Patterns of Software Development Process

This article presents a set of patterns that can be found to perform best practices in software processes that are directly related to the problem of implementing the activities of the process, the roles involved, the knowledge generated and the inputs and outputs belonging to the process. In this work, a definition of the architecture is encouraged by using different recurrent configurations that strengthen the process and yield efficient results for the development of a software project. The patterns presented constitute a catalog, which serves as a vocabulary for communication among project participants [1], [2], and also can be implemented through software tools, thus facilitating patterns implementation [3]. Additionally, a tool that can be obtained under GPL (General Public license) is provided for this purpose

Optimization of Nursing Scheduling in Emergency by Using Genetic Algorithm

Scheduling nurse duty is one of the problems in health organizations that is quite complicated to solve. Starting from the uncertain number of patients, serious patient illnesses, characteristics of organizational groups, requests for nurses to take time off, and the qualifications and specialization of the nurses themselves are why scheduling in the ER is difficult to optimize. The same thing is being experienced by one of the health institutions, RSUD Dr. Pirngadi. Preparing schedules or determining the number of nurses on duty is still done manually, resulting in a lack of optimization in scheduling and the number of nurses who must be on duty, especially in the emergency department. In solving this problem, an appropriate method is needed so that the process of scheduling and optimizing the number of nurses can be formed properly. This research applies the Genetic Algorithm in optimal emergency department (IGD) nurse duty scheduling. Genetic algorithms, also called search algorithms, are based on the mechanisms of natural selection and genetics. Genetic algorithms are one of the appropriate methods for solving complex optimization problems. This method is good enough to optimize shift scheduling for the Emergency Room Nursing Service in a Hospital. This Genetic Algorithm can be a solution to multi-criteria and multi-objective problems modeled using biological and evolutionary processes. So, the concept of this method can be applied in optimizing the Nursing Service schedule. The results of calculations using the Genetic Algorithm show quite significant comparisons, including several nurses losing their positions and being eliminated by mutation because they could not compete with several other strong individuals

Decision-making methods in engineering design: a designer-oriented approach

The use of decisional methods for the solution of engineering design problems has to be tackled on a "human" viewpoint. Hence, fundamental is the identification of design issues and needs that become a designer oriented viewpoint. Decision-based methods are systematically classified in MCDM methods, Structured Design methods and Problem Structuring methods. The results are organised in order to provide a first reference for the designer in a preliminary selection of decision-based methods. The paper shows the heterogeneous use of decision-based methods, traditionally expected to solve only some specific design problems, which have been used also in different design contexts. Moreover, several design issues, which emerged from the review process, have been pointed out and discussed accordingly. This review provided useful results for the enlargement of the state of the art on Decision Based Design methods in engineering design contexts

A Comprehensive Solution to Automated Inspection Device Selection Problems using ELECTRE Methods

Selection of an automated inspection device for an explicit industrial application is one of the most challenging problems in the current manufacturing environment. It has become more and more complicated due to increasing complexity, advanced features and facilities that are endlessly being integrated into the devices by different manufacturers. Selection of inspection devices plays a significant role in a manufacturing system for cost effectiveness and improved productivity. This paper focuses on the application of a very popular Multi-Criteria Decision-Making (MCDM) tool, i.e. ELimination and Et Choice Translating REality (ELECTRE) for solving an automated inspection device selection problem in a discrete manufacturing environment. Using a sample case study from the published literature, this paper attempts to show how different variants of the ELECTRE method, namely ELECTRE II, IS, III, IV and TRI can be suitably applied in choosing the most efficient alternative that accounts for both the decision maker’s intervention and other technical elements. Using different ELECTRE methods, a list of all the possible choices from the best to the worst suitable devices is obtained while taking into account different selection attributes. The ranking performance of these methods is also compared with that of the past researchers

Optimization of Nursing Scheduling in Emergency by Using Genetic Algorithm

Scheduling nurse duty is one of the problems in health organizations that is quite complicated to solve. Starting from the uncertain number of patients, serious patient illnesses, characteristics of organizational groups, requests for nurses to take time off, and the qualifications and specialization of the nurses themselves are why scheduling in the ER is difficult to optimize. The same thing is being experienced by one of the health institutions, RSUD Dr. Pirngadi. Preparing schedules or determining the number of nurses on duty is still done manually, resulting in a lack of optimization in scheduling and the number of nurses who must be on duty, especially in the emergency department. In solving this problem, an appropriate method is needed so that the process of scheduling and optimizing the number of nurses can be formed properly. This research applies the Genetic Algorithm in optimal emergency department (IGD) nurse duty scheduling. Genetic algorithms, also called search algorithms, are based on the mechanisms of natural selection and genetics. Genetic algorithms are one of the appropriate methods for solving complex optimization problems. This method is good enough to optimize shift scheduling for the Emergency Room Nursing Service in a Hospital. This Genetic Algorithm can be a solution to multi-criteria and multi-objective problems modeled using biological and evolutionary processes. So, the concept of this method can be applied in optimizing the Nursing Service schedule. The results of calculations using the Genetic Algorithm show quite significant comparisons, including several nurses losing their positions and being eliminated by mutation because they could not compete with several other strong individuals

Generic modelling and simulation of operations and stock levels in supply chains

Imperial Users onl

A new hybrid meta-heuristic algorithm for solving single machine scheduling problems

A dissertation submitted in partial ful lment of the degree of Master of Science in Engineering (Electrical) (50/50) in the Faculty of Engineering and the Built Environment Department of Electrical and Information Engineering May 2017Numerous applications in a wide variety of elds has resulted in a rich history of research into optimisation for scheduling. Although it is a fundamental form of the problem, the single machine scheduling problem with two or more objectives is known to be NP-hard. For this reason we consider the single machine problem a good test bed for solution algorithms. While there is a plethora of research into various aspects of scheduling problems, little has been done in evaluating the performance of the Simulated Annealing algorithm for the fundamental problem, or using it in combination with other techniques. Speci cally, this has not been done for minimising total weighted earliness and tardiness, which is the optimisation objective of this work. If we consider a mere ten jobs for scheduling, this results in over 3.6 million possible solution schedules. It is thus of de nite practical necessity to reduce the search space in order to nd an optimal or acceptable suboptimal solution in a shorter time, especially when scaling up the problem size. This is of particular importance in the application area of packet scheduling in wireless communications networks where the tolerance for computational delays is very low. The main contribution of this work is to investigate the hypothesis that inserting a step of pre-sampling by Markov Chain Monte Carlo methods before running the Simulated Annealing algorithm on the pruned search space can result in overall reduced running times. The search space is divided into a number of sections and Metropolis-Hastings Markov Chain Monte Carlo is performed over the sections in order to reduce the search space for Simulated Annealing by a factor of 20 to 100. Trade-o s are found between the run time and number of sections of the pre-sampling algorithm, and the run time of Simulated Annealing for minimising the percentage deviation of the nal result from the optimal solution cost. Algorithm performance is determined both by computational complexity and the quality of the solution (i.e. the percentage deviation from the optimal). We nd that the running time can be reduced by a factor of 4.5 to ensure a 2% deviation from the optimal, as compared to the basic Simulated Annealing algorithm on the full search space. More importantly, we are able to reduce the complexity of nding the optimal from O(n:n!) for a complete search to O(nNS) for Simulated Annealing to O(n(NMr +NS)+m) for the input variables n jobs, NS SA iterations, NM Metropolis- Hastings iterations, r inner samples and m sections.MT 201

Efeitos da liderança na melhoria da qualidade dos cuidados de enfermagem

O desenvolvimento de sistemas de qualidade em saúde, de acordo com o Conselho de Enfermagem da Ordem dos Enfermeiros Portugueses, é uma acção prioritária e os enfermeiros assumem um papel fundamental na definição de padrões de qualidade dos cuidados prestados. Alias, os enfermeiros correspondem ao maior grupo profissional dentro das organizações de saúde e a comunidade espera destes que os cuidados prestados sejam de qualidade. Neste contexto, as organizações de saúde devem promover um ambiente favorecedor do desenvolvimento profissional dos enfermeiros com vista ao empenhamento destes em prol da qualidade dos cuidados prestados aos utentes. Em contextos de melhoria da qualidade, é geralmente reconhecida a importância do factor liderança, que nos modelos de excelência assume inclusivamente um papel de destaque. No entanto, em termos de investigação, a significância dessa relação continua carecendo de suficiente comprovação empírica, principalmente no sector da saúde, sendo que a escassez de investigação relativamente à influência da liderança na melhoria da qualidade dos cuidados de saúde é uma realidade. Neste sentido, pretendeu-se com a elaboração deste estudo, investigar até que ponto a liderança em enfermagem, percepcionada pelos enfermeiros, influencia a melhoria da qualidade dos cuidados de enfermagem. Para o efeito, considerou-se liderança em enfermagem como uma variável multidimensional [i) Reconhecimento, ii) Desenvolvimento da equipa, iii) Comunicação e iv) Inovação] e a melhoria da qualidade dos cuidados de enfermagem baseou-se nos padrões da qualidade emanados pela Ordem dos Enfermeiros. A recolha de dados foi efectuada através de um questionário aplicado aos enfermeiros da ULS EPE (Unidade Local Saúde, Empresa Pública Empresarial) de Castelo Branco e foi realizada entre Agosto e Outubro de 2011. Foram inquiridos 283 enfermeiros colaboradores, dos quais foram recebidos 184 questionários correspondendo a uma taxa de resposta de 65,02%. Os resultados da investigação, obtidos através da análise de equações estruturais (AEE), sugerem claramente que a liderança em enfermagem influência directa e significativamente a qualidade dos cuidados de enfermagem, reforçando um vasto conjunto de ideias veiculadas ao longo de toda a literatura que sugerem a pertinência dessa relação, mas que carecia de comprovação empírica, sobretudo no contexto Português

Evaluasi Aturan Penugasan Dan Penentuan Jumlah Crane Pada Pt Terminal Petikemas Surabaya

PT Terminal Petikemas Surabaya (TPS) bergerak di bidang penyediaan fasilitas terminal petikemas bagi pelaku usaha di wilayah Indonesia Timur. Salah satu visi PT TPS adalah menyediakan dan memastikan bahwa layanan yang diberikan kepada para pelanggan tepat waktu dan terjadwal. Untuk mendukung visi tersebut, perusahaan terus berusaha meningkatkan layanan dalam hal waktu bongkar muat. Berdasarkan data pada periode observasi, diketahui bahwa terdapat beberapa berth yang masih memiliki nilai BSH (Boxes Ships Hours) di bawah target perusahaan. Untuk meningkatkan nilai BSH, maka dibutuhkan suatu solusi dengan cara mempersingkat total waktu kerja crane. Penentuan alokasi crane akan berpengaruh terhadap waktu pelayanan bongkar muat pada suatu kapal, sehingga pada penelitian ini akan dilakukan evaluasi aturan alokasi crane pada kapal untuk melihat dampak aturan penugasan crane terhadap waktu kerja crane pada tiap kapal di tiap berth. Penelitian ini bertujuan untuk melakukan evaluasi terhadap kondisi eksisting untuk setiap berth dan setiap crane, mengetahui aturan pengalokasian crane pada tiap kapal pada kondisi eksisting, dan mengembangkan skenario perbaikan berupa alternatif aturan penugasan crane pada tiap kapal di tiap berth. Metode yang digunakan adalah simulasi dengan skenario perbaikan berupa perubahan jumlah maksimal crane yang dialokasikan pada tiap berth. Skenario terpilih merupakan skenario yang menghasilkan rata-rata waktu kerja crane di berth terkecil ====================================================================== PT Terminal Petikemas Surabaya (TPS) provides container terminal facilities for the traders at the eastern regions of Indonesia. One of the vision of PT TPS is to give and ensure the services on time for its customers. In order to support their vision, PT TPS has to improve their services by doing loading and discharging container on time. Based on the data in observation period, there are some ships that had the BSH (Boxes Ships Hours) value below the target. In order to increase the BSH value, it needed a way to shorten the vessel service time. Crane allocation will affect the vessel service time. In this study, crane allocation rules will be evaluated to see the impact of the crane assignment rule to crane working time on each vessel at each berth. This study aims to evaluate the existing conditions, identify the crane allocation rules, and develop scenarios i.e the alternative of the crane assignment rules for each berth and each crane. The method used is the simulation with software Arena. In this study, the maximum number of crane that allocated on each vessel will be changed. The chosen scenario is the scenario that generates the smallest average of crane working time in each bert

Optimization Models and Algorithms for Workforce Scheduling with Uncertain Demand

A workforce plan states the number of workers required at any point in time. Efficient workforce plans can help companies achieve their organizational goals while keeping costs low. In ever increasing globalized work market, companies need a competitive edge over their competitors. A competitive edge can be achieved by lowering costs. Labour costs can be one of the significant costs faced by the companies. Efficient workforce plans can provide companies with a competitive edge by finding low cost options to meet customer demand. This thesis studies the problem of determining the required number of workers when there are two categories of workers. Workers belonging to the first category are trained to work on one type of task (called Specialized Workers); whereas, workers in the second category are trained to work in all the tasks (called Flexible Workers). This thesis makes the following three main contributions. First, it addresses this problem when the demand is deterministic and stochastic. Two different models for deterministic demand cases have been proposed. To study the effects of uncertain demand, techniques of Robust Optimization and Robust Mathemat- ical Programming were used. The thesis also investigates methods to solve large instances of this problem; some of the instances we considered have more than 600,000 variables and constraints. As most of the variables are integer, and objective function is nonlinear, a commercial solver was not able to solve the problem in one day. Initially, we tried to solve the problem by using Lagrangian relaxation and Outer approximation techniques but these approaches were not successful. Although effective in solving small problems, these tools were not able to generate a bound within run time limit for the large data set. A number of heuristics were proposed using projection techniques. Finally this thesis develops a genetic algorithm to solve large instances of this prob- lem. For the tested population, the genetic algorithm delivered results within 2-3% of optimal solution