On Dynamic Multiple Criteria Decision Making Models: A Goal Programming Approach

We use data from the Ethiopia Rural Household Survey and the Ethiopian Central Statistics Agency to demonstrate a set of techniques for estimating optimal investment allocation in smallholder farming. The approaches treat farming tasks, constraints, and investments as a portfolio problem, characterized by multiple competing objectives. We formulate several versions of the multi-objective problem and solve them in three alternative ways; 1) using a scalarized Markowitz portfolio optimization, 2) using a weighted goal programming model, and 3) a multi-horizon goal programming model, estimating all model parameters using real data. The main benefit of the goal programming formulation is the possibility to simplify in a single criterion problem complex situations in which the Decision Maker (DM) faces a trade-off between two or more objectives. We discuss the importance of portfolio allocations for smallholder farmers in minimizing risk and increasing return, and discuss how these approaches provide a framework that can be extended to practical applications in smallholder farming.

Polynomial goal programming: A procedure for modeling preference trade-offs

Multi-choice mixed integer goal programming optimization for real problems in a sugar and ethanol milling company

On the mixed binary goal programming problems

This paper presents the development of a goal programming (GP) model as an aid to strategic planning and allocation for limited human resources in a health-care organization. The purpose of this study is to assign the personnel to the proper shift hours that enable management to meet the objective of minimizing the total payroll costs while patients are satisfied. A GP model is illustrated using the data provided by a health-care organization in the midwest area. The goals are identified and prioritized. The model result is examined and a sensitivity analysis is performed to improve the model applicability. The GP model application adds insight to the planning functions of resource allocation in the health-care organizations. The proposed model is easily applicable to other human resource planning process.

This paper deals with the issue of efficiency in the Goal Programming (GP) model. A general approach for the determination of an efficient solution of GP is presented. An efficiency test for the GP solution is developed; moreover, when this solution is not efficient, an efficient solution that dominates it is determined.

ABSTRACTThe Goal Programming (GP) model is one of the first models that have been developed to deal with managerial decision‐making problems where several incommensurable and conflicting objectives are involved. The GP variants integrate the decision maker's preferences differently. This model has also been applied to group decision‐making situations. The aim of this paper is to propose a new typology based on preferences articulation of decision makers through the GP model. This typology is based on the articulation and the elucidation process of the group decision makers' preferences. Copyright © 2012 John Wiley & Sons, Ltd.

Asset Liability Management (ALM) can be overseen using financial ratios derived from financial statements. These statements provide a comprehensive picture of a company's status and necessitate analysis to evaluate performance. This research aims to analyze financial ratios to describe the financial condition, measure business development over time, and evaluate the achievement of the company's objectives. An optimization analysis of financial ratios is performed using the Weighted Goal Programming (WGP) model, which addresses multiple objectives by applying weights based on their priorities. The Best-Worst Method (BWM) was used to determine the priority weights of deviation variables from each financial ratio target. Financial ratios were selected based on their impact on profit using factor analysis. The constructed WGP model aims to minimize deviations in Return on Assets, Operating Ratios, Operating Income Ratio, Total Assets Turnover, and Current Ratio. Computational calculations to solve the WGP model are performed using Python, with pseudocode provided. A case study on a company in the garment and textile sector was conducted and found that the Operating Ratio, Return on Assets, Operating Income Ratio, and Current Ratio still need improvement by developing strategies to achieve the targets. Sensitivity analysis was also employed to assess the resilience of the model in response to alterations in data.

Financial management is important for the construction sector, as construction companies contribute to the development of countries. Malaysia encourages the construction sector to develop advanced infrastructure related to transport and housing. Financial management is a multi-criteria decision making (MCDM) problem, since companies have to consider multiple goals in order to make the optimal decision. Therefore, goal programming is proposed in financial management to solve optimization in MCDM problems. According to previous studies, there has been no comprehensive study conducted on optimization and comparison among the construction companies with a goal programming model. Thus, this study aims to propose a goal programming model to optimize and compare the financial management of listed construction companies in Malaysia for benchmarking purposes. Six goals of financial management, namely the total assets, total liabilities, equity, profit, earnings, and optimum management of construction companies are examined in this study. The results of this study show that the goal programming model is able to determine the optimal solution and goal achievement for each construction company. In addition, the model value can be further enhanced according to the optimal solution of the goal programming model. This study provides insights to the listed construction companies in Malaysia to identify the potential improvement based on the benchmarking and optimal solution of the goal programming model.

Goal programming approach to solving network design problem with multiple objectives and demand uncertainty

The strategic planning is important in bank financial management. The banks and financial institutions have to achieve multiple goals in optimizing asset, liability, equity, earnings, profit and optimum management item. The subjective judgment in assigning weight of the goals is a drawback in financial management of the banks since it may cause inconsistent judgment. In addition, there are no comprehensive studies done on comparison among the banks for benchmarking based on the past studies in optimizing the financial management. Hence, this paper aims to improve the bank financial management by proposing a two-stage goal programming (GP) model to compare and optimize the bank financial management. The proposed model is developed based on entropy method in determining the weight of the goal at the first stage before optimizing the financial management with GP model at the second stage. Four listed banks in Malaysia are investigated in this study. The results indicate that the goal for asset, equity and optimum management item have been achieved by all banks. Furthermore, the target value of asset, equity, earning and profit can be increased according to the optimal solution of the proposed model. The significance of this paper is to provide insights to the banks for further improvement based on the optimal solution of the proposed model.KeywordsEntropyGoal programmingOptimizationFinancial managementPotential improvement

Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models

In the present study, ranking of agricultural land use constraints for about 700 hectares of Kashmar plain lands were determined by studying the consumption of the chemical fertilizers and impacts of irregular harvest on the quality of the groundwater resources. The studied lands were divided into 8 groups. The quality and more use restrictions were ranked by Vicor multi-criteria programming method and the related factors and indicators. Using weighted goal programming model with consideration to the 4 environmental and economical goals for each group simultaneously,, optimal cultivating pattern, sustainable management of groundwater use and achievement to the sustainable agriculture were proposed. The required data were collected from the Agriculture Administration, the Regional Water administration and the meteorological office of Kashmar County in 2011. Results showed that priority ranking of land use restrictions should be considered to prevent more damages to the agricultural areas. The results on the agricultural water resources quality during 2009 and 2011 showed that salinity increment in different subdivisions has developed. This causes the regional agriculture to face serious damages in the near future. Therefore, in order to achieve a comprehensive management on exploiting the regional groundwater resources, it is necessary to pay attention to some programs such as groundwater quality management, cultivation pattern change, cultivating the more resistant plants to salinity, replacing the chemical fertilizers with organic ones and considering the sustainable agriculture in the region..

Weighted goal programming (WGP) is employed to reconcile the goals of food security, improved incomes and woodland conservation in households from selected sites in Malawi, Mozambique and Zimbabwe. The three goals are attainable simultaneously, albeit with trade-offs useful in guiding development of rural development policies. The WGP model provides a framework for evaluating impact, on the household goals and woodland condition, of some macroeconomic and sectoral policies and demographic changes. Keywords: goal programming, woodlands, households, simulation, southern Africa.

The Great Lakes, the largest group of freshwater lakes in the world, have profound impacts on residents, ecosystems, water resource utilization, shipping, and tourism industries. Addressing water level variability, this study integrates network science, goal programming algorithm, and Model Predictive Control to establish a comprehensive and adaptive model for optimizing dam adjustment mechanisms and maximizing stakeholder benefits. Initially, a Vector Autoregression Model is developed for the Great Lakes and connecting river flows toward the Atlantic Ocean to conditionally project future paths of specified variables. This model yields a network representation of the Great Lakes system. Subsequently, a Goal Programming Model is constructed to determine optimal water levels throughout the year based on extensive literature review and priority rankings. Leveraging insights from the 2014 plan, a detailed analysis is conducted on Lake Ontario water levels, focusing solely on stakeholders and influential factors. This research contributes a robust methodology for managing water levels in the Great Lakes region, providing valuable insights for sustainable water resource management.