Cost Coefficients Research Articles

Assessing the economic effects of drought using Positive Mathematical Planning model under climate change scenarios

In recent decades, regions all around the world have experienced severe droughts adversely affecting their agricultural production. Climate change, along with limited access to water will alter future production and agricultural development. The purpose of this study is to provide a perspective for the future cultivation regime in the Divandarre region in the Sepidrood catchment in Iran, using historical climatic, agricultural, and economic information. Future precipitation values are determined for three climate scenarios, then downscaled and converted to pixel-based precipitation maps using the Moving Least Squares method. Future droughts are identified using the Standardized Precipitation Index at 3, 6, and 9-month intervals based on precipitation values and the relationship between different types of droughts (meteorological, agricultural and hydrological). We introduce a new coefficient, the water cost coefficient, derived from drought characteristics that captures the added irrigation cost in drought years because of increased water price. Using the Positive Mathematical Planning method and considering limited land and water, predicted future prices and costs based on a linear regression of supply-demand, and the annual water cost coefficient values, an agroeconomic model is built. After prediction of future price and cost based on historical data from 2005 to 2018, we run future scenarios based on various price and cost values to determine the optimal annual cultivation area for each crop from 2020 to 2040. All scenarios indicate a decline in cultivation area for all crops making agriculture less beneficial in the future. The cultivation regime moves away from more water-consuming products with less economic value (e.g. watermelon) toward less water-consuming, more expensive products (e.g. lentils). The findings of this model along with expert economic judgments help determine the economic effects of climate change on irrigation, farmers' decisions, and water policies, including water markets, and improving irrigation efficiency. Authorities and farmers could adapt to drought shocks and changes in the market while experiencing less revenue loss.

Energy-saving and subsidy policy decisions for double competition manufacturers

With rising consumer environmental awareness (CEA) and strict carbon peak and carbon neutral targets, energy-intensive manufacturers are involving a fierce double competition in price and energy efficiency. However, their energy-saving and regulators' subsidy policy decisions are unclear. We addressed this gap here, where we have derived the manufacturers' optimal energy-saving decisions and have shown the effective conditions of the subsidy policy under three scenarios, including no difference, difference in unit original energy levels and difference in investment cost coefficients scenarios. Under the first scenario, we show the following: (1) those manufacturers in a weakly competitive environment should increase their unit energy saving, and counterintuitively, they should reduce it after the competitive intensity exceeds a threshold value; the increase in the CEA encourages the manufacturers to pursue a greater unit energy saving; (2) their profits are harmed by the increase in the competitive intensity; interestingly, the increase in the CEA is not always beneficial to those manufacturers; (3) The environmental concern degree of the regulator and society has a key impact on the effect of the subsidy policy. Under the latter two scenarios, many results depend on the manufacturers' type, such as dirty and clean manufacturers, outdated technology and advanced technology manufacturers.

Optimal Decisions in Green, Low-Carbon Supply Chain Considering the Competition and Cooperation Relationships between Different Types of Manufacturers.

In this study, we built a green, low-carbon supply chain including one green manufacturer, one green remanufacturer and one retailer in which the manufacturer produces new, green, low-carbon products and the remanufacturer recycles and remanufactures the green, low-carbon products. We assumed the manufacturer to be the Stackelberg leader and the remanufacturer and the retailer to be Stackelberg followers. The game model was solved using backward induction. We discuss the optimal operation strategies for green, low-carbon supply-chain members in a centralized decision-making model, decentralized decision-making model, manufacturer-remanufacturer cooperative decision-making model and manufacturer-retailer cooperative decision-making model. Furthermore, we discuss the impacts of the unit cost savings for remanufacturing, the recovery cost coefficient and the green improvement cost coefficient on the green supply-chain members' optimal decision and profits. The results show that increased unit cost savings from remanufacturing can increase the total profit of the supply chain and promote the recycling and remanufacturing of waste products. Moreover, the total profit of the green, low-carbon supply chain is the highest in the centralized decision-making model and lowest in the manufacturer cooperative decision-making model. When there is a cooperation relationship between the manufacturer and the retailer, the optimal recycling effort level and the optimal greenness level for the new product and the remanufactured product are the highest.

Grandfathering or benchmarking: Which is more viable for the manufacturer’s low-carbon activities?

Under the emissions trading policy, two typical carbon allowance allocation rules of grandfathering and benchmarking are widely adopted in the present carbon markets. Based on the mathematical modeling method, this paper explores which allocation rule is more viable for manufacturers’ low-carbon activities including abatement investment and remanufacturing activities. Meanwhile, the effects on total profit, total carbon emissions, consumer surplus, and social welfare are discussed through numerical analysis. The results show that benchmarking is more viable for abatement investment activities of manufacturers than grandfathering. Additionally, benchmarking is always more viable for remanufacturing activities of manufacturers only in a situation with a higher consumer low-carbon preference. Otherwise, which allocation rule is more beneficial for remanufacturing activities mainly depends on the abatement cost coefficient. Correspondingly, the higher the consumer low-carbon preference or the lower the abatement cost, the more viable the benchmarking is to achieve each performance target (e.g., total profit, emissions control, consumer surplus, and social welfare). Based on these findings, this paper also recommends managerial insights for manufacturers and policy implications for policy-makers.

Online scheduling using a fixed template: the case of outpatient chemotherapy drug administration

In this paper, we use a fixed template of slots for the online scheduling of appointments. The template is a link between planning the service capacity at a tactical level and online scheduling at an operational level. We develop a detailed heuristic for the case of drug administration appointments in outpatient chemotherapy. However, the approach can be applied to online scheduling in other application areas as well. The desired scheduling principles are incorporated into the cost coefficients of the objective function of a binary integer program for booking appointments in the template, as requests arrive. The day and time of appointments are decided simultaneously, rather than sequentially, where optimal solutions may be eliminated from the search. The service that we consider in this paper is an example to show the versatility of a fixed template online scheduling model. It requires two types of resource, one of which is exclusively assigned for the whole appointment duration, and the other is shared among multiple appointments after setting up the service. There is high heterogeneity among appointments on a day of this service. The appointments may range from fifteen minutes to more than eight hours. A fixed template gives a pattern for the scheduling of possibly required steps before the service. Instead of maximizing the fill-rate of the template, the objective of our heuristic is to have high performance in multiple indicators pertaining to various stakeholders (patients, nurses, and the clinic). By simulation, we illustrate the performance of the fixed template model for the key indicators.

A New Approached Method for Solving the Four Index Transportation Problem with Fuzzy Parameters and Application Perspective in Industry

The four index transportation problem (4ITP: origin, destination, goods type, vehicle type) is an extension of the Hitchcock problem, a problem best suited for the goods allocation planning in the case for using the shuttle type. Based on the research results in 1979, in 2011, we developed an exact method for fully solving the 4ITP with determinist parameters. However, because of the limitation of human in recognizing the imprecise natural events in the decision making science, it is necessary to use fuzzy numbers for uncertain condition. By extending the problem toward this direction, in this paper, we develop a method, an extension of fuzzy programming, solving the 4ITP when all parameters such as the cost coefficients, the supply and demand quantities, the goods type and vehicle type quantities are fuzzy numbers and obey the Gaussian law in order 2. We use Gauss method in the fuzzy programming to obtain the costs under form of point cloud. From these results, we use Pham’s method, a method for the conventional 4ITP, to obtain a point cloud with the agglomerating costs. By using Kronechker’s method, we extract the obtained minimal cost under form of an interval for a given solution. It allows us to obtain a goods allocation planning with the minimal total transportation cost.

Fix-and-optimize metaheuristics for minmax regret binary integer programming problems under interval uncertainty

The Binary Integer Programming problem (BIP) is a mathematical optimization problem, with linear objective function and constraints, on which the domain of all variables is {0, 1}. In BIP, every variable is associated with a determined cost coefficient. The Minmax regret Binary Integer Programming problem under interval uncertainty (M-BIP) is a generalization of BIP in which the cost coefficient associated to the variables is not known in advance, but are assumed to be bounded by an interval. The objective of M-BIP is to find a solution that possesses the minimum maximum regret among all possible solutions for the problem. In this paper, we show that the decision version of M-BIP is Σp2-complete. Furthermore, we tackle M-BIP by both exact and heuristic algorithms. We extend three exact algorithms from the literature to M-BIP and propose two fix-and-optimize heuristic algorithms. Computational experiments, performed on the Minmax regret Weighted Set Covering problem under Interval Uncertainties (M-WSCP) as a test case, indicates that one of the exact algorithms outperforms the others. Furthermore, it shows that the proposed fix-and-optimize heuristics, that can be easily employed to solve any minmax regret optimization problem under interval uncertainty, are competitive with ad-hoc algorithms for the M-WSCP.

Research on the application of search algorithm in computer communication network

Abstract This article mitigates the challenges of previously reported literature by reducing the operating cost and improving the performance of network. A genetic algorithm-based tabu search methodology is proposed to solve the link capacity and traffic allocation (CFA) problem in a computer communication network. An efficient modern super-heuristic search method is used to influence the fixed cost, delay cost, and variable cost of a link on the total operating cost in the computer communication network are discussed. The article analyses a large number of computer simulation results to verify the effectiveness of the tabu search algorithm for CFA problems and also improves the quality of solutions significantly compared with traditional Lagrange relaxation and subgradient optimization algorithms. The experimental results show that with the increase of the weighted coefficient of variable cost, the proportion of variable cost in the total cost increases from 10 to 35%. The growth is relatively slow, and the fixed cost is still the main component. In addition, due to the increase in the variable cost, the tabu search algorithm will also choose the link with large luxury to reduce the variable cost, which makes the fixed cost slightly increase, while the network delay cost and average delay slightly decrease. The proposed method, when compared with the genetic algorithm, has more advantages for large-scale or heavy-load networks.

Nano optical temperature sensor based on fiber Bragg grating using graphene

In this paper, a novel Graphene-Assisted Microfiber based temperature sensor is proposed that changing the surrounding temperature has a severe effect on the refractive index of the graphene, so these changes result in the transmission power of microfiber. In the proposed structure, the position of the graphene sheet affects the interaction of light and graphene, effectively. Simply, by examining the change in transmission power, the temperature sensitivity of 0.1038 dBC in the heating process and 0.1072 dBC in the high-resolution cooling process of 0.0098 ℃ is obtained, which is 16 times more than bare fiber. Moreover, in this paper, both MgF2 layer and the graphene ribbon affect fiber refractive index so a high sensitivity, compact size, low cost, linear operation and suitable transmission coefficient temperature sensor is achieved. This sensor has great potential in the field of measurement, especially surrounding temperature measurement. Also, a comprehensive and detailed study of the properties of surface conductivity, electric dispersion, refractive index and permittivity for graphene with sensitivity simulation is accomplished.

Incentive Regulation of Construction Waste Resource Recycling: Subsidy and Tax Incentive

Construction waste destroys the sustainability of environment and economy. In the study of game theory at present, the construction waste resource-based products supply chain only considers that there is only one type of enterprise producing renewable building materials or new building materials in the market, but in the construction market, there is no single type of enterprise. It isvery important for government to protect the environment and promote the resource utilization and development of construction waste. Therefore, this study establishes a game theory model between traditional building materials enterprise and construction waste recycling enterprise, which considers the decision-making behavior of enterprise under no regulation, subsidy incentive regulation, or tax incentive regulation. The findings are listed as follows: (1) both subsidies and tax incentives can effectively improve the economic benefits and production initiative of construction waste recycling enterprise. (2) The impact coefficient of unit new building materials on the environment and the tax rate of unit new building materials are the key factors that affect government decision-making. (3) Resource utilization cost coefficient and builder’s preference for renewable building materials have a great impact on the profits of construction waste recycling enterprise. These results will provide reference for the government to formulate incentive regulations and promote the development of construction waste resources.

Optimal maintenance service outsourcing strategy considering extended warranty service

AbstractIn recent years, extended warranty (EW) services are widely provided by brand firms. Given that third‐party maintenance service providers have more maintenance cost advantages than brand firms, brand firms face the challenge of whether to enter the maintenance service market. To address the above challenge and issue, we develop theoretical models. The research shows that the cost coefficient of developing maintenance service plays a critical role in the optimal maintenance service operation strategy. And the base market sizes of the EW and the charged maintenance service have significant influences on the results. Moreover, we propose conditions for a win–win outcome.

Determination of Efficiency of Weapon Systems Maintenance as Condition for DM Distribution

The article discusses the mathematical model of technical condition-based maintenance of weapon systems. The model his developed based on a semi-Markov stochastic process. The diffusion-monotonic (DM) distribution law, which is specific for airfield technical condition-based maintenance of aircraft, is has been used as a failure model, and type I errors are considered. For standard operating conditions, graphs of the dependence of the coefficient of technical use and specific costs per hour of operation in good condition from the basic parameters are shown. The optimal maintenances interval ensuring maximum maintenance coefficient value has been proved. The principal results have been achieved by using multiple calculation method.

Effects of channel power structures on pricing and service provision decisions in a supply chain: A perspective of demand disruptions

This paper investigates the service investment and pricing decisions under different power structures when there exist demand disruptions. We consider a supply chain consisting of a single manufacturer and a single retailer, and establish a game theory model under three typical channel power structures: Manufacturer Stackelberg game, Retailer Stackelberg game, and Nash game. We find that when the demand is disrupted, whether it is a centralized system or decentralized system with different power structures, the service level and production quantity are robust when the demand disruption is sufficiently small, and the wholesale price is also robust under certain circumstance. The comparison results of equilibrium pricing and service level under different power structures are affected by the supply chain power and service cost coefficient no matter who provides the demand-enhancing services. Moreover, it can be found that the power structure most suitable for the manufacturer or the retailer is not invariable and the results are related to the change of some parameters.

Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources.

A healthcare resource allocation generally plays a vital role in the number of patients treated (pnt) and the patient waiting time (wt) in healthcare institutions. This study aimed to estimate pnt and wt as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient (δi) in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the δi of resources cost. The AB algorithm performed best in almost all scenarios based on the results of the analysis. According to the AB algorithm based on the δ0.0, δ0.1, δ0.2, and δ0.3, the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for pnt; 0.9514, 0.9517, 0.9514, and 0.9514 for wt, respectively in the training stage. The GB algorithm had the best performance value, except for the results of the δ0.2 (AB had a better accuracy at 0.8709 based on the value of δ0.2 for pnt) in the test stage. According to the AB algorithm based on the δ0.0, δ0.1, δ0.2, and δ0.3, the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for pnt; 0.8820, 0.8821, 0.8819, and 0.8818 for wt in the training phase, respectively. All scenarios created by the δi coefficient should be preferred for ED since the income provided by the pnt value to the hospital was more than the cost of healthcare resources. On the contrary, the wt estimation results of ML algorithms based on the δi coefficient differed. Although wt values in all ML algorithms with δ0.0 and δ0.1 coefficients reduced the cost of the hospital, wt values based on δ0.2 and δ0.3 increased the cost of the hospital.

A Multiobjective Fuzzy Optimized Model for a Sustainable Facility Location of Life Vehicle Reverse Logistic Considering the Recycling

Today, reverse logistics networks are increasingly expanding in various industries due to considering parameters such as environmental conditions, customer awareness, economic savings, social responsibilities, and gaining points such as customer loyalty. In the design of the reverse logistics network, the options of taking parts, repair, recycling, reproduction, and disposal can be considered. In this research, an integrated forward/reverse logistics network is modeled and an attempt is made to provide a general model and by adding the recycling option to the previous models, to a more general optimization model that includes more recovery options. It is achieved and the network should be optimized in the forward and reverse mode at the same time, taking into account the production of various products, first with the assumption of known demand and return, and then by fuzzing the parameters with uncertainty, as well as the limited capacities of the network centers. To defuzzification of this model, a method based on alpha cut is used for cost coefficients of decision variables and the center of mass method for demand and fuzzy return in the objective function. In fuzzy limits, a method based on the comparison of fuzzy numbers is used. In order to show the application of the Mixed Integer Linear Programming results, several examples have been simulated and coded in GAMS software to obtain accurate answers. In order to solve the model in large dimensions of the problem, a multi-objective genetic algorithm has been developed and coded by MATLAB software. The results of solving the examples in both cases, the exact and near-optimal solutions obtained from the genetic algorithm, have been compared and at the end, the sensitivity analysis of the network has been done. The obtained results show that considering imprecise parameters in a fuzzy manner, firstly, a more realistic model is obtained. Secondly, in examining the answer, due to their connection, the decision maker has unlimited options for the correct decision according to the existing conditions.

ECONOMIC VALUATION AND THE DETERMINANTS OF DEMAND FACTORS OF BANDUNG STRAWBERRY AGROTOURISM, WEST JAVA, INDONESIA

Strawberry agrotourism is the most extensive strawberry farming in Bandung, West Java. This agrotourism has the potential for nature, tourism, conservation, and regional economic potential. This research aims to identify the characteristics and assessment of visitors, identify factors influencing tourism demand, and estimate the value of the agrotourism economy. The economic value is analyzed using the method of TCM (Travel Cost Method). This method is calculated based on total consumer surplus, travel cost coefficient, and respondent visits. The average travel cost incurred by 379 respondents during the trip is IDR. 413,699, and the total cost of the entire journey is IDR.156,792,000. The surplus of consumer visits is IDR. 12,670, and the individual consumer surplus visits are IDR.33. The economic value generated for one year is IDR. 237,600. The factors that influence the demand are income, distance, and time to know the location. At the same time, the factors that do not affect the market are travel cost, education, number of dependents in the family, gender, and age of visitors.

A multi‐priority hierarchical optimization method for double‐layer model predictive control

AbstractConsidering the demand for the sequential regulation of manipulated variables in actual industrial process control, the conventional solution of double‐layer model predictive control faces the problem that the weight coefficients are difficult to tune. This paper proposes an improved hierarchical optimization method for manipulated variables in the steady‐state optimization layer of double‐layer model predictive control. The proposed method can adjust the manipulated variables sequentially without an accurate weight coefficient to avoid difficulty in tuning the weight coefficients. The relation between the optimal solution and the feasible region of the steady‐state optimization layer is analysed to describe the reoptimization of the key manipulated variables. The impact of the economic cost coefficient on the optimal solution with the sensitivity analysis method is studied, and the complexity of using the weight coefficient to solve the priority optimization problem of the manipulated variables is assessed. The steady‐state optimization solution procedure is improved based on the theory of the multiobjective complete hierarchical method. The hierarchical and sequential optimization of the manipulated variables results in expanding the space and freedom of the key manipulated variables, increasing efficiency, reducing consumption, and improving economic performance. The improved hierarchical optimization method is direct and simple in achieving optimization sequentially and satisfies the need for adjusting the manipulated variables according to human intentions.

Green or non-green: A game to introduce decision making under fairness concerns

As such problems as scarcity of resources and environmental pollution have become increasingly prominent, the concept of emerging green products has been gradually entering the consciousness of manufacturers and retailers. However, will the supply chain members challenged by the high cost of green products be willing to make the transition to green production? To answer this question, this paper studies the production and sales decisions made by the manufacturers and retailers when taking the production of both common products and green products into consideration. In terms of product selection, the model of fairness and neutrality concerns is constructed and carefully studied. It is found in this study that with all the manufacturers under capacity constraints, when the manufacturers produce common products, their fairness concerns will increase their own utility but damage that of their downstream partners; when it comes to green products, the fairness concerns of the retailers will not decrease the utility of the manufacturers, which is the same case as the fairness-neutral retailers, but the manufacturers will be provoked to maximize their utility by their fairness concerns; the optimal level of green efforts by manufacturers and retailers should be obtained by measuring the cost coefficients of both parties. These conclusions will not only help improve the green level of the supply chain but also well exhibit the friendly development of surrounding conditions.

Cost-sharing contract design between manufacturer and dealership considering the customer low-carbon preferences

Manufacturers and dealerships usually have a long-term cooperation in selling low-carbon products produced by signing a contract without considering the dynamic changes in consumer’s low-carbon preferences. Consumers’ low-carbon awareness and the low-carbon reputation of those low-carbon products occasionally alternate affected by the market circumstances, which will influence the selling of low-carbon products. This paper focuses on the contract design between a manufacturer and a dealership using Stackelberg differential game models to formulate the long-term contract between a manufacturer and a dealership. To promote the low-carbon reputation of low-carbon products, two cost-sharing contracts, including a one-way cost-sharing contract (OWC) and a two-way cost-sharing contract (TWC), are proposed. The manufacturer in the OWC contract provides a certain amount of subsidies to incentivize the dealership. The manufacturer and the dealership share costs in the TWC contract. Besides, dual-channel models are also analyzed as an extension of the proposed model. Several findings were obtained by solving and analyzing the proposed game models. The manufacturer’s emission reduction efforts and the dealership’s low-carbon promotion efforts are influenced by dealership’s cost coefficient related to the promotion of low-carbon product, low carbon reputation sensitivity coefficient of consumers, influence coefficient of manufacturer’s emission reduction efforts on low-carbon reputation and etc. The proposed cost-sharing contracts improve the manufacturer and the dealership’s efforts by changing the factors. The OWC contract can achieve Pareto improvement, and the TWC contract can realize the coordination between the manufacturer and the dealership. The optimal conditions for its contract choice are also proposed from the manufacturer’s point of view. This paper also provides valuable insights for companies in the supply chain to design suitable contracts to coordinate the supply chain stakeholders’ activities, especially in promoting low-carbon efforts in a long-term cooperation.

Decision analysis of international joint prevention and control of public health emergencies.

COVID-19 has caused huge losses to countries around the world, and it will not end in a short time. The lack of motivation for international joint prevention and control is one of the important reasons for the global pandemic of COVID-19. How to improve the efforts and level of international joint prevention and control has become an urgent problem to be solved. Considering the long-term and dynamic nature of international joint prevention and control, the differential game method is used to compare and analyze the optimal decisions of countries in the three scenarios of spontaneous governance, external subsidies and internal cost sharing. The results show that the optimal prevention and control efforts of countries are negatively correlated with discount rates, prevention and control cost coefficients, decay rate and risk factors. It is positively correlated with the impact degree of social benefits, the impact degree of prevention and control efforts on the level of joint prevention and control, the distribution ratio of social benefits, and the impact degree of prevention and control level on social benefits. The prevention and control efforts, joint prevention and control level, social benefits and system benefits under spontaneous governance are the lowest and highest under the internal cost sharing. The internal cost sharing will only be carried out when social benefits distribution ratio obtained reach a certain threshold. This study provides decision-making support for the joint prevention and control of countries to defeat COVID-19 under the normalization of the epidemic.