Two-stage Decision Model Research Articles

Optimal investment and exit decision of venture capitals with multiple heterogeneous Beliefs

Generally, traders hold different beliefs about the same project in venture capital activities. We propose a two-stage decision model with multiple heterogeneous beliefs using real options game theory. Based on this model, we analyse the optimal timing and amount of invested capital for venture capital in stage 1 (investing) and stage 2 (exiting). The results show that different beliefs decrease the optimal amount of invested capital but have little influence on the optimal investment timing, which accounts for the “agree to disagree” phenomenon. This negative effect can be offset by the learning process but can also be exacerbated by the differences in required returns.

Bidding Strategy for Wind and Thermal Power Joint Participation in the Electricity Spot Market Considering Uncertainty

As the proportion of new energy sources, such as wind power, in the electricity system rapidly increases, their participation in spot market competition has become an inevitable trend. However, the uncertainty of clearing price and wind power output will lead to bidding deviation and bring revenue risks. In response to this, a bidding strategy is proposed for wind farms to participate in the spot market jointly with carbon capture power plants (CCPP) that have flexible regulation capabilities. First, a two-stage decision model is constructed in the day-ahead market and real-time balancing market. Under the joint bidding mode, CCPP can help alleviate wind power output deviations, thereby reducing real-time imbalanced power settlement. On this basis, a tiered carbon trading mechanism is introduced to optimize day-ahead bidding, aiming at maximizing revenue in both the electricity spot market and carbon trading market. Secondly, conditional value at risk (CVaR) is introduced to quantitatively assess the risks posed by uncertainties in the two-stage decision model, and the risk aversion coefficient is used to represent the decision-maker’s risk preference, providing corresponding strategies. The model is transformed into a mixed-integer linear programming model using piecewise linearization and McCormick enveloping. Finally, the effectiveness of the proposed model and methods is verified through numerical examples.

A two-stage decision model for sustainable-resilient supplier selection and order allocation under uncertain environment

PurposeThe paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.Design/methodology/approachFirst, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.FindingsThe paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.Originality/valueThe paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

Expanding Service Capabilities Through an On-Demand Workforce

Call Center Staffing: On-Demand Is in Demand Traditional call centers face challenges in quickly adapting their service capacity to meet fluctuations in demand, even with short staffing periods. In their research article “Expanding Service Capabilities Through an On-Demand Workforce,” Sun and Liu propose a solution for call centers to improve service levels and reduce operating expenses. They develop a two-stage decision model that determines the optimal combination of permanent and on-demand staff, along with an optimal on-demand staffing and call scheduling policy that minimizes costs. By utilizing diffusion approximation, they derive approximate solutions for the second-stage problem. The optimal staffing rule employs switching boundaries to determine when to bring in or dismiss on-demand agents, while the scheduling rule employs a nested threshold rule that prioritizes customer urgency. Interestingly, the call scheduling rule exhibits an intriguing pattern arising from the interaction between on-demand staffing and call scheduling decisions. Their research findings highlight significant cost savings achievable through the implementation of an on-demand workforce.

GERT-Q-learning model for intelligent QoS dynamic optimization of Inmarsat STN based on grey clustering of delay and delay variation

PurposeThis paper aims to implement quality of service(QoS) dynamic optimization for the integrated satellite-terrestrial network(STN) of the fifth-generation Inmarsat system(Inmarsat-5).Design/methodology/approachThe structure and operational logic of Inmarsat-5 STN are introduced to build the graphic evaluation and review technique(GERT) model. Thus, the equivalent network QoS metrics can be derived from the analytical algorithm of GERT. The center–point mixed possibility functions of average delay and delay variation are constructed considering users' experiences. Then, the grey clustering evaluation of link QoS is obtained combined with the two-stage decision model to give suitable rewards for the agent of GERT-Q-learning, which realizes the intelligent optimization mechanism under real-time monitoring data.FindingsA case study based on five time periods of monitoring data verifies the adaptability of the proposed method. On the one hand, grey clustering based on possibility function enables a more effective measurement of link QoS from the users' perspective. On the other hand, the method comparison intuitively shows that the proposed method performs better.Originality/valueWith the development trend of integrated communication, STN has become an important research object in satellite communications. This paper establishes a modular and extensible optimization framework whose loose coupling structure and flexibility facilitate management and development. The grey-clustering-based GERT-Q-Learning model has the potential to maximize design and application benefits of STN throughout its life cycle.

Early identification of autism spectrum disorder by multi-instrument fusion: A clinically applicable machine learning approach

Autism spectrum disorder (ASD), developmental language disorder (DLD), and global developmental delay (GDD) are common neurodevelopmental disorders in early childhood; however, the differential diagnosis of these disorders is difficult because of overlapping symptoms. Drawing on a cohort of 2004 children with ASD, DLD, or GDD, this study developed machine learning classifiers using decision trees, support vector machines, eXtreme gradient boosting (XGB), logistic regression, and neural networks by combining several easily accessible behavioral and developmental assessment instruments. The best-performing XGB model was further simplified into a two-stage decision model (TS-DM) to achieve better interpretability. Model performance was tested and compared with that of 12 pediatricians on an external dataset of 60 children. The accuracies of the resident pediatricians, senior pediatricians, TS-DM, and XGB were 53.3%, 66.7%, 75.0%, and 78.3%, respectively. Machine learning has the potential to identify these three neurodevelopmental disorders by integrating information from multiple instruments and thereby may increase our understanding of the roles of different behavioral and developmental characteristics in the different diagnoses.

Highway traffic flow prediction model with multi-component spatial–temporal graph convolution networks

In order to effectively solve the problems of redundant medical material allocation, unbalanced material allocation, high distribution cost and lack of symmetry caused by unreasonable prediction in the case of sudden epidemic disasters, the prospect theory is introduced to establish a two-stage robust allocation model of medical materials, and the HQDRO based on the two-stage decision model is proposed. Aiming at minimizing the emergency response time and the total number of allocated materials, and taking the dynamic change of medical material demand in the epidemic sealed control area as the constraint condition, a two-stage robust planning model of medical materials based on scenario is established to realize the symmetrical allocation of medical materials under the sudden epidemic situation. Then, the perception model based on demand prediction, symmetry optimization, targeted distribution and psychological expectation of medical materials are constructed. Through the comparative analysis with the fitness of three commonly used algorithms in this field, the effectiveness of the robust configuration model and HQDRO proposed in this paper is verified.

The Application of a Two-Stage Decision Model to Analyze Tourist Behavior in Accommodation

As tourism products are not necessities for people’s livelihood, zero consumption data are usually observed while conducting studies on topics that are relevant to tourism expenditure using cross-sectional research data, and a similar problem exists in tourist accommodation expenditure. This study adopts a two-stage process to examine the factors influencing tourist accommodation decisions in the domestic market, applying the dependent double-hurdle (DDH) model while using the dataset on Survey of Travel by R.O.C. (Taiwan) Citizens for the years 2014–2018. The findings reveal that, in the two decision-making equations, the social stratum, family life cycle, residential area, tourism behavior, vacation policy, and economic variables have different degrees and directions of influence on the intention to use and expenditure on tourist accommodation. Such information presents the processes involved in deciding to accommodate and how much to spend on accommodation, thereby indicating that it is inappropriate to use the single-equation analysis consisting of zero consumption expenditure data and to assume that the same variables influence the participation and consumption decisions.

Two-Stage Decision Model of Fresh Agricultural Products Supply Chain Based on Option Contract

Two-Stage Decision Model of Fresh Agricultural Products Supply Chain Based on Option Contract

Solving the dilemma in supplier selection by the group of weight vector with kernel

PurposeThe purpose of this paper is to solve the dilemma in the process of major selection decision-making.Design/methodology/approachFirstly, the group of weight vector with kernel has been defined. Then, the weighted comprehensive clustering coefficient vector was calculated based on the group of weight vector with kernel. Under the action of weighted comprehensive clustering coefficient vector, the information including in other components around component k and supporting object i to be classified into the k-th category has been gathered to component k. At last, a novel two-stage decision model based on the group of weight vector with kernel and the weighted comprehensive clustering coefficient vector is put forward to solve the dilemma in grey clustering evaluation. Then the overall evaluation conclusion can be consistent with the clustering result according to the rule of maximum value.FindingsA new way to solve the dilemma in the process of major selection decision-making has been found. People can obtain a consistent result with two-stage decision model at the case of dilemma. That is, the conclusion of the overall evaluation is consistent with the clustering result according to the rule of maximum value.Practical implicationsSeveral functional groups of weight vector with kernel have been put forward. The proposed model can solve the clustering dilemma effectively and produce consistent results. A practical application of decision problem to solve the dilemma in supplier evaluation and selection of a key component of large commercial aircraft C919 have been completed by the novel two-stage decision model.Originality/valueThe two-stage decision model, the group of weight vector with kernel and the weighted comprehensive clustering coefficient vector were presented in this paper firstly. People can solve the dilemma in grey clustering evaluation effectively by the novel two-stage decision model based on the group of weight vector with kernel and the weighted comprehensive clustering coefficient vector.

Optimal pricing decisions for multichannel healthcare service with differing consemers’ knowledge levels

PurposeThe purpose of this paper is to establish a stylized model to solve the pricing strategy, resource allocation and consumer surplus problems of multichannel healthcare services.Design/methodology/approachThis paper considers a two-stage decision model with different levels of consumers’ knowledge. Faced with physical problems, knowledgeable consumers can solve their problems by seeking online healthcare channels, while unknowledgeable consumers need to make a two-stage decision to try to solve their problems.FindingsThe effective diagnosis rate and the proportion of knowledgeable consumers positively impact the optimal pricing in online and offline channels. In addition, a higher proportion of knowledgeable consumers does not result in higher demand in the online and offline channels. Moreover, if service providers lower their prices a small amount, they will lose some profit, but the consumer surplus will be higher, which will encourage more consumers to access healthcare services.Research limitations/implicationsKnowledge levels are simplified into two categories. Also, the authors assume the resources of online and offline healthcare services are comparable.Originality/valueThis paper incorporates the knowledge level and misdiagnosis rate into the model framework to study the most effective pricing strategy for multichannel healthcare services.

Multi-stage risk-averse operation of integrated electric power and natural gas systems

Regional electric power and natural gas systems have become increasingly interdependent in recent years, and have become subject to increasing supply and demand uncertainties associated with renewable energy sources. The present work addresses these issues by developing a multi-stage risk-averse operation model at the level of a regional integrated energy system that considers integrated demand response. Specifically, the developed multi-stage operation model can make flexible wait-and-see decisions, which are adaptive to newly observed uncertainties in the output of renewable energy sources, and the conditional value at risk approach is employed to achieve an economic operating cost at a given risk level. As a result, the multi-stage decision model outperforms conventional two-stage decision models in terms of operating costs. Test results indicate that the operation cost of the multi-stage operation model is reduced by 4.2% compared with a regular two-stage operation model. The impact of congestion in both the electricity and natural gas networks on system operations and energy prices is also investigated.

Managing market risk caused by customer preference uncertainty in product family design with launch flexibility: Product option strategy

The uncertainty of customer preference is one of major sources of product family design risk. Since the prediction of customer preference entails forecasting and estimation error, the uncertainty is inevitable. The present study proposes the novel method for module-based product family design, product option strategy. Product option strategy provides the launch flexibility to companies. The firm has the right to release a maximum-utility product alternative among the product option set, corresponding to actual customer preference. Launch flexibility saves commercialization cost resulting from ineffective product launch and enhances market share by possessing diverse product alternatives. The strategy is implemented by the two-stage decision model with heuristic algorithm. The structure of decision model accommodates the request of consumer electronic industry on logical decision mechanism to persuade internal stakeholders. The decision model strikes the balance between development goals of main internal stakeholders, market effectiveness and operational efficiency. The proposed strategy is applied to product family design of LCD TV. In the case study, the proposed method achieves higher market share with the same or less operational complexity (cost) but the advantage might be limited under complex product architecture. In the future, operational cost model will be elaborated for the equivalent comparison between market share and operational cost.

Design of Flexible Vehicle Scheduling Systems for Sustainable Paratransit Services

The aging population has led to an increase in the variety and volume of transportation demands by people facing travel difficulties. Hence, transportation organisations need to provide flexible and sustainable paratransit services to meet these increasing demands. In this study, we investigate the design of flexible vehicle scheduling systems in order for a community organisation to serve more people and achieve higher operational efficiency. We analyse and propose a system design based on user requirements for different types of paratransit types. Further, we identify an integrated service option and process flow for dial-a-ride passengers to ride on a vehicle with schedule route passengers. Because this option involves a complex decision, we formulate the problem as a two-stage decision model. To verify the effectiveness of our proposed design, we perform numerical simulations and conduct a case study by collaborating with a transportation organisation. We found that the proposed system would enable the organisation to serve more people with fewer vehicles but without an increase in the travelling time. These results demonstrate the importance of a flexible vehicle scheduling system for accessible transportation organisations to sustain their service operations.

Economical user-generated content (UGC) marketing for online stores based on a fine-grained joint model of the consumer purchase decision process

User-generated content (UGC) is influential in reducing customer perceived risk and determining online store sales. E-sellers spend huge costs and efforts to improve UGC for it serves as a convenient and persuasive alternative for marketing and advertising purposes. Considering that consumers may set lower and/or upper limits (i.e., psychological thresholds) in which the good is expected to be, and purchase decisions are considered as a multi-stage decision process, yet models in previous research cannot uncover this decision-making process. Therefore, exploring the impact of UGC at each decision-making stage and detecting the psychological thresholds on various aspects of UGC (i.e., the fine-grained effects of UGC) contribute to optimizing the UGC with the best cost to boost sales. To this end, a fine-grained joint two-stage decision model, zero-inflated negative binomial regression (ZINB-P) model is proposed to support economical UGC marketing. Specifically, we compile a factors system composed of various types of aggregate-level statistics of UGC, which can impact risk perception. Afterward, change point analysis is used to find multi-level consumer psychological thresholds on UGC factors and consumers’ risk perception model is constructed to measure purchasing probabilities in the first decision-making stage. On the basis of consumers’ risk perception model, the ZINB-P model is built to fully capture the fine-grained effects of UGC factors on each stage of the consumer purchase decision. It integrates two stages of consumer decision: the consumer risk perception and non-compensatory choice in the first stage, and the second compensatory stage. A genetic algorithm is constructed to jointly estimate the parameters in ZINB-P model. Finally, an experiment on a kind of fresh produce from Taobao.com evidences the precision of our model. We demonstrate how our model can provide with economical UGC marketing strategies using a decision support table, in which some scenarios are identified. E-sellers can use this table to find the scenarios they are located in and identify the critical UGC factors that impede the sales in each scenario, and thus economical UGC marketing strategies can be obtained by improving these critical UGC factors.

Mass Customizing Paratransit Services With a Ridesharing Option

Paratransit services for people with travel difficulties have received increasing government and community attention due to the aging population. In this paper, we examine an integrated service design approach to customize paratransit services with a ridesharing option for a dial-a-ride (DAR) service. The fundamental challenge of DAR services is a lack of commonality across other services in the process, which results in low vehicle utilization. To mass customize paratransit services systematically, we identify the common service components of different paratransit services under the design framework of product family architecture. We then formulate the community organization's pricing and vehicle scheduling decisions with a ridesharing option for a DAR operation as a two-stage decision model. In stage one of the tactical plan, we determine user tolerance of earlier pick-up and later drop-off times, and a price discount for a shared ride. Based on the user tolerance of a longer traveling time, we optimize the vehicle scheduling of shared service orders. Our experimental results indicate that a ridesharing policy option for a DAR service providing a 20% discount for passengers who accept a 15-min earlier pick-up time or a later drop-off time leads to 15% more people being served.

Autistic traits influence the strategic diversity of information sampling: Insights from two-stage decision models.

Information sampling can reduce uncertainty in future decisions but is often costly. To maximize reward, people need to balance sampling cost and information gain. Here we aimed to understand how autistic traits influence the optimality of information sampling and to identify the particularly affected cognitive processes. Healthy human adults with different levels of autistic traits performed a probabilistic inference task, where they could sequentially sample information to increase their likelihood of correct inference and may choose to stop at any moment. We manipulated the cost and evidence associated with each sample and compared participants’ performance to strategies that maximize expected gain. We found that participants were overall close to optimal but also showed autistic-trait-related differences. Participants with higher autistic traits had a higher efficiency of winning rewards when the sampling cost was zero but a lower efficiency when the cost was high and the evidence was more ambiguous. Computational modeling of participants’ sampling choices and decision times revealed a two-stage decision process, with the second stage being an optional second thought. Participants may consider cost in the first stage and evidence in the second stage, or in the reverse order. The probability of choosing to stop sampling at a specific stage increases with increasing cost or increasing evidence. Surprisingly, autistic traits did not influence the decision in either stage. However, participants with higher autistic traits inclined to consider cost first, while those with lower autistic traits considered cost or evidence first in a more balanced way. This would lead to the observed autistic-trait-related advantages or disadvantages in sampling optimality, depending on whether the optimal sampling strategy is determined only by cost or jointly by cost and evidence.

Research on the evaluation of tourism development potential of tea intangible cultural heritage based on grey clustering

PurposeThe purpose of this paper is to construct a grey clustering evaluation model based on center-point mixed possibility function and to evaluate the tourism development potential of tea intangible cultural heritage. The research results provide a certain reference for the tourism development department and related researchers who are engaged in the tourism development of intangible cultural heritage.Design/methodology/approachThe study uses literature research, questionnaire investigation, expert interviews and factor analysis to determine the evaluation index system of tourism development potential of tea intangible cultural heritage and applies analytic hierarchy process (AHP) to determine the weight of each criteria. Then, according to the grey clustering evaluation theory and two-stage decision model, a grey clustering evaluation model is constructed to assess the tourism development potential of tea intangible cultural heritage. Finally, a new model is employed to evaluate the tourism development potential of tea intangible cultural heritage in Huangshan city.FindingsThe results show that there is a big difference in the tourism development potential of different tea intangible cultural heritages in Huangshan City and it further illustrates the scientificity and rationality of the method proposed in this paper.Practical implicationsThe model constructed in the paper can be effectively applied to the evaluation of tourism development potential of tea intangible cultural heritage scientifically and reasonably.Originality/valueThis manuscript not only constructs the evaluation index system of tourism development potential of tea intangible cultural heritage but also creatively applies the grey clustering theory to the evaluation of tourism development potential of tea intangible cultural heritage, which provides a new research idea for the evaluation of tourism development potential of tea intangible cultural heritage.

Collaborative stowage planning problem for a liner ship

This paper investigates a stowage planning problem, in which a liner ship will visit a sequence of ports, the number of available quay cranes in ports and the numbers of loading/unloading containers in ports are uncertain. This stowage planning problem is about how to assign the loaded containers to the bays of the ship considering uncertain information in the future, so as to minimise the sum of the expected quay crane handling time at the ports. Based on stochastic programming, a two-stage decision model is proposed for this problem. A particle swarm optimisation based solution method is developed to solve the model for large-scale problem instances. Numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method.

Coordinating the supplier-retailer supply chain under noise effect with bundling and inventory strategies

In current competitive market, the products and their demand's uncertainty are high. In order to reduce these uncertainties the coordination of supply chain is necessary. Supply chain can be managed under two viewpoints typically: 1) centralized supply chain and 2) decentralized supply chain, and the coordination can be done in both types of chains. In the centralized supply chain there exists a global decision maker who takes all the best decisions in order to maximize the profit of the whole supply chain. Here, the useful information required to make the best decisions is open to all members of the chain. On the other hand, in the decentralized supply chain all members decide in a separate and sequential way, how to maximize their profits. In order to coordinate efficiently the supply chain, both supplier and retailer are involved in a coordination contract that makes it possible for the decentralized decisions to maximize the profit of the entire supply chain. In this context, the situation that the supplier-retailer chain faces is a two-stage decision model. In the first stage the supplier, based on former knowledge about the market, decides the production capacity to reserve for the retailer. In the second stage, after that demand information is updated, the retailer determines the bundle price and the quantity of bundles to order. This paper considers a supply chain comprised of one supplier and one retailer in which two complementary fashion products are manufactured and sold as a bundle. The bundle has a short selling season and a stochastic price dependent on demand with a high level of uncertainty. Therefore, this research considers that the demand rates are uncertain and are dependent on selling prices and on a random noise effect on the market. Profit maximization models are developed for centralized and decentralized supply chains to determine decisions on production capacity reservation, order quantity of bundled products and the bundle-selling price. The applicability of the developed models and solution method are illustrated with a numerical example.