A Co-evolutionary, Nature-Inspired Algorithm for the Concurrent Generation of Alternatives

Engineering optimization problems usually contain multifaceted performance requirements that can be riddled with unquantifiable specifications and incompatible performance objectives. Such problems typically possess competing design requirements which are very difficult – if not impossible – to quantify and capture at the time of model formulation. There are invariably unmodelled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, when solving many “real life” mathematical programming applications, it is generally preferable to formulate several quantifiably good alternatives that provide very different perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables. This solution approach is referred to as modelling-to-generate-alternatives (MGA). This study demonstrates how the nature-inspired, Firefly Algorithm can be used to concurrently create multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces. This new co-evolutionary approach is very computationally efficient, since it permits the concurrent generation of multiple, good solution alternatives in a single computational run rather than the multiple implementations required in previous MGA procedures

A Stochastic Simulation-Optimization Method for Generating Waste Management Alternatives Using Population-Based Algorithms

While solving difficult stochastic engineering problems, it is often desirable to generate several quantifiably good options that provide contrasting perspectives. These alternatives should satisfy all of the stated system conditions, but be maximally different from each other in the requisite decision space. The process of creating maximally different solution sets has been referred to as modelling-to-generate-alternatives (MGA). Simulation-optimization has frequently been used to solve computationally difficult, stochastic problems. This paper applies an MGA method that can create sets of maximally different alternatives for any simulation-optimization approach that employs a population-based algorithm. This algorithmic approach is both computationally efficient and simultaneously produces the prescribed number of maximally different solution alternatives in a single computational run of the procedure. The efficacy of this stochastic MGA method is demonstrated on a waste management facility expansion case

Stochastic Modelling to Generate Alternatives Using the Firefly Algorithm: A Simulation- Optimization Approach

In solving many practical mathematicalprogramming applications, it is generally preferable to formulateseveral quantifiably good alternatives that provide very differentapproaches to the particular problem. This is because decisionmakingtypically involves complex problems that are riddled withincompatible performance objectives and possess competingdesign requirements which are very difficult – if not impossible –to quantify and capture at the time that the supporting decisionmodels are constructed. There are invariably unmodelled designissues, not apparent at the time of model construction, which cangreatly impact the acceptability of the model’s solutions.Consequently, it is preferable to generate several alternativesthat provide multiple, disparate perspectives to the problem.These alternatives should possess near-optimal objectivemeasures with respect to all known modelled objective(s), but befundamentally different from each other in terms of the systemstructures characterized by their decision variables. This solutionapproach is referred to as modelling to generate-alternatives(MGA). This paper provides a biologically-inspired simulationoptimizationMGA approach that uses the Firefly Algorithm toefficiently create multiple solution alternatives to stochasticproblems that satisfy required system performance criteria andyet remain maximally different in their decision spaces. Theefficacy of this stochastic MGA method is demonstrated using awaste facility expansion case study

Modeling all alternative solutions for highly renewable energy systems

As the world is transitioning towards highly renewable energy systems, advanced tools are needed to analyze such complex networks. Energy system design is, however, challenged by real-world objective functions consisting of a blurry mix of technical and socioeconomic agendas, with limitations that cannot always be clearly stated. As a result, it is highly likely that solutions which are techno-economically suboptimal will be preferable. Here, we present a method capable of determining the continuum containing all techno-economically near-optimal solutions, moving the field of energy system modeling from discrete solutions to a new era where continuous solution ranges are available. The presented method is applied to study a range of technical and socioeconomic metrics on a model of the European electricity system. The near-optimal region is found to be relatively flat allowing for solutions that are slightly more expensive than the optimum but better in terms of equality, land use, and implementation time.Comment: 25 pages, 7 figures, also available as preprint at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=368204

Facility layout planning. An extended literature review

[EN] Facility layout planning (FLP) involves a set of design problems related to the arrangement of the elements that shape industrial production systems in a physical space. The fact that they are considered one of the most important design decisions as part of business operation strategies, and their proven repercussion on production systems' operation costs, efficiency and productivity, mean that this theme has been widely addressed in science. In this context, the present article offers a scientific literature review about FLP from the operations management perspective. The 232 reviewed articles were classified as a large taxonomy based on type of problem, approach and planning stage and characteristics of production facilities by configuring the material handling system and methods to generate and assess layout alternatives. We stress that the generation of layout alternatives was done mainly using mathematical optimisation models, specifically discrete quadratic programming models for similar sized departments, or continuous linear and non-linear mixed integer programming models for different sized departments. Other approaches followed to generate layout alternatives were expert's knowledge and specialised software packages. Generally speaking, the most frequent solution algorithms were metaheuristics.The research leading to these results received funding from the European Union H2020 Program under grant agreement No 958205 `Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)'and from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-101344-B-I00 `Optimisation of zerodefectsproduction technologies enabling supply chains 4.0 (CADS4.0)'Pérez-Gosende, P.; Mula, J.; Díaz-Madroñero Boluda, FM. (2021). Facility layout planning. An extended literature review. International Journal of Production Research. 59(12):3777-3816. https://doi.org/10.1080/00207543.2021.189717637773816591

Depth Modulation in Radiotherapy

Intensity Modulated Radiotherapy (IMRT) has been a major field of research over the last thirty years and is today the standard in radiotherapy treatment of cancer. The introduction of IMRT into the clinical environment has greatly improved the ability of the treatment team to conform the radiation dose to the tumour volume. Alongside improvements in image guidance, IMRT has led to a reduction in side effects for patients and opened up the possibilities of dose escalation and hypofractionation. IMRT is however by no means perfect. IMRT and derivatives such as Volumated Arc Therapy (VMAT) are limited by the exit dose from the X-ray beams and deliver a significant amount of radiation dose to normal tissues. The much publicised alternative to IMRT is proton therapy. Proton therapy beams deposit dose over a narrow range resulting in minimal exit dose. The future of radiotherapy certainly involves a significant contribution from proton therapy but the availability to patients is likely to remain limited for a long time to come. The research in this thesis considers the possibility of further improving IMRT by modulating radiotherapy beams along their direction of travel as well as across their intensity, i.e. the so called ‘Depth Modulation’ of the thesis title. Although there are numerous possible ways to achieve depth modulation, this work proposes a combination of X-ray beams with electron beams of different energies with both modalities delivered with a conventional medical linear accelerator. The research in this thesis is concerned with developing a proof of principle for this method. It is to some extent a theoretical study, however at each step the possibility of practical implementation has been considered with the view that the method is only a viable proposition if it can be effectively implemented into clinical practice. The technique proposed in this work is to use electron beams delivered through X-ray MLC with a standard patient set up. To reduce scatter and photon contamination it is proposed to remove the scattering foils from the beamline and to employ optimisation of the electron and photon components to compensate for any remaining penumbra broadening. The research has shown that improvements to dosimetry through removal of the scattering foil would allow delivery without reducing the source to surface distance, making a single isocentre synergistic delivery for both the electron and photon components practical. Electron dose segments have been calculated using Monte Carlo radiation transport and a procedure to optimise dose for the combined photon and electron IMRT technique has been developed. Through development of the optimisation procedure the characteristics of the mixed modality technique have been examined. A number of findings are demonstrated such as the benefit of gaps between electron segments, the benefits of optimising for energy in three dimensions and the dependence of the cost function minimum on the electron to photon ratio. Through clinical examples it has been shown that for tumours close to the surface the mixed modality technique has the potential to reduce the dose to normal tissues, particular in the low dose wash. Calculations of relative malignant induction probability demonstrate that this reduction in dose has the potential to reduce the incidence of secondary cancer induction. Possible treatment sites for application of the technique include breast, head and neck, brain and sarcomas