Course scheduling is a combinatorial optimization problem with multiple constraints. To achieve the reasonable allocation of teaching resources, all courses should be arranged under a number of specified constraints. With the increased number of courses, the solution space increases exponentially. However, the traditional methods cannot quickly find the optimal solution. In order to overcome the disadvantages of low efficiency and the possibility of high conflict in traditional course scheduling, this research study proposes a course scheduling, method based on an improved binary cuckoo search algorithm (IBCS). First of all, a multi-objective and multi-constraint mathematical model of course scheduling is established. Course scheduling involves issues, such as allocating teachers, courses, classes, classrooms and time under specific constraints. The class element is defined to represent the class and the course that the teachers will teach. Therefore, the problem of course scheduling is transformed into the process of mapping from the class element to the classroom-time pair. Six hard constraints and three soft constraints are also defined accordingly. Then, the BCS algorithm is used to search the best course scheduling scheme in the binary-encoded solution space. In order to control the convergence rate effectively and avoid falling into the local optimum, a dynamically adjustable, equilibrium coefficient is introduced. Therefore, the algorithm retains the diversity of the solutions while converging. Finally, this research study constructs the data set and carries out the simulation experiment. It also compares the IBCS algorithm in this paper with the genetic algorithm and the standard binary cuckoo search algorithm (BCS). Experimental results show that the IBCS algorithm can converge in effective time. Meanwhile, it can achieve an improved global scheduling scheme with higher stability.
In this paper, a supply chain system is viewed as a maintainable system, and the economic-statistical design of a likelihood ratio control chart with a maintenance application is considered for this system. The supply chain system is described by a three-state: normal state, warning state and failure state. A likelihood ratio control chart is used to monitor the system given that only categorical observations can be obtained. When the chart signals, a full inspection is performed to determine the actual system state (normal or warning), and preventive maintenance is immediately performed in the warning state. In addition, the supply chain system must be corrected upon failure (i.e. corrective maintenance), and should be maintained in a scheduled time (i.e. planned maintenance). A mathematical model is developed for the joint optimisation of the control chart parameters and planned maintenance time based on renewal theory. An example is presented to illustrate how to determine the optimal design parameters. We also investigate the effect of coefficients and statistical constraints on the decision variables and the expected cost. 相似文献
