Goal Programming Model Research Articles

Minimizing production cost for kendang djembe production through goal programming model

Kendang djembe is a percussion instrument played by striking with the fingers and palms. The body of the kendang djembe is generally made of wood and shaped like a cup or mug, carved either by machine or traditionally only by hand. This problem affects the production costs, the employee working house, and the profit of kendang djembe. Customer orders and requirements determine the production process for kendang djembe. It leads to fluctuations in market demand, affecting production costs and the working hours of kendang djembe employees. As a result, employees work overtime when orders rush in, resulting in poor product finishes such as crude engraving and painting. This research aims to minimize the production cost of kendang djembe, maximize the employee working hours, and maximize the profit by using the goal programming method. Goal programming is applied to decide the number of kendang djembe, the minimum production cost, and the time for each kendang djembe. The result of this research is that CV. Maharani Abadi has to make 237 units of kendang djembe paintings, 1266 kendang djembe carvings, 870 kendang djembe painting carvings, and 91 kendang djembe deep carvings. CV. Maharani spent a production cost of Rp 399,413,400, with the employee working 1846.36 hours, and obtained a maximum profit of Rp 126,526,600. This research helps the company to avoid unprofitable options in the production process of kendang djembe.

ANALISIS OPTIMISASI DENGAN MENGGUNAKAN METODE GOAL PROGRAMMING TANPA PRIORITAS TERHADAP LAPORAN KEUANGAN BANK (STUDI KASUS: PT BANK NATIONAL NOBU TBK)

Periodically, Nobu Bank releases an annual financial report that describes the company's financial condition. The financial statements contain elements of financial statements, namely assets, liabilities, equity, income, and financial expenses. To analyze the optimization, the five elements of the financial statements are then expressed in a mathematical model as a constraint function to be processed using the Goal Programming Method. The Goal Programming method was chosen because it is considered a mathematical model that can be used to obtain solutions to problems that have many goals. After all, the deviation variable in the Goal Programming model can produce complete information on the relative achievement of existing goals so that optimal results can be achieved. The first step of the Goal Programming Method is to determine the decision variables, then determine the objective function, then determine the constraint function. The analysis and solution were carried out using the Simplex Method and validated with the LINGO. The results of the sensitivity analysis show that the five objectives have been achieved, namely, there are no deviations. This shows that the performance of the financial statements at Bank Nobu is optimal. However, two objectives can still be optimized, such as the value of liabilities can still be reduced by Rp.593,731.14 per year, and the value of financial expenses can still be reduced by Rp.30,599.66 per year.

Design of humanitarian supply chain system by applying the general two-stage network DEA model

PurposeThe recent COVID-19 outbreak and severe natural disasters make the design of the humanitarian supply chain network (HSCN) a crucial strategic issue in a pre-disaster scenario. The HSCN design problem deals with the location/allocation of emergency response facilities (ERFs). This paper aims to propose and demonstrate how to design an efficient HSCN configuration under the risk of ERF disruptions.Design/methodology/approachThis paper considers four performance measures simultaneously for the HSCN design by formulating a weighted goal programming (WGP) model. Solving the WGP model with different weight values assigned to each performance measure generates various HSCN configurations. This paper transforms a single-stage network into a general two-stage network, treating each HSCN configuration as a decision-making unit with two inputs and two outputs. Then a two-stage network data envelopment analysis (DEA) approach is applied to evaluate the HSCN schemes for consistently identifying the most efficient network configurations.FindingsAmong various network configurations generated by the WGP, the single-stage DEA model does not consistently identify the top-ranked HSCN schemes. In contrast, the proposed transformation approach identifies efficient HSCN configurations more consistently than the single-stage DEA model. A case study demonstrates that the proposed transformation method could provide a more robust and consistent evaluation for designing efficient HSCN systems. The proposed approach can be an essential tool for federal and local disaster response officials to plan a strategic design of HSCN.Originality/valueThis study presents how to transform a single-stage process into a two-stage network process to apply the general two-stage network DEA model for evaluating various HSCN configurations. The proposed transformation procedure could be extended for designing some supply chain systems with conflicting performance metrics more effectively and efficiently.

A Goal Programming Model for Dispatching Trucks in an Underground Gold Mine

The cost of transporting mined materials in an underground mine is major. This cost typically represents between 50 to 60 percent of a mine’s total operating costs. The problem of dispatching trucks in an underground gold mine is of major economic importance and warrants the use of a decision support model. The developments of a realistic decision-support model for the dispatching problem in an underground gold mine were addressed in this paper. The problem must address multiple conflicting objectives, and therefore, a goal programming model was formulated. The model was applied to a case study, the Red Lake underground gold mine, in Ontario, Canada. The results showed major improvements in meeting the multiple objectives of this problem versus a single objective model. The results also illustrate the flexibility that the dispatching problem (in underground gold mines) yields when solved for multiple objectives using a goal programming model.

Generative Text Secret Sharing with Topic-Controlled Shadows

Secret image sharing has been extensively and thoroughly researched. However, in the social network environment, shadow images are subject to compression or noise pollution during uploading and transmitting, which makes it challenging to recover secrets losslessly. Texts are more suited for transmission in social networks as shadows because of the broad variety of application scenarios and inherent robustness. Through a secret sharing technique of k , n threshold, a secret is encrypted as n shadows, where any k or more shadows can recover the secret, while less than k cannot obtain any information on the secret. In this article, we propose a generative text secret sharing scheme with topic-controlled shadows, which encrypts a secret message as a number of semantically natural shadow texts and controls the topics of shadow texts using bag-of-words models during text generation by the language model. This study also proposes two goal programming models to improve the shadow texts’ topic relevance and fluency. The shadow texts of the proposed scheme satisfy loss tolerance, semantic comprehensibility, topic controllability, and robustness. An ablation study, comparative test, and anti-detection experiment verify the effectiveness of the proposed scheme.

Goal programming duality revisited: Formulation of a set of variant duals

The formulation of the duals of the original goal programming variants took place in the decades following its introduction, principally for computational purposes. However the development of more recent Goal Programming variants has not been matched by the formulation and analysis of their associated duals. This paper furthers the topic of goal programming duality by formulating the duals of a progressive sequence of up to the most recent Goal Programming models: Weighted, Chebyshev, Extended and Extended Network Goal Programming models. It interprets the results and shows the insights that the duals can provide, principally by providing an understanding of the interactions between the multiple objectives in each case. The paper concludes with a comparison of the results and shows how the sequence of duals mirrors the sequence of the associated primals.

Research on raw material ordering and transportation scheme of production enterprises based on genetic algorithm

With the deepening of China's reform and opening up and the gradual improvement of productivity level,the production enterprises of construction and decoration panels have gradually sprung up. In order to improve the production efficiency of enterprises and reduce the loss of raw materials, this paper establishes an analytical hierarchical model, and a 0-1 goal programming model. Based on the analysis of the problem, it pays attention to the connection between the questions. A rational solution for ordering and transporting raw materials based on the 0-1 goal programming model is also proposed in conjunction with real-life practice. In this paper, the problem is solved by building several mathematical models and the results are entered in Annex A and Annex B. After the model is implemented and tested, significant improvements are found. In addition, the model is improved by a genetic algorithm so that the model is well applicable and generalizable.

OPTIMASI PENJADWALAN PERAWAT DI RUANG UGD RSUD LAPANGAN SAWANG MENGGUNAKAN METODE NON-PREEMPTIVE GOAL PROGRAMMING

The emergency room which requires a full 24-hour nurse certainly needs to pay attention to the right schedule. To get an optimal schedule, it is necessary to divide nurses according to the needs of the right shift. Schedules that are made manually often violate or pay less attention to the applicable rules. Based on the existing rules in the hospital, constraints are divided into main constraints and additional constraints which are modeled and solved using the Non-Preemptive Goal Programming method with the objective function being to minimize the advantages or disadvantages of additional constraints. Furthermore, for the computational process assisted by the LINGO 19.0 application. From the results and discussion, it is found that the optimal schedule using the Non-Preemptive Goal Programming model is fulfilled for every existing constraint.
 Keywords: Schedule, Nurse, Goal Programming, Non-Preemptive Goal Programming

A novel intuitionistic fuzzy goal programming method for heterogeneous MADM with application to regional green manufacturing level evaluation under multi-source information

The existed regional green manufacturing level assessment methods pay little attention to the multi-source information such as the expert’s psychological preference information, the preference information among regions, the heterogeneous information of regions on attributes and the interactions information between attributes at the same time, which is as a heterogeneous multi-attribute decision making (MADM) problems. A novel intuitionistic fuzzy goal programming is proposed for HMADM problems under multi-source information. The Choquet integral-based relative ratio is proposed on the basis of the Choquet integral measure. It is far away from the negative-ideal solution (NIS) and closeness to the positive-ideal solution (NIS) simultaneously with the interactions between attributes. Hereby the consistency and inconsistency indices with expert’s hesitation degrees are defined, the attribute weights are solved by using the developed intuitionistic fuzzy goal programming models with the optimistic, pessimistic and mixed methods. The Choquet integral-based relative ratios of alternatives can be calculated to generate their ranking orders. Finally, a regional green manufacturing level assessment example and compared with existed methods show that the superiority and validity of the proposed method, which improves the intuitionistic fuzzy goal programming methods, LINMAP methods and MADM methods with the interactions between attributes.

Multiplicative Consistency Ascertaining, Inconsistency Repairing, and Weights Derivation of Hesitant Multiplicative Preference Relations

This article investigates multiplicative consistency ascertaining, inconsistency repairing, and weights derivation for hesitant multiplicative preference relations (HMPRs). First, the completely multiplicative consistency and weakly multiplicative consistency of HMPRs are defined. Based on them, 0–1 mixed programming models and simple algebraic operations are proposed to ascertain the multiplicative consistency of HMPRs. Then, some goal programming models are developed to generate the weights from consistent HMPRs and to revise inconsistent HMPRs. An integrated procedure to manage the multiplicative consistencies of HMPRs is designed. The proposed methods are also extended to accommodate incomplete HMPRs, and to estimate missing values. Finally, some numerical examples, a comparative analysis with existent approaches, and a simulation analysis are included to illustrate the practicality and effectiveness of the developed models.

Goal Programming Approach For Frozen Food Production Planning

This paper examines the Goal Programming (GP) approach in food production planning in order to further enhance and find better solutions. The objective of this paper is to determine the optimum level of frozen food production for small and medium enterprise (SME). Azali Frozen Food, a small and medium enterprise located in Penang was selected as it can produce a range of frozen foods throughout the country. The problem is handled through Lexicographic Goal Programming. The results are compared to the available data that was given and other findings were reviewed. The findings of this paper are expected to assist community small and medium enterprise and other decision makers involved in production planning. The developed method will also be of use for those who are interested in the model of goal programming to solve complex planning issues involving uncertain parameters.

Multicriteria decision making and goal programming for determination of electric automobile aimed at sustainable green environment: a case study.

In this paper, we consider the problem of automobile selection for transportation in inner city using a hybrid multicriteria decision making approach. The electric automobiles that are a relatively new concept in the world of the automotive industry, are widely viewed as attractive among its alternatives day by day. Fuel-vehicles produce a lot of carbon emissions that are ejected into our natural atmosphere, leaving us vulnerable to things like pollution and greenhouse gases. So, electric vehicle and automobiles have emerged as a more efficient alternative and these vehicles have been a great step forward to help positively the environment with zero emissions and total energy consumption in their lifecycle. Many companies focus on electric vehicle production with the development of electric vehicle technology. Therefore, the selection process emerges among the various electric automobile technologies for the users. The selection process includes several conflicting factors which are such as economic, technical and technological factors. In the present study, we propose a hybrid approach for electric automobile selection that combines analytic hierarchy process (AHP), technique for order of preference by similarity to ideal solution (TOPSIS) and goal programming (GP) is used to determine the weights to assign to the factors that go into these selection decisions and TOPSIS method is used for preference ranking. These weights founded by AHP are inputted into a GP model to determine the best alternative among the electric automobiles. Finally, the study used three methods TOPSIS, AHP- TOPSIS and AHP-GP for better comparison and evaluation. The most suitable electric automobile is selected among their alternatives by using analytic methods and goal programming.

Analyzing the Application of the Sustainable Development Goals for Egypt Using a Neutrosophic Goal Programming Approach

Sustainable development necessitates the implementation of appropriate policies that integrate multiple competing objectives on economic, environmental, energy, and social criteria. Multi-criteria decision analysis with goal programming is a popular and widely used technique for studying decision problems with multiple competing objectives. Real-world situations frequently involve imprecise and incomplete information, making neutrosophic goal programming models the most appealing option. We presented a novel neutrosophic goal programming model that incorporates optimal resource allocation to simultaneously satisfy prospective goals on economic development, energy consumption, workforce, and greenhouse gas emission reduction by 2030, as applied to Egypt’s key economic sectors in this paper. We also compared the outcomes of fuzzy goal programming and neutrosophic goal programming. We show that neutrosophic goal programming approach is more accurate than fuzzy goal programming approach because it deals with incomplete and indeterminate information and has three independent degrees: truth membership degree, indeterminacy–membership degree, and falsity–membership degree. The presented model examines opportunities for improvement and the effort required to implement sustainable development plans. The model also provides valuable insights to decision makers for strategic planning as well as investment allocations for sustainable development. Numerical illustration is also provided for validation and application of the proposed model.

POLA HUBUNGAN OPTIMAL PADA AGRIBISNIS UBIKAYU DI KABUPATEN LAMPUNG TENGAH (Kasus Pada PT XYZ, Kecamatan Rumbia, Kabupaten Lampung Tengah)

The decline in the performance of cassava production has resulted in a decrease in the level of income of the agro-industry and the high uncertainty of the tapioca agro-industry in recent years. Weak coordination between farmers and industry worsens the existing condition of cassava agribusiness. On the one hand, factories have shortages and surpluses of raw materials in certain months of the year, but on the other hand, farmers experience extreme fluctuations in the price of cassava. This study aims to identify the optimal relationship pattern between the tapioca agroindustry at PT XYZ and farmers by using a goal programming model. The research used a case study method at PT XYZ in Central Lampung Regency and a survey of 78 cassava farmers in Central Lampung Regency. The research was conducted in February 2019. The analytical method used is an optimal planning model with a goal programming mathematical model and a qualitative descriptive analysis method to find the right relationship pattern. The results showed the optimal solution in the absorption of raw materials and tapioca production, which was an average of 5,328,554 kg of cassava per month and 1,323,390 kg of tapioca per month, or an increase of 70% and 85% from the actual condition. In addition, the ideal optimal relationship pattern to be able to provide justice for both factories and farmers is a strong sustainable coordination pattern. Key words: Cassava, Optimal Relationship, Tapioca Agroindustry

A Goal Programming Model for Dispatching Trucks in an Underground Gold Mine

The cost of transporting mined material in an underground mine is major. This cost typically represents between 50 to 60 percent of a mine’s total operating costs. The problem of dispatching trucks in an underground gold mine is, therefore, of major economic importance and warrants the use of a decision support model. The developments of a realistic decision-support model for the dispatching problem in an underground gold mine is addressed in this paper. The problem must address multiple conflicting objectives and therefore a goal programming model was formulated. The model was applied to a case study, the Red Lake underground gold mine, in Ontario, Canada. The results showed major improvements in meeting the multiple objectives of this problem versus a single objective model. The results illustrate the flexibility that the dispatching problem (in underground gold mines) yields when solved for multiple objectives using a goal programming model.

An Application of Fully Intuitionistic Fuzzy Multi-objective Linear Fractional Programming Problem in E-education System

This article addresses a special class of non-linear programming problems, viz., linear fractional programming problems having multiple objectives. In solving the real-life linear fractional optimization problems, the ambiguity and hesitation in the decision are inherent and ever-present, therefore, it is perfectly viable to formulate and solve these optimization models using the intuitionistic fuzzy environment. The purpose of this study is to propose a simple and computationally efficient approach to obtain the solution of multiple objective linear fractional programming problems having all the decision variables and parameters expressed in terms of triangular intuitionistic fuzzy numbers. The proposed solution algorithm is primarily based on the goal programming approach, fuzzy-based linearization technique, and a membership function strategy. The original linear fractional programming problem is first converted to its equivalent deterministic/crisp multi-objective linear fractional optimization problem using the weighted goal programming methodology along with the linear membership technique to resolve the intuitionistic fuzzy constraints into the crisp one. Finally, the variable transformation technique for the under- and over-deviational variables of the goal programming model is employed to linearize all the fractions involved in the problem so as to convert the original problem to an equivalent linear optimization problem. Further, this linear programming problem can be solved using any available commercial packages. Moreover, a numerical illustration is provided to demonstrate the steps of the proposed technique followed by the analysis and solution of an E-education set-up problem. The discussion and comparisons of the practical case establish the relevancy and usefulness of the proposed model.

A binary fuzzy goal programming model with fuzzy parameters to select the fruits and vegetables market hall location for Istanbul

In urban areas, delivery of the products necessary to survive to the desired points has vital importance, and public distribution centers (PDCs) are one of the most important components of this system. In this paper, we present a hybrid method to analyze the locations of F&VMHs which is one of the PDCs types. Firstly, the alternative numbers have been decreased by interval-valued trapezoidal fuzzy grey relational analysis (IVTFGRA) method. Secondly, we develop a binary fuzzy goal programming (BFGP) model with fuzzy parameters to determine the most appropriate F&VMH locations by taking into consideration costs, arrival times and CO2 emission goals. Then, the BFGP model is converted to standard FGP model. In the last stage, the constraints with fuzzy parameters are transformed to crisp constraints with Peidro et al. (2010)’s methodology. The results show that current F&VMHs need to be moved to the locations that are more cost effective and out of the city center.

A Goal Programming Model for Dispatching Trucks in an Underground Gold Mine

The cost of transporting mined material in an underground mine is major. This cost typically represents between 50 to 60 percent of a mine’s total operating costs. The problem of dispatching trucks in an underground gold mine is, therefore, of major economic importance and warrants the use of a decision support model. The developments of a realistic decision-support model for the dispatching problem in an underground gold mine is addressed in this paper. The problem must address multiple conflicting objectives and therefore a goal programming model was formulated. The model was applied to a case study, the Red Lake underground gold mine, in Ontario, Canada. The results showed major improvements in meeting the multiple objectives of this problem versus a single objective model. The results illustrate the flexibility that the dispatching problem (in underground gold mines) yields when solved for multiple objectives using a goal programming model.

Optimum stratum boundaries and sample sizes for Covid-19 data in Egypt.

Stratified random sampling is an effective sampling technique for estimating the population characteristics. The determination of strata boundaries and the allocation of sample size to the strata are two of the most critical factors in maximizing the precision of the estimates. Most surveys are conducted in an environment of severe budget constraints and a specific time is required to finish the survey. So cost and time are two important objectives that are taken under consideration in most surveys. The study suggested Mathematical goal programming model for determining optimum stratum boundaries for an exponential study variable under multiple objectives model when cost and time are under consideration. Compared to other techniques, Goal programming has many advantages in resources planning. Determining the required resources to satisfy the desired goals and the effectiveness of the available resources as well as providing best solutions under different amounts of resources are examples of the advantages of Goal programming. In addition the paper used data on Covid-19 to evaluate the performance of the suggested model for the exponential distribution. The study divided the number of new cases diseases into small, medium and high numbers. It also compared the results with the findings in the reports of the World Health Organization. The suggested mathematical goal programming revealed that Egypt was exposed to three waves of infection during the interval (5/3/2020 to 12/8/2021). These results are identical to the actual reality of covid-19 waves in Egypt.

A model for measuring D&A systems and their applications

Data is an indispensable strategic asset for various incorporations. In this paper, we deeply explore the model for evaluating the R&D and system of ICM company and effects on different scales and industries.we build a D&A system maturity evaluation model through the fuzzy analytical hierarchy process and recommend changes to the company system based on this model. The current D&A system maturity level of ICM company include key performance indicators of the success of staff, technology and processes, so as to obtain the quantitative evaluation index of the company's D&A process and technology level.we use the model at ICM to determine their current D&A maturity level and demonstrate how they use the model to optimize the system, allowing companies to maximize the potential of their data assets. It is of great essence to establish a goal programming model to optimize the indicators again, so as to formulate specific strategies.At last, we also summarize the strengths and weaknesses and provide insights to ICM customers about the D&A system.