Optimal Decision Model Research Articles

Research on Pricing Decision Considering Advertising Benefit and Consumer Psychological Benefit

For perishable goods, the optimal pricing models studied by current scholars seldom consider price effect, psychological benefit and consumption time. Based on the current basic newsboy model, this paper focuses on the price characteristics and consumer psychology of perishable products under the background of single dealer, in order to build a complete optimal price decision model of perishable products. According to the two-stage ladder pricing of perishable products in the pricing stage, this paper constructs the optimal price decision model of dealers in different sales stages by representing consumer demand and dealer profit equation, so as to provide enterprises with the maximum optimal strategy. Moreover, sensitivity analysis of parameters in the model is carried out to ensure the stability of the model.

Benefits evaluation of the Northeast Passage based on grey relational degree of discrete Z-numbers

The opening of the Arctic routes has great economic and strategic value, but the navigation environment is highly uncertain, unknown and complex. Aiming at the ambiguity and partial reliability of the uncertain information in the Arctic routes, based on the Z-number theory and the grey relational theory, this study establishes a grey relational optimization decision-making model for the comprehensive benefit evaluation of the Northeast Passage (NEP). First, the discrete Z-number is used to represent the comprehensive benefit evaluation information of channel, and Jensen Shannon divergence is introduced to measure the difference information between discrete Z-numbers. Then, on the basis of the grey relational degree of discrete Z-numbers, a new multi-dimensional fuzzy grey multi-criteria decision-making model is established and subsequently optimized using the PSO algorithm. Finally, the effectiveness and superiority of the new model are verified by comprehensively evaluating the benefit of using the Arctic NEP and the traditional route under different sea ice conditions. Results show that the comprehensive benefit of the Suez Passage is still the highest, but that of the NEP has been steadily increasing. Therefore, with future improvements in Arctic sea ice conditions and infrastructures, the NEP may become the best route for ocean shipping.

Joint optimization decision of service provider selection and CODP positioning based on mass customization in a cloud logistics environment

PurposeBy introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.Design/methodology/approachThis paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.Findings(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.Originality/valueThe originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

Bi-objective pavement maintenance and rehabilitation optimization decision-making model incorporating the construction length of preventive maintenance projects

Existing optimization decision-making approaches for pavement maintenance and rehabilitation (M&R) ignore the construction length of preventive maintenance (PM) projects, and its negative effects are difficult to be transformed into cost. To address this issue, this study proposes a bi-objective decision-making model that incorporates the problem as the second objective into the two-stage bottom-up approach. The proposed model contains selection of performance indicators, Bayesian neural network-based probabilistic deterioration model, evaluation of initial M&R actions on a segment level, and bi-objective decision-making. It is solved by the enumeration method and the non-dominated sorting genetic algorithm II. Finally, the Pareto solutions are obtained. A solution is an M&R plan, where an initial action (treatment type) is selected for a pavement segment. Among the Pareto solutions, the one with the second objective greater than or equal to and closest to the shortest construction length, is the optimal M&R plan. In addition, compared with the model that converts the problem into a constraint, the proposed model recommends a better plan that can achieve higher performance at lower cost, which validates the strength of the proposed model. Decision-makers can adopt the proposed model to optimize pavement M&R plans that consider the construction length of PM projects.

Shipbuilding Decision-Making Optimization Based on the Functional Technical Documentation Information Level Usage in Ship Production

_ Decision-makers in shipyards face the problem of determining the design cost concerning the information level contained in drawing and how it influences the shipbuilding process efficiency. Shipyards have their approach and shipbuilding practices about the level of functional technical documentation usage directly in ship production. However, decisions may vary related to the ship type, complexity, size, and deadlines. The shipbuilding decision-making optimization model based on functional technical documentation defines an algorithm for creating and analyzing the data representing the effects of applying functional technical documentation in ship production. Based on such analysis, it is possible to decide the preferred shipbuilding strategy, based on the level of functional technical documentation usage directly in ship production at the level other than one identified in the ship contractual stage, reducing overall shipbuilding costs. By varying different factors of the technological phase of ship production and the relative labor cost with the level of functional technical documentation usage directly in ship production, the curves of the shipbuilding cost and duration deviations, in relation with the value specified by the reference point, are obtained. Also, such analysis provides relevant conclusions about the trend of losses or savings of budget and labor hours based on the level of functional technical documentation usage directly in ship production.

Decarbonization roadmap for China’s energy sector in the context of carbon futures trading: An energy-environment-economy assessment perspective

By virtue of the hedging and price discovery function, carbon futures trading may help carbon market function more effectively. Is it necessary to establish carbon futures trading in China? The authorities have endorsed the idea since 2015, however, the scheme has not yet started; no antecedent pre-assessment quantitative research has been carried out. Therefore this study that attempts to fill this gap in the literature, could be of real significance. Through deriving a potential decarbonization roadmap, this study tries to give some clues pertaining to the converted mitigation strategy imposed by carbon futures trading in China. A model chain has been proposed, which is composed of the Optimal Production Decision-making Model for Producers, Life Cycle Impact Assessment, Monetization, and Genetic Algorithm based optimization, to quantify the environmental benefits (including improvements to human health, ecosystem damage and increased temperature induced GDP losses) of the assumed mitigation trajectories. By setting the maximization of environmental benefits as the objective, the optimal decarbonization roadmap with carbon futures trading is derived. Results show that the optimal emission reductions for power enterprises (covered by the carbon market) for the next 10 years (2021–2030) are around 3.27 billion tonnes CO2e. If 36% of this amount is assigned to previously discussed mitigation trajectories, it is found that 106.98 GW ultra-super critical units, 160.85 GW mono-Si PV facilities and 167.26 GW doubly-fed induction generator wind capacity should be installed. Overall environmental benefits are 4.6 trillion CNY2018, over 5% of China’s 2018 GDP. Results demonstrate the optimal emission reductions and potential decarbonization roadmap for China’s power enterprises (those covered by the carbon market) under the context of carbon futures trading, which can be an important reference for the authorities and therefore encourage the establishment of the scheme.

A Multi-Objective Decision Model for Water Pollution Load Allocation under Uncertainty

In order to control the discharge of regional total pollutants in the region and improve the ability of water environment management and decision making, a multi-objective decision-making optimization model of water pollution load allocation was constructed, which took into account economy and fairness. The model takes the maximum environmental benefit and the minimum weighted comprehensive Gini coefficient as the objective function and takes into account the uncertainty and multi-objectives of the model, which is conducive to promoting economic development and ensuring the fairness of regional water pollutant discharge. A method based on Monte Carlo simulation coupled with a genetic algorithm was designed to obtain the optimal solution set through multiple simulation optimization. This model is applied to Anhui Province to solve the allocation optimization problem of total pollutant reduction in the 13th Five-Year Energy Conservation and Emission Reduction Plan. After the optimization of water pollution load distribution, the comprehensive Gini coefficients of COD and NH3-N are reduced by different ranges. The comprehensive Gini coefficient after COD optimization decreased by 2.4–4.6%, and the comprehensive Gini coefficient after NH3-N optimization decreased by 25.1–32.5%, which verified the feasibility and rationality of the model in the optimal allocation of the total discharge of regional water pollutants. The model takes into account uncertain subjective and objective factors that have an important impact on water pollutant discharge targets and decision variables, thus optimizing the total emissions of the entire regional control unit in both space and time.

Research on optimal supply decision of manufacturers considering futures purchase under deferred payment

Taking the three-level supply chain consisting of a single supplier, a manufacturer and a strong brand as the research object, the manufacturer's optimal supply decision model is constructed, which considers the futures purchase under the deferred payment of the brand, and it is proved that the optimal solution not only exists but also is unique. Then, the influence of parameter variation on the optimal ordering strategy was analyzed by numerical example simulation and sensitivity analysis. The results show that when the period of deferred payment to the manufacturer is extended and the price discount is strengthened, the manufacturer can purchase from the futures market to lock the price and make up for its income loss through the supplier's delayed payment. Production rate should also be controlled, too high or too low production rate will affect the manufacturer's production cycle.

A Joint Optimization Algorithm for Renewable Energy System

Energy sustainability is a hot topic in both scientific and political circles. To date, two alternative approaches to this issue are being taken. Some people believe that increasing power consumption is necessary for countries’ economic and social progress, while others are more concerned with maintaining carbon consumption under set limitations. To establish a secure, sustainable, and economical energy system while mitigating the consequences of climate change, most governments are currently pushing renewable growth policies. Energy markets are meant to provide consumers with dependable electricity at the lowest possible cost. A profit-maximization optimal decision model is created in the electric power market with the combined wind, solar units, loads, and energy storage systems, based on the bidding mechanism in the electricity market and operational principles. This model utterly considers the technological limits of new energy units and storages, as well as the involvement of new energy and electric vehicles in market bidding through power generation strategy and the output arrangement of the virtual power plant’s coordinated operation. The accuracy and validity of the optimal decision-making model of combined wind, solar units, loads, and energy storage systems are validated using numerical examples. Under multi-operating scenarios, the effects of renewable energy output changes on joint system bidding techniques are compared.

Traffic flow modeling and optimization control in the approach airspace

<p style='text-indent:20px;'>Air traffic control behaviors in civil aviation approach airspace include arrival time control, aircraft sequencing and aircraft diversion. In the airspace with heavy air traffic flow, air traffic controllers need to make the best decision for multiple flights in a given space and time range. The topic of this paper is the optimization in civil aviation approach airspace. In this paper, the flight operation involves multiple different resource, and the flight path selection is reset based on the given flight constraints to optimize the objective function. This paper presents an optimal decision model for controlling the flight flow in the approach airspace. The performance of the model is evaluated in different simulation scenarios.</p>

Research and Application of intelligent purchasing and transportation model for coal-fired power plant

At present, the decision-making method of coal purchase in most coal-fired power stations is based on the experience of blending coal and burning in thermal power units, but this method does not consider the factors of economy and transportation. In order to solve this problem, this paper proposes a method to optimize the coal purchasing scheme of coal-fired power plants based on the joint optimization decision model of coal purchasing and dispatching. First fuel characteristics of mixed coal and purchasing cost model was constructed to determine the fountainhead procurement constraints, and mixing coal prices, then dispatching model was constructed to determine the scheduling constraints and transport price, the final will buy coal cost and transportation cost summation minimum and the nature of the mixed coal constraints as a joint decision model, and USES the particle swarm optimization algorithm to solve it. The decision model is applied to a power plant for verification, and the calculation results show that the total purchasing cost of power plant is greatly reduced.

Building a Multi-Objective Flexible Optimal Decision Model for Green Supply Chains

Due to climate change, the importance of environmental protection, and the operation of the global supply chain influenced by the pandemic, building a flexible green supply chain model can help enterprises keep operational sustainability and strengthen competitive advantages. However, it can be found from relevant literature that research on green supply chain flexibility is still insufficient. This study aims to fill the research gap, and attempts to develop a multi-objective mixed integer programming model for a flexible green supply chain network design to maximize the profit, the amicable production level. To our knowledge, this proposed model is the first effort to take economic factors, environmental factors, supply flexibility, manufacturing flexibility, distribution flexibility and reverse logistics flexibility into account simultaneously, and can be a reference for supporting effectively management of the green supply chain network design. The research result and findings can be a reference for related academic researches and also can be used to guide the development of a green supply chain model for making better decision.

Cost Sharing in Insurance Communities: A Hybrid Approach Based on Multiple-Choice Objective Programming and Cooperative Games

At present, utilizing the insurance community is a common method to deal with investment risks along the Belt and Road; however, there is no clear method or mechanism to deal with the decision-making optimization and cost allocation of the insurance community participants. We propose a hybrid approach to solve this problem. First, we construct an underwriting decision optimization model for the insurance community using the multi-choice goal programming method, which generates the cost characteristic function based on a cooperative alliance. Second, we use the cooperative game method combined with the modified Shapley value method to take risk factors into consideration, which allows us to optimize the cost allocation among members of the insurance community. Finally, our simulation analysis results show that the multi-choice goal programming method can optimize the insurance community’s underwriting decisions. Specifically, the total underwriting cost is lower than the sum of the underwriting costs under the insurance company’s single-action strategy, and the total underwriting scale is as large as possible. Compared with the classical Shapley value method, the modified Shapley value method can better reflect differences in the underwriting risks of different regions, encouraging governments to take measures to reduce underwriting risks. To conclude, we propose some suggestions based on our research findings. The possible contributions of this paper are as follows: our research provides a hybrid optimization method based on multiple-choice objective programming and cooperative games to solve the cost allocation problem facing the insurance community, and it has some reference value for improving the cost-sharing system of the insurance community.

Puncture site decision method for venipuncture robot based on near-infrared vision and multiobjective optimization.

Venipuncture robots have superior perception and stability to humans and are expected to replace manual venipuncture. However, their use is greatly restricted because they cannot make decisions regarding the puncture sites. Thus, this study presents a multi-information fusion method for determining puncture sites for venipuncture robots to improve their autonomy in the case of limited resources. Here, numerous images have been gathered and processed to establish an image dataset of human forearms for training the U-Net with the soft attention mechanism (SAU-Net) for vein segmentation. Then, the veins are segmented from the images, feature information is extracted based on near-infrared vision, and a multiobjective optimization model for puncture site decision is provided by considering the depth, diameter, curvature, and length of the vein to determine the optimal puncture site. Experiments demonstrate that the method achieves a segmentation accuracy of 91.2% and a vein extraction rate of 86.7% while achieving the Pareto solution set (average time: 1.458 s) and optimal results for each vessel. Finally, a near-infrared camera is applied to the venipuncture robot to segment veins and determine puncture sites in real time, with the results transmitted back to the robot for an attitude adjustment. Consequently, this method can enhance the autonomy of venipuncture robots if implemented dramatically.

A study on credit strategy for small and medium-sized enterprises based on hierarchical analysis

Micro, small and medium-sized enterprises (MSMEs) are enterprises with relatively small staff size and operation scale, which are important subjects for enhancing employment and scientific and technological innovation in China, as well as an important part of China's real economy, and play an irreplaceable role in promoting economic growth and social prosperity. This paper mainly provides research solutions to solve the credit decision problems of banks for MSMEs through quantitative analysis of credit risks of MSMEs, combined with the realistic basis for banks to pursue profit maximization and risk minimization. This paper selects some enterprise data as the research sample, pre-processes the original data, and then selects six indicators to quantify the risk: input invoice efficiency, output invoice efficiency, credit rating, total invoices, total price and tax, and average profit. Based on this, this paper uses AHP hierarchical analysis to calculate index weights of 0.0664, 0.115, 0.0755, 0.1067, 0.2899, and 0.3465, respectively, to weight the indicators of each enterprise to sum up the enterprise's reputation score. Then we derive the enterprise's compliance rate from the scores and build the optimal credit decision model accordingly. Finally, we use the greedy algorithm to solve for the final strategy.

Distributionally Robust Unit Commitment with N-k Security Criterion and Operational Flexibility of CSP

In order to reduce the conservatism of the robust optimization method and the complexity of the stochastic optimization method and to enhance the ability of power systems to deal with occasional line fault disturbance, this paper proposes a distributionally robust unit commitment (DRUC) model with concentrating solar power (CSP) operational flexibility and N-k safety criterion under distributed uncertainty. According to the limited historical sample data, under the condition of satisfying a certain confidence level, based on the imprecise Dirichlet model (IDM), an ambiguity set is constructed to describe the uncertainty of transmission line fault probability. Through the identification of the worst probability distribution in the ambiguity set, the adaptive robust optimal scheduling problem is transformed into a two-stage robust optimization decision model under the condition of deterministic probability distribution. The CSP flexibility column and constraint generation (C&CG) algorithm is used to process the model and the main problem and subproblem are solved by using the Big-M method, linearization technique, and duality principle. Then, a mixed integer linear programming problem (MILP) model is obtained, which effectively reduces the difficulty of solving the model. Finally, case studies on the IEEE 14 bus system and the IEEE 118 bus system demonstrate the efficiency of the proposed method, such as enhancing the ability of power systems to cope with occasional line fault disturbances and reducing the conservatism of the robust optimization method.

The Capacity Decision-Making of Omnichannel Catering Firms Based on Queueing System Considering Customer Reference Behavior

Omnichannel catering firms need to pay attention to the consumer behavior of customers in both online and offline channels to make the corresponding capacity decisions. However, customers’ consumption behavior is not only affected by base utility, but also by reference utility, which indicates that customers have reference dependence when making consumption decisions. In order to better help omnichannel catering firms to make capacity decisions more in line with customer consumption behavior in their actual operations, this paper adopts the queuing theory to construct an optimal capacity decision-making model for omnichannel catering firms. We also analyzed the impact of channel information availability on customer reference behavior and the subsequent impact on the firm’s optimal decision-making. The findings were as follows: when the channel information is unavailable, customers in each channel take their expectation of waiting time as the reference point. With the improvement of the sensitivity of customers in different channels to the reference point, firms should improve their safety capacity to meet customers’ needs. Moreover, at a higher reference point, the increase in customer sensitivity can make the firm obtain higher profits. When channel information is available, customers take the waiting time of customers in different channels as the reference point, and the increased sensitivity of online customers to the reference point will prompt firms to reduce safety capacity. Comparing the two scenarios, we also found that omnichannel catering firms could develop lower safety capacities and obtain higher profits more easily when channel information is available. This not only contributes to the development of omnichannel catering firms, but also expands the application scope of reference theory.

Optimal decision-making model for selecting residential green building technologies in China

China is expected to have an annual demand of 1 billion square meters of new residential floor areas in the next few years. Housing developers have adopted various green building technologies (GBTs) in building designs to meet the constantly rising green building (GB) standards. However, they often ignore users’ satisfaction and perception of these GBTs and doubt the cost-benefit assessment of GBTs. This study first constructed GBTs commonly used in residential construction in the Yangtze River Delta region, China. In particular, it adopted the Kano model and the Customer Satisfaction Coefficient (CSC) model to conduct questionnaire surveys on 171 Ningbo households to analyze the differences between developers’ and users’ perceptions of GBTs. Further, a zero-one integer programming (ZOIP) approach was used to build an optimal decision-making model for housing developers to select GBTs that take into account developers’ incremental cost and benefit assessment. The results showed that residents focus on technologies related to comfort and health issues, followed by energy-efficient technologies with lower costs. High-cost and low-scoring GBTs, such as new air systems and Low-E center louvered glass, will be excluded. The decision model clarifies the differences between developers’ and users’ perceptions of GBTs and effectively assists developers in rationalizing the selection of GBTs. Furthermore, it is suggested that the proposed model can be widely applied to other projects in different regions in the future to promote GB markets in China.

An Optimization Method for Distributing Emergency Materials Which Balances Multiple Decision Criteria

In emergency rescue operations, it is very important for emergency management to arrange emergency materials scientifically and reasonably according to emergency needs. However, the decision criteria for emergency material distribution are often diverse, coupled with the lack of information acquisition due to the suddenness and development complexity of disasters, which makes the scientific decisions about emergency material distribution face great challenges. This paper proposes a multi-criteria decision optimization model of emergency material distribution under uncertainty that combines the 4E criteria of performance evaluation (economic criterion, efficiency criterion, effectiveness criterion and equity criterion). Then, the solution method of the multiple decision criteria model is designed. Finally, a case study of emergency medical material distribution during COVID-19 in China was conducted for model verification. The results show that different decision criteria have an important impact on the distribution of emergency materials, and the proposed model has significant advantages in the allocation of large-scale disaster relief materials, which can provide a useful reference for emergency material distribution under uncertain information conditions.

Research on the Optimal Asset Allocation Decision of Chinese Retirees from the Perspective of Health

This paper constructs an optimal asset allocation decision model for retirees based on the characterization of the CRRA utility function and health stock, and uses the Ramsey equation to obtain the optimal intertemporal asset allocation under steady-state conditions. Combining with the actual situation of Chinese retirees, the theoretical model is numerically simulated and analyzed. The results show that different health shock intensities lead to different asset allocation decisions. Based on the research results, this paper puts forward relevant suggestions on how to make optimal asset allocation decisions for retirees and how the government formulates social policies to cope with the aging society.