DYNAMIC SIMULATION ANALYSIS FOR VARIOUS NUMBERS OF ORDERS IN AN INTEGRATED CAR-MANUFACTURING WAREHOUSE

The order-picking process in a warehouse is critical in managing customer orders, especially in retail stores. It is expensive because fulfilling online orders takes up to 70% of all warehouse activities. Procedures in order picking, including different route selection schemes, can significantly increase yield and reduce costs. The research shows that a suitable routing method can reduce the travel time of the order picker to fulfill the order. However, the number of orders may vary. This paper presented a dynamic simulation analysis based on a real scenario of a various number of orders in an integrated car manufacturing warehouse. The simulation reduced the travel time of the voters by about 44.89%. This simulation model helps to visualize the potential reduction in customer waiting times, leading to increased customer satisfaction

Forecasting of Paracetamol Demand in UMMC Pharmacy

Pharmaceutical inventory management is a critical operation in healthcare centres. This is due to the fact that most pharmaceutical products are perishable. Managing the inventory of perishable items can be a complicated process as the healthcare industry needs to maintain a high level of services. In order to manage the inventory of pharmaceutical products, it is important to forecast the demand, which will enable the distribution to be planned and scheduled effectively. In this research, we focus on one fast moving medicine which is paracetamol that commonly used to treat fever and pain across all ages group of patients. Data is obtained from University Malaya Medical Centre (UMMC) for the year 2017-2020. Before applying the forecasting techniques, the data pattern needs to be identified. Among the five forecasting techniques are Additive Decomposition Method, Multiplicative Decomposition Method, Simple Exponential Smoothing and Adaptive Response Rate Exponential Smoothing. The performance of these techniques was evaluated based on four error measurements; (i) Mean Absolute Deviation, (ii) Mean Squares Error, (iii) Mean Percentage Error and Mean Absolute Percentage Error. Multiplicative Decomposition method displays the lowest values of error measurements which indicates the greatest accuracy and implies the suitability for this research. The data predicts that the demand for paracetamol will likely continue to move downwards over the next five year

Impact of Financial Literacy Level on Financial Behavior among Higher Education Students: A Case Study in KDA University

Nowadays, financial literacy is becoming a concern in a society particularly, among youngsters. The biggest issue is, the youngsters are lack of understanding on how to control their finances. This study aims to evaluate the financial literacy level among youngsters based on their knowledge, attitude and behavior. At the same time investigating whether financial literacy, gender and household income have a significant influence on the financial behavior of KDA undergraduate students. Primary data was collected through a questionnaire which consisted of few different sections that measure the financial knowledge, attitude and behavior among KDA students. A total sample size of 186 was collected for this study. Reliability test, regression analysis and Pearson’s correlation coefficient were the methods used to analyze the data. The finding concluded that KDA students acquire a medium financial literacy level. Therefore, this study implies that individual, academic authorities, government and NGOs should educate and encourage students to practice good financial management and raise their financial literacy to avoid facing any financial issues in the futur

OPTIMIZATION OF RICE INVENTORY USING FUZZY INVENTORY MODEL AND LAGRANGE INTERPOLATION METHOD

Interpolation is a method to determine the value that is between two values and is known from the data. In some cases, the data obtained is incomplete due to limitations in data collection. Interpolation techniques can be used to obtain approximate data. In this study, the Lagrange interpolation method of degree 2 and degree 3 is used to interpolate the data on rice demand. A trapezoidal fuzzy number expresses the demand data obtained from the interpolation.  The other parameters are obtained from company data related to rice supplies and are expressed as trapezoidal fuzzy numbers. The interpolation accuracy rate is calculated using Mean Error Percentage (MAPE). The second-degree interpolation method produces a MAPE value of 30.76 percent, while the third-degree interpolation has a MAPE of 32.92 percent. The quantity of order  respectively  202677 kg, 384610 kg, 1012357 kg, 1447963 kg, and a Total inventory cost of Rp. 129231797951

The integrated inventory and production planning for time-varying demand process / Siti Suzlin Supadi

In the literature, integrated inventory model has received a lot of attention. Most previous works on this topic have been based on the assumption of constant demand rate. However this assumption is not reliable in reality; it is either increasing or decreasing with time. In this thesis, we considered the model which consists of a single vendor who manage the production and deliver to a single buyer with a linearly decreasing demand rate over a finite time horizon. Costs are attached to manufacturing set up, the delivery of a shipment and stockholding at the vendor and buyer. The objective is to determine the number of shipments and size of those shipments which minimize the total system cost - assuming the vendor and buyer collaborate and find a way of sharing the consequent benefits. We begin this thesis with the integrated inventory policy for shipping a vendor’s final production batch to a single buyer under linearly decreasing demand. The first case considered here is the holding cost at the vendor is less than at the buyer. We solve this model with equal shipment sizes policy, equal shipment periods policy and unequal shipment sizes and unequal shipment periods policy. Then, we develop a mathematical model when the unit holding cost is higher at the vendor rather than at the buyer (consignment stock problem). For this case, we also consider equal shipment sizes policy, equal shipment periods policy, and unequal shipment sizes and unequal shipment periods as in the previous case policy. It is followed by an integrated inventory model with n production batches which consists of the final batch at the end of the production cycle. This model also considers the case of the buyer’s holding cost being greater than the vendor’s and vice versa. We consider this model with equal cycle time and unequal cycle time for both policies. We show the solution procedure when the shipment sizes are equal and when they are unequal. We solve all the models in this thesis using Microsoft Excel Solver and illustrate all the policies with numerical examples and sensitivity analysis. Then we make some comparison of the model. Lastly we end the thesis with conclusion and some recommendations for further research

EOQ Models for Imperfect Items under Time Varying Demand Rate

In the classical Economic Order Quantity (EOQ) model, the common unrealistic assumptions are that all the purchased items are of perfect quality and the demand is constant. However, in a real-world environment, a portion of the purchased items might be damaged due to mishandling or an accident during the shipment process, and the demand rate may increase or decrease over time. Many companies are torn between repairing or replacing the imperfect items with new ones. The right decision on that options is crucial in order to guarantee that there is no shortage of stocks while at the same time not jeopardising the items’ quality and maximising the company’s profit. This paper investigates two EOQ models for imperfect quality items by assuming the demand rate varies with time. Under Policy 1, imperfect items are sent for repairs at an additional cost to the makeup margin; under Policy 2, imperfect items are replaced with equivalent quality items from a local supplier at a higher price. Two mathematical models are developed, and numerical examples along with sensitivity analyses are provided to illustrate these models. Our results reveal that Policy 1 is preferable to Policy 2 most of the time. However, Policy 2 outperforms Policy 1 if there is no minimum threshold on the purchased stock quantity. This research allows a company to discover solutions to previously identified inventory problems and make the inventory-patching process more controlled

Assessing Youth Unemployment Rate in Malaysia using Multiple Linear Regression

This study investigated the relationship between urbanization, inflation, gross domestic product and foreign direct investment towards youth unemployment in Malaysia. As youth unemployment has been increasing over the past decade, it is therefore crucial to decide which variables are the most critical in affecting Malaysia's youth unemployment rate as to ensure sustainable economic growth in the country. Data was obtained from Department of Statistics Malaysia and World Bank websites. Descriptive and multiple linear regressions were used in this study. The outcome revealed that foreign direct investment and gross domestic product growth are significant factors, while urbanization and inflation showed insignificant relationship towards the contribution to the youth unemployment rat

Greedy Reduction Algorithm as the Heuristic Approach in Determining the Temporary Waste Disposal Sites in Sukarami Sub-District, Palembang, Indonesia

Waste is one of the problems in Palembang, Indonesia. The amount of waste in Palembang increases proportionally to the population yearly and can adversely affect the community. Therefore, we determine the optimal temporary waste disposal site (TWDS) to optimize the problems. The set covering model is the proper model for solving the location and allocation problem. In this study, data on the distance between each TWDS is needed in the set covering modeling. The novelty in this research is developing the p-median problem model, which is formed from the optimal solution of the set covering location problem (SCLP) model. Palembang consists of 18 sub-districts, of which the Sukarami sub-district has the highest population density. This study discussed the determination of strategic TWDS in the Sukarami sub-district using the SCLP model, the p-median problem, and a heuristic approach, namely the greedy reduction algorithm in solving the model. Based on the solution of the p-median problem model with LINGO 18.0 and the p-median problem solved by the greedy reduction algorithm, only three strategic TWDS were found for the Sukarami sub-district. The study results recommend a review of the existing TWDS and particularly the addition of a TWDS in Sukodadi and Talang Betutu villages, respectively

Modelling the Inner Warehouse Shortest Route Planning using Dynamic Programming Block

Fulfilling the customer requirement has always been of utmost concern to logistics service companies, namely those providing warehouse and transportation services. In the warehouse, inner transportation problem affects its performance. Order picker problem is one of the problems that involves the transportation problem within the warehouse.  The problem can be handled properly by having proper storage assignment, proper tasking allocation and optimal routing for inner warehouse vehicles’ movement. This study proposed a modified Dynamic Programming model to determine the shortest route for the order pickers in completing and fulfilling the customers’ orders. The model shows stable solutions for numerous orders. Keywords: Order picking, dynamic programming, inner warehouse transportation eISSN: 2398-4287© 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DO

Improve Fuzzy Inventory Model of Fractal Interpolation with Vertical Scaling Factor

The inventory model is used to determine the optimal inventory of a product. In certain cases, parameters in the inventory model are uncertain. Fractal interpolation techniques can be used to overcome parameter with uncertainty. Fractal interpolation results are affected by the fractal interpolation function and the vertical scaling factor. The vertical scaling factor is positive and less than 1. In this study, fractal interpolation techniques are introduced with variations in vertical scaling factor to overcome the uncertainty of demand data in inventory models. Furthermore, the interpolation results are used in fuzzy inventory models and expressed by Trapezoidal Fuzzy Number. This paper considers an inventory model with varying demand to optimize rice inventory. Based on the data obtained, the accuracy level will increase for the vertical scaling factor values close to 1. Optimal rice inventory of each successive fuzzy parameter is 1447963, 1013914, 504950, 215312. If the cost parameter is increased, then the amount of inventory is decreases