Ship Lock Control System Optimization using GA, PSO and ABC: A Comparative Review

This paper presents the comparison of some well-known global optimization techniques in optimization of an expert system controlling a ship locking process. The purpose of the comparison is to find the best algorithm for optimization of membership function parameters of fuzzy expert system for the ship lock control. Optimization was conducted in order to achieve better results in local distribution of ship arrivals, i.e. shorter waiting times for ships and less empty lockages. Particle swarm optimization, artificial bee colony optimization and genetic algorithm were compared. The results shown in this paper confirmed that all these procedures show similar results and provide overall improvement of ship lock operation performance, which speaks in favour of their application in similar transportation problem optimization

Novel approach for integrated biomass supply chain synthesis and optimisation

Despite looming energy crises, fossil resources are still widely used for energy and chemical production. Growing awareness of the environmental impact from fossil fuels has made sustainability one of the main focuses in research and development. Towards that end, biomass is identified as a promising renewable source of carbon that can potentially replace fossil resources in energy and chemical productions. Although many researches on converting biomass to value-added product have been done, biomass is still considered underutilised in the industry. This is mainly due to challenges in the logistic and processing network of biomass. An integrated biomass supply chain synthesis and optimisation are therefore important. Thus, the ultimate goal of this thesis is to develop a novel approach for an integrated biomass supply chain. Firstly, a multiple biomass corridor (MBC) concept is presented to integrate various biomass and processing technologies into existing biomass supply chain system in urban and developed regions. Based on this approach, a framework is developed for the synthesis of a more diversified and economical biomass supply chain system. The work is then extended to consider the centralisation and decentralisation of supply chain structure. In this manner, P-graph-aided decomposition approach (PADA) is proposed, whereby it divides the complex supply chain problem into two smaller sub-problems – the processing network is solved via mixed-integer linear programming (MILP) model, whereas the binaries-intensive logistic network configuration is determined through P-graph framework. As existing works often focus on supply chain synthesis in urban regions with well-developed infrastructure, resources integrated network (RIN) – a novel approach for the synthesis of integrated biomass supply chain in rural and remote regions is introduced to enhance rural economies. This approach incorporates multiple resources (i.e. bioresources, food commodities, rural communities’ daily needs) into the value chain and utilises inland water system as the mode of transport, making the system more economically feasible. It extends the MBC approach for technology selection and adopts vehicle routing problem (VRP) for inland water supply and delivery network. To evaluate the performance of the proposed integrated biomass supply chain system, a FANP-based (fuzzy analytical network process) sustainability assessment tool is established. A framework is proposed to derive sustainability index (SI) from pairwise comparison done by supply chain stakeholders to assess the sustainability of a system. Fuzzy limits are introduced to reduce uncertainties in human judgment while conducting the pairwise comparison. To design a sustainable integrated biomass supply chain, a FANP-aided, a novel multiple objectives optimisation framework is proposed. This approach transforms multiple objective functions into single objective function by prioritising each of the objective through the FANP framework. The multiple objectives are then normalised via max-min aggregation to ensure the trade-off between objectives is performed on the same scale. At the end of this thesis, viable future works of the whole programme is presented for consideration

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

Logistic autonomous vehicles assessment using decision support model under spherical fuzzy set integrated Choquet Integral approach

Autonomous vehicles (AVs) are the newest products in the intelligent transportation system that can move around with minimal human intervention. These products continue their path with all kinds of sensors with different parts. Effective use of these technologies in the logistics industry can create a competitive advantage. Nowadays, there are many AVs, some of which are superior to others in terms of build quality, variety of features, and design. Choosing an efficient, optimal, and reliable vehicle is one of the most important challenges in logistics planning. Therefore, choosing an AV based on a series of criteria can be considered a multi-criteria decision-making (MCDM) problem. Due to the complication of decision-making issues, criteria are usually not independent of each other and there are relationships between them. Therefore, this study develop an extended MCDM framework based on Choquet integral (CI) under group decision-making with a Spherical fuzzy set (SFS) for assessing logistics AVs. The CI technique is expanded with SFS to increase the power of CI. Furthermore, the combination of CI with SFS leads to greater freedom for decision makers to express opinions and use three independent membership functions. Accordingly, the interactions between the criteria are considered and the skepticism and uncertainty present during the decision are controlled. The proposed approach is implemented in selecting the best AVs in the logistics industry, and the results are compared with Pythagorean fuzzy CI and Intuitionistic fuzzy CI. Moreover, sensitivity analysis is done by changing the weights and creating different scenarios to confirm and check the robustness of the proposed approach results. The results indicate the suggested approach's efficiency and the ranking's stability in different scenarios

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Incorporating Memory and Learning Mechanisms Into Meta-RaPS

Due to the rapid increase of dimensions and complexity of real life problems, it has become more difficult to find optimal solutions using only exact mathematical methods. The need to find near-optimal solutions in an acceptable amount of time is a challenge when developing more sophisticated approaches. A proper answer to this challenge can be through the implementation of metaheuristic approaches. However, a more powerful answer might be reached by incorporating intelligence into metaheuristics. Meta-RaPS (Metaheuristic for Randomized Priority Search) is a metaheuristic that creates high quality solutions for discrete optimization problems. It is proposed that incorporating memory and learning mechanisms into Meta-RaPS, which is currently classified as a memoryless metaheuristic, can help the algorithm produce higher quality results. The proposed Meta-RaPS versions were created by taking different perspectives of learning. The first approach taken is Estimation of Distribution Algorithms (EDA), a stochastic learning technique that creates a probability distribution for each decision variable to generate new solutions. The second Meta-RaPS version was developed by utilizing a machine learning algorithm, Q Learning, which has been successfully applied to optimization problems whose output is a sequence of actions. In the third Meta-RaPS version, Path Relinking (PR) was implemented as a post-optimization method in which the new algorithm learns the good attributes by memorizing best solutions, and follows them to reach better solutions. The fourth proposed version of Meta-RaPS presented another form of learning with its ability to adaptively tune parameters. The efficiency of these approaches motivated us to redesign Meta-RaPS by removing the improvement phase and adding a more sophisticated Path Relinking method. The new Meta-RaPS could solve even the largest problems in much less time while keeping up the quality of its solutions. To evaluate their performance, all introduced versions were tested using the 0-1 Multidimensional Knapsack Problem (MKP). After comparing the proposed algorithms, Meta-RaPS PR and Meta-RaPS Q Learning appeared to be the algorithms with the best and worst performance, respectively. On the other hand, they could all show superior performance than other approaches to the 0-1 MKP in the literature

Framework de Tomada de Decisão para Last-Mile Sustentável

The e-commerce growth, propelled by factors like globalization, urbanization, or the COVID-19 pandemic, has been raising the demand for logistic activities. This affects the entire supply chain, especially the last-mile, as it is considered the most ineffective part of the supply chain and a source of negative externalities. Although various solutions promise to alleviate these problems, understanding them and selecting the best has proven to be difficult due to conflicting criteria, multiple perspectives, and trade-offs. The vicissitudes of complex and sensitive urban contexts like historic centers also contribute to this difficulty. This work contributes an integrated framework that may assist the involved stakeholders in decision-making. To this end, this work is based on a three-part methodology. The extensive systematic literature review developed provided an integrated overview of this fragmented research area. This review confirmed the multidisciplinary nature of the topic, as there is an increasing number of studies conducted under very different perspectives. Furthermore, it was found that the economic dimension is the most considered; the most polluting countries contributed little to the research; and the solutions involve trade-offs. The literature review supported the definition of the hierarchical model that structures last-mile operations in historic centers. This model was evaluated by interviewing a group of experts. After integrating the experts’ feedback, the model was quantified by the same experts according to an AHP-TOPSIS approach. This quantification had as a case study the historic center of Porto, Portugal. The experts considered the three sustainability dimensions identically important. Air pollution was the most valued sub-criterion whereas Visual pollution was the least. All last-mile solutions considered in the model achieved similar results, therefore suggesting a combined distribution strategy. Nevertheless, the use of parcel lockers is the most favorable solution and seems adequate in Porto’s historic center.O crescimento do e-commerce, impulsionado por fatores como a globalização, a urbanização ou a pandemia de COVID-19, tem aumentado a procura por atividades logísticas. Isto afeta toda a cadeia de abastecimento, principalmente a última-milha, por ser considerada a parte mais ineficaz da cadeia de abastecimento e uma fonte de externalidades negativas. Embora existam várias soluções que prometem aliviar estes problemas, entendêlas e selecionar a melhor tem se provado difícil devido a critérios conflituosos, múltiplas perspetivas e trade-offs. As vicissitudes de contextos urbanos complexos e sensíveis como os centros históricos também contribuem para essa dificuldade. Este trabalho contribui um framework integrado que pode auxiliar os stakeholders envolvidos na tomada de decisão. Para este fim, este trabalho é baseado numa metodologia composta por três partes. A extensa revisão sistemática da literatura desenvolvida forneceu uma visão integrada desta área de investigação fragmentada. Esta revisão confirmou o caráter multidisciplinar do tema, pois há um número crescente de estudos conduzidos sob perspetivas muito diferentes. Além disso, verificou-se que a dimensão económica é a mais considerada; os países mais poluentes contribuíram pouco para a pesquisa; e as soluções envolvem trade-offs. A revisão da literatura suportou a definição do modelo hierárquico que estrutura as operações de última-milha em centros históricos. Este modelo foi avaliado entrevistando um grupo de experts. Após a integração do feedback dos experts, o modelo foi quantificado pelos mesmos de acordo com uma abordagem AHP-TOPSIS. Esta quantificação teve como caso de estudo o centro histórico do Porto, Portugal. Os experts consideraram as três dimensões da sustentabilidade identicamente importantes. O subcritério relativo à poluição atmosférica foi o mais valorizado, enquanto o menos foi o relativo à poluição visual. Todas as soluções de últimamilha consideradas no modelo alcançaram resultados semelhantes, sugerindo uma estratégia de distribuição combinada. No entanto, o uso de parcel lockers é a solução mais favorável e é aparentemente adequada para o centro histórico do Porto

Capturing Risk in Capital Budgeting

NPS NRP Technical ReportThis proposed research has the goal of proposing novel, reusable, extensible, adaptable, and comprehensive advanced analytical process and Integrated Risk Management to help the (DOD) with risk-based capital budgeting, Monte Carlo risk-simulation, predictive analytics, and stochastic optimization of acquisitions and programs portfolios with multiple competing stakeholders while subject to budgetary, risk, schedule, and strategic constraints. The research covers topics of traditional capital budgeting methodologies used in industry, including the market, cost, and income approaches, and explains how some of these traditional methods can be applied in the DOD by using DOD-centric non-economic, logistic, readiness, capabilities, and requirements variables. Stochastic portfolio optimization with dynamic simulations and investment efficient frontiers will be run for the purposes of selecting the best combination of programs and capabilities is also addressed, as are other alternative methods such as average ranking, risk metrics, lexicographic methods, PROMETHEE, ELECTRE, and others. The results include actionable intelligence developed from an analytically robust case study that senior leadership at the DOD may utilize to make optimal decisions. The main deliverables will be a detailed written research report and presentation brief on the approach of capturing risk and uncertainty in capital budgeting analysis. The report will detail the proposed methodology and applications, as well as a summary case study and examples of how the methodology can be applied.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Predicting business failure using artificial intelligence system

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPredicting business insolvency is considered one of the main supportive sources of information for decision making for financial institutions, investors, creditors, and other participants in the business market. Financial reporting systems provide relevant information that can be used to assess the financial position of firms. It is crucial to have classification and prediction models that can analyse this financial information and provide accurate assurance for users about business health. Recent studies have explored the use of machine learning tools as substitute for traditional statistical methods to develop classification models to classify firm insolvency according to financial statement information. However, these models have no ideal classifier, since each provides a certain percentage of wrong outputs, which is a crucial consideration; every percentage of wrong response can mean massive financial losses for stakeholders. Therefore, this study proposes new insolvency classification and perdition models based on machine learning modelling techniques to develop an improved classifier. Individual modelling techniques using statistical methods and machine learning were used to develop the classification model of business insolvency. The results showed that machine learning method outperformed statistical methods. Deep Learning (DPL) achieved the highest performance based on all performance measurements used in the study, and it was the best individual classifier, with average accuracy of 97.2% using all-years dataset. Ensemble- Boosted Decision Tree classifier ranked second, followed by Decision Tree classifier. Thus, it has been proven that DPL modelling approach is useful for business insolvency classification. A key contribution in enhancing individual classifier outputs is the use of traditional combining methods with two new aggregation methods in business insolvency (Fuzzy Logic and Consensus Approach). The Consensus Approach showed the best improvement in the results of all individual classifiers with average accuracy of 97.7%, and it is considered the best classification method not only in comparison with individual classifiers, but also with traditional combiners. This study pioneers the development of a time series business insolvency prediction model with Big Data for UK businesses. The aim of the model is to provide early prediction about a business health. Three prediction models were developed based on Nonlinear Autoregressive with Exogenous Input models (NARX), Nonlinear Autoregressive Neural Network (NAR), and Deep Learning Time-series model (DPL-SA) and achieved average accuracy rates of 83.6%, 89.5%, and 91.35%, respectively. The results show relatively high performance in comparison with the best individual classifier (deep learning)