New DTMDP model tackles lot-sizing with stochastic demand timing

Researchers have developed a discrete-time Markov decision process (DTMDP) model to address a multi-item capacitated lot-sizing problem with stochastic demand timing. This model accounts for factors like capacity competition and demand-specific backlog. When compared to a deterministic counterpart, the DTMDP model significantly increases computational demands. To tackle these complexities, a genetic algorithm (GA) was proposed, which demonstrated strong performance on benchmark instances, achieving an average optimality gap of 3.44% and an average optimization speedup of 6.89. AI

IMPACT Introduces a novel DTMDP approach for complex supply chain optimization problems, potentially improving efficiency in stochastic environments.

RANK_REASON This is a research paper detailing a new modeling approach for an optimization problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Bellman-time regression
• Discrete-Time Markov Decision Processes with First Passage Models
• genetic algorithm
• Markov decision process

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →