Heuristic approaches for mixed-model sequencing problem with stochastic processing times

2016 ◽  
Vol 55 (10) ◽  
pp. 2857-2880 ◽  
Author(s):  
H. Mosadegh ◽  
S.M.T. Fatemi Ghomi ◽  
G.A. Süer
Keyword(s):  
Mixed Model ◽  
Sequencing Problem ◽  
Heuristic Approaches ◽  
Processing Times ◽  
Stochastic Processing ◽  
Stochastic Processing Times ◽  
Model Sequencing

scholarly journals Stochastic mixed model sequencing with multiple stations using reinforcement learning and probability quantiles

OR Spectrum ◽  
2021 ◽  
Author(s):  
Janis Brammer ◽  
Bernhard Lutz ◽  
Dirk Neumann
Keyword(s):  
Reinforcement Learning ◽  
Mixed Model ◽  
Statistical Information ◽  
Stochastic Problem ◽  
State Representation ◽  
Solution Quality ◽  
Processing Times ◽  
Stochastic Processing ◽  
Stochastic Processing Times ◽  
Model Sequencing

AbstractIn this study, we propose a reinforcement learning (RL) approach for minimizing the number of work overload situations in the mixed model sequencing (MMS) problem with stochastic processing times. The learning environment simulates stochastic processing times and penalizes work overloads with negative rewards. To account for the stochastic component of the problem, we implement a state representation that specifies whether work overloads will occur if the processing times are equal to their respective 25%, 50%, and 75% probability quantiles. Thereby, the RL agent is guided toward minimizing the number of overload situations while being provided with statistical information about how fluctuations in processing times affect the solution quality. To the best of our knowledge, this study is the first to consider the stochastic problem variation with a minimization of overload situations.

The Electronic Kanban System for Mixed-Model Assembly Based on Simulated Annealing Algorithm

2011 ◽  
Vol 403-408 ◽  
pp. 4355-4359
Author(s):  
Hai Yan Wang
Keyword(s):  
Mixed Model ◽  
Simulated Annealing Algorithm ◽  
Inventory Level ◽  
Processing Times ◽  
Stochastic Processing ◽  
Order Completion ◽  
Lot Sizes ◽  
Kanban System ◽  
Annealing Algorithm ◽  
Stochastic Processing Times

After discussing the technology of electronic Kanban, simulation annealing algorithm is introduced to determine the number of Kanbans and lot sizes of part types under the environment of variant demand, stochastic processing times and different types of products. Also a newly developed Kanban system is presented to dynamically and timely adjust the number of Kanbans in order to offset the blocking and starvation caused by surging demands and stochastic processing times. A simulation model of Kanban system is built to verify the various performances of electronic Kanban and traditional one. At last the performance differences in order completion and average inventory level between the number-fixed Kanban system and the number-adjustable one are presented and verified.

On almost optimal priority rules for preemptive scheduling of stochastic jobs on parallel machines

1995 ◽  
Vol 27 (03) ◽  
pp. 821-839 ◽  
Author(s):  
Gideon Weiss
Keyword(s):  
Single Machine ◽  
Parallel Machines ◽  
Preemptive Scheduling ◽  
Priority Rule ◽  
Priority Rules ◽  
Holding Costs ◽  
Processing Times ◽  
Stochastic Processing ◽  
Stochastic Processing Times

We consider scheduling a batch of jobs with stochastic processing times on single or parallel machines, with the objective of minimizing the expected holding costs. Preemption of jobs is allowed, and the holding costs of preempted jobs may depend on the stage of completion. We provide a new proof of the optimality of a Gittins priority rule for the single machine and use the same proof to show that the Gittins priority rule is nearly optimal for parallel machines.

On almost optimal priority rules for preemptive scheduling of stochastic jobs on parallel machines

1995 ◽  
Vol 27 (3) ◽  
pp. 821-839 ◽  
Author(s):  
Gideon Weiss
Keyword(s):  
Single Machine ◽  
Parallel Machines ◽  
Preemptive Scheduling ◽  
Priority Rule ◽  
Priority Rules ◽  
Holding Costs ◽  
Processing Times ◽  
Stochastic Processing ◽  
Stochastic Processing Times

We consider scheduling a batch of jobs with stochastic processing times on single or parallel machines, with the objective of minimizing the expected holding costs. Preemption of jobs is allowed, and the holding costs of preempted jobs may depend on the stage of completion. We provide a new proof of the optimality of a Gittins priority rule for the single machine and use the same proof to show that the Gittins priority rule is nearly optimal for parallel machines.

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