A dai-liao hybrid conjugate gradient method for unconstrained optimization

One of todays’ best-performing CG methods is Dai-Liao (DL) method which depends on non-negative parameter and conjugacy conditions for its computation. Although numerous optimal selections for the parameter were suggested, the best choice of remains a subject of consideration. The pure conjugacy condition adopts an exact line search for numerical experiments and convergence analysis. Though, a practical mathematical experiment implies using an inexact line search to find the step size. To avoid such drawbacks, Dai and Liao substituted the earlier conjugacy condition with an extended conjugacy condition. Therefore, this paper suggests a new hybrid CG that combines the strength of Liu and Storey and Conjugate Descent CG methods by retaining a choice of Dai-Liao parameterthat is optimal. The theoretical analysis indicated that the search direction of the new CG scheme is descent and satisfies sufficient descent condition when the iterates jam under strong Wolfe line search. The algorithm is shown to converge globally using standard assumptions. The numerical experimentation of the scheme demonstrated that the proposed method is robust and promising than some known methods applying the performance profile Dolan and Mor´e on 250 unrestricted problems. Numerical assessment of the tested CG algorithms with sparse signal reconstruction and image restoration in compressive sensing problems, file restoration, image video coding and other applications. The result shows that these CG schemes are comparable and can be applied in different fields such as temperature, fire, seismic sensors, and humidity detectors in forests, using wireless sensor network techniques.

Analysis of marketing strategy at setia stores using ahp, clustering, and ar-mba method

A company can survive and thrive when the strategies and processes applied in its business are correct. One of the processes in determining strategy in decision making. The owner of Setia Store has difficulty in choosing a marketing strategy. The product layout shows this in the Setia Store, which confuses customers. Setia Store also rarely offers a promotion, making it difficult to compete with competitors. This study aims to help Setia Store increase sales by determining the right marketing strategy. To determine the right marketing strategy, there are three methods developed. First of all, the analytical hierarchy process (AHP) is employed to find the customer priorities. Then, clustering is proposed to find potential marketing targets that have similar characteristics from the results of the AHP method. Third, association rule-market basket analysis (AR-MBA) is developed to find the best rules for product marketing strategy. The first method shows that the housewives (EV=0.6270) are Setia Store's priority customers from the three methods. Second, cluster 3 (which has three characteristics in common) is a very potential target market. Third, the best rule is to buy products from departments 2 and 3 (Confidence 60%, Support 12%). From these results, the right marketing strategy is to create a buy 1 get 1 promo banner or label for products that are rarely purchased, such as household appliances. Then, create a catalog by bringing together frequently purchased products such as spices and food ingredients. Finally, improve the layout by bringing the departmental shelves closer to frequently purchased products.

Ceramic supplier selection using analytical hierarchy process method

This study tried to implement the Analytical Hierarchy Process (AHP) and the weights of the criteria and sub-criteria to find the best supplier. According to QCDFR (quality, cost, delivery, flexibility, and responsiveness). This study took place in one of the biggest tile producers, ranks fifth in the world and the first in Indonesia. However, the company currently only uses quality, cost, and delivery methods to choose the best supplier of raw material, namely feldspar. This research tries to use the systematic method to find the best supplier based on the importance of the criteria. The method used the quantitative approach to enumerate the data to analyze the information. The company analyzed six suppliers. The primary tool used in this research is a Super Decision Software version 3.2 to create and manage the AHP model, enter the judgments, get results, and perform sensitivity analysis on the results. The result found that Semarang is the best supplier. The company will choose Semarang to become the company's business partner compared to the other suppliers because Semarang has met the criteria that the company prioritizes the most. By having the best supplier selection, the company can provide the right material consistency and suitable material suitability.

Multi-item inventory policy with time-dependent pricing and rework cost

The price of broiler chickens at the consumer level varies daily. The price can be very low or otherwise. The price has resulted from the imbalance between the availability of chicken from suppliers and the market demand. As a result, demand will also fluctuate because it is influenced by consumer purchasing power. When the price of live chickens is low, the carcass company will usually buy in large quantities and expect to sell them at a higher price. The problem arises when the chicken overstock company will risk product damage due to product buildup in the refrigerated warehouse, so rework is necessary. In this paper, we will be developed a multi-item inventory model that considers material prices that vary to time, probabilistic demand, and rework costs. The aim is to determine the right policy for controlling frozen chicken products' inventory to minimize losses and total inventory costs. This model can evaluate the best time to order broiler chickens, how much to order, how long the interval between orders, and the optimal number of orders, resulting in minimum total inventory cost per period. The model solution is carried out with an optimization approach based on the parameters that affect the model. A numerical example is given at the end of this paper for model validation and illustrates the model solving algorithm.

Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

In the era of globalization, the internet has become a human need in doing various things. Many internet users are an opportunity for internet service providers, PT Telekomunikasi Indonesia (Telkom). One of PT Telkom's products is IndiHome. As the only state-owned enterprise engaged in telecommunications, PT Telkom is expected to meet the needs of the Indonesian people. However, based on the rating obtained by IndiHome products through the myIndiHome application on Google Play, it is 3.5 out of 87,000 more reviews. The reviews focus on how important the effect of word-of-mouth is on choosing and using internet provider products. The review data was collected on November 1, 2020 to December 15, 2020, with a total of 2,539 reviews as a sample. The sentiment analysis process that has been carried out shows that the number of reviews included in the negative sentiment class was 1.160 reviews, and the positive class was 1.374 reviews out of a total of 2,539 reviews. The results indicate that service errors in IndiHome services are still quite high, reaching 46.7% as indicated by the number of negative reviews. The classification results show that the average value of the total accuracy of the Support Vector Machine (SVM) method is 86.54% greater than Naïve Bayes Classifier (NBC) method which has an average total accuracy of 84.69%. Based on fishbone diagram analysis, there are 12nd problems on negative reviews that classify problems 5P factors: Price, People, Process, Place, and Product.

Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

The strength of the company's competitiveness is needed because the current industrial development is very rapid. It is necessary to maintain the quality and quantity of the products produced according to company standards. One of the companies that must maintain the quality and quantity is PT. XYZ is a clay tile company. The classification of products used by this company to maintain good quality is three classes: good tile, white stone tile, and cracked tile. However, quality control based on classification still uses the traditional way by relying on sight. It can increase errors and slow down the process. It can be overcome with artificial visual detectors. It is a result of the rapid development of automation. So to detect defects, this research can use image preprocessing, supervised learning algorithms, and measurement methods. Support Vector Machine (SVM) is used in this study to perform classification, while feature extraction on clay tiles used the Local Binary Pattern (LBP) method. The algorithm is made using python, while for image retrieval, raspberry pi is used. The linear kernel on the SVM algorithm is used in this study. The conclusion in this study obtained 86.95% is the highest accuracy with a linear kernel. It takes 10.625 seconds to classify.

Marketing strategy planning at alfamart lodadi stores using the clustering, ahp, and ar-mba method

Nowadays, people are very facilitated by the existence of various shopping centers, including retail. Because many retailers are close to each other, Alfamart Lodadi must have a good marketing strategy. So far, the strategy used is sometimes inaccurate because it is not based on customer segmentation. Therefore, the purpose of this research is to help retail owners to make decisions regarding the right marketing strategy with three methods so that Alfamart Lodadi can compete and increase sales. The Analytical Hierarchy Process (AHP) is employed to find the priority variables of customer segmentation; meanwhile, the K-Means Clustering is used to group customers based on the similarity of predetermined characteristics. AR-MBA is used to find out the best rules, and products are rarely, sufficient, and frequently purchased. The results of this research, based on AHP, obtained five segmentation priority variables based on the largest eigenvector values. There are income, age, expenditure, distance, and shopping intensity with each eigenvector value of 0.13; 0.16; 0.12; 0.12; 0.17. From clustering, there are three customer clusters with different strategies, including free shipping when shopping, product discounts for certain periods, and providing catalogs and discounts on each transaction and offer notifications. Then, this research also obtained three strategies based on AR-MBA. These include making a catalog by bringing frequently purchased products closer together, choosing a layout for shopping places by bringing frequently purchased products closer together, and making shopping coupons for rarely purchased products. With several strategic choices, companies can make decisions appropriately according to the desired criteria.

A Dai-Liao Hybrid Hestenes-Stiefel and Fletcher-Revees Methods for Unconstrained Optimization

Some problems have no analytical solution or too difficult to solve by scientists, engineers, and mathematicians, so the development of numerical methods to obtain approximate solutions became necessary. Gradient methods are more efficient when the function to be minimized continuously in its first derivative. Therefore, this article presents a new hybrid Conjugate Gradient (CG) method to solve unconstrained optimization problems. The method requires the first-order derivatives but overcomes the steepest descent method’s shortcoming of slow convergence and needs not to save or compute the second-order derivatives needed by the Newton method. The CG update parameter is suggested from the Dai-Liao conjugacy condition as a convex combination of Hestenes-Stiefel and Fletcher-Revees algorithms by employing an optimal modulating choice parameterto avoid matrix storage. Numerical computation adopts an inexact line search to obtain the step-size that generates a decent property, showing that the algorithm is robust and efficient. The scheme converges globally under Wolfe line search, and it’s like is suitable in compressive sensing problems and M-tensor systems.

Designing project schedule using crashing method to compress the fiber to the home project schedule

ABC Company is an agent of network construction, operation, and maintenance. ABC Company is currently implementing the STTF (Shit to the Front) project, which is the project to add FTTH (Fiber to the Home) networks in areas that can have high customer demand. One of the STTF project construction sites is the Indra Prahasta II housing location. However, the project is currently experiencing work delays due to the Covid-19 disaster in Indonesia. Delays in project execution can result in potential prospects choosing another company that provides similar services. The project schedule can be accelerated using the crashing method and TCTO (Time Cost Trade-Off) analysis to solve this problem. This research's acceleration will be carried out with alternatives for adding 3 hours, 2 hours, 1 hour, and an alternative to increasing workers' number. This project has an average duration of 55 working days with a total cost of Rp 604,124,460. The results obtained from data processing, on the alternative of adding 1 hour of overtime work, the total duration becomes 54 working days with total project cost is Rp 605,734,138. In addition to 2 hours of overtime work, the project's total duration can be reduced to 54 days with a total project cost Rp 606,803,619. And for the addition of 3 hours overtime, the total duration can be shortened to 54 days with a total cost of Rp 606,803,619. As for increasing the number of workers, project work duration can be shortened to 54 working days with a total project cost Rp 604,556,748

Using machine learning to predict the number of alternative solutions to a minimum cardinality set covering problem

Although the characterization of alternative optimal solutions for linear programming problems is well known, such characterizations for combinatorial optimization problems are essentially non-existent. This is the first article to qualitatively predict the number of alternative optima for a classic NP-hard combinatorial optimization problem, namely, the minimum cardinality (also called unicost) set covering problem (MCSCP). For the MCSCP, a set must be covered by a minimum number of subsets selected from a specified collection of subsets of the given set. The MCSCP has numerous industrial applications that require that a secondary objective is optimized once the size of a minimum cover has been determined. To optimize the secondary objective, the number of MCSCP solutions is optimized. In this article, for the first time, a machine learning methodology is presented to generate categorical regression trees to predict, qualitatively (extra-small, small, medium, large, or extra-large), the number of solutions to an MCSCP. Within the machine learning toolbox of MATLAB®, 600,000 unique random MCSCPs were generated and used to construct regression trees. The prediction quality of these regression trees was tested on 5000 different MCSCPs. For the 5-output model, the average accuracy of being at most one off from the predicted category was 94.2%.