A mathematical model and simulated annealing algorithm for solving the cyclic scheduling problem of a flexible robotic cell

Flexible robotic cells are used to produce standardized items at a high production speed. In this study, the scheduling problem of a flexible robotic cell is considered. Machines are identical and parallel. In the cell, there is an input and an output buffer, wherein the unprocessed and the finished items are kept, respectively. There is a robot performing the loading/unloading operations of the machines and transporting the items. The system repeats a cycle in its long run. It is assumed that each machine processes one part in each cycle. The cycle time depends on the order of the actions. Therefore, determining the order of the actions to minimize the cycle time is an optimization problem. A new mathematical model is presented to solve the problem, and as an alternative, a simulated annealing algorithm is developed for large-size problems. In the simulated annealing algorithm, the objective function value of a given solution is computed by solving a linear programming model which is the first case in the literature to the best of our knowledge. Several numerical examples are solved using the proposed methods, and their performances are evaluated.

[1]  Kouroush Jenab,et al.  Cycle time analysis in reentrant robotic cells with swap ability , 2012 .

[2]  Seyed Mahdi Shavarani,et al.  Mobile robot scheduling for cycle time optimization in flow-shop cells, a case study , 2018, Prod. Eng..

[3]  Kate Smith-Miles,et al.  Scheduling of two-machine robotic rework cells: In-process, post-process and in-line inspection scenarios , 2017, Robotics Auton. Syst..

[4]  Hüseyin Güden,et al.  An adaptive simulated annealing algorithm-based approach for assembly line balancing and a real-life case study , 2015 .

[5]  M. Selim Akturk,et al.  Pure cycles in two-machine dual-gripper robotic cells , 2017 .

[6]  Seyed Mahdi Shavarani,et al.  Application of hierarchical facility location problem for optimization of a drone delivery system: a case study of Amazon prime air in the city of San Francisco , 2018 .

[7]  Seyed Mahdi Shavarani,et al.  Trade-off between process scheduling and production cost in cyclic flexible robotic cells , 2018 .

[8]  Reza Tavakkoli-Moghaddam,et al.  Cyclic scheduling of a robotic flexible cell with load lock and swap , 2012, J. Intell. Manuf..

[9]  Ahmed Azab,et al.  An improved model and novel simulated annealing for distributed job shop problems , 2015 .

[10]  Huseyin Guden,et al.  AN ADAPTIVE SIMULATED ANNEALING METHOD FOR TYPE-ONE SIMPLE ASSEMBLY LINE BALANCING: A REAL LIFE CASE STUDY , 2013 .

[11]  Kate Smith-Miles,et al.  Increasing Throughput for a Class of Two-Machine Robotic Cells Served by a Multifunction Robot , 2017, IEEE Transactions on Automation Science and Engineering.

[12]  Oya Ekin Karasan,et al.  Scheduling in robotic cells: process flexibility and cell layout , 2008 .

[13]  Ehram Safari,et al.  A novel algorithm based on hybridization of artificial immune system and simulated annealing for clustering problem , 2012 .

[14]  Kouroush Jenab,et al.  Analysis of flexible robotic cells with improved pure cycle , 2013, Int. J. Comput. Integr. Manuf..

[15]  Seyed Mahdi Shavarani,et al.  An edge-based stochastic facility location problem in UAV-supported humanitarian relief logistics: a case study of Tehran earthquake , 2017, Natural Hazards.

[16]  Ali Vatankhah Barenji,et al.  A multi-agent RFID-enabled distributed control system for a flexible manufacturing shop , 2014 .

[17]  Manoj Kumar Tiwari,et al.  Constraint-based simulated annealing (CBSA) approach to solve the disassembly scheduling problem , 2012 .

[18]  John Stufken,et al.  Taguchi Methods: A Hands-On Approach , 1992 .

[19]  Oya Ekin Karasan,et al.  Pure cycles in flexible robotic cells , 2009, Comput. Oper. Res..

[20]  S. G. Ponnambalam,et al.  An elitist strategy genetic algorithm using simulated annealing algorithm as local search for facility layout design , 2013 .

[21]  Maghsoud Amiri,et al.  Application of a hybrid simulated annealing-mutation operator to solve fuzzy capacitated location-routing problem , 2013 .

[22]  Yousef Ibrahim,et al.  Scheduling rotationally arranged robotic cells served by a multi-function robot , 2014 .

[23]  Mostafa Zandieh,et al.  A simulated annealing/local search to minimize the makespan and total tardiness on a hybrid flowshop , 2013 .

[24]  Min Ji,et al.  Single-server parallel-machine scheduling with loading and unloading times , 2015, J. Comb. Optim..

[25]  H. Neil Geismar,et al.  Sequencing and Scheduling in Robotic Cells: Recent Developments , 2005, J. Sched..

[26]  Gianfranco Passannanti,et al.  A simulated annealing-based approach for the joint optimization of production/inventory and preventive maintenance policies , 2017 .

[27]  H. P. Williams,et al.  A Survey of Different Integer Programming Formulations of the Travelling Salesman Problem , 2007 .

[28]  Pan Chen,et al.  Particle swarm optimization with simulated annealing for TSP , 2007 .

[29]  Oya Ekin Karasan,et al.  An analysis of cyclic scheduling problems in robot centered cells , 2012, Comput. Oper. Res..

[30]  Kate Smith-Miles,et al.  A framework for stochastic scheduling of two-machine robotic rework cells with in-process inspection system , 2017, Comput. Ind. Eng..

[31]  Farooque Azam,et al.  Hybridization of simulated annealing with genetic algorithm for cell formation problem , 2016, The International Journal of Advanced Manufacturing Technology.