Recent developments in the interplay between Operational Research and Statistics allowed us to exploit advances in mixed-integer optimisation (MIO) solvers to improve the quality of statistical analysis. In this work,...
详细信息
Recent developments in the interplay between Operational Research and Statistics allowed us to exploit advances in mixed-integer optimisation (MIO) solvers to improve the quality of statistical analysis. In this work, we tackle Canonical Correlation Analysis (CCA), a dimensionality reduction method that jointly summarises multiple data sources while retaining their dependency structure. We propose a new technique for encoding sparsity in CCA by means of a mathematical programming formulation that allows one to obtain an exact solution using readily available solvers (such as Gurobi) or design solution algorithmic procedures based on it. Finally, we evaluate the performance of alternative solution strategies presented on multiple datasets from the literature. The results of the extensive comparison study highlight that the proposed approach is capable of finding the optimal correlation or finding good quality solutions, better than those provided by other conventional methods.
This article presents a novel approach for optimising maintenance scheduling and production in a process using decaying catalysts while considering uncertainties in the kinetic parameters involved. The approach formul...
详细信息
This article presents a novel approach for optimising maintenance scheduling and production in a process using decaying catalysts while considering uncertainties in the kinetic parameters involved. The approach formulates this problem as a multistage mixed-integer optimal control problem (MSMIOCP) and uses a solution methodology that can offer a number of potential advantages over conventional methodologies. The solution methodology involves using a multiple scenario approach to consider parametric uncertainties and formulating a stochastic version of the MSMIOCP, which is solved as a standard nonlinear optimisation problem using a technique developed in a previous work. The proposed formulation and solution methodology are applied to identify the effects on the optimal process operation, of individual uncertainty of each parameter, of simultaneous uncertainty of all parameters and of the number of scenarios generated, as four case studies. The results obtained provide insights into these aspects and indicate the approach's capability to solve this problem. (C) 2021 Elsevier Ltd. All rights reserved.
We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a...
详细信息
We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The fundamental behavioural principle behind our approach is that individuals schedule their day to maximise their overall derived utility from the activities they complete, according to their individual needs, constraints, and preferences. By combining multiple choices into a single optimisation problem, our framework is able to capture the complex trade-offs between scheduling decisions for multiple activities. These trade-offs could include how spending longer in one activity will reduce the time-availability for other activities or how the order of activities determines the travel-times. The implemented framework takes as input a set of considered activities, with associated locations and travel modes, and uses these to produce empirical distributions of individual schedules from which different daily schedules can be drawn. The model is illustrated using historic trip diary data from the Swiss Mobility and Transport Microcensus. The results demonstrate the ability of the proposed framework to generate complex and realistic distributions of starting time and duration for different activities within the tight time constraints. The generated schedules are then compared to the aggregate distributions from the historical data to demonstrate the feasibility and flexibility of our approach.
暂无评论