase in energy prices and to create an environmental friendly industry. Robotic assembly lines (RALs) are used in an industry for assembling different types of products in an assembly line due to the flexibility it offers to the production system. Since different types of robots are available with different specialization and capabilities, there is a requirement of efficiently balancing the assembly line by allocating equal amount of tasks to the workstations and allocate the best fit robot to perform the allocated tasks. The purpose of this paper is to maximize the line efficiency by minimizing the total energy consumption in a U-shaped RAL. Design/methodology/approach – Particle swarm optimization (PSO) and differential evolution (DE) are the two evolutionary algorithms used as the optimization tool to solve this problem. Performance of these proposed algorithm are tested on a set of randomly g...

[1]  Ali Wagdy Mohamed,et al.  An alternative differential evolution algorithm for global optimization , 2012 .

[2]  Gerhard Reinelt,et al.  The Linear Ordering Problem: Exact and Heuristic Methods in Combinatorial Optimization , 2011 .

[3]  J. Wijngaard,et al.  The U-line balancing problem , 1994 .

[4]  Konstantinos Salonitis,et al.  An Empirical Study of the Energy Consumption in Automotive Assembly , 2012 .

[5]  A. Kaveh,et al.  Hybrid charged system search and particle swarm optimization for engineering design problems , 2011 .

[6]  Mitsuo Gen,et al.  An efficient approach for type II robotic assembly line balancing problems , 2009, Comput. Ind. Eng..

[7]  Gregory Levitin,et al.  A genetic algorithm for robotic assembly line balancing , 2006, Eur. J. Oper. Res..

[8]  Janet M. Twomey,et al.  Operational methods for minimization of energy consumption of manufacturing equipment , 2007 .

[9]  Adriana Giret,et al.  Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm , 2013 .

[10]  Armin Scholl,et al.  Data of assembly line balancing problems , 1995 .

[11]  Lionel Amodeo,et al.  Solving a robotic assembly line balancing problem using efficient hybrid methods , 2014, J. Heuristics.

[12]  A. Gandomi,et al.  A novel improved accelerated particle swarm optimization algorithm for global numerical optimization , 2014 .

[13]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[14]  George L. Nemhauser,et al.  An Algorithm for the Line Balancing Problem , 1964 .

[15]  Adil Baykasoğlu,et al.  Stochastic U-line balancing using genetic algorithms , 2007 .

[16]  Michal Tzur,et al.  Design of flexible assembly line to minimize equipment cost , 2000 .

[17]  George Q. Huang,et al.  Hybrid flow shop scheduling considering machine electricity consumption cost , 2013 .

[18]  Lawrence Davis,et al.  Applying Adaptive Algorithms to Epistatic Domains , 1985, IJCAI.

[19]  Ashutosh Tiwari,et al.  A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches , 2012 .

[20]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[21]  A. Abraham,et al.  Simplex Differential Evolution , 2009 .

[22]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[23]  Ihsan Sabuncuoglu,et al.  Balancing of U-type assembly systems using simulated annealing , 2001 .

[24]  Joaquín B. Ordieres Meré,et al.  Optimizing the production scheduling of a single machine to minimize total energy consumption costs , 2014 .

[25]  R. K. Suresh,et al.  Discrete Particle Swarm Optimization (DPSO) Algorithm for Permutation Flowshop Scheduling to Minimize Makespan , 2005, ICNC.

[26]  Majid Aminnayeri,et al.  Type II robotic assembly line balancing problem: An evolution strategies algorithm for a multi-objective model , 2012 .

[27]  Dario Pacciarelli,et al.  Optimally balancing assembly lines with different workstations , 2002, Discret. Appl. Math..

[28]  E. Lenz,et al.  RALB – A Heuristic Algorithm for Design and Balancing of Robotic Assembly Lines , 1993 .

[29]  S. G. Ponnambalam,et al.  A Multi-Objective Genetic Algorithm for Solving Assembly Line Balancing Problem , 2000 .

[30]  S. G. Ponnambalam,et al.  An efficient PSO for type II robotic assembly line balancing problem , 2012, 2012 IEEE International Conference on Automation Science and Engineering (CASE).

[31]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[32]  Vitaliy Feoktistov.  Differential Evolution: In Search of Solutions , 2006 .

[33]  Naveen Kumar,et al.  Assembly Line Balancing: A Review of Developments and Trends in Approach to Industrial Application , 2013 .

[34]  Armin Scholl,et al.  State-of-the-art exact and heuristic solution procedures for simple assembly line balancing , 2006, Eur. J. Oper. Res..