The water supply system studied in this paper consists of a water treatment plant, a ground-level storage, a pumping station, and a distribution network in series. Expected served demand is employed to measure reliability taking into account both insufficient heads and flows at individual nodes in the network since it is the most important service level index provided to individual users. A basic method proposed is to assume that the insufficient nodal head reduces the effectiveness of flow supplied at the node and that the authority provides the maximum service to customers so that the real-time pump and network flow operations maximize the effective served system demand. The average value of the maximum effective served system demand relative to the total system demand over all system states is defined as system reliability, and the nodal reliability for each demand node is similarly defined. The Markov chain method introduced by Beim and Hobbs (1988) is employed to describe the evolution of the storage level over time so that the real-time pump and network flow operations can be accurately implemented by solving a nonlinear programming model. Two example systems are presented to demonstrate numerically the advantage of the method proposed in its consideration of the distribution network and nodal reliabilities.

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This paper considers operational issues that arise in repetitive manufacturing systems served by automated guided vehicles (AGVs) in loops with unidirectional material flow. The objective considered is the minimization of the steady state cycle time required to produce a minimal job set (or equivalently, throughput rate maximization). Our models allow for delays caused by AGV conflicts. We define and analyze three nondominated and widely used AGV dispatching policies. For each policy, we describe algorithms and intractability results for combined job scheduling and material handling problems. We describe a genetic algorithm that estimates the cycle time within 5% on average for instances with up to 10 machines and four AGVs. Some related fleet sizing and loop decomposition issues are discussed in the companion paper [19].