Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates

Manufacturing industry reflects a country’s productivity level and occupies an important share in the national economy of developed countries in the world. Jobshop scheduling (JSS) model originates from modern manufacturing, in which a number of tasks are executed individually on a series of processors following their preset processing routes. This study addresses a JSS problem with the criterion of minimizing total quadratic completion time (TQCT), where each task is available at its own release date. Constructive heuristic and meta-heuristic algorithms are introduced to handle different scale instances as the problem is NP-hard. Given that the shortest-processing-time (SPT)-based heuristic and dense scheduling rule are effective for the TQCT criterion and the JSS problem, respectively, an innovative heuristic combining SPT and dense scheduling rule is put forward to provide feasible solutions for large-scale instances. A preemptive single-machine-based lower bound is designed to estimate the optimal schedule and reveal the performance of the heuristic. Differential evolution algorithm is a global search algorithm on the basis of population, which has the advantages of simple structure, strong robustness, fast convergence, and easy implementation. Therefore, a hybrid discrete differential evolution (HDDE) algorithm is presented to obtain near-optimal solutions for medium-scale instances, where multi-point insertion and a local search scheme enhance the quality of final solutions. The superiority of the HDDE algorithm is highlighted by contrast experiments with population-based meta-heuristics, i.e., ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). Average gaps 45.62, 63.38 and 188.46 between HDDE with ACO, PSO and GA, respectively, are demonstrated by the numerical results with benchmark data, which reveals the domination of the proposed HDDE algorithm.

[1]  Joyce Friedman,et al.  A Non-Numerical Approach to Production Scheduling Problems , 1955, Oper. Res..

[2]  Ravi Sethi,et al.  The Complexity of Flowshop and Jobshop Scheduling , 1976, Math. Oper. Res..

[3]  W. Townsend The Single Machine Problem with Quadratic Penalty Function of Completion Times: A Branch-and-Bound Solution , 1978 .

[4]  Éric D. Taillard,et al.  Benchmarks for basic scheduling problems , 1993 .

[5]  Gerhard J. Woeginger,et al.  A Review of Machine Scheduling: Complexity, Algorithms and Approximability , 1998 .

[6]  Sheik Meeran,et al.  Deterministic job-shop scheduling: Past, present and future , 1999, Eur. J. Oper. Res..

[7]  T. C. Edwin Cheng,et al.  Parallel machine scheduling to minimize the sum of quadratic completion times , 2004 .

[8]  Rakesh Kumar Phanden,et al.  A genetic algorithm‐based approach for job shop scheduling , 2012 .

[9]  Thatchai Thepphakorn,et al.  Application of Firefly Algorithm and Its Parameter Setting for Job Shop Scheduling , 2012 .

[10]  Hao Gao,et al.  A Hybrid Particle-Swarm Tabu Search Algorithm for Solving Job Shop Scheduling Problems , 2014, IEEE Transactions on Industrial Informatics.

[11]  Henry Y. K. Lau,et al.  An AIS-based hybrid algorithm for static job shop scheduling problem , 2012, Journal of Intelligent Manufacturing.

[12]  Xiaohua Wang,et al.  A hybrid biogeography-based optimization algorithm for job shop scheduling problem , 2014, Comput. Ind. Eng..

[13]  R. V. Rao,et al.  Optimization of job shop scheduling problems using teaching-learning-based optimization algorithm , 2014 .

[14]  Mark Johnston,et al.  Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming , 2014, IEEE Transactions on Evolutionary Computation.

[15]  Danyu Bai Asymptotic analysis of online algorithms and improved scheme for the flow shop scheduling problem with release dates , 2015, Int. J. Syst. Sci..

[16]  Mohammad Saidi-Mehrabad,et al.  An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs , 2015, Comput. Ind. Eng..

[17]  Mohamed Kurdi,et al.  A new hybrid island model genetic algorithm for job shop scheduling problem , 2015, Comput. Ind. Eng..

[18]  T. C. Edwin Cheng,et al.  A tabu search/path relinking algorithm to solve the job shop scheduling problem , 2014, Comput. Oper. Res..

[19]  Marco Taisch,et al.  Multi-objective genetic algorithm for energy-efficient job shop scheduling , 2015 .

[20]  Leila Asadzadeh,et al.  A local search genetic algorithm for the job shop scheduling problem with intelligent agents , 2015, Comput. Ind. Eng..

[21]  T. C. Edwin Cheng,et al.  A hybrid evolutionary algorithm to solve the job shop scheduling problem , 2016, Ann. Oper. Res..

[22]  Adriana Giret,et al.  A genetic algorithm for energy-efficiency in job-shop scheduling , 2016 .

[23]  J. Christopher Beck,et al.  Mixed Integer Programming models for job shop scheduling: A computational analysis , 2016, Comput. Oper. Res..

[24]  Raymond Chiong,et al.  Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption , 2016 .

[25]  Christian Bierwirth,et al.  A study on local search neighborhoods for the job shop scheduling problem with total weighted tardiness objective , 2016, Comput. Oper. Res..

[26]  Osman Kulak,et al.  Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem , 2016, Comput. Ind. Eng..

[27]  Ponnuthurai N. Suganthan,et al.  A hybrid artificial bee colony algorithm for the job-shop scheduling problem with no-wait constraint , 2015, Soft Computing.

[28]  Trong-The Nguyen,et al.  Parallel bat algorithm for optimizing makespan in job shop scheduling problems , 2015, Journal of Intelligent Manufacturing.

[29]  Jian Zhang,et al.  Review of job shop scheduling research and its new perspectives under Industry 4.0 , 2017, Journal of Intelligent Manufacturing.

[30]  Mahdi Pourakbari-Kasmaei,et al.  An efficient particle swarm optimization algorithm to solve optimal power flow problem integrated with FACTS devices , 2019, Appl. Soft Comput..

[31]  Mahdi Pourakbari-Kasmaei,et al.  Transmission expansion planning integrated with wind farms: A review, comparative study, and a novel profound search approach , 2020 .