The performance of a manufacturing system with a defined level of flexibility is determined by the effectiveness of the control strategy employed. The success of the latter is critically dependent upon information intensive activities including information collection, transfer and processing. Each of these activities consumes time and thus causes delays. We refer to these delays as information delays. Most real-world manufacturing systems operate under conditions that entail significant information delays. Thus, there is a need to model, analyse and evolve control strategies that can perform well under such delays. This paper focuses on the design of a suitable control strategy for a simple system operating in a stochastic environment with information delays. System stochasticity coupled with information delay has a compounding effect on the uncertainty of the environment within which decisions must be taken thus providing motivation to explore the development of control strategies based on fuzzy logic. We introduce a novel fuzzy associative memory based control strategy (FCS) to cope with information delays. Fuzzy associative memories embody a bank of fuzzy rules that reflect expert knowledge in linguistic form. In demonstrating the use of FCS for the one machine, two queue dynamic sequencing problem wherein information delays manifest in the form of machine setup times, this paper identities suitable input and output control variables and suggests their appropriate fuzzification. We define the relative opportunity gain and the relative work-in-process as two fuzzy control variables. The output fuzzy variable is the switching confidence level. A comparison of FCS with the alternating priority heuristic is presented using average job flowtime as a performance measure. Simulation results show the efficacy and potential of using fuzzy control in situations where information delays are significant.
