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In a Big Data environment, in order to study the decision-making problem of Big Data information investment and the effects of using Big Data information to improve industry cost on supply chain coordination, firstly the importance of Data Company in supply chain was analyzed, and the original supply chain model was built. Meanwhile, some changes of consumer behavior were analyzed in a Big Data environment. Based on these, the market demand function and the benefit model of stakeholder were built and analyzed. Findings: (1) The first finding is whether an enterprise was suitable for gaining Big Data to improve its costs, which was determined by the cost improvement coefficient; namely, it was related to the ability of excavating and using the value of Big Data. (2) Whether the supply chain was the decentralized decision-making and the centralized decision-making, the thresholds of acquisition costs on Big Data information were equal. Moreover, the maximum value that they could undertake was same. (3) Meanwhile the fact that the quantity discount contract could achieve a win-win outcome for supply chain members was proved. The discount coefficient was related to consumers' behavior preference in a Big Data environment.

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.

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.

Cropping fields often have well-defined poor-performing patches due to spatial and temporal variability. In an attempt to increase crop performance on poor patches, spatially selective field operations may be performed by agricultural robotics to apply additional inputs with targeted requirements. This paper addresses the route planning problem for an agricultural robot that has to treat some poor-patches in a field with row crops, with respect to the minimization of the total non-working distance travelled during headland turnings and in-field travel distance. The traversal of patches in the field is expressed as the traversal of a mixed weighted graph, and then the problem of finding an optimal patch sequence is formulated as an asymmetric traveling salesman problem and solved by the parthenogenetic algorithm. The proposed method is applied on a cropping field located in Northwestern China. Research results show that by using optimum patch sequences, the total non-working distance travelled during headland turnings and in-field travel distance can be reduced. But the savings on the non-working distance inside the field interior depend on the size and location of patches in the field, and the introduction of agricultural robotics is beneficial to increase field efficiency.Additional key words: ASTP; autonomous machines; field operations; fieldwork pattern; patch spraying; precision agriculture; spatially selective application. * Corresponding author: lyl_mac@126.com Received: 01-07-12. Accepted: 31-01-13.Abbreviations used: ATSP (asymmetric traveling salesman problem); AU (application unit); GIS (geographical information system); GPS (global positioning system); RS (remote sensing); RU (refilling unit).

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