Minimizing Total Weighted Tardiness on Parallel Batch Process Machines Using Genetic Algorithms

2003 ◽  
pp. 229-234 ◽  
Author(s):  
Lars Mönch ◽  
Hari Balasubramanian ◽  
John W. Fowler ◽  
Michele E. Pfund
Keyword(s):  
Genetic Algorithms ◽  
Batch Process ◽  
Total Weighted Tardiness ◽  
Weighted Tardiness

Minimizing total weighted tardiness on a single batch process machine with incompatible job families

2005 ◽  
Vol 32 (2) ◽  
pp. 327-341 ◽  
Author(s):  
Imelda C. Perez ◽  
John W. Fowler ◽  
W.Matthew Carlyle
Keyword(s):  
Batch Process ◽  
Total Weighted Tardiness ◽  
Weighted Tardiness ◽  
Single Batch

scholarly journals Bi-criteria parallel batch machine scheduling to minimize total weighted tardiness and electricity cost

2020 ◽  
Vol 90 (9) ◽  
pp. 1345-1381
Author(s):  
Jens Rocholl ◽  
Lars Mönch ◽  
John Fowler
Keyword(s):  
Genetic Algorithms ◽  
Pareto Front ◽  
Total Weighted Tardiness ◽  
Mixed Integer ◽  
Due Dates ◽  
Semiconductor Wafer ◽  
Weighted Tardiness ◽  
Electricity Cost ◽  
Problem Instances ◽  
Special Case

Abstract A bi-criteria scheduling problem for parallel identical batch processing machines in semiconductor wafer fabrication facilities is studied. Only jobs belonging to the same family can be batched together. The performance measures are the total weighted tardiness and the electricity cost where a time-of-use (TOU) tariff is assumed. Unequal ready times of the jobs and non-identical job sizes are considered. A mixed integer linear program (MILP) is formulated. We analyze the special case where all jobs have the same size, all due dates are zero, and the jobs are available at time zero. Properties of Pareto-optimal schedules for this special case are stated. They lead to a more tractable MILP. We design three heuristics based on grouping genetic algorithms that are embedded into a non-dominated sorting genetic algorithm II framework. Three solution representations are studied that allow for choosing start times of the batches to take into account the energy consumption. We discuss a heuristic that improves a given near-to-optimal Pareto front. Computational experiments are conducted based on randomly generated problem instances. The $$ \varepsilon $$ ε -constraint method is used for both MILP formulations to determine the true Pareto front. For large-sized problem instances, we apply the genetic algorithms (GAs). Some of the GAs provide high-quality solutions.

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