Approximation formulations for the single-product capacitated lot size problem
Operations Research
A simple procedure for solving single level lot sizing problems
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A genetic algorithm for solving economic lot size scheduling problem
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Improved Rolling Schedules for the Dynamic Single-Level Lot-Sizing Problem
Management Science
Design and Analysis of Experiments
Design and Analysis of Experiments
Dynamic Lot Sizing with Batch Ordering and Truckload Discounts
Operations Research
IEEE Transactions on Evolutionary Computation
A comparative study of heuristic algorithms on Economic Lot Scheduling Problem
Computers and Industrial Engineering
Multi-product sequencing and lot-sizing under uncertainties: A memetic algorithm
Engineering Applications of Artificial Intelligence
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In this paper, genetic algorithms are applied to the deterministic time-varying lot sizing problem with batch ordering and backorders. Batch ordering requires orders that are integer multiples of a fixed quantity that is larger than one. The developed genetic algorithm (GA) utilizes a new '012' coding scheme that is designed specifically for the batch ordering policy. The performance of the developed GA is compared to that of a modified Silver-Meal (MSM) heuristic based on the frequency of obtaining the optimum solution and the average percentage deviation from the optimum solution. In addition, the effect of five factors on the performance of the GA and the MSM is investigated in a fractional factorial experiment. Results indicate that the GA outperforms the MSM in both responses, with a more robust performance. Significant factors and interactions are identified and the best conditions for applying each approach are pointed out.