Articles

Warehouse Location Optimization with Clustering Analysis to Minimize Shipping Costs in Indonesia’s E-Commerce Case

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Due to the growth of the Internet economy, the popularity of online shopping has escalated in recent years. One of the largest e-commerce enterprises in Indonesia, PT. S, is the subject of the research in this article. Instead of typical e-commerce, where anybody may start a store, PT. S is concentrating on social commerce, which makes use of several resellers to offer hand-picked SME brand partners. PT. S must expand the market for inter-island or non-java-to-non-java transactions to fulfill its vision. However, PT. S will have logistical difficulty completing this job. The business used performance indicators to keep track of the logistics process’ vision and mission. Gross merchandise value, pickup time service level, and shipping time service level are a few of the performance indicators that pertain to logistics. The process of managing the supply chain will become more complex as a result of the opening of the new warehouse, and the business will need to maximize its use of various selling channels, logistical services, and supply chain management. With the aid of clustering analysis, which assesses demand similarity and proximity, the enterprise can locate a new warehouse. Durairaj and Kasinathan developed the framework template for this study in 2015. Based on the case study, literature review, and clustering method framework, the framework will be modified in several ways, particularly clustering analysis. The alteration concerns framework-integrated theories as an input and as a data source. According to the simulation’s findings, shipping costs per kilogram decreased by about 35% for five clusters. But if the corporation does not have a problem with the number of warehouses, according to the simulation’s findings, because the cost of transportation will go down as the number of clusters increases, the number of warehouses can be expanded to more than five.

Solving A ML Problem Using The Grossone

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Machine learning (ML) has grown at a remarkable rate, becoming one of the most popular research directions. It is widely applied in various fields, such as machine translation, speech recognition, image recognition, recommendation system, etc. Optimization  problems lie at the heart of most machine learning approaches. So, the essence of most ML algorithms is to build an optimization model and learn the parameters in the objective function from the given data. A series of effective optimization methods were put forward, in order to promote the development of ML. They have improved the performance and efficiency of ML methods. The aim of this paper is to show that, among many other fields, the grossone may be used successfully in the ML. The grossone, the infinite unit of measure, has been proposed by Professor Y. Sergeyev in a number of noticeable works, as the number of elements of the set, N, of natural numbers. It is expressed by the numeral . This new computational methodology would allow one to work with infinite and infinitesimal quantities in the ―same way‖ as that working with finite numbers  More details about it are given in Section 4. We analyze the SVM from the viewpoint of mathematical programming, solving a numerical example using the grossone. The Iris dataset was chosen for the implementation of the support vector method. This is a wellknown set of data used in the area of ML.

Multi-Objective Optimization of Manufacturing Lot Size Under Stochastic Demand

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In many manufacturing problems, multi-objective optimizations are representative models, as objectives are considered a conflict with one another. In real-life applications, optimizing a specific solution concerning one objective may end up in unacceptable results concerning the other objectives. Many Manufacturing companies operate under uncertainties and this affects the system performance. Stochastic product demand is one of the challenges faced by manufacturing companies and often affects the manufacturing system’s performance and decision-making. Making the proper decisions regarding manufacturing lot-sizing problems is critical for any manufacturer because it makes the firm compete within the market. In this paper, Markov chains in conjunction with stochastic goal programming were used to develop an optimization model for the manufacturing lot size. The over-achievement or under-achievement of the manufacturing lot size was determined by defining the goal constraints, deviation variables, priorities, and objective function. The different states of demand for the product with stochastic demand were represented by states of a Markov chain. Using the applied mathematics solver in MATLAB TM, the optimization model was then solved, determining the quantity of product to be manufactured in a given quarter of the year as demand changes from one state to another.