Solving the cyclic job shop scheduling problem with linear precedence constraints using CP techniques

2003 ◽  
Author(s):  
F. Boussemart ◽  
G. Cavory ◽  
C. Lecoutre
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Precedence Constraints ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

scholarly journals A Job-Shop Scheduling Problem with Bidirectional Circular Precedence Constraints

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Pisut Pongchairerks
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Precedence Constraints ◽  
Middle Level ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Precedence Relation ◽  
Bottom Level ◽  
Parameter Values

This paper introduces a job-shop scheduling problem (JSP) with bidirectional circular precedence constraints, called BCJSP. In the problem, each job can be started from any operation and continued by its remaining operations in a circular precedence-relation chain via either a clockwise or counterclockwise direction. To solve BCJSP, this paper proposes a multilevel metaheuristic consisting of top-, middle-, and bottom-level algorithms. The top- and middle-level algorithms are population-based metaheuristics, while the bottom-level algorithm is a local search algorithm. The top-level algorithm basically controls a start operation and an operation-precedence-relation direction of each job, so that BCJSP becomes a JSP instance that is a subproblem of BCJSP. Moreover, the top-level algorithm can also be used to control input parameters of the middle-level algorithm, as an optional extra function. The middle-level algorithm controls input parameters of the bottom-level algorithm, and the bottom-level algorithm then solves the BCJSP’s subproblem. The middle-level algorithm evolves the bottom-level algorithm’s parameter values by using feedback from the bottom-level algorithm. Likewise, the top-level algorithm evolves the start operations, the operation-precedence-relation directions, and the middle-level algorithm’s parameter values by using feedback from the middle-level algorithm. Performance of two variants of the multilevel metaheuristic (i.e., with and without the mentioned extra function) was evaluated on BCJSP instances modified from well-known JSP instances. The variant with the extra function performs significantly better in number than the other. The existing JSP-solving algorithms can also solve BCJSP; however, their results on BCJSP are clearly worse than those of the two variants of the multilevel metaheuristic.

Memory Based Lamarckian Evolutionary Algorithm for Job Shop Scheduling Problem

2011 ◽  
Vol 21 (12) ◽  
pp. 3082-3093
Author(s):  
Zhu-Chang XIA ◽  
Fang LIU ◽  
Mao-Guo GONG ◽  
Yu-Tao QI
Keyword(s):  
Evolutionary Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling
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