Capacitated lot sizing and scheduling problems using hybrid GA/TS approaches

2003 ◽  
Vol 16 (01) ◽  
pp. 21 ◽  
Author(s):  
Honghong Yang
Keyword(s):  
Lot Sizing ◽  
Scheduling Problems ◽  
Capacitated Lot Sizing ◽  
Lot Sizing And Scheduling

scholarly journals Practical Tips for Modelling Lot-Sizing and Scheduling Problems

2009 ◽  
Vol 3 (2) ◽  
pp. 37-48
Author(s):  
Waldemar Kaczmarczyk
Keyword(s):  
Lot Sizing ◽  
Valid Inequalities ◽  
A Priori ◽  
Computational Effort ◽  
Scheduling Problems ◽  
Good Balance ◽  
Validation Of Models ◽  
Short Time ◽  
Lot Sizing And Scheduling

This paper presents some important alternatives for modelling Lot-Sizing and Scheduling Problems. First, the accuracy of models can improved by using short time buckets, which allow more detailed planning but lead to higher computational effort. Next, valid inequalities make the models tighter but increase their size. Sometimes it is possible to find a good balance between the size and tightness of a model by limiting a priori the number of valid inequalities. Finally, a special normalization of the variables simplifies the presentation of results and validation of models.

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

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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