Cormac Lucas

I. Introduction In recent times, China has started to rival the US financial and global hegemony. Asia with its undiscussed primate in population and the emerging strength of its youth majority is rivalling the cultural and strategic leadership of the old Europe (Bergsten, 2021). This new world frameset together with the rise of globalisation and financial market liberalisation increases the interest for developing a methodology that can show the integration and segmentation of different global economies. CAPM has been the prevalent method for analysing economies integration. As noted by Brooks et al. (2009), a world market portfolio that is mean variance efficient in a global integrated market should show that assets of different geographic areas with the same sensitivities to

This paper considers the scheduling of preventive maintenance for the boilers, turbines, and distillers of power plants that produce electricity and desalinated water. It models the problem as a mathematical program (MP) that maximizes the sum of the minimal ratios of production to the demand of electricity and water during a planning time horizon. This objective encourages the plants’ production and enhances the chances of meeting consumers’ needs. It reduces the chance of power cuts and water shortages that may be caused by emergency disruptions of equipment on the network. To assess its performance and effectiveness, we test the MP on a real system consisting of 32 units and generate a preventive maintenance schedule for a time horizon of 52 weeks (one year). The generated schedule outperforms the schedule established by experts of the water plant; it induces, respectively, 16% and 12% increases in the surpluses while either matching or surpassing the total production. The sensitivity analysis further indicates that the generated schedule can handle unforeseen longer maintenance periods as well as a 120% increase in demand—a sizable realization in a country that heavily relies on electricity to acclimate to the harsh weather conditions. In addition, it suggests the robustness of the schedules with respect to increased demand. In summary, the MP model yields optimal systematic sustainable schedules.

We examine the first phase of a known NP-hard 2-stage assembly problem. It consists of sequencing a set of jobs having multiple components to be processed. Each job has to be worked on independently on a specific machine. We consider these jobs to form a vector of tasks. Our objective is to schedule jobs on the particular machines in order to minimize the completion time before the second stage starts. We first develop a new mathematical programming formulation of the problem and test it on a small problem instance using an integer programming solver. Then, we develop a heuristic algorithm based on Ant Colony Optimization and Variable Neighborhood Search metaheuristics in order to minimize the total completion time. The performance of our implementation appears to be efficient and effective.

1Dipartimento di Informatica, Sistemistica e Comunicazione, University of Milano‐Bicocca, Italy 2CARISMA: The Centre for the Analysis of Risk and Optimisation Modelling Applications, Brunel University, UK