Setting planned leadtimes in serial production systems with tardiness costs
Management Science
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On a Model of Joint Control of Reserves in Automatic Control Systems of Production
Automation and Remote Control
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Computers and Operations Research
Engineering Applications of Artificial Intelligence
Multi-objective supply planning for two-level assembly systems with stochastic lead times
Computers and Operations Research
Multi-product sequencing and lot-sizing under uncertainties: A memetic algorithm
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
This paper examines supply planning for two-level assembly systems under lead time uncertainties. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. If the demand for the finished product is not delivered at the due date, a tardiness cost is incurred. In the same manner, a holding cost at each level appears if some components needed to assemble the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the lead time at each level is a random discrete variable. The expected cost is composed of the tardiness cost for finished product and the holding costs of components at levels 1 and 2. The objective is to find the release dates for the components at level 2 in order to minimize the total expected cost. For this new problem, a genetic algorithm is suggested. The proposed algorithm is evaluated with a variety of supply chain settings in order to verify its robustness across different supply chain scenarios. Moreover, the effect of a local search on the performance of the Genetic Algorithm in terms of solution quality, convergence and computation time is also investigated.