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Batch processors can concurrently process more than one job, and are commonly used in complex manufacturing systems such as wafer fabrication facilities (wafer fabs). Previous work has shown that there can be a significant reduction in cycle time when the control of the upstream serial processor is dependent on the batch processor. We wish to determine whether this is also true when the upstream processor is also a batch processor, with smaller capacity. We model a two-stage system with an upstream batch processor and a downstream batch processor under differing simple control policies as a continuous time-discrete state Markov chain. In doing so, we show that the concept of constraining the production of the upstream processor in accordance to the anticipated needs of the downstream batch processor can similarly reduce mean system cycle time when the upstream processor is a smaller batch processor. We also perform simulation experiments, using parameters close to those found in wafer fabs, and confirm that the strategy of prioritising the formation of larger batches for the downstream batch processor typically results in substantial cycle time reductions for the two-stage system.

Routing traffic across a capacitated hub-and-spoke network is very common in freight transportation and telecommunications. Most of the hub-and-spoke network models available in the literature assume a fully inter-connected hub network and allow traffic to pass through a maximum of two hubs. In the absence of a fully inter-connected hub network, the traffic may have to be routed through more than two hubs. In this paper, we develop a mathematical model and a simulated annealing (SA)-based solution methodology for routing freight through not more than three hubs in a given capacitated network. We generate test problem instances to evaluate the efficacy of the mathematical model and the meta-heuristic procedure. The effect of each network parameter on the performance of the SA procedure is analysed thoroughly with extensive computational experiments and relevant statistical tests. The results show that near-optimal solutions for large problem instances can be derived in little computational time.

Batch processors can concurrently process more than one job. In wafer fabrication, the processing time of a batch is independent of batch size, and only jobs from the same job family can be batched together. We consider a two-stage subsystem of a wafer fabrication facility (wafer fab), comprised of the diffusion furnace (a batch processor) and its upstream serial processor, with random job arrivals. We hypothesise that allowing the serial processor to anticipate the job family preference of the batch processor will reduce the overall cycle time of jobs passing through this system. To evaluate this hypothesis, we model the performance of the two-stage system under different system parameters and processor control policies as discrete state continuous time Markov chains. We characterise the system performance and show that the concept of constraining the production of the upstream processor according to the anticipated needs of the batch processor can reduce the mean cycle time of jobs being processed. We also perform simulation experiments to show that a simple heuristic based on this insight can translate to substantial cycle time reductions for systems with assumptions closer to those found in wafer fabs.

A cluster tool basically consists of one or more loadlocks where wafers enter and exit the cluster tool, two or more processing chambers where the processes are performed and one or more handlers that transport the wafers between loadlocks and processing chambers. This paper focuses on understanding the behaviour of a cluster tool system by simulation modelling, experimenting on the model and finding out the factors that influence the tool overall cycle time. The number of deposition chambers, number of robot grippers, clean cycle, MTTF and MTTR are identified as the most important factors for cycle time reduction at cluster tool in semiconductor wafer fabrication.

A supply chain is a network of different entities, such as suppliers, manufacturers, distributors and retailers. Each supply chain member adopts a myopic policy for replenishment and production-related decisions to maximise its revenue or minimise its costs. It results in local optimisation and hence reduced overall supply chain performance. This work investigates the benefits of coordinating the production and distribution decisions in a scenario comprising single supplier and a cluster of multiple buyers. The problem is modelled as a mixed-integer linear programme. Factor analysis is conducted to identify dominant factors that favour the benefits of coordination. A heuristic procedure is developed to solve large problem instances in very less computational time. Encouraging results are obtained.

We consider the problem of scheduling semiconductor burn-in operations, where burn-in ovens are modelled as batch processing machines. Most of the studies assume that ready times and due dates of jobs are agreeable (i.e., ri < rj implies di ≤ dj). In many real world applications, the agreeable property assumption does not hold. Therefore, in this paper, scheduling of a single burn-in oven with non-agreeable release times and due dates along with non-identical job sizes as well as non-identical processing of time problem is formulated as a Non-Linear (0-1) Integer Programming optimisation problem. The objective measure of the problem is minimising the maximum completion time (makespan) of all jobs. Due to computational intractability, we have proposed four variants of a two-phase greedy heuristic algorithm. Computational experiments indicate that two out of four proposed algorithms have excellent average performance and also capable of solving any large-scale real life problems with a relatively low computational effort on a Pentium IV computer.

In this paper, we consider the problem of scheduling semiconductor burn-in operation, where the burn-in oven is modelled as a batch processing machine. In this study, both oven capacity and the number of boards available are taken as constraints. The objective measure of the problem is minimising the total completion time of all jobs. The computational difficulty in getting optimal solution is shown empirically. Since the problem under study is NP-hard, two variants of Tabu Search method are proposed. A series of computational experiments show that the proposed variants of Tabu Search method outperforms the existing heuristic for most system configurations in very meagre CPU times on P4 machine.

Motivated by a major PC manufacturer in the consumer electronics industry, we study the problem of synchronisation of assembly manufacturing and final deliveries using air transportation to achieve accurate delivery with minimised delivery cost in consumer electronics supply chain (CESC). The customer orders are shipped to the customers by cargo flights. Orders are allocated to the available flight capacities based on customer delivery dates. Synchronisation is considered to match the assembly completion of each order with the departure time of its predetermined allocated flights. We break the synchronisation problem down into two sub-problems consisting of an air transportation allocation problem and an assembly scheduling problem. The first sub-problem is formulated as an Integer Linear Programming (ILP) model. Two solution methodologies are proposed for the second sub-problem. Computational results indicate that the proposed methodologies can achieve considerable cost reduction compared to the existing industry practice.