All Outputs (87)

Local-global methods for generalised solar irradiance forecasting (2024)
Journal Article

For efficient operation, solar power operators often require generation forecasts for multiple sites with varying data availability. Many proposed methods for forecasting solar irradiance / solar power production formulate the problem as a time-serie... Read More about Local-global methods for generalised solar irradiance forecasting.

UAV Path Planning for Area Coverage and Energy Consumption in Oil and Gas Exploration Environment (2023)
Conference Proceeding

This paper proposes a model for unmanned aerial vehicles (UAV) grid-based coverage path planning, considering coverage completeness and energy consumption in complex environments with multiple obstacles. The work is inspired by the need for more effi... Read More about UAV Path Planning for Area Coverage and Energy Consumption in Oil and Gas Exploration Environment.

EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification (2020)
Journal Article

© 2020 Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with the majority of the examples. Undersampling approaches reduce the size of... Read More about EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification.

Evolving Deep CNN-LSTMs for Inventory Time Series Prediction (2019)
Conference Proceeding

Inventory forecasting is a key component of effective inventory management. In this work, we utilise hybrid deep learning models for inventory forecasting. According to the highly nonlinear and non-stationary characteristics of inventory data, the mo... Read More about Evolving Deep CNN-LSTMs for Inventory Time Series Prediction.

An Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes (2019)
Book Chapter

© 2019, Springer Nature Switzerland AG. Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many-objective... Read More about An Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes.

A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food (2019)
Conference Proceeding

The taste and freshness of perishable foods decrease dramatically with time. Effective inventory management requires understanding of market demand as well as balancing customers needs and references with products’ shelf life. The objective is to av... Read More about A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food.

An agent based modelling approach for the office space allocation problem (2018)
Conference Proceeding

This paper describes an agent based simulation model to create solutions for the office space allocation (OSA) problem. OSA is a combinatorial optimization problem concerned with the allocation of available office space to a set of entities such as p... Read More about An agent based modelling approach for the office space allocation problem.

Lookahead policy and genetic algorithm for solving nurse rostering problems (2018)
Conference Proceeding

Previous research has shown that value function approximation in dynamic programming does not perform too well when tackling difficult combinatorial optimisation problem such as multi-stage nurse rostering. This is because the large action space that... Read More about Lookahead policy and genetic algorithm for solving nurse rostering problems.

Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem (2018)
Journal Article

The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits, across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise the operat... Read More about Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem.

A genetic algorithm with composite chromosome for shift assignment of part-time employees (2018)
Conference Proceeding

Personnel scheduling problems involve multiple tasks, including assigning shifts to workers. The purpose is usually to satisfy objectives and constraints arising from management, labour unions and employee preferences. The shift assignment problem is... Read More about A genetic algorithm with composite chromosome for shift assignment of part-time employees.

Fuzzy C-means-based scenario bundling for stochastic service network design (2018)
Conference Proceeding

Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a decomposition method for solvi... Read More about Fuzzy C-means-based scenario bundling for stochastic service network design.

Using goal programming on estimated Pareto fronts to solve multiobjective problems (2018)
Conference Proceeding

Modern multiobjective algorithms can be computationally inefficient in producing good approximation sets for highly constrained many-objective problems. Such problems are common in real-world applications where decision-makers need to assess multiple... Read More about Using goal programming on estimated Pareto fronts to solve multiobjective problems.

Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows (2018)
Journal Article

© 2018, The Author(s). An effective and fast hybrid metaheuristic is proposed for solving the pickup and delivery problem with time windows. The proposed approach combines local search, large neighbourhood search and guided ejection search in a novel... Read More about Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows.

An evolutionary strategy with machine learning for learning to rank in information retrieval (2018)
Journal Article

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Learning to rank (LTR) is one of the problems attracting researchers in information retrieval (IR). The LTR problem refers to ranking the retrieved documents for users in search engines,... Read More about An evolutionary strategy with machine learning for learning to rank in information retrieval.

Selecting genetic operators to maximise preference satisfaction in a workforce scheduling and routing problem (2017)
Conference Proceeding

The Workforce Scheduling and Routing Problem (WSRP) is a combinatorial optimisation problem that involves scheduling and routing of workforce. Tackling this type of problem often requires handling a considerable number of requirements, including cust... Read More about Selecting genetic operators to maximise preference satisfaction in a workforce scheduling and routing problem.

Towards collaborative optimisation in a shared-logistics environment for pickup and delivery operations (2017)
Conference Proceeding

This paper gives an overview of research work in progress within the COSLE (Collaborative Optimisation in a Shared Logistics Environment) project between the University of Nottingham and Microlise Ltd. This is an R&D project that seeks to develop opt... Read More about Towards collaborative optimisation in a shared-logistics environment for pickup and delivery operations.

Approximate dynamic programming with combined policy functions for solving multi-stage nurse rostering problem (2017)
Conference Proceeding

An approximate dynamic programming that incorporates a combined policy, value function approximation and lookahead policy, is proposed. The algorithm is validated by applying it to solve a set of instances of the nurse rostering problem tackled as a... Read More about Approximate dynamic programming with combined policy functions for solving multi-stage nurse rostering problem.

Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem (2017)
Conference Proceeding

The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise total opera... Read More about Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem.

Multi-start methods for the capacitated clustering problem (2017)
Conference Proceeding

In this work, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomi... Read More about Multi-start methods for the capacitated clustering problem.

Randomized heuristics for the Capacitated Clustering Problem (2017)
Journal Article

In this paper, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between random... Read More about Randomized heuristics for the Capacitated Clustering Problem.