A new approach for generating efficient solutions within the goal programming model

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

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.

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.

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

On the mixed binary goal programming problems

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

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

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.

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

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.

Aggregate planning involves planning the best quantity to be produced during time periods in the medium‐range horizon at the lowest cost. Usually, the production manager seeks a plan that simultaneously optimizes several incommensurable and conflicting objectives, such as total cost, level of inventories, level of customer service, fluctuation in workforce, and utilization level of the physical facility and equipment. The goal programming (GP) model is one of the best known multi‐objective programming models that considers simultaneously several conflicting objectives to select the most satisfactory solution among a set of feasible solutions. In the production planning problem, the goals and the technological parameters are naturally imprecise. Moreover, the existing GP formulations developed in industrial engineering and aggregate production planning do not explicitly incorporate the manager's preferences. The aim of this paper is to develop a GP formulation within an imprecise environment where the concept of satisfaction function will be utilized to explicitly introduce the manager's preferences into the aggregate planning model.

Dynamic multiple criteria decision making (DMCDM) represents an extension of classical multiple criteria decision making to a context in which all variables are depending on time. This complex decision making problem requires the development of methodologies able to incorporate different and conflicting goals in a satisfying design of policies. We formulate two different goal programming models, namely a weighted goal programming model and a goal programming model with satisfaction functions, for solving DMCDM models. We present an application of this methodology to analyze the trade-off between consumption and investment in a traditional Ramsey-type macroeconomic model with heterogeneous agents. For a specific realistic parameterization, such a model is solved by means of the proposed goal programming formulations.

The nurse-scheduling is a common problem in hospitals. The problem requires a timetable considering hospital regulations and nurses’ demands and preferences. This study proposes an integrated Lexicographic goal programming and dynamic satisfaction function model for solving nurse-scheduling problems. The proposed model provides the head nurses with a balanced and fair automated schedule. The model considers two aspects related to hospital regulation constraints. First, the dynamic aspect allows the decision-makers to integrate their judgment to the changing circumstances caused by nurse absenteeism. Second, the satisfaction aspect considers the decision maker’s preferences in assigning nurses’ surplus or shortages to the shifts. The model’s primary goal is to give nurses a fair and balanced schedule with an evenly distributed workload. A fair and balanced schedule helps maintain nurses’ welfare and provide quality patient service. We present a case study in an intensive care unit to demonstrate the effectiveness of our approach compared to the conventional goal programming method.

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.

This study presents a goal programming approach to determine the best self-drive tourism route in Terengganu. The goal programming was formulated based on the model of Traveling Salesman Problem (TSP). This study involved seven cities in Terengganu which can be connected by road network. The data of the tourist attractions for each city was used in this study. The model of self-drive tourism route was constructed so as to connect all these seven cities by minimizing the travel distance, minimizing accommodation costs and maximizing the total number of tourist attractions. To achieve the objectives of this study, we develop the goal programming model that considers three model structures with each of them consists three goal constraints which are subjected to four hard constraints. This model has been solved using LINGO12.0 software. The results obtained from each model structures will be compared in order to get the most optimum route for self-drive tourism. The result showed that the first model structure was suitable to be used because this model structure gave the optimum result compared to other model structures. It produced the minimum total traveling cost of RM967.70 compared to the model structure two of RM977.10 and model structure three of RM1233.10.