A GP-Based Hierarchical Objectives Decision-Making Method for Building Energy Efficiency Optimization

Building energy efficiency, which is critical in reducing environmental impact, has become one of the most important objectives of building designs. In order to precisely express the goals of building designs, and help decision makers estimate the ultimate performance of design schemes in advance when searching for the optimal building design, the Goal Programming Model (GPM) is introduced in this study to provide a solution for explicit design objective delivery and multi-stakeholder involved decision-making support. In this proposed method, EnergyPlusTM works as a simulation engine to search for the relationship between design parameter combinations and building energy consumption. Simultaneously, Genetic Algorithm (GA) is used to improve the efficiency of overall building energy performance optimization by processing multiple iterations. A case study with five possible design scenarios was dedicated in this study to implement the proposed optimization method, and the optimization results verified the capacity of the established GP-based optimization method to satisfy various design requirements for decision makers and/or stakeholders, especially in facing the hierarchical objectives with different priorities. In this case, the envelope-related variables, including the exterior wall and window, serve as optimization objectives. The optimization is carried out under the ideal air conditioning system, considering different energy usage patterns. Meanwhile, comparing with the vague and restricted expression of objectives in multi-objective optimization, the proposed GP-based optimization method provides explicit trade-off relationships among various objectives for designers, which improves the practical value of the optimized designs, so as to ensure the project success and facilitate the development of green buildings.

Ranking the Performance of Universities: The Role of Sustainability

University rankings assess the performance of universities in various fields and aggregate that performance into a single value. In this way, the aggregate performance of universities can be easily compared. The importance of rankings is evident, as they often guide the policy of Higher Education Institutions. The most prestigious multi-criteria rankings use indicators related to teaching and research. However, many stakeholders are now demanding a greater commitment to sustainable development from universities, and it is therefore necessary to include sustainability criteria in university rankings. The development of multi-criteria rankings is subject to numerous criticisms, including the subjectivity of the decision makers when assigning weights to the criteria. In this paper we propose a methodology based on goal programming that allows objective, transparent and reproducible weighting of the criteria. Moreover, it avoids the problems associated with the existence of correlated criteria. The methodology is applied to a sample of 718 universities, using 11 criteria obtained from two prestigious university rankings covering sustainability, teaching and research. A sensitivity analysis is carried out to assess the robustness of the results obtained. This analysis shows how the weights of the criteria and the universities’ rank change depending on the λ parameter of the goal programming model, which is the only parameter set by the decision maker.

Hybrid Uncertainty-Goal Programming Model with Scaled Index for Production Planning Assessment

Goal programming (GP) can be thought of as an extension or generalization of linear programming to handle multiple, normally conflicting objective measures. Each of these measures is given a goal or target value to be achieved. Unwanted deviations from this set of target values are then minimized in an achievement function. Production planning is an important process that aims to leverage the resources available in industry to achieve one or more business goals. However, the production planning that typically uses mathematical models has its own challenges where parameter models are sometimes difficult to find easily and accurately. Data collected with various data collection methods and human experts’ judgments are often prone to uncertainties that can affect the information presented by quantitative results. This study focuses on resolving data uncertainties as well as multi-objective optimization using fuzzy random methods and GP in production planning problems. GP was enhanced with fuzzy random features. Scalable approaches and maximum minimum operators were then used to solve multi-object optimization problems. Scaled indices were also introduced to resolve fuzzy symbols containing unspecified relationships. The application results indicate that the proposed approach can mitigate the characteristics of uncertainty in the analysis and achieve a satisfactory optimized solution.

Lexicographic Goal Programming Model for Bank’s Performance Management

Any bank’s financial management is essential to preparing the assets and liabilities for multiple goals. In this paper, we develop an optimal bank model for the financial management department in the Kingdom of Saudi Arabia. The lexicographic goal programming model was used to formulate the banks’ performance management. In this study, the six goals of one of the leading banks in Saudi Arabia, namely, maximize asset, minimize liability, maximize equity, maximize operating income, maximize net income, and maximizing total goal achievements in the financial statement, were studied. To illustrate the model, we have focused on Al Rajhi Bank’s financial statements as a case study. The data was obtained from the banks’ financial statements. The outcomes of the study exhibited that all goals were accomplished. This proposed model is dynamic because it will help examine the banks’ financial strengths located in the kingdom. As a result, the proposed model can guide banking firms in making decisions and developing strategies to deal with numerous monetary circumstances.

Supplier Selection Analysis Using Minmax Multi Choice Goal Programming Model

Production control, inventory and distribution is an important factor in trading activities. These three factors are discussed in a system called Supply Chain Management (SCM). Procurement of goods from a company or trading business related to suppliers. In some cases, there are several supplier that can be assessed by considering certain factors. In certain cases, the data from several factors that are considered are uncertainty, so the fuzzy approach can be used. The MINMAX Multi Choice Goal Programming model can be used to solve fuzzy supplier selection problems with linear membership function. It can be applied to selecting supplier of Brastagi Oranges. There are four suppliers, namely Jaya, Mako, Baros. Gina. There are three factor to consider, cost, quality and delivery. The decision maker select the best supplier for ordering 17000 kg Brastagi oranges. The results, the best supplier is Gina with an order quantity of 10000 kg and Mako with a total order of 7000 kg

Optimizing the Financial Management of Electronic Companies using Goal Programming Model

The swift development and transformation of emerging technologies such as augmented reality, robotics, biometrics and 3D printing place varying degree of pressure to the electronic industry to play a trailblazing role in making the world a smarter place of living. The concept of smart city increases the demand for the upgrades and sophistication of electronic components. Shorter product life cycles of personal and commercial electronic products also keep the electronic companies in a never-ending loop for huge investments in materials, equipment and expertise. Electronic companies in Malaysia are still facing financial stress in their operations. Therefore, this paper aims to optimize the financial management of listed electronic companies, namely D&O, GTRONIC, UNISEM and VITROX with asset, liability, equity, earning, profit and optimum management item as the objectives using goal programming model. The benchmarks of all the goals are obtained by comparing the maximum and minimum values of the optimal values of these companies. The results of this study show that the goal programming model is able to generate the optimal solution for each company. Besides liability and earnings, all the goals have been attained by these companies upon analysis using goal programming. Possible refinement values particularly for liabilities for all the companies have been generated from this model to provide insights for these companies to benchmark for risk alleviation and strategic decision making.

Discussion on the Feasibility of the Integration of Wind Power and Coal Chemical Industries for Hydrogen Production

To improve the utilization rate of the energy industry and reduce high energy consumption and pollution caused by coal chemical industries in northwestern China, a planning scheme of a wind-coal coupling energy system was developed. This scheme involved the analysis method, evaluation criteria, planning method, and optimization operation check for the integration of a comprehensive evaluation framework. A system was established to plan the total cycle revenue to maximize the net present value of the goal programming model and overcome challenges associated with the development of new forms of energy. Subsequently, the proposed scheme is demonstrated using a 500-MW wind farm. The annual capacity of a coal-to-methanol system is 50,000. Results show that the reliability of the wind farm capacity and the investment subject are the main factors affecting the feasibility of the wind-coal coupled system. Wind power hydrogen production generates O2 and H2, which are used for methanol preparation and electricity production in coal chemical systems, respectively. Considering electricity price constraints and environmental benefits, a methanol production plant can construct its own wind farm, matching its output to facilitate a more economical wind-coal coupled system. Owing to the high investment cost of wind power plants, an incentive mechanism for saving energy and reducing emissions should be provided for the wind-coal coupled system to ensure economic feasibility and promote clean energy transformation.

Growth Scale Optimization of Discrete Innovation Population Systems with Multichoice Goal Programming

How are limited resources efficiently allocated among different innovation populations? The performances of different innovation populations are quite different with either synergy or competition between them. If the innovation population is kept under an appropriate scale, full use can be made of the allocated resources. The maximization of the development and performance for a certain scale of innovation population is a typical multichoice development problem. Therefore, the scale optimization of the innovation population should be analyzed. According to the population dynamics, a resource constraint model for the growth of innovation population is developed, and the growth of innovation population under resource constraints is in equilibrium accordingly. With the help of a multichoice goal programming model, the scale optimization of innovation population performance can be obtained. The results of the resource constraint model and multichoice goal programming model are used to determine the optimal scale of the innovation population. From the panel data of the innovation population in Jiangsu Province from 2000 to 2017, we have found that R&D investment was the main innovation resource variable and that patent number was the main innovation output variable. Based on these data, the scale optimization of the innovation population under resource constraints can be calculated. The results of the study show that, in the observation period, the enterprise innovation population is often in the appropriate scale state. The scale development of enterprise innovation population is often more suitable for innovation ecosystem than that of scientific research institutions. According to these results, the government can provide appropriate guiding policies and incentives for different innovation populations. The innovative population can adjust its own development strategy and plan in time accordingly.