An Evolutionary Algorithm For The Nurse Scheduling Problem With Circadian Rhythms [RT89.N76 R278 2004 f rb][Microfiche 7574].

This thesis investigates the use of memetic algorithm (MA) for solving the nurse scheduling problem

A Comparison Study on the Performances Of X , EWMA and CUSUM Control Charts for Skewed Distributions Using Weighted Standard Deviations Methods

In many statistical process control (SPC) applications, the ease of use of control charts leads to ignoring the fact that the process population of the quality characteristic being measured may be highly skewed. However, in many situations, the normality assumption is usually violated. Among the recent heuristic charts for skewed distributions proposed in the literature are those based on the weighted standard deviation (WSD) method. Thus, this paper compares the performances of certain WSD charts, such as WSD X , WSD Exponential weighted moving Average (WSDEWMA) and WSD Cumulative Sum (WSD-CUSUM) charts for skewed distributions. The skewed distributions being considered are weibull, gamma and lognormal. The false alarm and mean shift detection rates were computed so as to evaluate the performances of the WSD charts. The WSD X chart was found to have the lowest false alarm rate in cases of known and unknown parameters. Moreover, when parameters are known and unknown, the WSD-CUSUM provided the highest mean shift detection rates. The chart with the lowest false alarm and the highest mean shift detection rates for most level of skewness and sample size, n is assumed to be have a better performance

An improved DSS for a local human resource development emphasizing basic TQM practice

Nowadays, it is very crucial to increase competency skills of unemployed fresh graduates in Malaysia so as to overcome the rising of unemployment rate.Hence, training schemes and development courses are fruitful solutions to fill up the gap between what students studied at universities and what exactly labor markets need. Therefore, in this paper we propose an improved Decision Support System (DSS) for a Local Human Resource Development (LHRD), which emphasized the Total Quality Management practice.Specifically, the output is the DSS for the incorporative LHRD model, which is further improved by the Web-based training process to enhance the overall delivery of various training and development schemes. The implementation of the model is able to increase knowledge, skills and capacities of fresh graduates, thus increase their productivity as employees along with job satisfaction

Adopting AHP in evaluating nurse scheduling methods

When comparing and evaluating two or more scheduling methods the need to use a multiple criteria decision making technique arises.Instead of just employing a qualitative approach of evaluating the methods, one can integrate the qualitative and the quantitative data in the evaluation process through the use of the analytic hierarchy process (AHP).Hence, this paper reports the evaluation of two nurse scheduling methods where the AHP technique is employed to support the selection process of these methods.Five decision criteria are used in the process.The main objective of the scheduling methods (models) is to assign work shifts and off days of the nurses in a particular hospital unit such that, it fulfils certain specified constraints while ensuring continuous high-quality patient care services.The first method is a heuristic procedure that is currently in practice.The other one is a prototype in which a memetic algorithm is adopted in the approach.The application of AHP has been found to provide a better transparency of the capability and efficiency of the scheduling methods. Consequently, the memetic algorithm approach stands out to be the better one based on the evaluation scheme

A simulation approach to determine the probability of demand during lead-time when demand distributed normal and lead-time distributed gamma

Globalization and advances in information and production technologies make inventory management can be very difficult even for organizations with simple structures. The complexities of inventory management increase in multi-stage networks, where inventory appears in multiple tiers of locations. Due to massive practical applications in the reality of the world, an efficient inventory system policy whether single location or multi-stage location will avoid falling into overstock inventory or under stock inventory. However, the optimality of inventory and allocation policies in a supply chain is still unknown for most types of multi-stage systems. Hence, this paper aims to determine the probability distribution function of demand during lead-time by using a simulation model when the demand distributed normal and the lead-time distributed gamma. The simulation model showed a new probability distribution function of demand during lead-time in the considered inventory system, which is, Generalized Gamma distribution with 4 parameters. This probability distribution function makes the mathematical expression more difficult to build the inventory model especially in multistage or multi-echelon inventory model

Investigating feed mix problem approaches: An overview and potential solution

Feed is one of the factors which play an important role in determining a successful development of an aquaculture industry. It is always critical to produce the best aquaculture diet at a minimum cost in order to trim down the operational cost and gain more profit. However, the feed mix problem becomes increasingly difficult since many issues need to be considered simultaneously.Thus, the purpose of this paper is to review the current techniques used by nutritionist and researchers to tackle the issues. Additionally, this paper introduce an enhance algorithm which is deemed suitable to deal with all the issues arise. The proposed technique refers to Hybrid Genetic Algorithm which is expected to obtain the minimum cost diet for farmed animal, while satisfying nutritional requirements. Hybrid GA technique with artificial bee algorithm is expected to reduce the penalty function and provide a better solution for the feed mix problem

Predicting Completion Time for Production Line in a Supply Chain System through Artificial Neural Networks

Completion time in manufacturing sector is the time needed to produce a product through production processes in sequence and it reflects the delivery performance of such company in supply chain system to meet customer demands on time. However, actual completion time always deviated from the standard completion time due to unavoidable factors and consequently affect delivery due date and ultimately lead to customer dissatisfaction. Therefore, this paper predicts completion time based on historical data of production line activities and discovers the most influential factor that contributes to the tardiness or a late jobs due date from its completion time. A well-known company in producing audio speaker is selected as a case company. Based on the review of previous works, it is found that Artificial Neural Networks (ANN) has superior capability in prediction of future occurrence by capturing the underlying relationship among variables through historical data. Besides, ANN is also capable to provide final weight for each of related variable. Variable with the highest value of final weight indicates the most influential variable and should be concerned more to solve completion time issue which has persisted among entities in supply chain system. The obtained result is expected to become an advantageous guidance for every entity in supply chain system to fulfil completion time requirement as requested by customer in order to survive in this turbulent market place

Roulette-tournament selection for shrimp diet formulation problem

This paper aims to propose a new selection procedure for real value encoding problem, specifically for shrimp diet problem.This new selection is a hybrid between two well-known selection procedure; roulette wheel selection and binary tournament selection.Shrimp diet problem is investigated to understand the hard constraints and the soft constraints involved.The comparison between other existing selections is also described for evaluation purposes.The result shows that roulette-tournament selection is better in terms of number of feasible solutions achieved and thus suitable for real value encoding problem.However, the combination with other crossover or mutation might be investigated to find the most suited combination that can obtain better best so far solution

A Slack Based Enhanced DEA Model with Undesirable Outputs for Rice Growing Farmers Efficiency Measurement

Agricultural production process typically produces two types of outputs which are economic desirable as well as environmentally undesirable outputs (such as greenhouse gas emission, nitrate leaching, effects to human and organisms and water pollution). In efficiency analysis, these undesirable outputs cannot be ignored and need to be included in order to obtain the actual estimation of firms efficiency. There are several approaches that has been proposed in DEA literature to account for undesirable outputs. Many researchers have pointed that directional distance function (DDF) approach is the best as it allows for simultaneous increase in desirable outputs and reduction of undesirable outputs. Additionally, slack based DEA approaches considers the output shortfalls and input excess in determining efficiency. The proposed model uses an enhanced DEA model which is based on DDF approach and incorporates slack based measure to determine efficiency in the presence of undesirable factors. Later the proposed increase in desirable outputs and reduction in undesirable outputs can be found for inefficient farmers. The developed model is used to determine rice farmers efficiency form Kepala Batas, Kedah. The study found 13 out of 30 farmers are CRS efficient and 17 out of 30 farmers are VRS efficient. From the basic DEA model, higher number of efficient farmers are identified due to the fact that the effect of undesirable outputs is not included in the model. Generally, DEA models which considers the effects of undesirable outputs produces more robust results