Matthijs C. van der Heijden

Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems ☆

We consider the real-time scheduling of full truckload transportation orders with time windows that arrive during schedule execution. Because a fast scheduling method is required, look-ahead heuristics are traditionally used to solve these kinds of problems. As an alternative, we introduce an agent-based approach where intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. This approach offers several advantages: it is fast, requires relatively little information and facilitates easy schedule adjustments in reaction to information updates. We compare the agent-based approach to more traditional hierarchical heuristics in an extensive simulation experiment. We find that a properly designed multi-agent approach performs as good as or even better than traditional methods. Particularly, the multi-agent approach yields less empty miles and a more stable service level.

Service differentiation in spare parts supply through dedicated stocks

We investigate the option of keeping dedicated stocks at customer sites in addition to stock kept at some central location as a tool for applying service differentiation in spare parts supply. We study the resulting two-echelon system in a multi-item setting, both under backordering and under the use of emergency shipments (equivalent to lost sales for the inventory system). In an extensive computational experiment, we show that dedicated stocks have significant added value: the approach results in clear savings compared to a strategy of providing all customers with uniform service. Furthermore, the savings found with dedicated stocks are close to those with critical level policies, with dedicated stocks usually being much easier to implement in practice.

An Exact Solution for the State Probabilities of the Multi-Class, Multi-Server Queue with Preemptive Priorities

We consider a multi-class, multi-server queueing system with preemptive priorities. We distinguish two groups of priority classes that consist of multiple customer types, each having their own arrival and service rate. We assume Poisson arrival processes and exponentially distributed service times. We derive an exact method to estimate the steady state probabilities. Because we need iterations to calculate the steady state probabilities, the only error arises from choosing a finite number of matrix iterations. Based on these probabilities, we can derive approximations for a wide range of relevant performance characteristics, such as the moments of the number of customers of a certain type in the system en the expected postponement time for each customer class. We illustrate our method with some numerical examples. Numerical results show that in most cases we need only a moderate number of matrix iterations (∼20) to obtain an error less than 1% when estimating key performance characteristics.

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.

Look-ahead strategies for dynamic pickup and delivery problems

In this paper we consider a dynamic full truckload pickup and delivery problem with time-windows. Jobs arrive over time and are offered in a second-price auction. Individual vehicles bid on these jobs and maintain a schedule of the jobs they have won. We propose a pricing and scheduling strategy based on dynamic programming where not only the direct costs of a job insertion are taken into account, but also the impact on future opportunities. Simulation is used to evaluate the benefits of pricing opportunities compared to simple pricing strategies in various market settings. Numerical results show that the proposed approach provides high quality solutions, in terms of profits, capacity utilization, and delivery reliability.

Near cost-optimal inventory control policies for divergent networks under fill rate constraints

We deal with the optimisation of stock levels in general divergent networks under a periodic review, order-up-to (R, S) policy. The goal is to attain target fill rates, while the total holding costs in the entire network are minimised. To this end, we first present a method for the fast calculation of the control parameters, given central and intermediate stock levels. Next we develop an approximate procedure to determine stock levels sequentially. Extensive numerical experimentation shows that this procedure yields satisfactory results. It also shows that significant stocks at intermediate stockpoints are only useful if unit holding costs in these stockpoints are considerably less than in the end stockpoints that deliver directly to the final customers.

Design choices for agent-based control of AGVs in the dough making process

We consider a multi-agent system for the logistics control of Automatic Guided Vehicles that are used in the dough making process at an industrial bakery. Here, logistics control refers to constructing robust schedules for transportation jobs. We discuss how alternative MAS designs can be developed and compared. Qualitative design guidelines turn out to be insufficient to select the best agent architecture. Therefore, we also use simulation to support decision making, where we use real-life data from the bakery to evaluate alternative designs. We show that depending on the degree of dynamism and objectives of the bakery, different architectures are preferred.

Approximations for Markovian multi-class queues with preemptive priorities

We discuss the approximation of performance measures in multi-class M/M/k queues with preemptive priorities for large problem instances (many classes and servers) using class aggregation and server reduction. We compared our approximations to exact and simulation results and found that our approach yields small-to-moderate approximation errors.

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.

Selecting parts for additive manufacturing in service logistics

PurposenFor more than ten years, the value of additive manufacturing (AM) for after-sales service logistics has been propagated. Today, however, only few applications are observed in practice. In this paper, possible reasons for this discrepancy are discussed and a method is developed to simplify the identification of economically valuable and technologically feasible business cases.nnDesign/methodology/approachnThe approach is based on the Analytic Hierarchy Process (AHP) and relies on spare part information that is easily retrievable from the company databases. This has two advantages: first, the approach can be customized towards specific company characteristics, and second, a very large number of spare parts may be assessed simultaneously. A field study is discussed in order to demonstrate and validate the approach in practice. Furthermore, sensitivity analyses are performed to evaluate the robustness of the method.nnFindingsnResults provide evidence that the method allows a valid prioritization of a large spare part assortment. Also, sensitivity analyses clarify the robustness of the approach and illustrate the flexibility of applying the method in practice. More than 1000 positive business cases of AM for after-sales service logistics have been identified based on the method.nnOriginality/valuenThe developed method enables companies to rank spare parts according to their potential value when produced with AM. As a result, companies can evaluate the most promising spare parts first. This increases the effectiveness and efficiency of identifying business cases and thus may support the adoption of AM in after-sales service supply chains.