In practice, order acceptance and production planning are often functionally separated. As a result, order acceptance decisions are made without considering the actual workload in the production system, or by only regarding the aggregate workload. We investigate the importance of a good workload based order acceptance method in over-demanded job shop environments, and study approaches that integrate order acceptance and resource capacity loading. We present sophisticated methods that consider technological restrictions, such as precedence relations, and release and due dates of orders. We use a simulation model of a generic job shop to compare these methods with straightforward methods, which consider capacity restrictions at an aggregate level and ignore precedence relations. We compare the performance of the approaches based on criteria such as capacity utilisation. The simulation results show that the sophisticated approaches significantly outperform the straightforward approaches in case of tight due dates (little slack). In that case, improvements of up to 30% in utilisation rate can be achieved. In case of much slack, a sophisticated order acceptance method is less important.

To avoid road congestion, we are developing a highly automated underground transport system using automatic guided vehicles (AGVs) around Schiphol Airport. It is unique in its scale, incorporating 16 to 25 km tubes connecting five to 20 terminals, and it includes 200 to 400 AGVs to transport an estimated 3.5 million tons of cargo in 2020 with different ordering priorities. According to the current plans, the system will run from 2006 on. Since 1997, we have used object-oriented simulations to plan the dimensions of the system (number of AGVs, terminal sizes) and to design the layout (network, terminals). We showed that an investment reduction of plus or minus 20 percent is feasible using periodically switched one-way tube sections. We developed a variety of logistics optimization algorithms and heuristics, including allocating AGVs between terminals, scheduling terminals, and controlling traffic. We used simulation control structures to test prototype AGVs on a test site. Performing distributed simulations with a mixture of simulated and real objects, we could reduce the risks of the new technology.

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One of the major planning issues in large scale automated transportation systems is so-called empty vehicle management, the timely supply of vehicles to terminals in order to reduce cargo waiting times. Motivated by a Dutch pilot project on an underground cargo transportation system using Automated Guided Vehicles (AGVs), we developed several rules and algorithms for empty vehicle management, varying from trivial First-Come, First-Served (FCFS) via look-ahead rules to integral planning. For our application, we focus on attaining customer service levels in the presence of varying order priorities, taking into account resource capacities and the relation to other planning decisions, such as terminal management. We show how the various rules are embedded in a framework for logistics control of automated transportation networks. Using simulation, the planning options are evaluated on their performance in terms of customer service levels, AGV requirements and empty travel distances. Based on our experiments, we conclude that look-ahead rules have significant advantages above FCFS. A more advanced so-called serial scheduling method outperforms the look-ahead rules if the peak demand quickly moves amongst routes in the system. We thank the Dutch Centre for Transportation Technology (CTT) for their funding of the simulation study that has been the basis of our research results. CTT is initiator and coordinator of the project to design and develop the underground logistics system around Amsterdam Airport Schiphol that has been used as a case study in this paper.Correspondence to: M. van der Heijden