A novel artificial bee colony algorithm for the workforce scheduling and balancing problem in sub-assembly lines with limited buffers

Abstract In this study, a workforce scheduling and balancing problem is solved in unpaced sub-assembly lines with buffers feeding the paced body assembly line of a car manufacturer. The goal is to determine the minimum workforce required to process split lots at sub-assembly stations to feed the paced line over a periodic time window. Limited by a given buffer capacity at each station but with flexible start times for each split lot, an efficient workforce scheduling is possible to prevent shortages in downstream stations. Therefore, a stock-continuity equation has been proposed yielding the size of those split lots. Next, a single-objective Mixed Integer Programming (MIP) model is formulated for the problem as a combination of two implicitly weighted goals to minimise the workforce and the unbalanced workloads. The problem is a variant of workforce scheduling and routing problem with time windows and negligible walking distances. Due to the non-deterministic polynomial-time -hardness of the problem, we proposed an improved Artificial Bee Colony (ABC) algorithm named as discrete ABC with solution acceptance rule and multi-search (SAMSABC). The proposed algorithm is compared with different variants of ABC and other well-known metaheuristic algorithms such as Particle Swarm Optimisation and Differential Evolution on generated test cases. The computational results demonstrate the superiority of the proposed ABC algorithm and reveal that the SAMSABC can achieve accurate results within short computational times.

[1]  Maurizio Faccio,et al.  Lot splitting scheduling procedure for makespan reduction and machine capacity increase in a hybrid flow shop with batch production , 2012 .

[2]  Dingwei Wang,et al.  Aircraft Ground Service Scheduling Problems and Their Genetic Algorithm With Hybrid Assignment and Sequence Encoding Scheme , 2013, IEEE Systems Journal.

[3]  Mohammed Azmi Al-Betar,et al.  A hybrid artificial bee colony for a nurse rostering problem , 2015, Appl. Soft Comput..

[4]  Richard F. Hartl,et al.  Adaptive large neighborhood search for service technician routing and scheduling problems , 2012, J. Sched..

[5]  Wei-Chang Yeh,et al.  Solving reliability redundancy allocation problems using an artificial bee colony algorithm , 2011, Comput. Oper. Res..

[6]  Mustafa Sonmez,et al.  Discrete optimum design of truss structures using artificial bee colony algorithm , 2011 .

[7]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[8]  Alkın Yurtkuran,et al.  A Modified Artificial Bee Colony Algorithm for p-Center Problems , 2014, TheScientificWorldJournal.

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

[10]  Quan-Ke Pan,et al.  A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion , 2015, Expert Syst. Appl..

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

[12]  Tunchan Cura,et al.  An artificial bee colony algorithm approach for the team orienteering problem with time windows , 2014, Comput. Ind. Eng..

[13]  Dario Landa Silva,et al.  A genetic algorithm for a workforce scheduling and routing problem , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[14]  Alkın Yurtkuran,et al.  A discrete artificial bee colony algorithm for single machine scheduling problems , 2016 .

[15]  Andrew Lim,et al.  Manpower allocation with time windows and job teaming constraints , 2003 .

[16]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[17]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[18]  Liping Zhang,et al.  An effective discrete artificial bee colony algorithm with idle time reduction techniques for two-sided assembly line balancing problem of type-II , 2016, Comput. Ind. Eng..

[19]  Pisal Yenradee,et al.  PSO-based algorithm for home care worker scheduling in the UK , 2007, Comput. Ind. Eng..

[20]  Sanyang Liu,et al.  Improved artificial bee colony algorithm for global optimization , 2011 .

[21]  Stefan Nickel,et al.  Mid-term and short-term planning support for home health care services , 2012, Eur. J. Oper. Res..

[22]  Raik Stolletz,et al.  Branch-and-price approaches for the Multiperiod Technician Routing and Scheduling Problem , 2017, Eur. J. Oper. Res..

[23]  Quan-Ke Pan,et al.  Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems , 2011 .

[24]  Sam Kwong,et al.  Gbest-guided artificial bee colony algorithm for numerical function optimization , 2010, Appl. Math. Comput..

[25]  Tapabrata Ray,et al.  Parameters adaptation in Differential Evolution , 2012, 2012 IEEE Congress on Evolutionary Computation.

[26]  Mehmet Fatih Tasgetiren,et al.  A discrete differential evolution algorithm for the permutation flowshop scheduling problem , 2008, Comput. Ind. Eng..

[27]  Lorenzo Tiacci,et al.  Simultaneous balancing and buffer allocation decisions for the design of mixed-model assembly lines with parallel workstations and stochastic task times , 2015 .

[28]  Lothar Thiele,et al.  A Comparison of Selection Schemes Used in Evolutionary Algorithms , 1996, Evolutionary Computation.

[29]  Min Liu,et al.  An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling , 2012 .

[30]  Dario Landa Silva,et al.  A Variable Neighbourhood Search for the Workforce Scheduling and Routing Problem , 2015, NaBIC.

[31]  Dervis Karaboga,et al.  A modified Artificial Bee Colony algorithm for real-parameter optimization , 2012, Inf. Sci..

[32]  L. Song,et al.  Manpower allocation with time windows , 2004, J. Oper. Res. Soc..

[33]  Shengyao Wang,et al.  An effective artificial bee colony algorithm for the flexible job-shop scheduling problem , 2012 .

[34]  Loo Hay Lee,et al.  Heuristic methods for vehicle routing problem with time windows , 2001, Artif. Intell. Eng..

[35]  Harish Sharma,et al.  Artificial bee colony algorithm: a survey , 2013, Int. J. Adv. Intell. Paradigms.

[36]  Jesper Larsen,et al.  The Home Care Crew Scheduling Problem: Preference-based visit clustering and temporal dependencies , 2012, Eur. J. Oper. Res..

[37]  Gilbert Laporte,et al.  Scheduling technicians and tasks in a telecommunications company , 2008, J. Sched..

[38]  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..

[39]  Min Liu,et al.  A hybrid artificial bee colony algorithm for the fuzzy flexible job-shop scheduling problem , 2013 .

[40]  Christian Bierwirth,et al.  The home health care routing and scheduling problem with interdependent services , 2014, Health care management science.

[41]  Xiao-Yan Sun,et al.  A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking , 2015 .

[42]  Rung Ching Chen,et al.  An artificial bee colony algorithm for data collection path planning in sparse wireless sensor networks , 2013, International Journal of Machine Learning and Cybernetics.

[43]  Pedro Larrañaga,et al.  Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators , 1999, Artificial Intelligence Review.

[44]  Dinu Calin Secui,et al.  A new modified artificial bee colony algorithm for the economic dispatch problem , 2015 .

[45]  Dario Landa Silva,et al.  Workforce scheduling and routing problems: literature survey and computational study , 2014, Annals of Operations Research.

[46]  Yan-Feng Liu,et al.  A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem , 2013, Appl. Soft Comput..

[47]  Fernando Ordóñez,et al.  A robust optimization approach to dispatching technicians under stochastic service times , 2013, Optim. Lett..

[48]  Alok Singh,et al.  A swarm intelligence approach to the quadratic minimum spanning tree problem , 2010, Inf. Sci..

[49]  Harish Sharma,et al.  Hybrid Artificial Bee Colony algorithm with Differential Evolution , 2017, Appl. Soft Comput..

[50]  Chris N. Potts,et al.  Iterated local search for workforce scheduling and routing problems , 2017, J. Heuristics.

[51]  Janny Leung,et al.  Solving a manpower scheduling problem for airline catering using metaheuristics , 2010, Eur. J. Oper. Res..

[52]  Esben Kolind,et al.  The manpower allocation problem with time windows and job-teaming constraints: A branch-and-price approach , 2009, Comput. Oper. Res..

[53]  Mehmet Fatih Tasgetiren,et al.  A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops , 2011, Inf. Sci..

[54]  Juan Lin,et al.  Hybrid discrete artificial bee colony algorithm with threshold acceptance criterion for traveling salesman problem , 2017, Inf. Sci..

[55]  Reza Akbari,et al.  On the performance of bee algorithms for resource-constrained project scheduling problem , 2011, Appl. Soft Comput..

[56]  Quan-Ke Pan,et al.  Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm , 2015, Inf. Sci..

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

[58]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

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

[60]  W. Y. Szeto,et al.  An artificial bee colony algorithm for the capacitated vehicle routing problem , 2011, Eur. J. Oper. Res..

[61]  Mehmet Fatih Tasgetiren,et al.  A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem , 2008, Comput. Oper. Res..

[62]  Alok Singh,et al.  An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem , 2009, Appl. Soft Comput..

[63]  Marius M. Solomon,et al.  Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints , 1987, Oper. Res..