Ordering policy using temporal association rule mining

In traditional association rule mining algorithms, if the minimum support is set too high, many valuable rules will be lost. However, if the value is set too low, then numerous trivial rules will be generated. To overcome the difficulty of setting minimum support values, global and local patterns are mined herein. Owing to the temporal factor in association rule mining, an itemset may not occur frequently in the entire dataset (meaning that it is not a global p attern), but it may appear frequently over specific intervals (meaning that it is a local pattern). This paper proposed a temporal ass ociation rule mining algorithm for interval frequent-patterns, called GLFMiner, which automatically and efficiently generates all intervals w ithout prior domain knowledge in an efficient manner. GLFMiner considers not only global frequent-patterns, but also local frequent-patterns. Using the same value of minimum support, it can locate many valuable temporal rules without losing the rules that traditio nal algorithms may find. Experimental results reveal that our novel algorithm mines more temporal frequent-patterns than traditional association rule mining algorithms and is effective in real-world applications such as market basket analysis and intrusion detection systems.

Incremental sequential patterns for multivariate temporal association rules mining

This study aims to determine whether the calculation of Economic Order Quantity (EOQ) can be used as Control of Raw Material Inventory at PT. X. This research was conducted by comparing between inventory control methods according to company policy with the Economic Order Quantity (EOQ) method. The research method used is descriptive quantitative, do to collect techniques used are interviews, observation, and documentation. Data analysis techniques used in this study are steps (1) gathering information about the amount of raw material inventory in 2018. (2) processing these data determine the control of raw material inventory based on company policy. (3) analyzing the precessed data using the Economic Order Quantity (EOQ) method. The results of the study prove that Economic Order Quantity (EOQ) is more efficient than the method used by the company. The application of the Economic Order Quantity (EOQ) method can reduce the total costs that must be incurred by the company. Which is IDR 299,399,040. with this (EOQ) method, it can avoid delays in the supply of raw materials, because the supply of raw materials can be ensured constant and according to lead time. Then reaching the research objectives, companies should use the Economic Order Quantity (EOQ) method.

Blast furnace (BF) is the main method of modern iron-making. Ensuring the stability of the BF conditions can effectively improve the quality and output of iron and steel. However, operations of BF depend on mainly human experience, which causes two problems: (1) human experience is not objective and is difficult to inherit and learn and (2) it is difficult to acquire knowledge that contains time information among multiple variables in BF. To address these problems, a data-driven method is proposed. In this article, we propose a novel and efficient algorithm for discovering underlying knowledge in the form of temporal association rules (TARs) in BF iron-making data. First, a new TAR mining framework is proposed for mining temporal frequent patterns. Then, a novel TAR mining algorithm is proposed for mining underlying, up-to-date, and effective knowledge in the form of TARs. Finally, considering the updating of the BF database, a rule updating method is proposed that is based on the algorithm that is proposed in this article. Our extensive experiments demonstrate the satisfactory performance of the proposed algorithm in discovering TARs in comparison with the state-of-the-art algorithms. Experiments on BF iron-making data have demonstrated the superior performance and practicability of the proposed method.

In this paper, we developed a sound conceptual framework for temporal fuzzy association rules mining based on fuzzy information granulation. First, the definition of the support rate of traditional fuzzy association rules is extended to the temporal data, including the fuzzy support rate of both continuous and discontinues temporal fuzzy item set and their association rules. It is found that the computation of support rate under our definition is a simply dynamic programming problem with very low complexity. Then an algorithm based on Apriori is demonstrated. At last, experiments were carried out to show the good performance of our new algorithm in mining continuous rules and discontinuous rules by our definition.

Early detection of adverse climatic conditions saves life and prevents harmful health impacts. One of the factors that causes such climatic conditions is the changes in ozone layer. Hyper levels of ozone concentration may cause several lung diseases and respiratory disorders. Many data mining algorithms perform analysis on the given data only after performing discretization. Discretisation involves the process of partitioning the data intervals of the attributes with crisp boundaries which leads to over estimation or under estimation of the boundary values. Fuzzy logic overcomes this problem by considering the natural boundaries of attributes. Genetic algorithm overcomes this crisp boundary problem by considering the entire interval of the attribute and yields optimal boundaries. Temporal data mining involves the process of extracting useful information from temporal database. The proposed method identifies these unusual climatic conditions using fuzzy logic and Genetic algorithm. Our method aims to find out the unexpected parameters(in terms of climatological variables) that causes ozone day/normal day from ozone level detection database which is of quantitative, multidimensional and time series in nature by using temporal outliers. These parameters which causes ozone day/normal day are identified by using Temporal Association Rule mining. Then the temporal association rules are pruned by the unexpectedness measure, Leverage to generate temporal outliers. Finally our proposed approach is validated through a set of experiments which show its efficiency in terms of accuracy.

Nowadays due to the rapid advances in the field of information systems, transactional databases are being updated regularly and/or periodically. The knowledge discovered from these databases has to be maintained, and an incremental updating technique needs to be developed for maintaining the discovered association rules from these databases. The concept of Temporal Association Rules has been introduced to solve the problem of handling time series by including time expressions into association rules. In this paper we introduce a novel algorithm for Incremental Mining of General Temporal Association Rules (IMTAR) using an extended TFP-tree. The main benefits introduced by our algorithm are that it offers significant advantages in terms of storage and running time and it can handle the problem of mining general temporal association rules in incremental databases by building TFP-trees incrementally. It can be utilized and applied to real life application domains. We demonstrate our algorithm and its advantages in this paper.

We developed an economic order quantity (EOQ) model with a learning effect and carbon emissions under inflationary conditions and inspection for retailers where the items deteriorate naturally. Finally, the total profit of the retailer is maximized with respect to cycle length. A sensitivity analysis was also performed to understand the robustness of the model. In the sensitivity analysis, we discuss the impact of learning rate, inflation rate, and deterioration rate on lot size and length of the cycle, as well as the retailer’s entire profit function. Observations and managerial insights are discussed. The effect of inventory parameters on the total profit is shown in the sensitivity section.

Inventory control is a very important element in the management of pharmaceutical supplies. Inventory control at Hospital “X” in Palopo City is currently using the consumption method, which has the risk of running out of medicine (stock out). Methods that can minimize this risk are the ABC, EOQ, and ROP analysis methods. The research aims to group drug items into 3 ABC groups based on investment value, find out the optimal order quantity using the economic order quantity (EOQ) calculation, and determine the time to reorder drugs using the reorder point (ROP) calculation. This type of research is non-experimental and uses mixed methods; data collection is done retrospectively for quantitative data and directly for qualitative data. The results of the analysis of the ABC method of branded generic drug included in group A were 18 types (12%) with an investment value of 70.17% of the total investment value, group B, namely 22 types (14.67%) with an investment value of 20.38 % of the total investment value, and group C drugs, namely 110 types (73.33%) with an investment value of 9.46% of the total investment value. Based on the EOQ calculation analysis, the optimal number of orders for group A branded generic drugs varied between 683 and 34 units. The results of the calculation and analysis using the ROP method show that the reorder point for group A branded generic drugs varies between 234 and 7 for each drug unit.

EOQ model with imperfect items and backorder with allowable proportionate discount using cross selling effects

The classical economic order quantity (EOQ) model assumes that items produced are of perfect quality and that die unit cost of production is independent of demand. Product quality is not always perfect but directly affected by the reliability of the production process used to produce the products. In addition, a relationship between unit production cost and demand may exist under certain circumstances. We propose an EOQ model with demand-dependent unit production cost and imperfect production processes. We formulate this inventory decision problem as a geometric program (GP) and solve it to obtain closed-form optimal solutions. An illustrative example is provided to demonstrate the point that GP has potential as a valuable analytical tool for studying a certain class of inventory control problems. We also discuss the aspect of sensitivity analysis based on the GP approach.

The COVID-19 global health pandemic significantly affected the global economy and the automotive industry when lockdown measures were implemented. This study seeks to investigate the influence of inventory forecasting and control decisions on effective inventory management in the South African automotive component manufacturing (SAACM) industry. Using a positivistic paradigm with a quantitative research approach, data was sourced from 162 Automotive Component Manufacturers (ACMs) to establish whether their inventory forecasting and control decisions changed from prior COVID-19 to the lockdown period during the COVID-19 pandemic. The multiple regression analysis found statistically significant relationships between inventory forecasting decisions and effective inventory management prior to COVID-19, inventory control decisions and effective inventory management prior to COVID-19 and inventory forecasting decisions and effective inventory management during the lockdown period. Thus, regardless of the COVID-19 pandemic, inventory managers in ACMs in South Africa (SA) should use inventory forecasting methods such as demand forecasting, economic order quantity and materials requirement planning. They should further consider using an inventory information sharing system and inventory replenishment procedure to manage inventory more effectively.

Inventory control is one of the important things of working capital in a company. Economic Order Quantity (EOQ) could be decided from the safety stock, maximum inventory, and reorder point in a company. The aim of the research was to know the number of stock and inventory cost savings. The research used EOQ method on controlling the packaging cup 240 ml and carton box to know the amount of safety stock, maximum inventory, and reorder point in PT. XYZ. The result showed that the sum of buying the material based on EOQ method in the 2015, 2016 and 2017.In the 2015 the amount f packaging cup 240ml were 1.492.031 cups, the amount f carton box were 25.164 cartons, in the 2016 the sum of packaging cup 240 ml were 1.492.084 cups, the sum of cartoon box were 24.697 cartons, while in the 2017 the sum of packaging cup were 240 cups. Using the EOQ method can be known that inventory cost savings as the result of the decreasing of the order. Safety stock were around 447.406 for a 240ml cups and 9.319 for cartons box. Maximum inventory based on EOQ method were around 992.445 for 240 ml cups and 20.549 for cartons. Reorder point based on EOQ method were around 860.066 for a 240ml cups and 17.918 for cartons.
 Keywords: EOQ, inventory control, maximum inventory, reorder point, safety stock

After studying this chapter, you should be able to: justify which costs are relevant and should be included in the calculation of the economic order quantity (EOQ); calculate the EOQ using the formula and tabulation methods; determine whether or not a company should purchase larger quantities in order to take advantage of quantity discounts; calculate the optimal safety stock when demand is uncertain; describe the ABC classification method; explain just-in-time purchasing and list the benefits arising from adopting JIT concepts.

Inventory control is important action in calculating the optimal amount of the inventory level that required. The raw materials’ supply is the one factors in the production process. The lack of raw materials can make the production process gonna be hung up, otherwise the excess of raw materials can make increasing in storage cost and other costs, This research is the application of the Economic Order Quantity (EOQ) method. It used Lilliefors Normality Test to found the normality of the data. From the calculations from the EOQ method in this study found that the total cost of raw material supplies according to Neko Neko Bakery & Cake was amounted to Rp. 9.699.331,24. While from EOQ was amounted to Rp. 6.713.210,99. And using this method, the company can saving their cost to Rp. 2.986.120,25 from the Total Inventory Cost Calculation.