Observation of temporary accommodation for construction workers according to the code of practice for temporary construction site workers amenities and accommodation (ms2593:2015) in Johor, Malaysia

The Malaysian government is currently improving the quality of workers temporary accommodation by introducing MS2593:2015 (Code of Practice for Temporary Site Workers Amenities and Accommodation) in 2015. It is in line with the initiative in the Construction Industry Transformation Programme (2016-2020) to increase the quality and well-being of construction workers in Malaysia. Thus, to gauge the current practice of temporary accommodation on complying with the particular guideline, this paper has put forth the observation of such accommodation towards elements in Section 3 within MS2593:2015. A total of seventeen (17) temporary accommodation provided by Grade 6 and Grade 7 contractors in Johor were selected and assessed. The results disclosed that most of the temporary accommodation was not complying with the guideline, where only thirteen (13) out of fifty-eight (58) elements have recorded full compliance (100%), and the lowest compliance percentage (5.9%) are discovered in the Section 3.12 (Signage). In a nutshell, given the significant gap of compliance between current practices of temporary accommodation and MS2593:2015, a holistic initiative need to be in place for the guideline to be worthwhile

Evaluating decision-making units under uncertainty using fuzzy multi-objective nonlinear programming

This paper proposes a new method to evaluate Decision Making Units (DMUs) under uncertainty using fuzzy Data Envelopment Analysis (DEA). In the proposed multi-objective nonlinear programming methodology both the objective functions and the constraints are considered fuzzy. The coefficients of the decision variables in the objective functions and in the constraints, as well as the DMUs under assessment are assumed to be fuzzy numbers with triangular membership functions. A comparison between the current fuzzy DEA models and the proposed method is illustrated by a numerical example

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Insight into the Sustainable Integration of Bio- and Petroleum Refineries for the Production of Fuels and Chemicals.

A petroleum refinery heavily depends on crude oil as its main feedstock to produce liquid fuels and chemicals. In the long term, this unyielding dependency is threatened by the depletion of the crude oil reserve. However, in the short term, its price highly fluctuates due to various factors, such as regional and global security instability causing additional complexity on refinery production planning. The petroleum refining industries are also drawing criticism and pressure due to their direct and indirect impacts on the environment. The exhaust gas emission of automobiles apart from the industrial and power plant emission has been viewed as the cause of global warming. In this sense, there is a need for a feasible, sustainable, and environmentally friendly generation process of fuels and chemicals. The attention turns to the utilization of biomass as a potential feedstock to produce substitutes for petroleum-derived fuels and building blocks for biochemicals. Biomass is abundant and currently is still low in utilization. The biorefinery, a facility to convert biomass into biofuels and biochemicals, is still lacking in competitiveness to a petroleum refinery. An attractive solution that addresses both is by the integration of bio- and petroleum refineries. In this context, the right decision making in the process selection and technologies can lower the investment and operational costs and assure optimum yield. Process optimization based on mathematical programming has been extensively used to conduct techno-economic and sustainability analysis for bio-, petroleum, and the integration of both refineries. This paper provides insights into the context of crude oil and biomass as potential refinery feedstocks. The current optimization status of either bio- or petroleum refineries and their integration is reviewed with the focus on the methods to solve the multi-objective optimization problems. Internal and external uncertain parameters are important aspects in process optimization. The nature of these uncertain parameters and their representation methods in process optimization are also discussed

Fuzzy Linear Programming with Interval Type-2 fuzzy constraints

Doctorad

Improved two-phase solution strategy for multiobjective fuzzy stochastic linear programming problems with uncertain probability distribution

Multiobjective Fuzzy Stochastic Linear Programming (MFSLP) problem where the linear inequalities on the probability are fuzzy is called a Multiobjective Fuzzy Stochastic Linear Programming problem with Fuzzy Linear Partial Information on Probability Distribution (MFSLPPFI). The uncertainty presents unique difficulties in constrained optimization problems owing to the presence of conflicting goals and randomness surrounding the data. Most existing solution techniques for MFSLPPFI problems rely heavily on the expectation optimization model, the variance minimization model, the probability maximization model, pessimistic/optimistic values and compromise solution under partial uncertainty of random parameters. Although these approaches recognize the fact that the interval values for probability distribution have important significance, nevertheless they are restricted by the upper and lower limitations of probability distribution and neglected the interior values. This limitation motivated us to search for more efficient strategies for MFSLPPFI which address both the fuzziness of the probability distributions, and the fuzziness and randomness of the parameters. The proposed strategy consists two phases: fuzzy transformation and stochastic transformation. First, ranking function is used to transform the MFSLPPFI to Multiobjective Stochastic Linear Programming Problem with Fuzzy Linear Partial Information on Probability Distribution (MSLPPFI). The problem is then transformed to its corresponding Multiobjective Linear Programming (MLP) problem by using a-cut technique of uncertain probability distribution and linguistic hedges. In addition, Chance Constraint Programming (CCP), and expectation of random coefficients are applied to the constraints and the objectives respectively. Finally, the MLP problem is converted to a single-objective Linear Programming (LP) problem via an Adaptive Arithmetic Average Method (AAAM), and then solved by using simplex method. The algorithm used to obtain the solution requires fewer iterations and faster generation of results compared to existing solutions. Three realistic examples are tested which show that the approach used in this study is efficient in solving the MFSLPPFI

Defuzzification of groups of fuzzy numbers using data envelopment analysis

Defuzzification is a critical process in the implementation of fuzzy systems that converts fuzzy numbers to crisp representations. Few researchers have focused on cases where the crisp outputs must satisfy a set of relationships dictated in the original crisp data. This phenomenon indicates that these crisp outputs are mathematically dependent on one another. Furthermore, these fuzzy numbers may exist as a group of fuzzy numbers. Therefore, the primary aim of this thesis is to develop a method to defuzzify groups of fuzzy numbers based on Charnes, Cooper, and Rhodes (CCR)-Data Envelopment Analysis (DEA) model by modifying the Center of Gravity (COG) method as the objective function. The constraints represent the relationships and some additional restrictions on the allowable crisp outputs with their dependency property. This leads to the creation of crisp values with preserved relationships and/or properties as in the original crisp data. Comparing with Linear Programming (LP) based model, the proposed CCR-DEA model is more efficient, and also able to defuzzify non-linear fuzzy numbers with accurate solutions. Moreover, the crisp outputs obtained by the proposed method are the nearest points to the fuzzy numbers in case of crisp independent outputs, and best nearest points to the fuzzy numbers in case of dependent crisp outputs. As a conclusion, the proposed CCR-DEA defuzzification method can create either dependent crisp outputs with preserved relationship or independent crisp outputs without any relationship. Besides, the proposed method is a general method to defuzzify groups or individuals fuzzy numbers under the assumption of convexity with linear and non-linear membership functions or relationships

Advanced methods and models in uncertainty for the order promising process in supply chain characterized by the lack of homogeneity in product

Tesis por compendioThe Lack of Homogeneity in the Product (LHP) appears in productive processes with raw materials, which directly stem from nature and/or production processes with operations that confer heterogeneity to the characteristics of the outputs obtained, even when the inputs used are homogeneous. LHP appears in different sectors such as ceramic tile, horticulture, marble, snacks, among others. LHP becomes a managerial problem when customers require to be served with homogeneous product. Supply chains responsible to provide homogeneous product face the need to include classification activities in their productive processes to obtain sub-lots of homogeneous product. Due to the inherent LHP uncertainty, these homogeneous sub-lots will not be known until the product have been produced and classified. An improper management of the LHP can have a very negative impact on the customers' satisfaction due to inconsistencies in the answer to their requirements and also on the Supply Chain's efficiency. The Order Promising Process (OPP) appears as a key element for properly managing the LHP in order to ensure the matching of uncertain homogeneous supply with customer order proposals. The OPP refers to the set of business activities that are triggered to provide a response to the orders from customers. These activities are related to the acceptance/rejection decision, and to set delivery dates. For supply chains affected by the LHP, the OPP must consider the homogeneity as another requirement in the answer to the orders. Besides, due to the LHP inherent uncertainty, discrepancies between the real and planned homogeneous quantities might provoke that previously committed orders cannot be served. The Shortage Planning (SP) process intends to find alternatives in order to minimise the negative impact on customers and the supply chain. Considering LHP in the OPP brings a set of new challenging features to be addressed. The conventional approach of assuming homogeneity in the product for the master production schedule (MPS) and the quantities Available-To-Promise (ATP) derived from it is no longer adequate. Instead, both the MPS and ATP should be handled in terms of homogeneous sub-lots. Since the exact quantity of homogeneous product from the planned lots in the MPS is not exactly known until the classification activities have been performed, the ATP also inherits this uncertainty, bringing a new level of complexity. Non-homogeneous product cannot be accumulated in order to fulfil future incoming orders. Even more, if the product handled is perishable, the homogeneity management becomes considerably more complex. This is because the state of the product is dynamic with time and related variables to it, like quality, price, etc., could change with time. This situation could bring unexpected wasting costs apart from the shortages already mentioned. The perishability factor is itself another source of uncertainty associated to the LHP. This dissertation proposes a conceptual framework and different mathematical programming models and tools, in both deterministic and uncertainty environments, in order to support the OPP and SP under LHP's effect. The aim is to provide a reliable commitment with customer orders looking for a high service level not just in the due date and quantity but also in the homogeneity requirements. The modelling of the characteristics inherent to LHP under deterministic context constitutes itself one of the main contribution of this dissertation. Another novelty consists in the inclusion of uncertainty in the definition of homogeneous sub-lots, their quantities and their dynamic state and value. The uncertainty modelling approach proposed is mainly based on the application of fuzzy set theory and possibility theory. The proposed mathematical models and tools have been validated in real cases of SC, specifically in the ceramic tile sector for non perishables, and in the fruit sector for perishables. The results show a ...La Falta de Homogeneidad en el Producto (LHP, por sus siglas del inglés ``Lack of Homogeneity in the Product'') aparece en procesos productivos con materias primas que derivan directamente de la naturaleza y/o procesos de producción con operaciones que confieren heterogeneidad a las características de los productos obtenidos, incluso cuando los insumos utilizados son homogéneos. La LHP aparece en diferentes sectores como la cerámica, horticultura, mármol, snacks, entre otros. Se convierte en un problema gerencial cuando los clientes requieren homogeneidad en el producto y las cadenas de suministro enfrentan la necesidad de incluir actividades de clasificación en sus procesos productivos para obtener sub-lotes de producto homogéneo. Debido a la incertidumbre inherente a la LHP, los sub-lotes homogéneos y su cantidad no serán conocidos hasta que el producto haya sido producido y clasificado. Una gestión inadecuada de la LHP puede tener un impacto muy negativo en la satisfacción de los clientes debido a inconsistencias en la respuesta a sus requerimientos y también en la eficacia de la Cadena de Suministro. El Proceso de Comprometer de Pedido (OPP, por sus siglas del inglés ``Order Promising Process'') aparece como un elemento clave para gestionar adecuadamente la LHP, con el fin de asegurar la coincidencia entre el suministro incierto de producto homogéneo y las propuestas de pedido del cliente. El OPP se refiere al conjunto de actividades empresariales realizadas para proporcionar una respuesta a las órdenes de los clientes. Estas actividades están relacionadas con las decisiones de aceptación/rechazo, y establecimiento de fechas de entrega para las órdenes del cliente. En las cadenas de suministro afectadas por la LHP, el OPP debe considerar la homogeneidad como otro requisito adicional en la respuesta a los pedidos. Además, debido a la incertidumbre intrínseca de la LHP, las discrepancias entre las cantidades homogéneas reales y planificadas podrían provocar que las órdenes comprometidas anteriormente no puedan ser completadas debido a la escasez de producto. El proceso de planificación de la escasez (SP, por sus siglas del inglés "Shortage Planning") se encarga de encontrar alternativas para minimizar este impacto negativo en los clientes y la cadena de suministro. Considerar la LHP dentro del OPP implica un conjunto nuevo de características desafiantes que deben ser abordadas. El enfoque convencional de asumir la homogeneidad en el producto para el programa maestro de producción (MPS, por sus siglas del inglés "Master Production Schedule") y las cantidades disponibles a comprometer (ATP, por sus siglas del inglés "Available-To-Promise") derivadas de él, no es adecuado. En cambio, tanto el MPS como el ATP deben manejarse en términos de sub-lotes homogéneos. Dado que la cantidad exacta de producto homogéneo de los lotes previstos en el MPS no se sabe exactamente hasta que se han realizado las actividades de clasificación, el ATP también hereda esta incertidumbre, trayendo un nuevo nivel de complejidad. El producto no homogéneo no se puede acumular para satisfacer futuras órdenes entrantes. Más aún, si el producto manipulado es perecedero, el manejo de la homogeneidad se vuelve mucho más complejo. Esto se debe a que el estado del producto es dinámico en el tiempo, y variables relacionadas como calidad, precio, etc., podrían también cambiar con el tiempo. Esta situación puede provocar costos inesperados de desperdicio aparte de la escasez ya mencionada. El factor de perecedero es en sí mismo otra fuente de incertidumbre asociada a la LHP. Esta disertación propone un marco conceptual y diferentes modelos y herramientas de programación matemática, tanto en entornos deterministas como de incertidumbre, para apoyar al OPP y SP considerando el efecto de LHP. El objetivo es proporcionar un compromiso fiable con los pedidos de los clientes en busca de un alto nivel de servicio no sLa Falta d'Homogeneïtat en el Producte (LHP, per les seues sigles de l'anglés ''Lack of Homogeneity in the Product'') apareix en processos productius amb matèries primes que deriven directament de la natura i/o processos de producció amb operacions que conferixen heterogeneïtat a les característiques dels productes obtinguts, fins i tot quan les entrades utilitzades són homogènies . La LHP apareix en diferents sectors com la ceràmica, horticultura, marbre, snacks, entre altres. Es convertix en un problema gerencial quan els clients requereixen homogeneïtat en el producte i les cadenes de subministrament enfronten la necessitat d'incloure activitats de classificació en els seus processos productius per a obtindre sublots de producte homogeni. A causa de la incertesa inherent a la LHP, els sublots homogenis i la seua quantitat no seran coneguts fins que el producte haja sigut produït i classificat. Una gestió inadequada de la LHP pot tindre un impacte molt negatiu en la satisfacció dels clients degut a inconsistències en la resposta als seus requeriments i també en l'eficàcia de la Cadena de Subministrament. El Procés de Comprometre Comandes (OPP, per les seues sigles de l'anglés ''Order Promising Process'') apareix com un element clau per a gestionar adequadament la LHP, a fi d'assegurar la coincidència entre el subministrament incert de producte homogeni i les propostes de comanda del client. L'OPP es refereix al conjunt d'activitats empresarials realitzades per a proporcionar una resposta a les ordres dels clients. Aquestes activitats estan relacionades amb les decisions d'acceptació/rebuig, i establiment de dates de lliurament per a les ordres del client. En les cadenes de subministrament afectades per la LHP, l'OPP ha de considerar l'homogeneïtat com un altre requisit addicional en la resposta a les comandes. A més, a causa de la incertesa intrínseca de la LHP, les discrepàncies entre les quantitats homogènies reals i planificades podrien provocar que les ordres compromeses anteriorment no puguen ser completades a causa de l'escassetat de producte. El procés de planificació de l'escassetat (SP, per les seues sigles de l'anglés "Shortage Planning") s'encarrega de trobar alternatives per a minimitzar aquest impacte negatiu en els clients i en la cadena de subministrament. Considerar la LHP dins de l'OPP implica un conjunt nou de característiques desafiants que han de ser abordades. L'enfocament convencional d'assumir l'homogeneïtat en el producte per al programa mestre de producció (MPS, per les seues sigles de l'anglés "Master Production Schedule") i les quantitats disponibles a comprometre (ATP, per les seues sigles de l'anglés "Available-To-Promise") derivades d'ell, no és adequat. En canvi, tant el MPS com l'ATP han de manejar-se en termes de sublots homogenis. Atés que la quantitat exacta de producte homogeni dels lots previstos en el MPS no se sap exactament fins que s'han realitzat les activitats de classificació, l'ATP també hereta aquesta incertesa, portant un nou nivell de complexitat. El producte no homogeni no es pot acumular per a satisfer futures ordees entrants. Més encara, si el producte manipulat és perible, el maneig de l'homogeneïtat es torna molt més complex. Açò es deu al fet que l'estat del producte és dinàmic en el temps, i variables relacionades com qualitat, preu, etc., podrien també canviar amb el temps. Aquesta situació pot provocar costos inesperats de rebuig a banda de l'escassetat ja esmentada. El factor de perible és en si mateix un altra font d'incertesa associada a la LHP. Aquesta dissertació proposa un marc conceptual i diferents models i eines de programació matemàtica, tant en entorns deterministes com d'incertesa, per a recolzar a l'OPP i SP considerant l'efecte de LHP. L'objectiu és proporcionar un compromís fiable amb les comandes dels clients a la recerca d'un alt nivell de servei no sols en la data i la quantitat esperades, sGrillo Espinoza, H. (2017). Advanced methods and models in uncertainty for the order promising process in supply chain characterized by the lack of homogeneity in product [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/91110TESISCompendi