Mark Ebben

Workload based order acceptance in job shop environments

Abstract.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.

Using Simulation to Design an Automated Underground System for Transporting Freight Around Schiphol Airport

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.

Scheduling vehicles in automated transportation systems : algorithms and case study

Abstract. 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.

Dynamic one-way traffic control in automated transportation systems

In a project on underground freight transportation using Automated Guided Vehicles, single lanes for traffic in two directions are constructed to reduce infrastructure investment. Intelligent control rules are required to manage vehicle flows such, that collision is avoided and waiting times are minimised. In contrast to standard traffic control at intersections, these control rules should take into account significant driving times along the single lane (in our application up to 8 min). Whereas periodic control rules are often applied in traffic theory, we focus on adaptive rules such as look-ahead heuristics and dynamic programming algorithms. Numerical experiments show that our control rules reduce waiting times by 10–25% compared to a straightforward periodic rule. Dynamic programming yields the best results in terms of mean waiting times.

Scheduling vehicles in automated transportation systems

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.

Dynamic transport scheduling under multiple resource constraints

This paper presents a heuristic for the dynamic vehicle scheduling problem with multiple resource capacity constraints. In the envisaged application, an automated transport system using Automated Guided Vehicles, bottleneck resources are (1) vehicles, (2) docks for loading/unloading, (3) vehicle parking places, and (4) load storage space. This problem is hard, because interrelated activities (loading, transportation, unloading) at several geographical locations have to be scheduled under multiple resource constraints, where the bottleneck resource varies over time. Besides, the method should be suitable for real-time planning. We developed a dedicated serial scheduling method and analyzed its dynamic behavior using discrete event simulation. We found that our method is very well able to find good vehicle schedules satisfying all resource constraints. For comparison, we used a simple approach where we left out the resource constraints and extended the processing times by statistically estimated waiting times to account for finite capacities. We found that our newly designed method finds better schedules in terms of service levels.

Multi-agent simulation of purchasing activities in organizations

In this paper we present a multi-agent simulation model to investigate purchasing activities in an organizational environment. The starting point is the observation that the majority of purchasing activities in organizations are usually performed without any involvement of the organizations purchasing department. The purpose of the experiments is to investigate if and how certain factors determine the degree to which purchasing professionals become involved in the purchasing of non-product related (NPR) items and services. Among the factors investigated are: corporate purchasing policies, available information, and the nature of the various purchasing activities. Preliminary results show that the behavior of the multi-agent simulation model is an acceptable representation of reality. Furthermore, the results show the limits of a purchasing departments added value and the important role of organizational learning in that respect. The paper provides directions for further research.