Search Results (11)

Probabilistic selling is a retail strategy in which consumers purchase products without knowing their exact identities until after purchase, with various applications like gaming and retail; a real-world practice involves retailers may unsealing and reselling goods to meet consumer demand for transparency. This disrupts manufacturers’ strategies designed to adopt the uncertainty for segmentation and pricing. Using a vertically differentiated supply chain model structured as a Stackelberg game framework, this study examines how transparency from retailer unsealing affects profitability, consumer surplus, and market dynamics. Key findings include the following: (1) Unsealing increases retailer profits by aligning pricing with heterogeneous consumer willingness to pay. (2) Introducing a manufacturer’s direct channel reduces unsealing profits via price competition. (3) Unsealing creates conflicts between manufacturers’ design goals and retailers’ profit-driven incentives. By applying a Stackelberg game framework to model unsealing as a downstream transparency decision, this work advances the probabilistic selling literature by offering a structured approach to analyzing how downstream transparency and retailer strategies reshape probabilistic selling and supply chain dynamics. It highlights the need for manufacturers to balance segmentation, pricing, and channel control, offering insights into mitigating conflicts between design intentions and downstream market behaviors. Full article

Accurate demand forecasting is essential for retail operations as it directly impacts supply chain efficiency, inventory management, and financial performance. However, forecasting retail time series presents significant challenges due to their irregular patterns, hierarchical structures, and strong dependence on external factors such as promotions, pricing strategies, and socio-economic conditions. This study evaluates the effectiveness of Transformer-based architectures, specifically Vanilla Transformer, Informer, Autoformer, ETSformer, NSTransformer, and Reformer, for probabilistic time series forecasting in retail. A key focus is the integration of explanatory variables, such as calendar-related indicators, selling prices, and socio-economic factors, which play a crucial role in capturing demand fluctuations. This study assesses how incorporating these variables enhances forecast accuracy, addressing a research gap in the comprehensive evaluation of explanatory variables within multiple Transformer-based models. Empirical results, based on the M5 dataset, show that incorporating explanatory variables generally improves forecasting performance. Models leveraging these variables achieve up to 12.4% reduction in Normalized Root Mean Squared Error (NRMSE) and 2.9% improvement in Mean Absolute Scaled Error (MASE) compared to models that rely solely on past sales. Furthermore, probabilistic forecasting enhances decision making by quantifying uncertainty, providing more reliable demand predictions for risk management. These findings underscore the effectiveness of Transformer-based models in retail forecasting and emphasize the importance of integrating domain-specific explanatory variables to achieve more accurate, context-aware predictions in dynamic retail environments. Full article

Probabilistic selling (PS) is widely used in the travel industry and marketing practices; how to design a win-win contract using PS in multiple participants’ supply chain to achieve sustainable development has been a worthy concerned issue in the green supply chain development in China. To end this, we consider a three-players supply chain involving two firms and a common retailer in which the firms sell their transparent products via a dual channel, while the retailer sells probabilistic goods by a direct channel. First, the three players’ demand functions are derived using both a Hotelling model and taking account of consumers’ price reference effects. Second, we construct a probabilistic selling model for both decentralized and centralized supply chain systems. The optimal policies for the decentralized system are determined by employing Stackelberg sequential games and an analytical approach. Third, we propose a new three-way revenue sharing contract to deal with channel conflict. Furthermore, we determine the conditions under which the dual-channel supply chain can be coordinated to achieve a win–win situation for all participants. The results indicate that the retailer adopts probabilistic selling depending upon the relative values of the two manufacturers’ production costs in the three-way revenue sharing contract. More interestingly, PS not only can improve the supply chain’s efficiency, but also can coordinate the dual-channel supply chain to achieve a win-win situation for all participants. Finally, we present a numerical analysis to verify our results and a sensitivity analysis of the parameters involved. Full article

Defining prices and in which consumers’ segments to put the company’s efforts within competitive markets selling bundles is challenging. On the one hand, methodologies focused on competition are usually appropriate for analyzing market dynamics but not for helping decision makers in specific tasks regarding pricing. On the other hand, simplistic cost-oriented methods may fail to capture consumer behavior. We see these characteristics in such markets as telecommunications, retail, and financial service providers, among others. We propose a framework to support pricing decisions for products with multiple attributes in competitive markets, considering consumers’ willingness to pay and multiple segments. The proposed model is a nonlinear profit maximization probabilistic problem. We represent the demands for products and services through a multinomial logit model and then include consumers’ maximum willingness to pay through soft constraints within the demand function. Since the profit function is non-concave, we deal with the nonlinearity and the multiple optima to solve the model through an equivalent nonlinear model and a particle swarm optimization (PSO) heuristic. This setting allows us to find the prices that achieve equilibrium for the game among the firms that maximize their profits. Including the features shown, our approach enables decision makers to set prices optimally. Estimating the parameters needed to run our model requires more effort than traditional multinomial approaches. Nevertheless, we show that it is essential to include these aspects because the optimal prices are different from those obtained with more simplified models that do not have them. Additionally, there are well-established methodologies available to estimate those parameters. Both the determination of the first-order optimality conditions and the PSO implementation allow to find equilibria, quantify the effect of the consumers’ maximum willingness to pay, and assess the competition’s relevance. As complementary material, we analyze a case from a Chilean telecommunications company and show the results regarding price decisions and market share effects. According to our literature review, these aspects have not been handled and quantified jointly, as we do to support pricing. Full article

This paper proposes a probabilistic model with the aim to reduce the solar energy operation cost and CO₂ emissions of a multi-carrier microgrid. The MCMG in this study includes various elements such as combined heat and power (CHP), electrical heat pump (EHP), absorption chiller, solar panels, and thermal and electrical storages. A MILP model is proposed to manage the commitment of energy producers, energy storage equipment, the amount of selling/buying of energy with the upstream network, and the energy consumption of the responsible electrical loads for the day-ahead optimal operation of this microgrid. The proposed operation model is formulated as a multi-objective optimization model based on two environmental and economic objectives, using a weighted sum technique and a fuzzy satisfying approach. In this paper, the 2 m + 1-point estimate strategy has been used to model the uncertainties caused by the output power of solar panels and the upstream power supply price. In order to evaluate the performance of the proposed model, and also for minimizing cost and CO₂ emissions, the simulation was conducted on two typical cold and hot days. Numerical results show the proposed model’s performance and the effect of electrifying the heating and cooling of the microgrid through the EHP unit on greenhouse gas emissions in the scenarios considered. Full article

Production, processing, transporting, selling, and consumption of food are highly resource intensive. Therefore, if they are not well managed the consequences for the environment are far-reaching. This study aimed at investigating behaviors and attitudes of the Croatian population concerning the influence of environmental and political determinants of food choices, and the socio-demographic factors associated with pro-environmental behavior. Data analysis involved a non-probabilistic sample of 1534 adult participants from Croatia who responded to a validated questionnaire from November 2017 to March 2018. To test differences between sociodemographic groups, Welch’s t-test (two groups) and ANOVA (multiple groups) were used. The relationship between age and motivators of food choices was analyzed with Pearson’s r correlation coefficient. Participants reported a neutral rate of agreement with the items, with the exception of items related to food waste and food origin, for which they expressed a moderate amount of agreement. Socio-demographic factors that influence environmentally or politically concerned food choices in our study were age (older participants, p < 0.001), gender (women in comparison to men, p < 0.05), education level (higher education in comparison to elementary/high school, p < 0.05), marital status (married/cohabiting in comparison to unmarried, p < 0.05), responsibility for food supply (those who are responsible for food supply in comparison to those who are not responsible for food supply, p < 0.05), eating practices (participants with specific eating practices in comparison to participants without specific eating practices, p < 0.05), and smoking (those who have never smoked score and those who used to smoke in comparison to active smokers, p < 0.05). The results show that there are no statistically significant differences in environmental and political determinants of food choices based on the place of residence and employment status. The findings indicate that environmental and political determinants do not play a significant role in the food choices among the Croatian population. Full article

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin–Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model. Full article

In this paper we propose an optimization scheme for a selling strategy of an electricity producer who in advance decides on the share of electricity sold on the day-ahead market. The remaining part is sold on the complementary (intraday/balancing) market. To this end, we use probabilistic forecasts of the future selling price distribution. Next, we find an optimal share of electricity sold on the day-ahead market using one of the three objectives: maximization of the overall profit, minimization of the sellers risk, or maximization of the median of portfolio values. Using data from the Polish day-ahead and balancing markets, we show that the assumed objective is achieved, as compared to the naive strategy of selling the whole produced electricity only on the day-ahead market. However, an increase of the profit is associated with a significant increase of the risk. Full article

With the deregulation of electricity market, generation companies must take part in strategic bidding by offering its bidding quantity and bidding price in a day-ahead electricity wholesale market to sell their electricity. This paper studies the strategic bidding of a generation company with thermal power units and wind farms. This company is assumed to be a price-maker, which indicates that its installed capacity is high enough to affect the market-clearing price in the electricity wholesale market. The relationship between the bidding quantity of the generation company and market-clearing price is then studied. The uncertainty of wind power is considered and modeled through a set of discrete scenarios. A scenario-based two-stage stochastic bidding model is then provided. In the first stage, the decision-maker determines the bidding quantity in each time period. In the second stage, the decision-maker optimizes the unit commitment in each wind power scenario based on the bidding quantity in the first stage. The proposed two-stage stochastic optimization model is an NP-hard problem with high dimensions. To tackle the problem of “curses-of-dimensionality” caused by the coupling scenarios and improve the computation efficiency, a modified Benders decomposition algorithm is used to solve the model. The computational results show the following: (1) When wind power uncertainty is considered, generation companies prefer higher bidding quantities since the loss of wind power curtailment is much higher than the cost of additional power purchases in the current policy environment. (2) The wind power volatility has a strong negative effect on generation companies. The higher the power volatility is, the lower the profits, the bidding quantities, and the wind power curtailment of generation companies are. (3) The thermal power units play an important role in dealing with the wind power uncertainty in the strategic bidding problem, by shaving peak and filling valley probabilistic scheduling. Full article

Considering consumers’ attitudes to risks for probabilistic products and probabilistic selling, this paper develops a dynamic Stackelberg game model of the supply chain considering the asymmetric dual-channel structure. Based on entropy theory and dynamic theory, we analyze and simulate the influences of decision variables and parameters on the stability and entropy of asymmetric dual-channel supply chain systems using bifurcation, entropy diagram, the parameter plot basin, attractor, etc. The results show that decision variables and parameters have great impacts on the stability of asymmetric dual-channel supply chains; the supply chain system will enter chaos through flip bifurcation or Neimark–Sacker bifurcation with the increase of the system entropy, and thus the system is more complex and falls into a chaotic state, with its entropy increased. The stability of the system can become robust with the increase of the probability that product $a$ becomes a probabilistic product, and it weakens with the increase of the risk preference of customers for probabilistic products and the relative bargaining power of the retailer. A manufacturer using the direct selling channel may obtain greater profit than one using traditional selling channels. Using the method of parameter adjustment and feedback control, the entropy of the supply chain system will decline, and the supply chain system will fall into a stable state. Therefore, in the actual market of probabilistic selling, the manufacturers and retailers should pay attention to the parameters and adjustment speed of prices and ensure the stability of the game process and the orderliness of the dual-channel supply chain. Full article

In unrestricted combinatorial auctions, we put forward a mechanism that guarantees a meaningful revenue benchmark based on the possibilistic beliefs that the players have about each other’s valuations. In essence, the mechanism guarantees, within a factor of two, the maximum revenue that the “best informed player” would be sure to obtain if he/she were to sell the goods to his/her opponents via take-it-or-leave-it offers. Our mechanism is probabilistic and of an extensive form. It relies on a new solution concept, for analyzing extensive-form games of incomplete information, which assumes only mutual belief of rationality. Moreover, our mechanism enjoys several novel properties with respect to privacy, computation and collusion. Full article