A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions

The heuristic method we propose solves the flexible job-shop scheduling problem (FJSP) using a solution construction procedure with priority rules. FJSP is more complex than classical scheduling problems in that operations are processed on one of multiple candidate machines, one of which must be selected to get a feasible solution. The solution construction procedure with priority rules is implemented on top of the efficient existing method for solving the FJSP which consists of a genetic algorithm and a local search method. The performance of the proposed method is analyzed using various benchmark problems and it is confirmed that our proposed method outperforms the existing method on problems with particular conditions. The conditions are further investigated by applying the proposed method on newly created benchmark.

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AIS-SFHM APPROACH FOR OPTIMIZATION OF MULTI OBJECTIVE JOB SHOP PROBLEMS

JOURNAL OF ADVANCES IN CHEMISTRY ◽

10.24297/jac.v13i7.5633 ◽

2017 ◽

Vol 13 (7) ◽

pp. 6363-6368

Author(s):

Chandrasekaran Manoharan

Keyword(s):

Job Shop ◽

Job Shop Scheduling ◽

Hybrid Approach ◽

Flow Time ◽

Scheduling Problem ◽

Scheduling Problems ◽

Job Shop Scheduling Problem ◽

Mean Flow ◽

Shop Scheduling ◽

Multi Objective

The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems. The JSP problem is a scheduling problem, where a set of â€˜nâ€™ jobs must be processed or assembled on a set of â€˜mâ€™ dedicated machines. Each job consists of a specific set of operations, which have to be processed according to a given technical precedence order. Job shop scheduling problem is a NP-hard combinatorial optimization problem. Â In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. The hybrid approach of Sheep Flocks Heredity Model Algorithm (SFHM) is used for finding optimal makespan, mean flow time, mean tardiness. The hybrid SFHM approach is tested with multi objective job shop scheduling problems. Initial sequences are generated with Artificial Immune System (AIS) algorithm and results are refined using SFHM algorithm. The results show that the hybrid SFHM algorithm is an efficient and effective algorithm that gives better results than SFHM Algorithm, Genetic Algorithm (GA). The proposed hybrid SFHM algorithm is a good problem-solving technique for job shop scheduling problem with multi criteria.

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Pruning by dominance in best-first search for the job shop Scheduling problem with total flow time

Journal of Intelligent Manufacturing ◽

10.1007/s10845-008-0167-4 ◽

2008 ◽

Vol 21 (1) ◽

pp. 111-119 ◽

Cited By ~ 12

Author(s):

María R. Sierra ◽

Ramiro Varela

Keyword(s):

Job Shop ◽

Job Shop Scheduling ◽

Flow Time ◽

Scheduling Problem ◽

Job Shop Scheduling Problem ◽

Total Flow ◽

Total Flow Time ◽

Shop Scheduling ◽

Best First Search

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Chaotic Multi-Objective Simulated Annealing and Threshold Accepting for Job Shop Scheduling Problem

Mathematical and Computational Applications ◽

10.3390/mca26010008 ◽

2021 ◽

Vol 26 (1) ◽

pp. 8

Author(s):

Juan Frausto-Solis ◽

Leonor Hernández-Ramírez ◽

Guadalupe Castilla-Valdez ◽

Juan J. González-Barbosa ◽

Juan P. Sánchez-Hernández

Keyword(s):

Simulated Annealing ◽

Job Shop ◽

Job Shop Scheduling ◽

Scheduling Problem ◽

Job Shop Scheduling Problem ◽

Objective Functions ◽

Threshold Accepting ◽

Shop Scheduling ◽

Multi Objective ◽

Specific Order

The Job Shop Scheduling Problem (JSSP) has enormous industrial applicability. This problem refers to a set of jobs that should be processed in a specific order using a set of machines. For the single-objective optimization JSSP problem, Simulated Annealing is among the best algorithms. However, in Multi-Objective JSSP (MOJSSP), these algorithms have barely been analyzed, and the Threshold Accepting Algorithm has not been published for this problem. It is worth mentioning that the researchers in this area have not reported studies with more than three objectives, and the number of metrics they used to measure their performance is less than two or three. In this paper, we present two MOJSSP metaheuristics based on Simulated Annealing: Chaotic Multi-Objective Simulated Annealing (CMOSA) and Chaotic Multi-Objective Threshold Accepting (CMOTA). We developed these algorithms to minimize three objective functions and compared them using the HV metric with the recently published algorithms, MOMARLA, MOPSO, CMOEA, and SPEA. The best algorithm is CMOSA (HV of 0.76), followed by MOMARLA and CMOTA (with HV of 0.68), and MOPSO (with HV of 0.54). In addition, we show a complexity comparison of these algorithms, showing that CMOSA, CMOTA, and MOMARLA have a similar complexity class, followed by MOPSO.

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