TL;DR
This paper shows how an intuitive mapping from descriptive problem to optimization model can be realized with Constraint Programming (CP), and shows how a CP model can be constructed given a simulation model and a set of business goals.
This study presents an overview of stochastic integer programming (SIP) and its applications and highlights key methodologies such as two-stage and multi-stage models, LP-relaxation, and recourse strategies.
A decomposed approach with two stages, a planning stage that minimizes the expected number of superfluous activities subject to a feasibility constraint, and a scheduling stage that minimizes the makespan over the planned activities; and an integrated approach that combines planning and scheduling into a single formula...
The development of the combination of the artificial intelligence (AI) and optimization models is a revolutionary way to solve problems and make decisions in a variety of areas. This paper examines the synergistic opportunity of integrating AI methods, i.e., machine learning and natural language processing, with differ...
Many decision-making situations are characterized by hierarchical structures with two optimization tasks, modeled as Bi-level Optimization Problem (BOP). These problems are challenging due to the upper-level optimization being constrained by the lower-level solution, making evaluations computationally expensive. For...
A simulation-based optimization framework that implements discrete-event simulation with evolutionary search methods to optimize stochastic manufacturing systems efficiently is investigated, enabling more reliable parameter tuning with reduced computational effort and facilitating the implementation of digital twin tec...
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