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In the face of increasingly complex market environment, supply chain members pay attention to the possibility of obtaining expected profits and various risks while considering profit maximization. The behaviors of decision makers are often influenced by their psychological preferences, and loss aversion is one of the basic characteristics of human economic behavior. We research the supply chain coordination problem with capital constraint and loss-averse retailer in the case of stochastic production and demand. In order to address the loss aversion, a novel utility function is structured according to the theory of mental account. Under wholesale price contract, the optimal expected utility of supply chain under decentralized decision is smaller than that under centralized decision due to the retailer's constrained capital. Based on that, a model is constructed by making revenue sharing contract act as the dual role of supply chain coordination and delayed payment strategy without seeking external financing or internal financing, and the contract conditions and optimal decisions of the coordinated supply chain are solved. Theoretical analysis indicates that when the contract parameters are properly selected, even though the retailer's capital is constrained, the revenue sharing contract can not only coordinate the supply chain but also realize the arbitrary distribution of supply chain profits within a certain range. Finally, the change of decision variables and expected utility or profit with contract parameters is analyzed by several numerical examples. [ABSTRACT FROM AUTHOR]

This paper analyses loss aversion mechanism (LAM) of the shipping company's decision-makers about the risk-based decision (RBD) for slow steaming and generalizes a novel optimization model for the sailing speed through the trade-off between fuel consumption, SOx emissions and delivery delay. The value functions against the benchmark speed were constructed based on physiological expected utility (PEU) to reveal the features of loss aversion, and the objective function was derived from these value functions with the aim to optimize the sailing speed. After that, a Genetic Algorithm (GA) solution with fitness function and special operators was built to solve the proposed model. Finally, the model was applied to pinpoint the PEU for the optimal sailing speed against the benchmark speed, and the sensitivity of the model was discussed with different benchmark speeds, value function weights and input parameters. The analysis shows that the proposed model can assist the slow steaming RBD based on the inner feelings of the shipping company's decision-makers, offering a novel tool for sailing speed optimization. [ABSTRACT FROM AUTHOR]