Due to the rise of social and environmental concerns on global climate change, developing the low-carbon economy is a necessary strategic step to respond to greenhouse effect and incorporate sustainability. As such, there is a new trend for the cold chain industry to establish the low-carbon vehicle routing optimization model which takes costs and carbon emissions as the measurements of performance. This paper studies a low-carbon vehicle routing problem (LC-VRP) derived from a real cold chain logistics network with several practical constraints, which also takes customer satisfaction into account. A low-carbon two-echelon heterogeneous-fleet vehicle routing problem (LC-2EHVRP) model for cold chain third-party logistics servers (3PL) with mixed time window under a carbon trading policy is constructed in this paper and aims at minimizing costs, carbon emissions and maximizing total customer satisfaction simultaneously. To find the optimal solution of such a nondeterministic polynomial (NP) hard problem, we proposed an adaptive genetic algorithm (AGA) approach validated by a numerical benchmark test. Furthermore, a real cold chain case study is presented to demonstrate the influence of the mixed time window’s changing which affect customers’ final satisfaction and the carbon trading settings on LC-2EHVRP model. Experiment of LC-2EHVRP model without customer satisfaction consideration is also designed as a control group. Results show that customer satisfaction is a critical influencer for companies to plan multi-echelon vehicle routing strategy, and current modest carbon price and trading quota settings in China have only a minimal effect on emissions’ control. Several managerial suggestions are given to cold chain logistics enterprises, governments, and even consumers to help improve the development of cold chain logistics.

Purpose This study aims to investigate the factors from four dimensions that have an effect both on formal and informal knowledge sharing (FKS and IKS) and the relationship between knowledge sharing (KS) and task performance in Chinese manufacturing. Design/methodology/approach The structural equation modeling approach was applied to hypothesis testing according to the data collected from employees of manufacturing companies through the online questionnaire. A total of 530 valid responses were obtained. Findings The results indicate that level of knowledge structure, self-efficacy, leadership support and KS culture all have a significant positive effect on both FKS and IKS while trust only positively affects FKS and information technology support positively affects IKS. Both FKS and IKS positively contribute to the task performance of manufacturing companies. Research limitations/implications This study merely considered the impact of six factors on KS from four perspectives. Consequently, the relationship between some important other factors and KS is not revealed. In addition, the results of this study indicate that there might be a more complicated relationship between these factors and KS than the model constructed by this study. Therefore, in future research, more influencing factors could be considered in the research framework, and a multilevel model, such as a model considering the mediation effect, could be further explored. Practical implications According to the results, both FKS and IKS play a significant role in promoting organizational task performance, which is worthy of attention by the managers of manufacturing companies. In addition, the relationship between the different factors and the FKS and IKS found in this study provides specific guidance for improving the organizational KS practice. Originality/value First, previous studies considered the construction of explicit KS and tacit KS models based on the content of KS while this study considered FKS and IKS from the perspective of the process and approach of KS. Second, this research has clearly defined the level of knowledge structure from the perspective of knowledge ontology and verifies the positive effect of this factor on KS, providing a new theoretical perspective for exploring KS factors.

We study an agent-based scheduling problem of two identical parallel machines:. The machines and tasks are regarded as agents. A new multi-agent scheduling model is proposed to achieve the optimum from the two task agents, agent A and agent B. The objective is divided into two classes. The objectives of agent A and agent B are to minimize the total tardiness time and minimize the makespan, respectively. In this article, we research two identical parallel machines in which one job category can be processed by one machine agent only or two machine agents and propose a new multi-agent model for two identical parallel machines, divided into two subsystems. For subsystem 1, the shortest processing time order is used to solve job priorities. A single distribution strategy is proposed to assign jobs to machine agents and is applied to the dynamic scheduling environment. For subsystem 2, a centralized distribution strategy is applied to the static scheduling environment. The proposed model performs more efficiently and is better able to handle complex and dynamic scheduling environments.

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Process planning and job shop scheduling problems are the two classical but crucial activities in manufacturing system. With the approach of integrated process planning and scheduling, the two actual activities are combined to conduct operation selection and operation sequencing with the constraints of practical job shop status. In this article, a quantuminspired hybrid algorithm with the objective of minimum makespan is proposed, aiming to solve integrated process planning and scheduling problems in dynamic manufacturing systems. A hybrid-coding representation is suggested, which is a three-layer structure in numerical representation and Q-bit representation adopted from quantum-inspired evolutionary algorithm. Based on the hybrid-coding representation, customized converting and repairing rules and methods are presented to generate feasible individuals. Q-gate rotation and group leader optimization algorithm are integrated systematically for the population evolution to accelerate the convergence speed of the proposed algorithm. In order to increase the diversity of population, a chaotic map called logistic map is introduced, bringing the stochastic initial individuals. Experiments show that the proposed hybrid algorithm can generate outstanding outcomes for integrated process planning and scheduling instances.