Objective Programming Model Research Articles

Research on Bi-Level Objective Programming Model of Water Resources Uncertainty Based on Water Rights Trading—A Case Study of the Yehe Irrigation District in Hebei Province, China

Research on Bi-Level Objective Programming Model of Water Resources Uncertainty Based on Water Rights Trading—A Case Study of the Yehe Irrigation District in Hebei Province, China

Optimal Decision on Pricing and Replenishment of Vegetable Products Based on Goal Planning

Developing replenishment and pricing plans has always been the key for fresh food supermarkets to gain more profits. This paper analyzes various situations and provides the optimal solution through Spearman correlation analysis, Lasso regression, goal programming and other methods and models. We use normality testing and box plots to present the distribution patterns of major categories and individual products from both vertical and horizontal perspectives. Establish a single objective programming model and solve it with Lingo to obtain a maximum profit of 98513 yuan for 7 days. The largest total replenishment amount is for the flower and leaf category, the smallest is for edible mushrooms, the highest average cost markup rate is for aquatic roots and stems, and the lowest is for cauliflower. This has certain reference value and theoretical significance for fresh food supermarkets to formulate replenishment and pricing plans.

Research on Pricing and Replenishment Strategies for Vegetable Commodities Based on an Enhanced Nonlinear Objective Programming Model

With the increasing consumption of fresh food ingredients in the Chinese market and the perishable nature of vegetable commodities, which represent the largest proportion, optimizing procurement strategies plays a crucial role in enhancing the profitability of supermarkets. This has garnered significant attention from scholars and practitioners. Drawing on principles from microeconomics, this study employs multiple linear regression to describe the relationship between pricing and sales volume. Additionally, time series forecasting methods are utilized. By considering both supermarket profitability and social welfare maximization as optimization objectives, single-objective optimization and multi-objective nonlinear programming models are established. Various decision strategies for supermarkets under different scenarios are discussed, and a satisfactory decision is obtained by solving the models using differential evolution algorithm. Through research on the expected conditions and data provided by real supermarkets, this paper provides guidance for intelligent supermarket procurement strategies and supply chain management in practical settings.

Research On Replenishment Decision of Vegetable Commodities Based on Seasonal Decomposition Exponential Smoothing Method and Objective Programming Model

Vegetables are indispensable in people’s daily lives and represent a high proportion of the goods sold in supermarkets and bring in a large amount of profit. Therefore, the replenishment and pricing decisions on the day of sales are particularly important. In order to make the best replenishment decision, this paper firstly solves the relationship equation between the sales volume of each vegetable category and the cost-plus ratio coefficient based on the least square fitting; secondly, considering the cyclical change of the vegetable sales volume, the seasonal decomposition of the exponential smoothing method is used to predict the demand for each vegetable category in the first week of July; lastly, the profitability optimization function of the superstore is set up: the objective function is the total profit of the superstore, and the decision variables are the daily supply of each vegetable category. The objective function is the total profit of the hypermarket, the decision variables are the daily supply of each vegetable category and the cost-plus ratio coefficient, and the constraints consider the maximum inventory of the hypermarket, the supply is greater than the demand and is less than the maximum value of the historical sales volume. The maximum daily profit and the optimal cost-plus ratio of the hypermarket are finally solved. This study has reference value for vegetable commodity replenishment decision.

Price And Replenishment of Vegetables Based on NARX Neural Network

The research on the replenishment and pricing of vegetable commodities is helpful for supermarket to make more scientific purchase and sale decisions. Based on this, this paper mainly uses NARX neural network to study the replenishment and pricing of vegetable commodities. Firstly, it preprocesses the missing and abnormal values of the data, constructs a correlation analysis model to study the relationship between the sales of various categories of vegetables, then establishes a NARX neural network model to solve the daily replenishment total, and finally constructs a single objective programming model to get the pricing strategy, which provides a theoretical basis for supermarket to make scientific pricing and replenishment decisions.

A spatiotemporal optimization method for nutrient control in lake watersheds

Developing an efficient strategy for managing nutrients in less-developed lake watersheds that can balance the need for socioeconomic progress with the protection of aquatic ecosystems has become an urgent research subject for achieving sustainable development. This paper improves the optimization method for environmental and economic management of lake watersheds proposed in our previous research. A spatiotemporal optimization method based on a coupling model consisting of the Soil and Water Assessment Tool, system dynamics model, and objective programming model was applied to an agricultural non-point source (ANPS) pollution control program and a rural sewage treatment program at the Yilong Lake watershed as a case study. A simulation evaluation showed that the efficiency of the previous scheme was significantly improved after conducting spatiotemporal optimization. This scheme was dynamic and distributed, demonstrating an annual and high-resolution control program that can provide a basis for the precise management of ANPS. Although it still requires improvement, a framework for coupling simulation and two-step optimization was achieved in this study.

Optimization of Resources using Sen’s Multi Objective Programming (MOP) Model for Potato Growers of Nalanda District, Bihar

An appropriate resource use planning is required for the development of farming sector. Increasing income, employment with lesser use of irrigation, fertilizer may be the major objectives to be achieved. There are several mathematical models for achieving multiple objectives. Sen’s Multi Objective Programming (MOP) model is most popular for achieving several objectives simultaneously. In the present study, an optimal cropping plan was proposed for potato growers of Nalanda district of Bihar for increasing income, employment with lesser use of fertilizer.

Fuzzy Multi-Objective Optimization to Evaluate the Performance of Suppliers Taking Into Account the Visibility and Supply Chain Risk

Abstract Adequate and desirable connections between suppliers and customers necessitate an appropriate flow of information. Therefore, a promising and proper data collaboration in the supply chain is of tremendous significance. Thus, the study’s main objective is to provide multiple objective programming models under uncertain conditions to assess the performance of suppliers. To meet that aim, a case study for the reliability assessment of the presented model is carried out. That section is associated with supply chain visibility (SCV). Likewise, the likelihood of unpredicted and undesirable incidents involving supply chain risk (SCR) is taken into consideration. The intimate relation between visibility and risk of the supply chain is deemed efficient for the performance of the supply chain. Incoherence in maximization and minimization of SCR and SCV and other factors, including costs, capacity, or demand, necessitates multiple objective programming models to assess suppliers’ performance to accomplish the before-mentioned aims. The study’s results indicate the high reliability of the proposed model. Besides, the numeral results reveal that decision-makers in selecting suppliers mainly decrease SCR and then attempt to enhance SCV. In conclusion, the provided model in the study can be a desirable model for analyzing and estimating supplier performance with SCR and SCV simultaneously.

Quantitative trading strategy and portfolio optimization analysis based on LSTM model

This paper focuses on the optimal risk portfolio problem. Based on the principle of return maximization, an LSTM neural network model is used to predict the price movements of gold and bitcoin; then an objective planning method is used to find the optimal trading strategy for each day. Finally, the final amount of the initial asset is calculated for the end date. To accommodate the huge price changes of Bitcoin, an LSTM neural network model was built for prediction in this paper, which produced well-fitted Bitcoin price data. After obtaining the predicted prices of gold and bitcoin, an objective programming model is built with daily returns as the objective function, constraints are set, and market days (gold can be traded) and non-market days (gold cannot be traded) are discussed to derive the optimal investment strategy; a linear programming solution method is used to derive the optimal solution under the constraints to ensure the maximum amount of daily returns. The LSTM neural network forecasting model used in this paper has a long memory function and good predictability. Meanwhile, the heuristic algorithm greatly accelerates the convergence of the model for the optimal solution.

Optimization of the Territorial Spatial Patterns Based on MOP and PLUS Models: A Case Study from Hefei City, China.

Optimization of the territorial spatial patterns can promote the functional balance and utilization efficiency of space, which is influenced by economic, social, ecological, and environmental factors. Consequently, the final implementation of spatial planning should address the issue of sustainable optimization of territorial spatial patterns, driven by multiple objectives. It has two components-the territorial spatial scale prediction and its layout simulation. Because a one-sided study of scale or layout is divisive, it is necessary to combine the two to form complete territorial spatial patterns. This paper took Hefei city as an example and optimized its territorial spatial scale using the multiple objective programming (MOP) model, with four objective functions. A computer simulation of the territorial spatial layout was created, using the patch-generating land use simulation (PLUS) model, with spatial driving factors, conversion rules, and the scale optimization result. To do this, statistical, empirical, land utilization, and spatially driven data were used. The function results showed that carbon accumulation and economic and ecological benefits would be ever-increasing, and carbon emissions would reach their peak in 2030. The year 2030 was a vital node for the two most important land use types in the spatial scale-construction land and farmland. It was projected that construction land would commence its transition from reduced to negative growth after that time, and farmland would start to rebound. The simulation results indicated that construction land in the main urban area would expand primarily to the west, with supplemental expansion to the east and north. In contrast, construction land in the counties would experience a nominal increase, and a future ecological corridor would develop along the route south of Chaohu County-Chaohu Waters-Lujiang County-south of Feixi County.

Optimal ordering and transportation scheme of raw materials in production enterprises based on objective programming model

In order to better realize the development of construction production enterprises and create greater profit space for them, based on the relevant data of raw material suppliers and forwarders of a production enterprise within five years, this paper first uses ARIMA algorithm to solve the enterprise's ordering scheme, and then uses 0-1 integer programming to find the constraints between suppliers and forwarders, Finally, LINGO software is used to build the target programming model to solve the 24-week transfer scheme.

A coupling simulation and optimization method developed for environmental-economic management of Lake watershed.

Many lake basins are facing the challenge of mitigating water shortage and water pollution while maintaining economic growth. Existing planning method for water pollution control often focus on how to alleviate water pollution effectively at the lowest cost, but rarely pay attention to the dynamic feedback and synergy effects between water pollution abatement, water conservation and social economy. This article proposes a method which consists of system dynamics model (SDM), Soil and Water Assessment Tool (SWAT) and objective programming model (OPM). It could be used to create insights on basin-wide water problems from a systematic perspective. The case study on Yilong Lake Watershed evaluates the proposed measures in existing local planning, calculates the optimal scheme, and discusses issues including the uncertainty of effectiveness, choice between recycling sewage and transferring outside, and the necessity of restricting food processing industry. This method could be improved on the simulation of social and industrial economy, the simulation of water cycle, and the spatial planning.

Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China

The rapid development of Internet finance has impacted traditional investment patterns, and Internet money funds (IMFs) are involved extensively in finance. This research constructed a long short-term memory (LSTM) neural network model to predict the return rates of IMFs and utilized the value-at-risk (VaR) and liquidity-adjusted VaR (La-VaR) methods to measure the IMFs’ risk. Then, an objective programming model based on prediction and risk assessment was established to design optimal portfolios. The results indicate the following: (1) The LSTM model results show that the forecast curves are consistent with the actual curves, and the root-mean-squared error (RMSE) result is mere 0.009, indicating that the model is suitable for forecasting data with reliable time-periodic characteristics. (2) With unit liquidity cost, the La-VaR results match the actuality better than the VaR as they demonstrate that the fund-based IMFs (FUND) have the most significant risk, the bank-based IMFs (BANK) rank 2nd, and the third-party-based IMFs (THIRD) rank 3rd. (3) The programming model based on LSTM and the La-VaR can meet different investors’ preferences by adjusting the objectives and constraints. It shows that the designed models have more practical significance than the traditional investment strategies.

An omega portfolio model with dynamic return thresholds

AbstractThe Omega ratio grants flexibility to a decision‐maker regarding the selection of a return threshold (τ). Although the return threshold can affect the effectiveness of the Omega portfolio, the literature has not soundly concluded how to determine it. In this paper, we develop a multiple objective programming model to generate time‐varying return thresholds by maximizing the τ value and maximizing the deviation between gain and loss, considering short sales and trading costs in portfolio rebalances. The empirical results using the daily returns of the composite stocks in the S&P 500 index over 13 years suggest that the proposed Omega (Max τ) model realizes higher performances than the widely applied portfolio models and the fixed‐τ Omega models under different rebalancing frequencies. The time‐varying return thresholds generated by our model capture the dynamics of the overall market and can be suitable for index enhancement.

An interactive multi-objective programming approach for project portfolio selection and scheduling

Organizations have to allocate resources, time, and workforce in many projects at the same time. Selection and scheduling of the projects have a significant impact on effective project management. However, most of the studies in the literature do not solve the selection and scheduling problems simultaneously. This study aims to design an interactive process to integrate selection and scheduling processes in the project management. For this purpose, a new multi objective programming model is proposed. The project scores are presented as belief degrees (i.e., distributions to linguistic term levels) that are gathered as a result of the weighted cumulative belief degree approach. By the use of the belief degrees, projects could be selected and scheduled based on the satisfaction level of the problem owner. The proposed model considers conditions and restrictions in management of business development projects such as the progress percentage of the projects in a period, the complementary and mutual exclusive relations between projects, etc. An interactive solution procedure is developed in order to solve the proposed model. The proposed model and the solution procedure are applied in an information technology company for their business development projects.

Dual-objective Programming Model and Optimization Based on Factor Analysis

In this paper, through the establishment of factor analysis, multiple linear regression, and objective programming models, the optimal task pricing plan is given by analyzing the problem of meeting production requirements. Specifically, by using the R-type factor analysis method, using MATLAB to calculate the selected factor data. Import SPSS to calculate the KMO test, through factor analysis and comprehensive evaluation model, use the formula to calculate the total score of the comprehensive evaluation of suppliers, and select the top 50. In addition, we use the objective programming model to select from 131 companies, so as to obtain the plan with the least loss, the most economical, and meet the production needs of the enterprise. Further, the dual goal programming model is used to obtain the plan with the least loss. The transfer process in the objective planning problem is optimized.

Dynamic Multi-Attribute Decision Analysis on Three-Parameter Interval Grey Number of Upper (Lower) Deviation Quasi-Normal Distribution

Aiming at the dynamic multi-attribute decision making problem where the weight of each decision stage and attribute weight are completely unknown and the attribute value is unknown distributed three-parameter interval grey number, a three-parameter interval grey number dynamic multiattribute grey target decision making method with attribute value following quasi-normal distribution is proposed. Firstly, the position relationship between the “center of gravity” point and the kernel of the three-parameter interval grey number is discussed. According to the characteristic that the attribute value obeys the quasi-normal distribution, a new weight is given to the “center of gravity” point, and a new distance measure formula of the three-parameter interval grey number is defined. Secondly, according to the principle of maximum entropy, the objective programming model is constructed to determine the stage weight and attribute weight. Then, the schemes are sorted according to the size of the comprehensive bull’s-eye-distance. Finally, an example is given to illustrate the effectiveness of the decision model.

Selection of countermeasure portfolio for shipping safety with consideration of investment risk aversion

This paper proposes optimization models to select shipping safety countermeasure portfolios with consideration of the investment risk preferences of decision-makers. The formulation is extended from the basic economic safety model, where losses are scenario-specific and are expressed as a function of the countermeasure selection variables based on analysing the chain of events along accident paths. Conditional value-at-risk is introduced to measure the investment risk represented by the worst-case cost. Several single -objective programming models are formulated first to optimize the safety-related costs using either the expectation criterion in risk-neutral environments or the conditional value-at-risk criterion in risk-averse environments. Then a bi-objective model is presented by combining those two criteria. Simulation results indicate that the best countermeasures for the expectation objectives are different from those for the conditional value-at-risk objectives. Such findings provide shipping safety investment planners insights in balancing not only the active investment and the accident loss but the return and the risk of the investment, to achieve a selection of countermeasures that is cost-effective as well as leads to low costs in extreme situations.

Unmanned Aerial Vehicles Rescue System Design and Traffic Model Planning

Unmanned Aerial Vehicles (UAV) are widely used in disaster relief and road exploration in recent years. This paper mainly studied the emergency response of UAVs after disasters. The UAV response system is mainly suitable for the distribution of necessities and road exploration after geological disasters and tsunamis in coastal areas. By analyzing the problem and making reasonable assumptions, the optimization model was established with the traffic planning theory, and MATLAB software was used to program and solve the problem. An optimal scheduling scheme was presented to solve these problems. The normalization method was used to select a highly capable UAV. Taking the minimum volume of idle space buffer material as the objective function and taking into account the constraints, such as payload of unmanned aerial vehicle, a single objective programming model was established. The results are as follows: Each International Standards Organization (ISO) cargo container has five UAVs B, one UAV C, one UAV F and one UAV H. It provides 188 days of relief requirements with ISO cargo containers’ space utilization of 71.4%. The research shows that the UAV response system has the functions of necessities distribution and road exploration after disasters, and can be used to deal with the emergency response after disasters in coastal areas, and has a wide range of applicability.

Analysis and Construction of EEMD Smart Model and Fuzzy Forecasting through Improved Bayesian Estimation

Different portfolio models will have a great impact on the model rate of return. In order to meet the data needs of portfolio investment forecasting, a big data generation method based on incremental Bayesian network model is proposed. Firstly, the fuzzy interval number is constructed by using the global GM (1pare 1) prediction model, then the fuzzy Mmurv model is established based on the interval fuzzy number, and the model is optimized based on the midpoint, radius and acceptability of the interval number, thus a single objective programming model with parameters is obtained. The future data is partially generated by using the time series generation algorithm, and the Bayesian network model trained by historical data is updated with the newly generated data. So that the updated Bayesian network can reflect the relationship between the variables and the laws contained in the new and old financial data in this period of time. And use the model to predict the amount of foreign direct investment absorbed by China in 2020, test the robustness and analyze the impact of major random events to show the reliability of the prediction. The analysis is carried out on multiple scales. The results show that the integration of IMF1 and IMF2 components through the white noise test can be regarded as the random impact of external factors; the cycle length of IMF3 component is about 3 years, which can be regarded as the inventory cycle caused by the change of enterprise inventory; the cycle length of IMF4 component is 5 years, which can be regarded as political cycle; the cycle length of IMF5 component is about 20 years, which can be regarded as construction cycle. The set of portfolio paths is generated by path search algorithm in Bayesian network, and the big data set with real data characteristics is generated according to the probability distribution of each path. The experimental results show that the method is feasible and ensures a certain accuracy. The forecast results show that the amount of foreign direct investment in China will continue to fluctuate seasonally and in a trend in recent years.