Mathematical Modeling and Discrete Firefly Algorithm to Optimize Scheduling Problem with Release Date, Sequence-Dependent Setup Time, and Periodic Maintenance

An evolutionary discrete firefly algorithm (EDFA) is presented herein to solve a real-world manufacturing system problem of scheduling a set of jobs on a single machine subject to nonzero release date, sequence-dependent setup time, and periodic maintenance with the objective of minimizing the maximum completion time “makespan.” To evaluate the performance of the proposed EDFA, a new mixed-integer linear programming model is also proposed for small-sized instances. Furthermore, the parameters of the EDFA are regulated using full factorial analysis. Finally, numerical experiments are performed to demonstrate the efficiency and capability of the EDFA in solving the abovementioned problem.

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A Mixed Integer Programming Model for Unrelated Parallel Machine Scheduling Problem with Sequence Dependent Setup Time to Minimize Makespan and Total Tardiness

2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA) ◽

10.1109/iciea49774.2020.9102086 ◽

2020 ◽

Author(s):

Papimol Kongsri ◽

Jirachai Buddhakulsomsiri

Keyword(s):

Programming Model ◽

Setup Time ◽

Parallel Machine Scheduling ◽

Parallel Machine ◽

Mixed Integer ◽

Scheduling Problem ◽

Unrelated Parallel Machine Scheduling ◽

Sequence Dependent Setup Time ◽

Parallel Machine Scheduling Problem ◽

Sequence Dependent Setup

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Study on column generation for the lot-sizing and scheduling problem with sequencedependent setup time

MATEC Web of Conferences ◽

10.1051/matecconf/201818906002 ◽

2018 ◽

Vol 189 ◽

pp. 06002

Author(s):

Dandan Zhang ◽

Canrong Zhang

Keyword(s):

Lot Sizing ◽

Setup Time ◽

Mixed Integer ◽

Scheduling Problem ◽

Optimal Solutions ◽

Sequence Dependent ◽

Sequence Dependent Setup Time ◽

Capacitated Lot Sizing ◽

Sequence Dependent Setup ◽

Lot Sizing And Scheduling

The capacitated lot-sizing and scheduling problem with sequence-dependent setup time and carryover setup state is a challenge problem in the semiconductor assembly and test manufacturing. For the problem, a new mixed integer programming model is proposed, followed by exploring its relative efficiency in obtaining optimal solutions and linearly relaxed optimal solutions. On account of the sequence-dependent setup time and the carryover of setup states, a per-machine Danzig Wolfe decomposition is proposed. We then build a statistical estimation model to describe correlation between the optimal solutions and two lower bounds including the linear relaxation solutions, and the pricing sub-problem solutions of Danzig Wolfe decomposition, which gives insight on the optimal values about information regarding whether or not the setup variables in the optimal solution take the value of 1, and the information is further used in the branch and select procedure. Numerical experiments are conducted to test the performance of the algorithm.

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A Mathematical Model and a Simulated Annealing Algorithm for Balancing Multi-manned Assembly Line Problem with Sequence-Dependent Setup Time

Mathematical Problems in Engineering ◽

10.1155/2020/8510253 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Wucheng Yang ◽

Wenming Cheng

Keyword(s):

Simulated Annealing ◽

Assembly Line ◽

Simulated Annealing Algorithm ◽

Assembly Line Balancing ◽

Setup Time ◽

Mixed Integer ◽

Sequence Dependent ◽

Sequence Dependent Setup Time ◽

Annealing Algorithm ◽

Sequence Dependent Setup

Multi-manned assembly lines have been widely applied to the industrial production, especially for large-sized products such as cars, buses, and trucks, in which more than one operator in the same station simultaneously performs different tasks in parallel. This study deals with a multi-manned assembly line balancing problem by simultaneously considering the forward and backward sequence-dependent setup time (MALBPS). A mixed-integer programming is established to characterize the problem. Besides, a simulated annealing algorithm is also proposed to solve it. To validate the performance of the proposed approaches, a set of benchmark instances are tested and the lower bound of the proposed problem is also given. The results demonstrated that the proposed algorithm is quite effective to solve the problem.

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