Table of Content

30 December 2022, Volume 10 Issue 4

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Preface: Special Issue on Modeling, Simulation, and Optimization in Operational Research

Gerhard-Wilhelm Weber, J. Joshua Thomas, José Antonio Marmolejo Saucedo, Ugo Fiore, Igor Litvinchev, Pandian Vasant

2022, 10(4):  685-688.  doi:10.1007/s40305-021-00363-8

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Online Scheduling on Two Parallel Identical Machines Under a Grade of Service Provision

Shuang Cai, Ke Liu

2022, 10(4):  689-702.  doi:10.1007/s40305-020-00325-6

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In this paper, we investigate online scheduling problems on two parallel identical machines under a grade of service provision with makespan as the objective function. The jobs arrive over time and each job can be scheduled only when it has already been arrived. We consider three different versions: (i) the two machines cannot be idle at the same time until all arrived jobs have been processed; (ii) further to the first problem, jobs are processed on a first-come, first-serviced basis; (iii) each job must be assigned to one of the two machines as soon as it arrives. Respectively for three online scheduling problems, optimal online algorithms are proposed with competitive ratio ($\sqrt{5}$ + 1)/2, ($\sqrt{5}$ + 1)/2 and 5/3.

Chaotic Simulator for Bilevel Optimization of Virtual Machine Placements in Cloud Computing

Timothy Ganesan, Pandian Vasant, Igor Litvinchev

2022, 10(4):  703-723.  doi:10.1007/s40305-020-00326-5

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The drastic increase in engineering system complexity has spurred the development of highly efficient optimization techniques. Many real-world optimization problems have been identified as bilevel/multilevel as well as multiobjective. The primary aim of this work is to present a framework to tackle the bilevel virtual machine (VM) placement problem in cloud systems. This is done using the coupled map lattice (CML) approach in conjunction with the Stackelberg game theory and weighted-sum frameworks. The VM placement problem was modified from the original multiobjective (MO) problem to an MO bilevel formulation to make it more realistic albeit more complicated. Additionally comparative analysis on the performance of the CML approach was carried out against the particle swarm optimization method. A new bilevel metric called the cascaded hypervolume indicator is introduced and applied to measure the dominance of the solutions produced by both methods. Detailed analysis on the computational results is presented.

Analytics of an Imperfect Four-Layer Production Inventory Model Under Two-Level Credit Period Using Branch-and-Bound Technique

Subrata Panja, Shyamal Kumar Mondal

2022, 10(4):  725-748.  doi:10.1007/s40305-020-00300-1

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This paper explains an integrated production inventory supply chain model, which consists of a supplier, a manufacturer and a retailer under two-level credit period. One is manufacturer’s credit period offered by the supplier, and other is retailer’s credit period offered by the manufacturer. Here, the manufacturer replenishes raw materials from the supplier and produces non-deteriorating products in some cycles of equal length. It is noted that here four inventory structures have been considered to show the flow of the materials as either raw materials or finished products from supplier to retailer via manufacturer. Also in this model, manufacturer’s imperfect production has been considered. Here imperfect items are not repairable. The retailer receives good finished products from the manufacturer and sells these to his/her customers during some cycles of equal length in total time horizon. Our main objective is to find the optimal number of production and business cycles of the manufacturer such that the integrated system can get the maximum profit. The above discussed model is elaborated with the help of a numerical example, and a sensitivity analysis is done with respect to some parameters used in this model.

A Hybrid of Grey Wolf Optimization and Genetic Algorithm for Optimization of Hybrid Wind and Solar Renewable Energy System

Diriba Kajela Geleta, Mukhdeep Singh Manshahia

2022, 10(4):  749-762.  doi:10.1007/s40305-021-00341-0

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In this paper, a hybrid of grey wolf optimization (GWO) and genetic algorithm (GA) has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system. It was named as hybrid of grey wolf optimization and genetic algorithm (HGWOGA). HGWOGA was applied to this hybrid problem through three procedures.First,thebalancebetweentheexplorationandtheexploitationprocesswas done by grey wolf optimizer algorithm. Then, we divided the population into subpopulation and used the arithmetical crossover operator to utilize the dimension reduction and the population partitioning processes. At last, mutation operator was applied in the whole population in order to refrain from the premature convergence and trapping in local minima. MATLAB code was designed to implement the proposed methodology. The result of this algorithm is compared with the results of iteration method, GWO, GA, artificial bee colony (ABC) and particle swarm optimization (PSO) techniques. The results obtained by this algorithm are better when compared with those mentioned in the text.

An Alternative Approach to Rank Efficient DMUs in DEA via Cross-Efficiency Evaluation, Gini Coefficient, and Bonferroni Mean

Zahra Behdani, Majid Darehmiraki

2022, 10(4):  763-783.  doi:10.1007/s40305-019-00264-x

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In this paper, we focus on a critical problem in data envelopment analysis (DEA) and propose a simple resolution for it. The major problem of the DEA is the existence of several efficient decision-making units (DMUs). To deal with this issue, we introduce a method that involves cross-efficiency evaluation, Gini coefficient, and Bonferroni mean. First, a cross-efficiency matrix is developed. Then, mixing the Gini coefficient and Bonferroni mean, a Gini–Bonferroni (GB) index is proposed for ranking efficient DMUs, where the DMUs with bigger GB are ranked higher. The proposed method broke the tie between efficient DMUs. Finally, a numerical example and real application of this method are presented in the ranking of research and development (R&D) investment companies in the pharmaceutical and biotechnology industries.

On Equilibrium in a Constant Retrial Queuing System with Reserved Time and Vacations

Lin-Lin Wang, Li-Wei Liu, Xu-Dong Chai, Zhen Wang

2022, 10(4):  785-800.  doi:10.1007/s40305-019-00290-9

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This paper investigates an M/M/1 constant retrial queue with reserved time and vacations. A new arriving customer will take up the server and accept service immediately if the server is idle. Otherwise, if the server is busy or on vacation, customers have to join a retrial orbit and wait for retry. Once a service is completed, the server will reserve a random time to seek a customer from the orbit at a constant retrial rate. If there is no arrivals (from the orbit or outside) during the idle period, to save energy, the server will take a vacation. This paper studies the fully unobservable case. First, the steady-state condition of the system is analyzed by using the Foster’s criterion, and the customers’ expected waiting time is obtained based on the generating function technique. And then, by introducing an appropriate revenue structure, the equilibrium strategies of customers and the socially optimal strategy are all derived. Furthermore, a comparison between them is made and the effect of some main system parameters is studied.

TKES: A Novel System for Extracting Trendy Keywords from Online News Sites

Tham Vo, Phuc Do

2022, 10(4):  801-816.  doi:10.1007/s40305-020-00327-4

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As the Smart city trend especially artificial intelligence, data science, and the internet of things has attracted lots of attention, many researchers have created various smart applications for improving people’s life quality. As it is very essential to automatically collect and exploit information in the era of industry 4.0, a variety of models have been proposed for storage problem solving and efficient data mining. In this paper, we present our proposed system, Trendy Keyword Extraction System (TKES), which is designed for extracting trendy keywords from text streams. The system also supports storing, analyzing, and visualizing documents coming from text streams. The system first automatically collects daily articles, then it ranks the importance of keywords by calculating keywords’ frequency of existence in order to find trendy keywords by using the Burst Detection Algorithm which is proposed in this paper based on the idea of Kleinberg. This method is used for detecting bursts. A burst is defined as a period of time when a keyword is continuously and unusually popular over the text stream and the identification of bursts is known as burst detection procedure. The results from user requests could be displayed visually. Furthermore, we create a method in order to find a trendy keyword set which is defined as a set of keywords that belong to the same burst. This work also describes the datasets used for our experiments, processing speed tests of our two proposed algorithms.

Optimization Problem for the Integral Model of Developing Systems

Evgeniia Markova, Inna Sidler, Victor Trufanov

2022, 10(4):  817-833.  doi:10.1007/s40305-020-00302-z

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This paper addresses an integral model of development a large electric power system using the example of the Unified Energy System of Russia. The model takes account of the age structure of the plants main equipment. Besides, generating equipment is divided into components depending on the types of energy resources used. The mathematical model is presented by a system of nonclassical Volterra integral equations of the first kind. One of the equations describes the balance between the total available capacity of the electric power system, commissioning of new equipment, and dismantling of obsolete one. The other equations define the shares of different types of power plants in the total composition of the electric power system equipment. Based on the considered model, we set a problem that searches the optimal lifetimes of electric power system capacities for a given demand for electricity. The optimality criterion is a functional reflecting cost of commissioning and operating the capacities. The specifics of the optimization problem are that the optimization parameter is in the lower limit of integration. An algorithm for solving of this optimal control problem numerically is developed. The influence of economic indices on the solution to the optimal control problem is studied. Calculations of the optimal development of the Unified Energy System of Russia until 2050 are carried out using real-life data.

SMS Encryption and Decryption Using Modified Vigenere Cipher Algorithm

B. Bazeer Ahamed, Murugan Krishnamoorthy

2022, 10(4):  835-848.  doi:10.1007/s40305-020-00320-x

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Despite many online messaging services, short message service (SMS) is still used because of its simplicity. However, such services can be easily cracked, making SMS more vulnerable to send confidential information. Therefore, appropriate cryptographic algorithms must be applied to ensure security. One of the most common cryptographic techniques used for encrypting and decrypting messages is the Vigenere cipher. However, Vigenere cipher can be cracked easily because the previous approach involved 26×10 matrixes, and the key comprised of alphabets. If the key length is small, then it becomes easier to crack the plain text by permutation. So here in this work, we have proposed a modified Vigenere cipher technique to enhance its security standards. Existing Vigenere cipher attack is not more secured, and thus we developed a method that improves the security in Vigenere cipher using Rivest-Shamir-Adleman (RSA). RSA is an asymmetric algorithm that has public and private keys. The advantage of using this algorithm is that finding factors for the large composite numbers is difficult. This method is secured but time consuming. In this proposed method, we combined RSA with Vigenere cipher so that the proposed algorithm is secured and consumes less time than RSA algorithm. This approach is widely used to encrypt SMS.

Automatic Identification Fingerprint Based on Machine Learning Method

Long The Nguyen, Huong Thu Nguyen, Alexander Diomidovich Afanasiev, Tao Van Nguyen

2022, 10(4):  849-860.  doi:10.1007/s40305-020-00332-7

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The fingerprint identification technology has been developed and applied effectively to security systems in financial transactions, personal information security, national security, and other fields. In this paper, we proposed the development of a fingerprint identification system based on image processing methods that clarify fingerprint contours, using machine learning methods to increase processing speed and increase the accuracy of the fingerprint identification process. The identification system consists of the following main steps: improving image quality and image segmentation to identify the fingerprint area, extracting features, and matching the database. The accuracy of the system reached 97.75% on the mixed high- and low-quality fingerprint database.

Multiple Cross-docks Scheduling with Multiple Doors using Fuzzy Approach and Metaheuristic Algorithms

Mitra Movassaghi, Soroush Avakh Darestani

2022, 10(4):  861-911.  doi:10.1007/s40305-021-00362-9

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The issue of supply chain in today’s world is a major competitive advantage in reducing costs. Supply chain includes procurement, logistics and transportation, marketing, organizational behavior, networking, strategic management, information systems management and operations management. One of the most important practices in logistics is cross-docking which sets its goals as inventory reduction and customer satisfaction increase. Customers receive goods through docks. Docks are responsible to provide a place for goods before being delivered to the customers. Then, these materials are directly loaded into outbound trucks with little or no storage in between to send to customers in the shortest possible time. This paper is mainly aimed at introducing a mixed integer linear programming model to solve scheduling several cross-docking problems. The proposed model is highly facilitated to allocate the optimal destinations to storage doors and truck scheduling in docks while selecting the collection and delivery routes. Using optimization approaches at uncertainty conditions is also of great importance. Mathematical programming techniques vividly fail to solve transportation problems that include fuzzy objective function coefficients. A fuzzy multi-objective linear programming model is proposed to solve the transportation decision-making with fuzzy objective function coefficients in this paper. On the other hand, the existences of computational complexities lead this model to be categorized as a NP-Hard one. Therefore, we applied metaheuristic algorithms such as genetic and ant colony in order to solve our proposed problem.