Showing papers on "Goal programming published in 2022"

A hybrid approach to supporting physical-change-based renewal projects selection decisions in high-density city: a case study of Chongqing

Abstract: Purpose The aims of this paper is to establish an appropriate physical-change-based renewal (PCBR) projects selection mechanism capable of selecting the combination of the PCBR projects that can make up an integrated urban renewal program in high-density cities. Design/methodology/approach The research design follows a sequential integrated methodology that combines the calculation algorithms of Fuzzy Analytic Network Process (Fuzzy-ANP) with Zero-One Goal Programming (ZOGP) to support decisions for the selection of PCBR projects. In the first phase, general criteria for assessing the sustainability performance of PCBR projects were collected from relevant literature. In the second phase, the Fuzzy-ANP was used to identify the priority weights of the candidate projects through clarifying the interdependent degree between the criteria and candidate projects. Finally, ZOGP method was selected as a predetermined number of PCBR projects among candidate projects. Findings The feasibility and effectiveness of this hybrid approach is then verified in a case study of Yuzhong District, Chongqing in China. The results of this study indicate that the integrated method is capable of directing the decision maker toward the best compromising solution of PCBR program that can achieve the maximization of sustainable benefits and allocate limited resources most efficiently. Originality/value The novelty of this paper consists in combining the algorithms of the Fuzzy-ANP method with those of the ZOGP model that serves as an effective analysis tool to address practical decision problems. This is the first hybrid algorithms to make PCBR projects selection decision that reach the maximization of the sustainable benefits, both in economic and socio-environmental terms.

Optimization of Tire Production Planning Using The Goal Programming Method and Sensitivity Analysis

Abstract: In Indonesia there are many companies that are engaged in tire retreading or reuse of unused used tires. One of the problems in the retread business in Indonesia is that the tire production target is often not fulfilled and the lack of available tires is used as the main raw material for retreading due to the long process of sending tires from consumers to companies. The purpose of this study is to determine the optimal needs of resources, determine the priority of achievement and determine the value of sensitivity to the optimum solution achieved. The method used in this study is the Goal Programming Method, because it is suitable for problems that have many goals because through its deviation, the method can automatically capture information about the relative achievement of the goals to be achieved. Based on the results of research that has been done, it can be concluded that the maximum income earned by the company is IDR 474,426,000 or an increase of 36% from the limit set by the company. In addition, it can also be concluded that the sensitivity range for the boundary quantity value is the distance at which the shadow price remains valid. If it increases above the upper limit of sensitivity (increases) or decreases below the lower limit (decreases), the value of the shadow price will change.

Distribution Centre Location Selection for Disaster Logistics with Integrated Goal Programming-AHP based TOPSIS Method at the City Level

Abstract: The importance of disaster logistics and its share in the logistics sector are increasing significantly. Most disasters are difficult to predict; therefore, a set of measures seems to be necessary to reduce the risks. Thus, disaster logistics needs to be designed with the pre-disaster and post-disaster measures. These disasters are experienced intensely in Turkey and the importance of these measures becomes more evidential. Therefore, accurate models are required to develop an effective disaster preparedness system. One of the most important decisions to increase the preparedness is to locate the centres for handling material inventory. In this context, this paper analyses the response phase designing the disaster distribution centres in Turkey at the provincial level. AHP (Analytical Hierarchy Process) based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and goal programming model integration is used to decide alternative locations of distribution centres. TOPSIS method is employed for ranking the locations, which is based on hazard scores, total area, population, and distance to centre. Two conflicting objectives are first proposed in the goal programming formulation, in which maximization of the TOPSIS scores and minimization of the number of distribution centres covering all demands named set covering model are included. Although Gecimli has the highest priority with 0.8 p score in the TOPSIS ranking, Altincevre (0.77) and Buzlupınar (0.75) ensure both the TOPSIS score and coverage of the demand nodes. The results from this paper confirm that the computational results ensure disaster prevention insights especially in regions with limited data.

Maximization of Strategic Crops Production in Iraq with Fuzzy Goal Programming

Abstract: The need to increase agricultural production has become a challenging task for most countries. Generally, many resource factors affect the deterioration of production level, such as low water level, desertification, soil salinity, low on capital, lack of equipment, the impact of export and import of crops, lack of fertilizers, pesticide, and the ineffective role of agricultural extension services which are significant in this sector. The main objective of this research is to develop fuzzy goal programming (GP) model to improve agricultural crop production, leading to increased agricultural benefits (more tons of produce per acre) based on the minimization of the main resources (water, fertilizer, and pesticide) to determine the weight in the objectives function subject to different constraints (land area, irrigation, labor, fertilizer, pesticide, equipment, and seed). Fuzzy GP (FGP) and GP were utilized to solve multi-objective decision-making (MODM) problems. From the results, this research has successfully presented a new alternative method that introduced multi-interval weights in solving a multi-objective FGP and GP model problem in a fuzzy manner, in the current uncertain decision-making environment for the agricultural sector. The significance of this research lies in the fact that some of the farming zones have resource limitations while others adversely impact their environment due to misuse of resources.

Green supplier selection and order allocation using AHP-SAW and goal programming

Abstract: In the supply chain, environmental aspects become a concern for stakeholders. One of the strategic decisions in the supply chain field is Green Supplier Selection and Order Allocation (GSSOA). This study proposes the Analytical Hierarchy Process (AHP) and simple additive weight (SAW) and Goal Programming procedures in addressing the GSSOA problem. The five criteria and 13 sub-criteria are utilized in applied to plastic manufacturing companies. Based on AHP, the results indicate that quality criteria present a higher level of importance than cost, delivery, service, and environment. Furthermore, the suitability of the material with specifications (QU1) serves as the sub-criteria with the highest weight. The result of the preference assessment with SAW further indicates that supplier 2 presents the highest preference. The application of goal programming is advantageous to determine the optimal order allocation. The results depict that suppliers 2 and 3 are eligible to supply 150 each to fulfill demand.

Performance Optimisation of Public Transport Networks Using AHP-Dependent Multi-Aspiration-Level Goal Programming

Abstract: This study proposes an optimisation approach to improve multiple-criteria aspiration-level public transportation performance by combining public transport criteria matrix analytic hierarchy process (PTCM-AHP) models and multi-aspiration-level goal programming. The approach uses the PTCM-AHP to calculate the system weights. Based on the weight values, the approach combines the multi-aspiration goal-level selection process in three different ways. The proposed approach was used to optimise public transportation networks in Bayswater, Cockburn, and Stonnington, Australia, to demonstrate the public transportation network performance optimisation process. By controlling the criteria goal value interval, this new approach combines decision-making plans and strategies to optimise various scenarios. The optimisation outcomes can be applied to provide guidelines for improving the performance of public transportation networks.

Modeling and Analyzing a Multi-Objective Financial Planning Model Using Goal Programming

Abstract: Optimal financial planning plays a vital role in maintaining concentration and on the path as the organization extends, when new challenges materialize, and when unpredictable situations pounded. This study aims to develop and implement a goal programming model to evaluate financial planning based on the annual financial report of Saudi Basic Industries Corporation (SABIC), which assisted it in developing the financial planning model. This study is mainly designed to analyze SABIC’s budgeting structure; therefore, in order to maximize the benefits from the whole budget, goal programming is implemented for the entire budget. As a result of this study, we identified the following objectives as specific: reduced expenses, increased revenue, increased net profit, increased fixed assets, reduced debt, and increased equity share participation as a result of this project. Moreover, the analysis involved determining whether all objectives were met at the end of the study. Consequently, this study will benefit industrial institutions in achieving their financial objectives.

Optimization Techniques for Response Predication in Metal Cutting Operation: A Review

Abstract: At the Present time speedily changing in manufacturing industries, so optimization techniques is use for response predication in metal cutting operation. Now a day’s increasing demand of quality of product. These all types of optimization techniques play important role in quality and productivity. In this paper show a review about both types of optimization techniques such as conventional and non-conventional optimization techniques. Many types of conventional optimization techniques are used for optimization of machine parameters included dynamic programming, goal programming, linear programming, sequential unconstrained minimization technique, simplex method etc. And the latest optimization techniques included Taguchi design, Response surface method (RSM), Fuzzy logic system (FLS), Genetic algorithm, Scatter search techniques, Partial Swarm optimization (PSO), Multi optimization genetic algorithm etc.

Dynamic programming approach for fuzzy linear programming problems FLPs and its application to optimal resource allocation problems in education system

Abstract: This research is about the development of a dynamic programming model for solving fuzzy linear programming problems. Initially, fuzzy dynamic linear programming model FDLP is developed. This research revises the established dynamic programming model for solving linear programming problems in a crisp environment. The mentioned approach is upgraded to address the problem in an uncertain environment. Dynamic programming model can either be passing forward or backward. In the proposed approach backward dynamic programming approach is adopted to address the problem. It is then followed by implementing the proposed method on the education system of Pakistan. The education system of Pakistan comprises of the Primary, Middle, Secondary, and Tertiary education stages. The problem is to maximize the efficiency of the education system while achieving the targets with minimum usage of the constrained resources. Likewise the model tries to maximize the enrollment in the Primary, Middle, Secondary and Tertiary educational categories, subject to the total available resources in a fuzzy uncertain environment. The solution proposes that the enrollment can be increased by an amount 9997130, by increasing the enrollment in the Middle and Tertiary educational categories. Thus the proposed method contributes to increase the objective function value by 30%. Moreover, the proposed solutions violate none of the constraints. In other words, the problem of resources allocation in education system is efficiently managed to increase efficiency while remaining in the available constrained resources. The motivation behind using the dynamic programming methodology is that it always possesses a numerical solution, unlike the other approaches having no solution at certain times. The proposed fuzzy model takes into account uncertainty in the linear programming modeling process and is more robust, flexible and practicable.

Optimization of aggregate planning of rice husk charcoal production with Fuzzy Goal Programming approach

Abstract: This study proposes a Fuzzy Goal Programming approach to optimize the problem of aggregate planning of production processes in companies that produce charcoal husks. The application proposed to the company describes the process of Rice Husk Charcoal Production taking into account the uncertain factors involved in the aggregate planning process of Rice Husk charcoal production. Decision-making related to the level of material needs in each type of rice husk charcoal product is considered based on planning for the next 12 months by including weighting value in membership function, determination of membership function of each function objectives with equivalent Crip of fuzzy goal programming. Fuzzification is based on three main objectives with three membership levels for each purpose of Goal Programming. This research provides the results of the proposed adaptive model applied to companies that produce charcoal husks.

Comparison of Selected Mathematical Programming Models Used for Sustainable Land and Farm Management

Abstract: The aim of this study is to compare three mathematical programing models used for sustainable land and farm management. The sample for the comparison was 219 agricultural holdings participating as beneficiaries of the measure ‘Modernization of agricultural holdings’ in the Rural Development Plan at the Region of Central Macedonia in Greece. Using the crop plan of the agricultural land of these farms the mathematical programming models calculate the optimum solution under different and conflicting goals. The results of the methodologies of Linear Programming (LP), of Positive Mathematical Programming (PMP) and Weighted Goal Programming (WGP), are compared in terms of the proposed agricultural land changes. The sustainability of farms is measured with the use of eleven economic, social, and environmental indicators. Each model has some unique advantages and disadvantages that can enable it to be implemented in particular situations. In the conclusions to this research the characteristics of each model are highlighted.

Performance Optimisation of Public Transport Networks Using Ahp-Dependent Multi-Aspiration Level Goal Programming

Abstract: This study proposes an optimisation approach to improve multiple-criteria aspiration-level public transportation performance by combining public transport criteria matrix analytic hierarchy process (PTCM-AHP) models and multi-aspiration-level goal programming. The approach uses the PTCM-AHP to calculate the system weights. Based on the weight values, the approach combines the multi-aspiration goal-level selection process in three different ways. The proposed approach was used to optimise public transportation networks in Bayswater, Cockburn, and Stonnington, Australia, to demonstrate the public transportation network performance optimisation process. By controlling the criteria goal value interval, this new approach combines decision-making plans and strategies to optimise various scenarios. The optimisation outcomes can be applied to provide guidelines for improving the performance of public transportation networks.

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

Abstract: Abstract 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.

Application of goal programming in the textile apparel industry to resolve production planning problems a meta-goal programming technique using weights

Abstract: In the present business environment, rapidly developing technology and the competitive world market pose challenges to the available assets of industries. Hence, industries need to allocate and use available assets at the optimum level. Thus, industrialists must create a good decision plan to guide their performance in the production sector. As a result, the present study applies the Meta-Goal Programming technique to attain several objectives simultaneously in the textile production sector. The importance of this study lies in pursuing different objectives simultaneously, which has been almost ignored till now. The production scheduling problem in a textile firm is used to illustrate the practicability and mathematical validity of the suggested approach. Analysis of the results obtained demonstrates that the solution met all three meta-goals with some original goals being met partially. An analysis of the sensitivity of the approach to the weights of the preferences was conducted.

Solving Two-Stage Multi-objective Transportation Problem Using Goal Programming and Its Application to Sustainable Development

Abstract: In modern civilization, people are very fast and busy, and they try to achieve their goals in a short time. Most of the time, they do not consider the damage of nature to access their profits. In this regard, corporate world is moving based on use of technologies and machines. As a consequence, common people are losing their jobs in offices, factories, corn fields, etc. and living their life in anxiety and poverty. Every technology or machine is running by power of fuel, which produces pollution directly or indirectly to the nature. Misusing of technologies and machines for better profit cause high rate of pollution day by day. Now it is the time to recover the nature for sustaining human life in earth along with the consideration of removing among unemployment of youths. In this study, we incorporate a two-stage Multi-Objective Transportation Problem (MOTP) and solve it through goal programming. Considering goals corresponding to the objective functions of both stages, we solve two-stage MOTP and then derive Pareto-optimal solution. Thereafter, a numerical example based on a real life scenario is presented to show the effectiveness of the study in sustainable development. Finally, conclusions and future directions are presented regarding the present study.

Nonpre-Emptive Integer Nonlinear Goal Programming Model for Multi-Item Inventory Problem: Case Study of a Car Retail Centre in Lagos State.

Abstract: Most of the real-world optimization problems involve multiple objectives with constraint resources. Retailers oftentimes anticipate demand, they stock, and maintain warehouses with different variety of products at high cost to meet prospecting customers' demand. In this paper, a Non-preemptive Integer Nonlinear Goal Programming (NINGP) model was developed for obtaining Economic Order Quantities (EOQ) of multi-item inventory problems that satisfy the multiple and conflicting objectives of the Decision Maker (DM). The particular case considered was that of a motor vehicle dealer who sells 10 brands of Tokunbo vehicles and wants to determine the EOQ for each brand such that the deviations from the aspiration level are minimized. Using LINGO 17.0 Software to solve the NINGP model, the EOQ allocated to each brand type from 1 through 10 are 2, 2, 5, 2, 2, 3 3, 3, 3, and 3 cars respectively. The optimal number of cars was 28 with the associated cost of ₦53,825,915. Compared to the estimated budget of ₦60,000,000, the NINGP approach was able to achieve a 10% (₦6,174,085) below budget. With proper modifications considering the associated constraints, related inventory problems can be solved using the NINGP model.

Penggunaan Forecasting dan Goal Programming dalam Optimasi Perencanaan Produksi Beras

Abstract: Abstract. Rice is an important commodity in Indonesia because of its role as a staple food, which the majority of Indonesians consume every day as a carbohydrate intake. The “SM” Rice Shop is a place that manages rice production as well as sells it personally and to distributors. This shop produces 3 kinds of rice, namely white rice, brown rice, and white glutinous rice. The "SM" Rice Shop strives to always fulfill the existing demand based on market demand. Therefore shop owners always try to make optimal production planning. Forecasting is an integral part of production planning. In planning the production, forecasting is carried out to find out sales predictions in the following year based on data from the previous year. Forecasting is done with the help of MINITAB software. On the other hand, the shop tries to minimize production costs and maximize profits, one method that can accomplish these three goals is goal programming. Goal programming is an extension of the linear programming model. The main difference lies in the structure and use of the objective function. In general, goal programming is used to solve problems that have multiple goals or more than one goal. In completing this goal programming model using the LINGO software. The results obtained get the optimal solution, namely the achievement of rice production targets, the minimum costs of rice production and the maximum profits from rice sales. Abstrak. Beras merupakan komoditas penting di Indonesia karena perannya sebagai makanan pokok yang mayoritas setiap penduduk Indonesia mengkonsumsinya setiap hari sebagai asupan karbohidrat. Toko Beras “SM” merupakan tempat yang mengelola produksi beras sekaligus menjualnya secara pribadi maupun kepada distributor. Toko ini memproduksi 3 macam beras, yaitu beras putih, beras merah, dan beras ketan putih. Toko Beras “SM” berusaha untuk selalu memenuhi permintaan yang ada berdasarkan permintaan pasar. Maka dari itu pemilik toko selalu berusaha untuk membuat perencanaan produksi yang optimal. Peramalan merupakan bagian integral dari perencanaan produksi. Dalam melakukan perencanaan produksi tersebut dilakukan peramalan untuk mengetahui prediksi penjualan pada tahun selanjutnya berdasarkan data pada tahun sebelumnya. Peramalan tersebut dilakukan dengan bantuan software MINITAB. Disisi lain toko tersebut berusaha untuk meminimalkan biaya produksi dan memaksimalkan keuntungan, salah satu metode yang dapat menyelesaikan ketiga tujuan tersebut adalah goal programming. Goal programming merupakan perluasan dari model linear programming. Perbedaan utamanya terletak pada struktur dan penggunaan fungsi tujuan. Secara umum goal programming digunakan untuk menyelesaikan persoalan yang memiliki tujuan ganda atau lebih dari satu tujuan. Dalam penyelesaian model goal programmming ini menggunakan bantuan software LINGO. Hasil yang diperoleh mendapatkan solusi yang optimal, yaitu tercapainya target produksi beras, hasil biaya produksi beras yang minimal dan hasil keuntungan penjualan beras yang maksimal.

Application of the goal programming method in health care

Abstract: The Subject is the application of goal programming to the planning of medical care. The paper, in particular, introduces a resource allocation model for hospital management based on goal programming. In a health–care agency with insufficient human resources, the Goal Programming (GP) model will help in strategic planning and shipment. This study aims to delegate staff to the correct shift hours so that management can achieve the goal of lowering overall payroll costs while keeping patients happy. The data generated by a Midwest-based health-care agency is used to demonstrate a Goal Programming model. The objectives have been established and prioritized. In health-care organizations, the Goal Programming model implementation provides understanding of resources allocation planning functions. The proposed model can be easily applied to other HR planning processes.

Application of Goal Programing for Financial Management of a Listed Industrial Goods Firm in Nigeria

Abstract: In today’s competitive business environment, companies are faced with a lot of problems such as setting goals, planning how these goals can be achieved, organization and control of how the available scarce resources can be used to satisfy the aim and objectives of the company. Every decision made determine if the company will maintain, increase or lose its market share in today’s competitive market. Thus, there is need for mathematical modeling tools to help in making the right decision. Although we have different mathematical techniques that can be used, Goal Programing technique is chosen in this study since it enables the decision to strive toward multiple objectives, thereby enable optimum use of resources. This paper is aimed at demonstrating the use of goal programing for financial management of a listed Industrial Goods Firm in Nigeria. The result shows that two out of the five formulated goals were met. The least expected total of revenue, expenses, asset and employer benefit should be 10.61 billion naira annually if the company wants to meet the asset and expenses goal.