144 research outputs found
Application of DEO Method to Solving Fuzzy Multiobjective Optimal Control Problem
In the present paper a problem of optimal control for a single-product dynamical macroeconomic model is considered. In this model gross domestic product is divided into productive consumption, gross investment, and nonproductive consumption. The model is described by a fuzzy differential equation (FDE) to take into account imprecision inherent in the dynamics that may be naturally conditioned by influence of various external factors, unforeseen contingencies of future, and so forth. The considered problems are characterized by four criteria and by several important aspects. On one hand, the problem is complicated by the presence of fuzzy uncertainty as a result of a natural imprecision inherent in information about dynamics of real-world systems. On the other hand, the number of the criteria is not small and most of them are integral criteria. Due to the above mentioned aspects, solving the considered problem by using convolution of criteria into one criterion would lead to loss of information and also would be counterintuitive and complex. We applied DEO (differential evolution optimization) method to solve the considered problem
Soft Computing
Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering
Supply chain management for the process industry
This thesis investigates some important problems in the supply chain management
(SCM) for the process industry to fill the gap in the literature work, covering
production planning and scheduling, production, distribution planning under
uncertainty, multiobjective supply chain optimisation and water resources
management in the water supply chain planning. To solve these problems, models
and solution approaches are developed using mathematical programming, especially
mixed-integer linear programming (MILP), techniques.
First, the medium-term planning of continuous multiproduct plants with sequence-dependent
changeovers is addressed. An MILP model is developed using Travelling
Salesman Problem (TSP) classic formulation. A rolling horizon approach is also
proposed for large instances. Compared with several literature models, the proposed
models and approaches show significant computational advantage.
Then, the short-term scheduling of batch multiproduct plants is considered. TSP-based
formulation is adapted to model the sequence-dependent changeovers between
product groups. An edible-oil deodoriser case study is investigated.
Later, the proposed TSP-based formulation is incorporated into the supply chain
planning with sequence-dependent changeovers and demand elasticity of price.
Model predictive control (MPC) is applied to the production, distribution and
inventory planning of supply chains under demand uncertainty.
A multiobjective optimisation problem for the production, distribution and capacity
planning of a global supply chain of agrochemicals is also addressed, considering
cost, responsiveness and customer service level as objectives simultaneously. Both ε-
constraint method and lexicographic minimax method are used to find the Pareto-optimal
solutions Finally, the integrated water resources management in the water supply chain
management is addressed, considering desalinated water, wastewater and reclaimed
water, simultaneously. The optimal production, distribution and storage systems are
determined by the proposed MILP model. Real cases of two Greek islands are
studied
Soft Computing
Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering
Dynamic small-series fashion order allocation and supplier selection: a ga-topsis-based model
The fashion industry is currently confronted with significant economic and environmental challenges, necessitating the exploration of novel business models. Among the promising approaches is small series production on demand, though this poses considerable complexities in the highly competitive sector. Traditional supplier selection and production planning processes, known for their lengthy and intricate nature, must be replaced with more dynamic and effective decision-making procedures. To tackle this problem, GA-TOPSIS hybrid model is proposed as the methodology. The model integrates Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) evaluation into the fitness function of Genetic Algorithm (GA) to comprehensively consider both qualitative and quantitative criteria for supplier selection. Simultaneously, GA efficiently optimizes the order sequence for production planning. The model's efficacy is demonstrated through implementation on real orders, showcasing its ability to handle diverse evaluation criteria and support supplier selection in different scenarios. Moreover, the proposed model is employed to compute the Pareto front, which provides optimal sets of solutions for the given objective criteria. This allows for an effective demand-driven strategy, particularly relevant for fashion retailers to select supplier and order planning optimization decisions in dynamic and multi-criteria context. Overall, GA-TOPSIS hybrid model offers an innovative and efficient decision support system for fashion retailers to adapt to changing demands and achieve effective supplier selection and production planning optimization. The model's incorporation of both qualitative and quantitative criteria in a dynamic environment contributes to its originality and potential for addressing the complexities of the fashion industry's supply chain challenge
Optimización de la gestión de redes de riego a presión a diferentes escalas mediante Inteligencia Artificial
Factors such as climate change, world population growth or the
competition for the water resources make freshwater availability
become an increasingly large and complex global challenge.
Under this scenario of reduced water availability, increasing
droughts frequency and uncertainties associated with a changing
climate, the irrigated agriculture sector, particularly in the
Mediterranean region, will need to be even more efficient in the
use of the water resources. In Spain, many irrigation districts have
been modernized in recent years, replacing the obsolete open
channels by pressurized water distribution networks towards
improvements in water use efficiency. Thanks to this, water use
has reduced but the energy demand and the water costs have
dramatically increased. Thus, strategies to reduce simultaneously
water and energy uses in irrigation districts are required.
This thesis consists of nine chapters, which include several
models to optimize the management of the irrigation districts and
increase the efficiency of water and energy use.Factores tales como el cambio climático, el crecimiento de la
población mundial o la competencia por los recursos hÃdricos
hacen que la disponibilidad de agua se esté convirtiendo en un
desafÃo global cada vez más grande y complejo. En este escenario
de reducción de la disponibilidad de agua, aumento de la
frecuencia de las sequÃas y de las incertidumbres asociadas a un
cambio climático, el sector de la agricultura de regadÃo, en
particular en la región mediterránea, tendrá que ser aún más
eficiente en el uso de los recursos hÃdricos. En España, muchas
comunidades de regantes se han modernizado en los últimos
años, sustituyendo los obsoletos canales abiertos por redes de
distribución de agua a presión con el objetivo de mejorar la
eficiencia en el uso del agua. Gracias a esto, el uso del agua se ha
reducido, pero la demanda de energÃa y los costos del agua se han
incrementado drásticamente. Por lo tanto, se requieren
estrategias para reducir simultáneamente el uso de agua y energÃa
en las comunidades de regantes.
Esta tesis consta de nueve capÃtulos que incluyen varios modelos
para optimizar la gestión de las comunidades de regantes y
aumentar la eficiencia en el uso del agua y la energÃa
An efficient hybrid approach for multiobjective optimization of water distribution systems
An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.Feifei Zheng, Angus R. Simpson and Aaron C. Zecchi
Integrated Planning in Hospitals: A Review
Efficient planning of scarce resources in hospitals is a challenging task for
which a large variety of Operations Research and Management Science approaches
have been developed since the 1950s. While efficient planning of single
resources such as operating rooms, beds, or specific types of staff can already
lead to enormous efficiency gains, integrated planning of several resources has
been shown to hold even greater potential, and a large number of integrated
planning approaches have been presented in the literature over the past
decades.
This paper provides the first literature review that focuses specifically on
the Operations Research and Management Science literature related to integrated
planning of different resources in hospitals. We collect the relevant
literature and analyze it regarding different aspects such as uncertainty
modeling and the use of real-life data. Several cross comparisons reveal
interesting insights concerning, e.g., relations between the modeling and
solution methods used and the practical implementation of the approaches
developed. Moreover, we provide a high-level taxonomy for classifying different
resource-focused integration approaches and point out gaps in the literature as
well as promising directions for future research
Groundwater level prediction using a multiple objective genetic algorithm-grey relational analysis based weighted ensemble of anfis models
Predicting groundwater levels is critical for ensuring sustainable use of an aquifer’s limited groundwater reserves and developing a useful groundwater abstraction management strategy. The purpose of this study was to assess the predictive accuracy and estimation capability of various models based on the Adaptive Neuro Fuzzy Inference System (ANFIS). These models included Differential Evolution-ANFIS (DE-ANFIS), Particle Swarm Optimization-ANFIS (PSO-ANFIS), and traditional Hybrid Algorithm tuned ANFIS (HA-ANFIS) for the one-and multi-week forward forecast of groundwater levels at three observation wells. Model-independent partial autocorrelation functions followed by frequentist lasso regression-based feature selection approaches were used to recognize appropriate input variables for the prediction models. The performances of the ANFIS models were evaluated using various statistical performance evaluation indexes. The results revealed that the optimized ANFIS models performed equally well in predicting one-week-ahead groundwater levels at the observation wells when a set of various performance evaluation indexes were used. For improving prediction accuracy, a weighted-average ensemble of ANFIS models was proposed, in which weights for the individual ANFIS models were calculated using a Multiple Objective Genetic Algorithm (MOGA). The MOGA accounts for a set of benefits (higher values indicate better model performance) and cost (smaller values indicate better model performance) performance indexes calculated on the test dataset. Grey relational analysis was used to select the best solution from a set of feasible solutions produced by a MOGA. A MOGA-based individual model ranking revealed the superiority of DE-ANFIS (weight = 0.827), HA-ANFIS (weight = 0.524), and HAANFIS (weight = 0.697) at observation wells GT8194046, GT8194048, and GT8194049, respectively. Shannon’s entropy-based decision theory was utilized to rank the ensemble and individual ANFIS models using a set of performance indexes. The ranking result indicated that the ensemble model outperformed all individual models at all observation wells (ranking value = 0.987, 0.985, and 0.995 at observation wells GT8194046, GT8194048, and GT8194049, respectively). The worst performers were PSO-ANFIS (ranking value = 0.845), PSO-ANFIS (ranking value = 0.819), and DE-ANFIS (ranking value = 0.900) at observation wells GT8194046, GT8194048, and GT8194049, respectively. The generalization capability of the proposed ensemble modelling approach was evaluated for forecasting 2-, 4-, 6-, and 8-weeks ahead groundwater levels using data from GT8194046. The evaluation results confirmed the useability of the ensemble modelling for forecasting groundwater levels at higher forecasting horizons. The study demonstrated that the ensemble approach may be successfully used to predict multi-week-ahead groundwater levels, utilizing previous lagged groundwater levels as inputs
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