Application Of Goal Programming Research Articles

Practical and Adaptable Applications of Goal Programming: A Literature Review

Practical and Adaptable Applications of Goal Programming: A Literature Review

Goal programming and genetic algorithm in multiple objective optimization model for project portfolio selection: a review

Many scientific fields, such as engineering, data analytics, and deep learning, focus on optimization. Optimization problems are classified into two types based on the number of optimized objective functions: single objective and multi objective optimization problems. In this paper, a comparative review since 2000 using one of the deterministic and stochastic modelling approaches called goal programming (GP) and genetic algorithm (GA) in multi-objective optimization problem is discussed. This study gives a prime review of the application of GP and GA in various criteria of project portfolio selection problem. GP is a method for solving large-scale multi-objective optimization problems to assist decision makers in finding solutions that satisfy several competing goals. GA on the other hand are global meta-heuristic search algorithms that are used to provide approximation or optimal solutions to large-scale optimization problems. Of the 23 articles considered in this review showed that, from more than 100 projects, GA proved near optimal, feasible solution and efficient frontier in projects ranking, projects interaction and a preferred decision support tool of project portfolio selection. In addition, the two models select projects on risk-based approach, but GA proved to be more effective in terms of number of projects proposed, central processing unit (CPU) time and accuracy. The review concludes that, in multi-objective optimization model for project portfolio selection problems on a large-scale, very large or complex problems and less CPU time, GA is more effective than GP in multi-objective optimization problems. The review also showed gaps in previous studies of GP and GA application on project portfolio selection problem (PPSP). This review will aid scholars and demanding practitioners in gaining a broader understanding of goal programming and genetic algorithms in the context of project portfolio selection problems.

APPLICATION OF THE GOAL PROGRAMMING METHOD IN HEALTH CARE

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

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.

Applying the goal programming in the management of the 7P marketing mix model at universities-case study.

The decision-making relating to effective marketing is less supported by operational methodologies and optimizing methods in high education sectors than in the companies. This paper presents an application of goal programming as an aid for the optimizing marketing mix elements. It describes a project undertaken at one of the Universities in Poland considering the marketing mix includes all 7Ps elements at University. The constraints are first identified based on interviews with academic experts and survey. The analytic hierarchy process method is used to select the forms of promotional activities and Activity-Based Costing method are performed to determine the costs of the activities. Then, based on constraints, the multiple-criteria-programming model is built and applied to construct the marketing mix model at university, and it was solved using LP-Solve software AMPL. A comparison between the newly designed marketing mix and the existing one in terms of each of the criteria shows that the overall objective function could be greatly improved, optimized value can be obtained, and the model can be easily applied in any other high education sectors. The consequence of using the model is the optimal selection (with existing limitations) of promotional activities, taking into account their impact on the perception of the image of the university, significant from the perspective of students and impact on profitability, which is important from the perspective of the university management.

Goal Programming Application for Contract Pricing of Electric Vehicle Aggregator in Join Day-Ahead Market

Selecting an appropriate contract price between electric vehicle aggregators and electric vehicle owners is an uncertain, multi-criteria decision-making issue. In addition, the results can cause strong conflict due to different aims: the optimal value for increasing electric vehicle aggregator (EVA) profit negatively affects the cost for owners. The value of the contract price can change the optimal scheduling of EVAs in the day-ahead market. Taking into consideration this context, the current paper proposes to solve the multi-objective scheduling problem of an aggregator with a goal programming approach. The presented approach sets a satisfaction level for each goal according to decision-makers’ preference. Numerical results illustrate the validity of this approach to balance different performance measures. Furthermore, optimal scheduling of electric vehicle aggregators in the day-ahead market is created.

Five sector sustainability model: A proposal for assessing sustainability of production systems

The decision-making process towards sustainability is usually based on quantitative indicators, and becomes more complex the more indicators are considered. The lack of a clear criterion (i.e. a conceptual model of sustainability) supporting the choice of one indicator rather than others creates doubts about the real attainments of such studies. The holistic-based approach in choosing indicators to express sustainability claims for a multicriteria perspective. This work proposes a sustainability assessment tool based on the FIVE SEctor SUstainability (5SEnSU) model that is capable to show the relationships between humans and the natural environment, and the use of goal programming as a multicriteria method. The advantages of the proposed tool are based on clear criteria in choosing indicators supported by the 5SEnSU model, which recognizes the double functions (as a donor and receiver) of natural environment and society, as well the application of goal programming to obtain an easy-to-understand final indicator: the sustainability synthetic indicator of systems (SSIS). The countries that form Mercosur economic bloc were used as a case study to illustrate the application of the proposed model. Results have shown that Uruguay holds the highest sustainability performance among all Mercosur countries, although still demanding efforts from public policies to improve its national happiness level (K52 indicator), employment rate (K41 indicator) and total emergy flow per capita (K12 indicator). Instead of using a single criterion in choosing indicators, this work provides a scientific-based tool in choosing, calculating, and supporting discussions on the sustainability of production systems under a more holistic perspective.

Goal programming application for the decision support in the daily production planning of sawmills

Sawmills are a key element for the forest industry. It is not a simple task to plan correctly their production, taking into account that there are many criteria that are appropriate for their operation. Besides, the results obtained from the use of different objectives present strong conflicts: the optimal value in a performance measure negatively affects the rest. Usual criteria, such as benefits, yield, productivity, inventory level, demand satisfaction, etc., generate very different solutions. Taking into account this context, this paper presents a formulation for the optimal planning of the daily production of sawmills. The problem is solved using a Goal Programming approach that allows different criteria to be appropriately weighted. Firstly, the magnitude of the conflicts among objectives is evaluated and then the proposed formulation using Goal Programming is applied to an example to show the ability of this proposal to balance different performance measures. This type of approach highlights the capability of the decision-maker, who must adjust different parameters to find a solution that fits the possibilities and requirements of the production plan and the context.

An Application of Goal Programming: The Best Route to Discover a Wonderful West Sumatera

This study presents an application of nonpreemptive goal programming to find the best route to visit tourist sites in West Sumatera. The goal programming model is constructed based on traveling salesman problem. This study involves seven cities which can be connected by road network in West Sumatera. In this study, two cases are considered with the assumption that the tourists start to self-drive from different city. The results show that the goal programming based on traveling salesman problem model gives a route with minimum distance, time, and traveling cost compared to the traditional route.

An Application of Goal Programming in Data Envelopment Analysis

This research paper describes the transformation of the CCR fractional Programming Problem in to the CCR linear programming problem and the dual of the CCR problem. Here the Dual of CCR problem is viewed as BCC [1] formulation Obtained by axiomatic considerations. A goal programming method for finding common weights in DEA with an improved discriminating power for efficiency was proposed by A. Makui et.al [2]. Hamid Reza Babaee Asil and Sara Fanati Rashdi [3] investigated the relationship between resource allocation problems with the problem of weight control and target selection. M. Izadikhah, R. Roostaee et.al [4] proposed a method in which DEA problem can be solved by transforming it into MOLP formulation. The present research study explains the method of finding extremely efficient DMUs and their marveric indexes, the procedure to increase the discriminating power of DEA and the Process of obtaining a set of multiplier weights common to all the DMUs which leads to Global efficiency. In the following discussion a procedure to improve the discriminative power of DEA with goal Programming is proposed.

Solving fuzzy transportation problem using multi-choice goal programming

This paper explores the study of fuzzy transportation problem (FTP) using multi-choice goal-programming approach. Generally, the decision variable in transportation problem (TP) is considered as real variable, but here the decision variable in each node is chosen from a set of multi-choice fuzzy numbers. Here, we formulate a mathematical model of FTP considering fuzzy goal to the objective function. Thereafter, the solution procedure of the proposed model is developed through multi-choice goal programming approach. The proposed approach is not only improved the applicability of goal programming in real world situations but also provided useful insight about the solution of a new class of TP. A real-life numerical experiment is incorporated to analyze the feasibility and usefulness of this paper. The conclusions about our proposed work including future studies are discussed last.

An Empirical Application of Goal Programming and AHP in Real Estate Assessment

The Goal Programming (GP) is one of the many models which have been developed to deal with the multiple objectives decision-making problems. This model allows taking into account, simultaneously, many objectives while the decision-maker is seeking the best solution from among a set of feasible solutions. At first, the aim of this study is to compare the objectives of the decision maker based on the Analytical Hierarchy Process (AHP) and to rank the objectives according to the AHP process and once the objectives are set then apply the GP to the facility allocation in a real estate rental property. The objective of decision maker is not only to maximize profit rather the decision maker has multiple objective to achieve. The methodology presented in this study can help the decision maker to formulate viable location strategies in the volatile and complex facility allocation environment. This is an empirical study applied to a local business developer utilizing multi objective decision analysis tools.

Estimating Cost Analysis using Goal Programming

Objectives: The purpose of this paper is to develop an application of goal programming for a mathematical economics problem. In economic optimization given a cost target, the producer conditionally maximizes output or revenue. Methods/ Statistical Analysis: The paper aims in estimating cost limited maximal output based on full and stochastic frontiers of Cobb-Douglas and variable returns to scale production structure. The estimation procedure constitutes primarily formulating goal programming problems, later solving them by mathematical programming. The cost limited output expressions are derived. Findings: The method developed was applied to six manufacturing sectors of all India and the expression derived for cost limited outputs showed that the cost limited maximal output is cost efficient. Empirical analysis carried out resulted in showing the states whose cost efficiency was underachieved, overachieved and one and the same. Application/Improvements: The model has practical application in manufacturing sectors and was applied six sectors of all India. In future fuzziness can be applied to analyse cost efficiency in the manufacturing sectors of different states of all India.

Goal programming model for management accounting and auditing: a new typology

In practice, generally the accountants are facing complex decision-making situations where they aggregate simultaneously several conflicting and incommensurable factors (dimensions). They look for the decision of the best compromise. The goal programming (GP) is one of the multi-criteria decision aid models that have been applied to the field of accounting. The aim of this paper is to provide an exhaustive literature review of the GP application within the field of accounting and to propose a new typology which serves as a guideline for the accountants to identify the most appropriate variant of GP to deal with specific accounting related decision making situation.

Multi-criteria decision analysis with goal programming in engineering, management and social sciences: a state-of-the art review

Goal programming (GP) is an important class of multi-criteria decision models widely used to analyze and solve applied problems involving conflicting objectives. Originally introduced in the 1950s by Charnes et al. (Manag Sci 2:138–151, 1955) the popularity and applications of GP has increased immensely due to the mathematical simplicity and modeling elegance. Over the recent decades algorithmic developments and computational improvements have greatly contributed to the diverse applications and several variants of GP models. In this paper we present a state of the art literature review on GP applications in three selected (prominent and popular) areas, namely engineering, management and social sciences.

DEVELOPING OF DECISION SUPPORT SYSTEM FOR BUDGET ALLOCATION OF AN R&D ORGANIZATION

The problem of optimizing fund allocation in the public sector through performance based budgeting is a complex process. This paper considered only a very small part of this process, namely the final allocation of public funds through goal programming technique. While analyzing the last nine years of budget data in an R&D organization, it is inferred that there is wide gap between fund utilization and its allocation. The paper proposes a performance based model to assist decision makers in assessing the R&D programmes of an organization and accordingly make budget allocation in a more realistic and accurate manner than before. The model has been finally implemented using Excel spreadsheet and the same can be used as a DSS (Decision Support System) for the performance-based allocation of public funds. This study has made an approach to assess the influence of priority and risks associated with R&D programmes using Fuzzy Set Theory (FST). In the present scenario of economic context, optimum utilization of public funds requiring optimum public fund allocation is a dynamic and complex process. The problem of optimizing expenditure in the public sector was first discussed in early 1990s especially in the U.S.A. Various models of performance based budgeting were developed and implemented from then. Given the complexity of the operational context, public funds allocation is difficult to optimize(1).This paper addresses the problem of public fund allocation in an R&D organization using a performance based goal programming model with fuzzy logic . Performance based budgeting has been emerged as a tool for optimizing the public funds allocation. It has been widely employed in several OECD countries. Performance budgeting focuses on results rather than inputs compared to traditional budgeting. It helps managers to have more confidence in their policy making decisions. Here we develop a decision support system to implement performance based budgeting to improve the existing fund allocation in the concerned R&D organization. The organization of this paper is as follows: First section provides an introduction to Performance based budgeting and the problem addressed in this paper. The second section gives a short review of the literature referred. The third section describes the problem identified. The methodology is described in detail in the fourth section. A case study is included in the fifth section.Section six summarizes the study in a brief manner. 2. LITERATURE REVIEW This section deals with a brief review of researches conducted in the area of decision making in order to identify the approach and tool to be adopted in the work. The literature shows several applications of goal programming (GP) and fuzzy logic techniques. It is seen from the literature that GP has been the most widely used multi- objective technique in management science. Among the different risk analysis techniques, the application of Fuzzy Set Theory (FST) to risk analysis seems appropriate from the review of literature conducted.

Joint venture formation and partner selection in upstream crude oil section: goal programming application

In the past three decades, the magnitude of business dynamics has increased rapidly due to increased complexity, uncertainty and risk of international projects. This fact made it increasingly tough to ‘go alone’ into the international projects. As a consequence, companies with diverse strengths and weaknesses cooperatively bid for joint ventures (JV) formation. Joint venture is also a well-established aspect of the crude oil industry, specifically in the upstream segment. Making decision on the optimal form of JVs is still a challenging problem. In addition, the success of a JV is intertwined with the accuracy of the partner selection phase. Therefore, this paper formulates a multi-criteria mathematical model to select the best partners and form an optimal JV for undertaking oilfield projects. The lexicographic goal programming technique is employed to minimise undesirable deviations from diverse goals such as resources needs (technological and expertise), budgetary requirements, time, etc. The model is validated with a real-life-based example and provides insightful views on alternative formats of cooperation.

Contractor Selection Using Integrated Goal Programming and Fuzzy ELECTRE

Outsourcing plays an important role in the success of organizations. One of the risks associated with outsourcing is inappropriate contractor selection which significantly influences the implementation of projects in terms of time, quality, and cost. In this study, we applied multi-criteria decision-making techniques in order to determine the best contractor using criteria such as reputation, offered price, and technical capacity. This study is primarily aimed at identifying important criteria of contractor selection, determining the significance of the criteria, and designing a framework for selection of the most appropriate contractor. Important criteria for selecting contractors were extracted from the literature and experts' views were collected using questionnaire. Accordingly, six criteria were selected and their weights were determined by the application of goal programming. Finally, contractors were ranked and the best contractor was selected using fuzzy ELECTRE technique with trapezoidal fuzzy numbers.

A Multi-stage Multi Criteria Model for Portfolio Management

Today, portfolio management is an important task for all companies. The aim of portfolio selection is to choose a set of projects from a pool of possible projects such that the value of financial actions is maximized conditional on resource availability and technical limitations. However, existing portfolio selection methods and models do not significantly consider the dimension of time. Nevertheless, the best portfolio selection decisions depend on investor characteristics and expectations as well as the coordination of portfolio cash flows with financial plans. In this paper, we provide a goal programming model based on the current perception of the future prices of existing portfolio projects and present a plan for the selection of the optimal project portfolio by taking into account the flows desired by investors. Goal Programming (GP) is a very useful approach in the area of multiple criteria decision making that allows the decision maker to include multiple deviations of restrictions and goals, especially in the area of portfolio selection. This paper gives an application of GP to portfolio selection and analysis problems. To incorporate uncertainty into the model, portfolio prices and multiple criteria are considered in fuzzy conditions. In addition, we consider real option valuation in project portfolio management (PPM). We also present a method of fuzzifying PPM parameters using expert opinions. In applying the model, we consider a 6-project portfolio over five investment periods and compare the use of the model in fuzzy and non-fuzzy situations.

Application of Goal Programming to Swap Analysis in Oil Industry

Swap practices in the oil industry have been mostly ad-hoc, with no theoretical foundation, the main objective being to swap products on an as-needed basis. This research, using goal programming (GP), proposes a multiple objective methodology to solve the swap problem of the firms in the oil industry. The proposed model offers attractive alternatives and various practical GP-based scenarios, allowing the managers in the oil industry to consider viable options which take into consideration preferences, tradeoffs and outcomes when planning their logistics strategies and supply chain management activities. The swap strategies offered in this research have been developed with close cooperation of the officials from the oil industry—thus have practical implications and can be readily implemented.