Research objectives and content
This research involves the formulation, analysis and computational solution of several fundamental models, deterministic and stochastic, of production planning and scheduling problems using a mathematical programming approach.
Our objectives are as follows:
1. To develop improved mathematical programming formulations. 2. To develop efficient solution algorithms.
3. To assess, theoretically and empirically, the quality of these formulations and algorithms.
The models on which we shall focus our research are the following: 1. Deterministic multi-item multi-stage lot-sizing problems (mixed-integer programming model).
2. Stochastic multi-item multi-stage production inventory systems (queueing network control model).
The methods we intend to develop will draw on and combine recent results from deterministic and stochastic optimization, and their interplay. Training content (objective, benefit and expected impact)
The training will allow me to develop a deeper knowledge and a wider competence in the area of mathematical programming approaches to production planning and scheduling problems. Since my current research deals primarily with stochastic models I expect to complement this expertise with state-of-the-art research skills in deterministic optimization. I expect that the combination of ideas and methods from the deterministic and stochastic optimization areas will lead to a fruitful integrated research approach, as the results of my PhD research indicate. Links with industry / industrial relevance (22)
The research group on Discrete Optimization at CORE, that will be hosting me, has ongoing industrial collaborations in the subject area of the project, mathematical programming approaches to production planning and scheduling problems. Some of these collaborations are in the context of ESPRIT European Union projects.