Game-theoretic approach to power and admission control in hierarchical wireless sensor networks

Power efficiency and link reliability are of great impor- tance in hierarchical wireless sensor networks （HWSNs）, espe- cially at the key level, which consists of sensor nodes located only one hop away from the sink node called OHS. The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability. This problem is modeled as a non-cooperative game in which the active OHSs are con- sidered as players. By applying a double-pricing scheme in the definition of OHSs＇ utility function, a Nash Equilibrium solution with network properties is derived. Besides, a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium. Finally, the simulation results demonstrate the effec- tiveness of the proposed algorithm.

Omnichannel retail operations with ship-to-store and ship-from-store options under supply disruption

Omnichannel retailing strategies are widely used in practice and have been extensively studied in recent years, but few studies have explored omnichannel retailing operations in response to supply disruption in the post-pandemic era. To fill this gap, this study explores whether the adoption of omnichannel fulfillment options (i.e., ship-from-store and ship-to-store options) can mitigate the risk of supply disruption in a supply chain where a retailer orders products from a reliable supplier and a risky supplier, respectively. Under the omnichannel retailing strategy, the retailer’s order quantity from the risky supplier may increase or decrease while that from the reliable supplier may increase. Interestingly, it is possible to achieve a win–win–win outcome when the supply disruption risk is high and the market share of the channel offered by the risky supplier is low. Moreover, the entire supply chain benefits from the omnichannel retailing strategy even if it faces a high level of disruption risk.

How can manufacturers make decisions on product appearance design? A research on optimal design based on customers' emotional satisfaction

With changing customer attitudes toward consumption and function homogenization,product appearance designs have an increasing influence on the purchase decision.Customer characteristics and emotional factors play an important role here.This study proposes a novel approach for modelling satisfaction and accomplishing a configuration that overcomes the limitations of conventional methods to precisely predict satisfaction,provide optimal product recommendations,and advise manufacturers on product appearance design.The newly proposed approach considers satisfaction,clusters customers through the Kansei perspective,and constructs a satisfaction model for each cluster.Additionally,the study employs data mining to understand the basic design principles and conflicted combinations that must be followed and avoided,respectively.The bidirectional association rules-constrained genetic algorithm is presented to limit configuration freedom,ensuring that results are in the range of control.Comparing prediction errors and recommended sample votes between the novel and conventional approaches revealed the presented approach’s efficiency and accuracy,thereby providing suggestions for manufacturers to make precise decisions on launching new product appearance designs through predicting customer emotional satisfaction.