Mohsen Tabatabaei; Abbas Afrazeh; Abbas Seifi
Abstract
This paper aims to propose a knowledge sharing optimization model based on the game theory that optimizes both employer and employee(s) decisions simultaneously. This model is a bi-level programming model. The upper-level problem includes employer decision about the reward, and the lower-level problem ...
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This paper aims to propose a knowledge sharing optimization model based on the game theory that optimizes both employer and employee(s) decisions simultaneously. This model is a bi-level programming model. The upper-level problem includes employer decision about the reward, and the lower-level problem contains employee(s) decisions about time and effort allocation to knowledge sharing activity. Mathematical formulation of the model designed based on previous literature and in the framework of Motivation-Opportunity-Ability. The proposed bi-level programming model provides a foundation to investigate more different parameters comparing with previous models introduced in the literature. This model considers opportunity and ability factors in addition to the motivation. Also, payoff functions in this model are non-linear and therefore is more consistent with real cases relative to previous linear models. Additionally, this model analysis the effects of available time as a key factor. The bi-level model coded in GAMS using EMP syntax and solved for a set of randomly generated data using BARON algorithm. Results show that the increase of applicability of codified knowledge and impact coefficient of social comparison could improve organizational performance and also save the cost of reward system. Therefore, neglecting these two parameters in designing a reward system could lead to non-optimized decision making. This research provides a basis to consider more parameters simultaneously and help to improve organizational decisions. However, based on the results, BARON algorithm is not efficient to solve big problems, so developing a more efficient algorithm is needed
Omid Amirtaheri; Mostafa Zandieh; Behrouz Dorri
Abstract
In this paper, we investigate a decentralized manufacturer-distributer supply chain. In addition to global advertisement, the manufacturer participates in the local advertising expenditures of distributer. Bi-level programming approach is applied to model the relationship between the manufacturer and ...
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In this paper, we investigate a decentralized manufacturer-distributer supply chain. In addition to global advertisement, the manufacturer participates in the local advertising expenditures of distributer. Bi-level programming approach is applied to model the relationship between the manufacturer and retailer under two power scenarios of stackelberg game framework and the optimal policies in pricing, advertising, inventory management and logistics are identified. Two hierarchical genetic algorithms are proposed to solve the bi-level programming models. Based on collected data from Iranian automotive spare parts aftermarket, several numerical experiments are carried out to evaluate the validity of proposed models as well as the efficiency and effectiveness of solution procedures.
