As a new management mode, great attention has been paid to virtual enterprise (VE). While there is much research material on risk management of VE, a relationship perspective on owner and partner performance assessment and management can bring an added dimension. The coordination of risk management in fashion and textiles (FTs) supply chain organized as a VE is studied in this paper. The aim of this study is to find proper decision mechanisms that can improve the overall performance of risk management for the whole VE as well as each member. For the risk management problem in VE, a centralized mechanism is given as the base case, and then a distributed decision-making (DDM) mechanism with incentive scheme is introduced to establish a practicable strategic partnership. Under the DDM mechanism, a relationship performance definition that incorporates the financial dimension is investigated. For the two resulting optimization problems, a particle swarm optimization (PSO) algorithm is designed. In the numerical examples, the study shows that the DDM mechanism with incentive scheme can improve the overall benefit of risk management beyond the centralized one. Additionally, sensitivity analysis is conducted with respect to the bonus parameter, and suggestions are made for further research.

Revenue distribution is an important issue in the operations of a logistics service supply chain (LSSC). The existing works on revenue distribution are mostly based on the assumption of rational economic people that are purely self-interested. However, people also have a fairness preference, which impacts people’s decision-making behavior or even the success operations of the LSSC. For a two-level supply chain consisting of logistics service integrator (LSI) and several functional logistics service providers (FLSP), this paper establishes an improved revenue distribution model considering FLSPs’ inequity aversion. Specifically, the BO model (abbreviation of a model proposed by Bolton and Ockenfels in 2000) is improved to describe the FLSPs’ inequity aversion, which is combined into the conventional revenue distribution model. The proposed model aims to maximize the revenue of logistics service supply chain and obtains the best revenue distribution ratio of each member under equilibrium. In the numerical cases, the impacts of inequity aversion and the number of members with inequity aversion on the revenue distribution are discussed, respectively. The results show that a higher degree of FLSP’s advantageous inequity aversion corresponds to a lower revenue distribution ratio; a higher degree of FLSP’s disadvantageous inequity aversion corresponds to a higher revenue distribution ratio. Increasing the number of FLSP members with inequity aversion results in a higher profit of LSI and lower total utility of FLSPs and the utility of the supply chain. The more FLSP members with inequity aversion there are, the higher the LSI’s profit is, and the lower the total utility of FLSPs and the utility of supply chain are. In addition, the revenue distribution ratio of the FLSP increases with its relative fairness revenue coefficient among FLSPs.

<p style='text-indent:20px;'>Disasters such as earthquakes, typhoons, floods and COVID-19 continue to threaten the lives of people in all countries. In order to cover the basic needs of the victims, emergency logistics should be implemented in time. Location-routing problem (LRP) tackles facility location problem and vehicle routing problem simultaneously to obtain the overall optimization. In response to the shortage of relief materials in the early post-disaster stage, a multi-objective model for the LRP considering fairness is constructed by evaluating the urgency coefficients of all demand points. The objectives are the lowest cost, delivery time and degree of dissatisfaction. Since LRP is a NP-hard problem, a hybrid metaheuristic algorithm of Discrete Particle Swarm Optimization (DPSO) and Harris Hawks Optimization (HHO) is designed to solve the model. In addition, three improvement strategies, namely elite-opposition learning, nonlinear escaping energy, multi-probability random walk, are introduced to enhance its execution efficiency. Finally, the effectiveness and performance of the LRP model and the hybrid metaheuristic algorithm are verified by a case study of COVID-19 in Wuhan. It demonstrates that the hybrid metaheuristic algorithm is more competitive with higher accuracy and the ability to jump out of the local optimum than other metaheuristic algorithms.</p>

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