On a Modular Approach to the Design of Integrated Social Surveys

This article considers a modular approach to the design of integrated social surveys. The approach consists of grouping variables into ‘modules’, each of which is then allocated to one or more ‘instruments’. Each instrument is then administered to a random sample of population units, and each sample unit responds to all modules of the instrument. This approach offers a way of designing a system of integrated social surveys that balances the need to limit the cost and the need to obtain sufficient information. The allocation of the modules to instruments draws on the methodology of split questionnaire designs. The composition of the instruments, that is, how the modules are allocated to instruments, and the corresponding sample sizes are obtained as a solution to an optimisation problem. This optimisation involves minimisation of respondent burden and data collection cost, while respecting certain design constraints usually encountered in practice. These constraints may include, for example, the level of precision required and dependencies between the variables. We propose using a random search algorithm to find approximate optimal solutions to this problem. The algorithm is proved to fulfil conditions that ensure convergence to the global optimum and can also produce an efficient design for a split questionnaire.

Decentralized Sub-Optimal Minimum-Time Consensus

In many multi-agent scenarios, agents must balance both local and global performance and collaboration objectives with the constraints of efficient resource utilization. Here, consensus problems with fixed communication graphs are considered, where agents strive to reach a common decision value in minimum time while simultaneously minimizing a performance cost that measures the deviation of the decision value from the desired value. The paper offers preliminary results on the selection of meaningful decision values that approximate optimal solutions. The proposed approach shows a fast convergence time and also reduces the overall number of information transmissions by the agents in the network.

An EOQ model with stock dependent demand and imperfect quality items

This paper deals with an economic order quantity model where demand is stock dependent. Items received are not of perfect quality and each lot received contains percentage defective imperfect quality items, which follow a probability distribution. Two cases are considered. 1) Imperfect quality items are held in stock and sold in a single batch after a 100 percent screening process. 2) A hundred percent screening process is performed but the imperfect quality items are sold as soon as they are detected. Approximate optimal solutions are derived in both cases. A numerical example is provided in order to illustrate the development of the model. Sensitivity analysis is also presented, indicating the effects of percentage imperfect quality items on the optimal order quantity and total profit.