International Journal of Industrial Optimization (IJIO) 

About: International Journal of Industrial Optimization (IJIO) is an academic journal published by Ahmad Dahlan University. The journal publishes majorly in the area(s): Computer science & Engineering. It has an ISSN identifier of 2714-6006. It is also open access. Over the lifetime, 16 publications have been published receiving 6 citations. The journal is also known as: IJIO.

Generating bounded solutions for multi-demand multidimensional knapsack problems: a guide for operations research practitioners

Abstract: A generalization of the 0-1 knapsack problem that is hard-to-solve both theoretically (NP-hard) and in practice is the multi-demand multidimensional knapsack problem (MDMKP). Solving an MDMKP can be difficult because of its conflicting knapsack and demand constraints. Approximate solution approaches provide no guarantees on solution quality. Recently, with the use of classification trees, MDMKPs were partitioned into three general categories based on their expected performance using the integer programming option of the CPLEX® software package on a standard PC: Category A—relatively easy to solve, Category B—somewhat difficult to solve, and Category C—difficult to solve. However, no solution methods were associated with these categories. The primary contribution of this article is that it demonstrates, customized to each category, how general-purpose integer programming software (CPLEX in this case) can be iteratively used to efficiently generate bounded solutions for MDMKPs. Specifically, the simple sequential increasing tolerance (SSIT) methodology will iteratively use CPLEX with loosening tolerances to efficiently generate these bounded solutions. The real strength of this approach is that the SSIT methodology is customized based on the particular category (A, B, or C) of the MDMKP instance being solved. This methodology is easy for practitioners to use because it requires no time-consuming effort of coding problem specific-algorithms. Statistical analyses will compare the SSIT results to a single-pass execution of CPLEX in terms of execution time and solution quality.

The best location selection using analytical hierarchy process method

Abstract: CHUUO Plain Shirt Factory is a plain shirt manufacturer founded in 2016 and is located at Kaliurang road Km 9, Yogyakarta. They not only sell plain t-shirts but also sell screen printing shirts, receive screen printing services, and orders to make collared shirts (polo). For CHUUO Plain Shirt Factory, business location has an important role in the marketing process related to reaching the customers. One method that can be used to determine the location of a new business is Analytical Hierarchy Process (AHP). This study focuses on selecting the best alternative location by considering seven criteria: geography, cost, population, risk, facilities & infrastructure, availability of human resources, and developer credibility. The research method used was observation and direct interviews using a questionnaire. The result shows that alternative location A (Shop at Gejayan Road No.30) has the highest all weight evaluation value (0.45). Alternative location B (Shop at Kaliurang Road Km 4) has a value of all weight evaluation of 0.3. Alternative location C (Shop at Magelang Road Km 7) has valued all weight evaluations the lowest (0.25). Based on the analytical research conducted, it can be concluded that alternative location A (Shop at Gejayan Road No.30) is the best location to open a branch shop for CHUUO Plain Shirt Factory.

Long term currency forecast with multiple trend corrected exponential smoothing with shifting lags

Abstract: In the current global economy, exchange rate forecasting is critical for investors and businesses seeking to make informed investment decisions and manage risk. While many short-term exchange rate forecasting methods exist, long-term forecasting methods are limited and often fail to account for the complex macroeconomic factors that influence exchange rate trends. However, investors need to have an analytically examined basis for deciding to invest, which requires knowing more about the future values of the related market currency. This paper proposes a new Multiple Trend Corrected Exponential Smoothing with Shifting Lags model to forecast long-term exchange rates, which incorporates multiple trend corrections and shifting lags to provide more accurate predictions of future currency values. We apply the proposed method to six currency pairs (USD/EUR, USD/NOK, USD/TRY, USD/CNY, USD/XOF, and USD/MGF) from 2006 to 2018 and compare its performance to existing methods, such as moving average, weighted moving average, and exponential smoothing. Our results show that the proposed model provides more accurate long-term exchange rate forecasts for developed countries than existing methods. Our findings have important implications for investors and businesses seeking to manage currency risk and make informed investment decisions in the global economy.

An estimation of distribution algorithm for combinatorial optimization problems

Abstract: This paper considers solving more than one combinatorial problem considered some of the most difficult to solve in the combinatorial optimization field, such as the job shop scheduling problem (JSSP), the vehicle routing problem with time windows (VRPTW), and the quay crane scheduling problem (QCSP). A hybrid metaheuristic algorithm that integrates the Mallows model and the Moth-flame algorithm solves these problems. Through an exponential function, the Mallows model emulates the solution space distribution for the problems; meanwhile, the Moth-flame algorithm is in charge of determining how to produce the offspring by a geometric function that helps identify the new solutions. The proposed metaheuristic, called HEDAMMF (Hybrid Estimation of Distribution Algorithm with Mallows model and Moth-Flame algorithm), improves the performance of recent algorithms. Although knowing the algebra of permutations is required to understand the proposed metaheuristic, utilizing the HEDAMMF is justified because certain problems are fixed differently under different circumstances. These problems do not share the same objective function (fitness) and/or the same constraints. Therefore, it is not possible to use a single model problem. The aforementioned approach is able to outperform recent algorithms under different metrics for these three combinatorial problems. Finally, it is possible to conclude that the hybrid metaheuristics have a better performance, or equal in effectiveness than recent algorithms.

Graphology analysis for detecting hexaco personality and character through handwriting images by using convolutional neural networks and particle swarm optimization methods

Abstract: Graphology or handwriting analysis can be used to infer the traits of the writers by examining each stroke, space, pressure, and pattern of the handwriting. In this study, we infer a six-dimensional model of human personality (HEXACO) using a Convolutional Neural Network supported by Particle Swarm Optimization. These personalities include Honesty-Humility, Emotionality, eXtraversion, Agreeableness (versus Anger), Conscientiousness, and Openness to Experience. A digital handwriting sample data of 293 different individuals associated with 36 types of personalities were collected and derived from the HEXACO space. A convolutional neural network model called GraphoNet is built and optimized using Particle Swarm Optimization (PSO). The PSO is used to optimize epoch, minibatch, and droupout parameters on the GraphoNet. Although predicting 32 personalities is quite challenging, the GraphoNet predicts personalities with 71.88% accuracy using epoch 100, minibatch 30 and dropout 52% while standard AlexNet only achieves 25%. Moreover, GraphoNet can work with lower resolution (32 x 32 pixels) compared to standard AlexNet (227 x 227 pixels).