Conference Agenda

A fundamental assumption in addressing real-world problems is acknowledging the presence of uncertainty and dynamism. Dismissing these factors can lead to the formulation of an optimal solution for an entirely different problem. This paper presents a novel variant of the capacitated dispersion problem (CDP) referred to as the stochastic and dynamic CDP. The main objective of this problem is to strategically position facilities to achieve maximum dispersion while meeting the capacity demand constraint. The proposed approach combines stochastic and dynamic elements, introducing a new paradigm to address the problem. This innovation allows us to consider more realistic and flexible environments. To solve this challenging problem, a novel sim-learnheuristic algorithm is proposed. This algorithm combines a biased-randomized metaheuristic (optimization component) with a simulation component (to model the uncertainty) and a machine learning component (to model dynamic behavior). Based on an extended set of traditional benchmarks for the CDP, a series of computational experiments are carried out. The results demonstrate the effectiveness of the proposed sim-learnheuristic approach for solving the CDP under dynamic and stochastic scenarios. The adoption of this innovative methodology opens up avenues for future research in the field of operations research and optimization. Exploring extensions and adaptations of this approach to other related problem domains could yield valuable insights and contribute to the development of more robust decision support systems. In conclusion, the integration of stochastic and dynamic elements into traditional optimization problems like the CDP represents a promising direction for advancing the state-of-the-art in operations research.

We consider a multi-project setting where projects arrive stochastically over time and activity durations are stochastic. Each activity of a project has to seize one unit of a resource in order to be processed. Resources have multiple capacity units. For each unit of a resource, a priority rule decides on the next activity processed once the unit becomes available. The objective is to minimize the average weighted tardiness of the projects. In a previous study we have studied the performance of well-known priority rules from multi-project and job shop scheduling. In this study we investigate the performance of these rules when assumptions such as probability distributions and project networks are generalized.