Goal Programming Technique Research Articles

A novel bi-objective optimization model for an eco-efficient reverse logistics network design configuration

Ecological sustainability has become a top priority for governments and business practitioners because of the massive surge in electronic waste (e-waste). This ecological context has compelled electronic firms to adopt reverse logistics (RL) and to invest in advanced technologies with a focus on sustainable recovery practices and on reducing excessive carbon emissions (CEs). This study proposes a multi-objective mixed integer programming (MOMIP) model for configuring a RL network design; it incorporates multiple products, multiple recovery facilities, multiple processing technologies, and a selection of vehicle types. The novelty of this study lies in its consideration of four green technologies (inspection, dismantling, repair/refurbishing, and recycling facilities), and its efforts to maximize return yield potential in the form of product, components, and material recovery. The main objective of the model is to minimize overall RL network cost and environmental impact due to processing and transportation. Further, a weighted goal programming technique is used to determine efficient solutions and furnish a trade-off among the conflicting objectives. The mathematical model is validated utilizing a real life case study of an electronics manufacturing firm in India. The total RL network cost and emissions are both reduced, and different technologies are selected automatically for the RL processing facilities. The results demonstrate that the return yield increases significantly with the greener technology selections. The study also draws significant implications, specifically that carbon tax regulatory policies aid in significantly reducing carbon emissions to a large extent along with increasing product return yield. Hence, the results will help industrial managers in their strategic and tactical decision making. Their evaluations of recovery options, technology selection, and vehicle selection with regard to economic and ecological impact will permit decision makers to gain valuable managerial insights.

A study on uncertain fixed charge bi-objective 4-dimensional transportation problems under budget constraints

<span lang="EN-US">Generally, in traditional transportation problems, single items are transported from a set of origins to a set of destinations in such a way that the transportation cost is minimized. To face the challenges that arise in modelling transportation problem various parameters are to be considered. In this work to provide an optimal transportation policy to the decision maker, we have studied a fixed-charge bi-objective multi-product <br /> 4-dimensional transportation problem with vehicle speed under budget constraints under uncertain environment. The objective of the proposed model is to have the maximum profit with the minimum time taken of transporting the goods. An equivalent deterministic model of the proposed model is obtained to deal the uncertain variable using expected value-chance constraint properties on uncertainty theory and its compromise solution is obtained by using the goal programming technique. A numerical example is discussed to provide more clarity on the proposed model.</span>

Multi-goal Multi-product Fuzzy Solid Transportation Problem via Goal Programming Technique

Multi-goal Multi-product Fuzzy Solid Transportation Problem via Goal Programming Technique

A new integrated circular economy index and a combined method for optimization of wood production chain considering carbon neutrality

The wood industry is potentially advantageous to applying the concepts of circular economy for sustainable development and can contribute to the commitment of carbon neutrality. This study developed an integrated circular economy index based on five different quantitative indicators for assessment of the wood production chain: heat recovery rate, CO2 sequestration rate, fossil fuel substitution rate, renewable electricity usage rate, and revenue increase from the by-products. A combination of best-worst method (BWM) and linear goal programming (LGP) techniques was investigated to develop an optimal circular economy model of wood processing chain for reduction in CO2 emission. The integrated circular economy index and the combined method were tested in a case-study of a rubberwood processing chain in Vietnam. The proposed model suggests that the woodchips and biomass from the harvesting and processing of rubberwood could be collected and treated using microwave thermolysis techniques; the enzyme hydrolysis technique is appropriate for bioethanol and biomethane recovery from the sawdust; and the hot air technique is preferable in the drying process. The proposed model could result in a significant reduction of the total net carbon emission from +552,750 tons CO2eq to −1,145,940 tons CO2eq per year. This could support the achievement of Vietnam's zero CO2 emission goal and hopefully contribute to the country's commitment to carbon emission neutrality by the year 2050.

Hazardous waste management system for Thailand’s local administrative organization via route and location selection

ABSTRACT This study proposed a decision support system (DSS) for optimizing transportation routing and disposal hub location for Thailand’s local administrative organizations’ hazardous waste management. The first step is to choose the origin and destination, as well as to collect data for each route. Each route’s decision criteria were based on traffic quality and risk measures from the perspective of users. An analytic hierarchy process (AHP) was used to compute effective weights for decision criteria. To achieve optimal routing, AHP weights were combined with a zero-one goal programming technique. The DSS operates systematically and successfully, taking into account both transportation risk and socioeconomic factors, and provides appropriate routes. Overall, this waste management system may provide useful information to decision-makers for prioritizing disposal site alternatives and implementing feasible waste management actions. The zero-one location design was then used to determine the best hub location. Based on two scenarios of six and fifteen CoG hubs, the results revealed that the best model for managing a case company’s hazardous waste was to establish 15 hubs scattered throughout the country for collecting points and then transporting them to the waste disposal plant in Phitsanulok province. Locating the hub near sink points would reduce transportation costs and backhaul issues, reducing the environmental impact of GHG emissions and increasing system efficiency. Implications: The problem of waste pollution has become increasingly serious all over the world, particularly in developing countries that face significant pollution control challenges. End-of-life wastes cause economic, health, and environmental problems if they are not properly managed. As the world’s population and living standards rise, so does the amount of waste produced. The environmental impact is significant, with massive amounts of waste generated each year with only basic or minimal treatment to mitigate its impact. As a result, there is an urgent need for a method that can precisely optimize transportation routing and disposal hub location for Thailand’s local administrative organizations’ hazardous waste management (LAOs). The proposed decision support system (DSS) may provide useful information to decision-makers in prioritizing disposal site alternatives and implementing feasible waste management actions.

Multi-objective Mathematical Model for Asset Portfolio Selection using Neutrosophic Goal Programming Technique

Multi-objective Mathematical Model for Asset Portfolio Selection using Neutrosophic Goal Programming Technique

A Fuzzy Multi Objective Inventory Model with Production Cost and Set-up-Cost Dependent on Population

AbstractIn this paper, I have developed a multi item production inventory model for the non-deteriorating items with constant demand rate under the limitation on set up cost. The production price and set-up price are the most vital problem within the inventory system of the marketplace in international. Here the production cost is dependent on the demand as well as populations. Set up cost is dependent on the average inventory level. Holding cost is the most challenging issue in the business world. In order to reduce the holding cost, the holding cost function has been considered as on the number of peoples. Due to uncertainty all the cost parameters are taken as the generalized triangular fuzzy number. Multi objective fuzzy inventory model has been solved by various techniques like Fuzzy programming technique with hyperbolic membership function, Fuzzy non-linear programming technique and Fuzzy additive goal programming technique. Numerical example is given to illustrate the inventory model. Sensitivity analysis and the graphical representations have been shown to illustrate the reality of the inventory model.

Bi-criteria optimization of cleaner biofuel supply chain model by novel fuzzy goal programming technique

In engineering, the biofuel supply chain (BSC) is a significant supply chain optimization challenge. Here, the multi-objective optimization problem for biofuel distribution is based on the life cycle energy consumption (LCEC) and the emission of CO2 involve in supply network of a biofuel. It includes numerous feedstocks, means of transportation, and locations for biofuel plant construction, as well as multiple technologies, the number of feedstocks in the agricultural system, transportation capabilities, crop yields, and market needs. To provide a modelling framework for optimization issues with multi-fold uncertainty, different reduction approaches to turn a Type-2 Fuzzy variable into a Type-1 Fuzzy variable through the expected value method are described. The deterministic BSC model is then solved utilizing Goal Programming and Weighted Sum Method using Generalized Reduced Gradient Technique. The goal BSC models are (i) Minimization of life cycle energy consumption (ii) total emission of CO2 in uncertain environment. The numerical experiments demonstrated with different optimistic labels by which managers can control their objects by choosing optimistic values. Here, we have developed a systematic optimization approaches for the advancement of biofuel transportation, supply and process chain analysis, sustainability, and industrial optimization. Finally, a real-world case study is used to demonstrate the applicability of the suggested approach. The findings show that the proposed solutions are successful in reducing logistical expenses.

Evaluation of insurance companies considering uncertainty: A multi-objective network data envelopment analysis model with negative data and undesirable outputs

Uncertainty is an important issue to consider when evaluating entities in both public and private sectors. On the other hand, many operations have more than one stage process when some inputs are fed to the system to produce a number of intermediate measures. The intermediate measures are then transformed into final products in the subsequent stages. The composition method in network data envelopment analysis (NDEA) is a popular method for measuring the efficiency of a two-stage process. The composition method is fractional bi-objective programming that is solved by non-linear programming techniques such as bisection search. In this paper, the two-stage NDEA is extended with negative data and undesirable outputs. First, we propose an alternative linear model based on the goal programming technique to avoid complex non-linear calculations. Then, we use a method to transform negative data into positive and undesirable outputs into desirable ones. Finally, we develop the proposed model using the fuzzy α-cut approach in order to incorporate data uncertainty in the linear goal programming (GP) model. To validate the accuracy of the proposed model, a numerical example is solved. To show the applicability of the proposed model, a real case of 22 insurance companies is examined. We also perform a comparative analysis to specify the benchmark and inefficient companies. Comparative analysis can help managers to recognize where improvement should be investigated with priority.

Goal Programming and Mathematical Modelling for Developing a Capacity Planning Decision Support System-Based Framework in Higher Education Institutions

Achieving the Saudi Kingdom’s vision 2030 in the higher education sector requires higher education institutions to make a significant simultaneous change in their current practices. This encompasses the transitioning of government-funded educational institutions to be financially independent. Therefore, a prompt, agile transition is required while maintaining a positive socioeconomic impact, entrepreneurship and innovation, and high-quality education. This necessitates the transition to lean processes and the review of current practices. One of the most vital processes in educational institutions is student admission/enrollment capacity planning. This study puts forward a capacity planning decision support system (DSS)-based framework for university student enrollment. The framework was applied to the case of KAU, where current practice and challenges are presented, and from which data were collected. A top-down/bottom-up approach was followed and applied using the goal programming technique and a developed mathematical model, respectively. Results show that the proposed framework effectively affects student admission/enrollment capacity planning on strategic and operational levels. Moreover, it can be used in other planning aspects of higher education in universities, such as human resources planning, teaching load planning, faculty-to-student ratios, accreditation, quality requirements, lab capacity planning, equipment/teaching aids procurement, and financial planning, to mention a few. The implications of this study include assisting decision-makers in higher education institutions in matching their admission/enrollment capacity of student numbers between the macro-strategic and the micro-operational level.

Stable Goal-Based Management Strategy for Supply Chain Finance

The article is devoted to improving the methodology for managing the total working capital of the supply chain to reduce costs for given ranges of liquidity. The paper analyzes the current state of research on working capital management in supply chain finance and substantiates the multi-objective nature of managing joint working capital in the supply chain. The possibilities of individual financial instruments to reduce the cost of total working capital for given ranges of liquidity have been explored. The necessity of using the technique of dynamic goal programming in the problem of working capital management is substantiated. A model and algorithm of goal management have been developed, which is implemented on cases of real supply chains and has shown its effectiveness

Estimation and Optimization for System Availability Under Preventive Maintenance

Engineers design systems to be reliable and work to fulfil their missions without failure for a specific period. However, the system components deteriorate with time and lead to its failures. A frequent system failure increases the management costs, hence posing a challenge to decision-makers. Therefore, for the avoidance of frequent system failures, preventive maintenance is necessary. The objective of any manufacturing firm is to maximize profit and minimize costs. The interval for preventive maintenance can be optimized if the system’s availability is maximized and its cost function minimized. This study evaluates the availability and cost function for a continuous operating series-parallel system under a fixed time environment. A multiobjective model is formulated to maximize the availability and minimize the cost function of the system. The study illustrated a numerical example and solved using goal programming (GP), fuzzy goal programming (FGP), genetic algorithm (GA), and particle swarm optimization (PSO) techniques. The results are compared using a robust statistical test and, the PSO proves to be better. A simulation study was carried out further to evaluate the availability and cost function using R and MATLAB packages.

Mathematical modeling of sustainable development goals of India agenda 2030: a Neutrosophic programming approach.

Since 2015, the United Nations sustainable development goals (SDGs) agenda 2030 has been designed with 17 goals, 169 targets, and 232 unique indicators to help address environmental, development, and sustainability issues globally. India, like other developing nations, desired to achieve its vision 2030 targets. Several authors studied India’s SDGs with different approaches. However, none of the studies explores the concept of neutrosophic programming (NP). It is against this drawback; this study presents an optimization model for India’s socio-economic and environmental goals based on the NP concept. The NP model is capable of handling indeterminacy in optimization-related problems for which other techniques do not. The formulated models simultaneously optimized the gross domestic product (GDP) growth, electricity consumption, and greenhouse gas (GHG) emissions. The solutions revealed that the degree of satisfaction for the goals related to GDP, electricity consumption, and GHG emissions could be achieved partially. Also, it suggested the required optimal number of employment for each economic sector to achieve India’s vision 2030. The SDGs model was further solved with the goal programming (GP) technique and compared with the NP results to validate the proposed concept. It has been found that the proposed model gives a better compromise solution than the GP model. The study can help and guide policymakers in working toward vision 2030 attaintment. Other interested researchers can use the concept in other countries to help decision-makers understand managerial policy implications.

A robust fuzzy multi-objective programming model to design a sustainable hospital waste management network considering resiliency and uncertainty: A case study.

With the increase in the number of patients and activity of hospitals, the issue of hospital waste management (HWM) is becoming more and more challenging and worrying. In addition to financial losses, there will be irreparable damage to the ecosystem and environment which will create many problems for people (because the job of some people in the area is livestock and agriculture and they have a lot to do with their surroundings). It also doubles the need to pay attention to the issue of sustainable development (simultaneous attention to social, economic and environmental dimensions) in waste management. Moreover, the climatic and geographical conditions and lack of proper waste management in this area lead to major problems. Therefore, in this research, by developing a novel multi-objective mixed integer linear programming model, HWM is addressed in the hospitals of Sari, Iran. The aim is to design an HWM network considering sustainability, resiliency and uncertainty. In order to deal with uncertainty, a robust fuzzy programming approach is employed, and then an improved goal programming technique and Lp-metric method is proposed to solve the model. It was revealed that goal programming outperforms the Lp-metric method in terms of all objectives. Furthermore, the obtained results demonstrate the applicability and efficiency of the proposed methodology to design an efficient sustainable HWM network.

Solving multi-level multiobjective fractional programming problem with rough interval parameter in neutrosophic environment

In this study, a novel algorithm is developed to solve the multi-level multiobjective fractional programming problems, using the idea of a neutrosophic fuzzy set. The co-efficients in each objective functions is assumed to be rough intervals. Furthermore, the objective functions are transformed into two sub-problems based on lower and upper approximation intervals. The marginal evaluation of pre-determined neutrosophic fuzzy goals for all objective functions at each level is achieved by different membership functions, such as truth, indeterminacy/neutral, and falsity degrees in neutrosophic uncertainty. In addition, the neutrosophic fuzzy goal programming algorithm is proposed to attain the highest degrees of each marginal evaluation goals by reducing their deviational variables and consequently obtain the optimal solution for all the decision-makers at all levels. To verify and validate the proposed neutrosophic fuzzy goal programming techniques, a numerical example is adressed in a hierarchical decision-making environment along with the conclusions.

A combined approach for modeling multi-echelon multi-period decentralized supply chain.

In this paper, a multi-echelon, multi-period, decentralized supply chain (SC) with a single manufacturer, single distributor and single retailer is considered. For this setting, a two-phase planning approach combining centralized and decentralized decision-making processes is proposed, in which the first-phase planning is a coordinated centralized controlled, and the second-phase planning is viewed as independent decentralized decision-making for individual entities. This research focuses on the independence and equally powerful behavior of the individual entities with the aim of achieving the maximum profit for each stage. A mathematical model for total SC coordination as a first-phase planning problem and separate ones for each of the independent members with their individual objectives and constraints as second-phase planning problems are developed. We introduce a new solution approach using a goal programming technique in which a target or goal value is set for each independent decision problem to ensure that it obtains a near value for its individual optimum profit, with a numerical analysis presented to explain the results. Moreover, the proposed two-phase model is compared with a single-phase approach in which all stages are considered dependent on each other as parts of a centralized SC. The results prove that the combined two-phase planning method for a decentralized SC network is more realistic and effective than a traditional single-phase one.

A Hybrid Grid Partitioning and Fuzzy Goal Programming Model for Enhanced Performance and Decision Support

This study introduces an innovative Hybrid Grid Partitioning and Fuzzy Goal Programming Model for Enhanced Performance and Decision Support. The proposed model combines grid partitioning and fuzzy goal programming techniques in order to address the challenges of complex systems and optimize performance while taking into account subjective preferences and uncertainties. Using grid partitioning, the hybrid model divides the problem space into smaller grid cells, facilitating localized analysis and efficient processing. Fuzzy goal programming handles ambiguous objectives and uncertain constraints, providing decision-making flexibility and robustness. The effectiveness of the model is demonstrated through a numerical example, highlighting enhanced performance and decision support for complex problems. The applicability of the hybrid paradigm to diverse fields, including transportation, logistics, and resource management, is discussed. The study also acknowledges limitations, such as computational complexity and the need for validation in the actual world. In the future, it will be necessary to resolve these limitations and improve the hybrid model. Overall, the research advances optimization methodologies and decision support systems by providing a comprehensive framework for confronting complex systems and facilitating well-informed decision making.

An interactive approach to multi-objective mean semi-variance model with transaction cost for portfolio optimization

Portfolio selection may be considered as a complicated decision making under uncertain conditions. Regardless of an unknown future, a fund manager has to make critical financial choices based on the investors’ preferences towards risk and return and perceptions. Since the pioneer work of Markowitz, a lot of studies have been published considering his Mean-Variance (MV) model as a basis. In our proposed model we had considered Semivariance as the risk measure in our proposed portfolio optimization model. We also extend our model to include the effect of transaction cost in portfolio optimization and then the concept of entropy is incorporated as an objective function in our model to obtain a well diversified portfolio within optimal asset allocation. An interactive approach is used to solve the model. A numerical example is used to illustrate that the model can be efficiently used in practice. Finally the result obtained in interactive method has been compared with the result obtained by Fuzzy Non-Linear Programming (FNLP) and Fuzzy Additive Goal Programming (FAGP) techniques.

OPTIMIZING BALANCE SHEET FOR BANKS IN INDIA USING GOAL PROGRAMMING

In earlier years, there was abundance of funds in banks in the form of demand and savings deposits. Hence, the focus of banks was mainly on asset management. But intense competition and volatility of interest rate due to banking reforms reduced the availability of low-cost funds and therefore, banks focused on liability management as well. These pressures call for structured and comprehensive measures and not just ad hoc action. This is how banks started to concentrate more on the management of both sides of the balance sheet. As a result, the concept of asset-liability management originated in India and introduced in the Indian banking industry since 1st April 1999 to administer the risk management aspects. This paper attempts to optimize assets and liabilities of banks using goal programming technique. Secondary data is collected from annual reports of Allahabad bank from 2010-2019 and RBI website is used for modelling. The findings show that in Allahabad bank, goal programming help in achieving optimization and increase profitability. The model incorporating constraints and set objectives. It model can support banks in decision making process, planning, budgeting, and forecasting. An attempt is made to use realistic goals and constraints after discussing with bank officials.
 JEL Classification Codes: C61, G21, G32.

Optimizing Design of Smart Workplace through Multi-Objective Programming

Smart environments have proven very supportive to the improvement of the performance of people in different workplaces. Plenty of applications have been introduced spanning different settings including healthcare, ambient assisted living, homes, offices, and manufacturing environment, etc. However, subjectivity and ambiguity prevail in the majority of research, and still, up to date, rare approaches found quantitatively and objectively constructing or assessing the impact of smart enabling technologies on the performance of the subject environment. Further, no approaches have considered optimizing the adoption of those smart technologies with respect to objectives achievement. This article presents a novel optimization methodology for designing a smart workplace environment in conditions of ambiguity or fuzziness. The methodology begins with defining and weighing the overall goals and objectives of the workplace. The Prometthe multi-criterion decision-making technique is used to weigh the operational objectives with respect to the overall workplace goals. Next, the relation among basic building blocks of the model; namely: the operational objectives, smartness features, and smart enabling technologies are quantified, utilizing fuzzy relations. Then, the fuzzy goal programming techniques will be utilized to optimize the impact relation values while considering the budget constraint. The proposed optimization methodology is implemented on the development and optimization of the smart clinic, as a typical instance of the workplace.