Max Gath

Algorithm and knowledge engineering for the TSPTW problem

The well-known traveling salesman problem (TSP) is concerned with determining the shortest route for a vehicle while visiting a set of cities exactly once. We consider knowledge and algorithm engineering in combinatorial optimization for improved solving of complex TSPs with Time Windows (TSPTW). To speed-up the exploration of the applied Nested Monte-Carlo Search with Policy Adaption, we perform beam search for an improved compromise of search breadth and depth as well as automated knowledge elicitation to seed the distribution for the exploration. To evaluate our approach, we use established TSPTW benchmarks with promising results. Furthermore, we indicate improvements for real-world logistics by its use in a multiagent system. Thereby, each agent computes individual TSPTW solutions and starts negotiation processes on this basis.

Monte-Carlo Tree Search for 3D Packing with Object Orientation

In this paper we look at packing problems that naturally arise in container loading. Given a set of 3D iso-oriented objects and a container, the task is to find a packing sequence of the input objects consisting of the ID, location, and orientation that minimizes the space wasted by the packing. Instead of the decision problem, we look at the packing optimization problem, minimizing the total height of a packing. Our solutions uses extreme points and applies Monte-Carlo tree search with policy adaptation, a randomized search technique that has been shown to be effective for solving single-agent games and, more recently, complex traveling salesman and vehicle routing problems. The implementation is considerably simple and conceptually different from mathematical programming branch-and-bound and local search approaches. Nonetheless, the results in solving 2D and 3D packing problems are promising.

Monte-Carlo Tree Search for Logistics

In this paper we review recent advances of randomized AI search in solving industrially relevant optimization problems. The method we focus on is a sampling-based solution mechanism called Monte-Carlo Tree Search (MCTS), which is extended by the concepts of nestedness and policy adaptation to establish a better trade-off between exploitation and exploration. This method, originating in game playing research, is a general heuristic search technique, for which often less problem-specific knowledge has to be added than in comparable approaches.

Agent-Based Multimodal Transport Planning in Dynamic Environments

The development and maintenance of traffic concepts in urban districts is expensive and leads to high investments for altering transport infrastructures or for the acquisition of new resources. We present an agent-based approach for modeling, simulation, evaluation, and optimization of public transport systems by introducing a dynamic microscopic model. Actors of varying stakeholders are represented by intelligent agents. While describing the inter-agent communication and their individual behaviors, the focus is on the implementation of information systems for traveler agents as well as on the matching between open source geographic information systems, and standardized transport schedules provided by the Association of German Transport Companies. The performance, efficiency, and limitations of the system are evaluated within the public transport infrastructure of Bremen. We discuss the effects of passengers’ behaviors to the entire transport network and investigate the system’s flexibility as well as consequences of incidents in travel plans.

Agent-based dispatching in groupage traffic

The complexity and dynamics in group age traffic requires flexible, efficient, and adaptive planning and controlling processes. While the general problem refers to the Vehicle Routing Problem (VRP), additional requirements have to be fulfilled in application. Individual properties and priorities of orders, a heterogeneous fleet of vehicles, dynamically incoming orders, unexpected events etc. require a proactive and reactive system behavior. To enable automated dispatching processes, we have implemented a multiagent system where the decision making is shifted from a central system to autonomous, interacting, intelligent agents. To evaluate the approach we used multi agent-based simulation and modeled several scenarios on real world infrastructures with orders provided by our industrial partner. The results reveal that agent-based dispatching meets the increasing requirements in groupage traffic while supporting the combination of pickup and delivery tours and accommodating request priorities, time-windows, as well as capacity constraints.

Solving Single Vehicle Pickup and Delivery Problems with Time Windows and Capacity Constraints using Nested Monte-Carlo Search

Transporting goods by courier and express services increases the service quality through short transit times and satisfies individual demands of customers. Determining the optimal route for a vehicle to satisfy transport requests while minimizing the total cost refers to the Single Vehicle Pickup and Delivery Problem. Beside time and distance objectives, in real world operations it is mandatory to consider further constraints such as time windows and the capacity of the vehicle. This paper presents a novel approach to solve Single Vehicle Pickup and Delivery Problems with time windows and capacity constraints by applying Nested Monte-Carlo Search (NMCS). NMCS is a randomized exploration technique which has successfully solved complex combinatorial search problems. To evaluate the approach, we apply benchmarks instances with up to 400 cities which have to be visited. The effects of varying the number of iterations and the search level are investigated. The results reveal, that the algorithm computes state-of-the-art solutions and is competitive with other approaches.

Agent-based planning and control for groupage traffic

In this research and technology transfer project, the planning and control processes of the industrial partner Hellmann Worldwide Logistics GmbH & Co. KG are analyzed. An agent-based approach is presented to model current processes and to exploit the identified optimization potential. The developed system directly connects the information flow and the material flow as well as their interdependencies in order to optimize the planning and control in groupage traffic. The software system maps current processes to agents as system components and improves the efficiency by intelligent objects. To handle the high complexity and dynamics of logistics autonomous intelligent agents plan and control the way of represented objects through the logistic network by themselves and induce a flexible and reactive system behavior. We evaluate the implemented dispatching application by simulating the groupage traffic processes using effectively transported orders and process data provided by our industrial partner. Moreover, we modeled real world infrastructures and considered also the dynamics by the simulation of unexpected events and process disturbances. The results show that the system significantly decreases daily cost by reducing the required number of transport providers and shifting conventional orders to next days, which need no immediate delivery. Thus the system increases the efficiency and meets the special challenges and requirements of groupage traffic. Moreover, the system supports freight carriers and dispatchers with adequate tour and routing proposals. Computed tours were successfully validated by human dispatchers. Due to the promising results, Hellmann is highly interested in transferring the prototype to an application that optimizes the daily operations in numerous distribution centers. Finally, provide further research perspectives, and emphasize the advantages of the developed system in Industry 4.0 applications.

Autonomous and flexible multiagent systems enhance transport logistics

This paper presents an autonomous multiagent system which optimizes the planning and scheduling of industrial processes using the example of courier and express services. In order to handle the rising demands and to capitalize on the increasing optimization potential in transport logistics, which both result from the consequent integration of industrial processes into the Internet of Things and Services, the presented dispAgent solution ensures a flexible, adaptive, and proactive system behavior. Intelligent, selfishly acting agents represent logistic entities, which communicate and negotiate with each other to optimize the allocation of orders to transport facilities. The system has been developed in cooperation with our industrial partner tiramizoo, which is an expert in courier and express services. In order to determine the quality of the computed solutions, we evaluated the system using an established benchmark set and compared the results to best-known solutions. In addition, we further validated the systems performance by multiple simulations of real-world scenarios relying on data which was provided by our industrial partner. The results show that the system achieves high quality solutions for the benchmark set and outperforms a standard dispatching software product in real-world scenarios.

Autonomous, Adaptive, and Self-Organized Multiagent Systems for the Optimization of Decentralized Industrial Processes

This chapter presents the concepts, an example implementation, and the evaluation of an autonomous, self-organized, and adaptive multiagent system to optimize industrial processes in dynamic environments. In order to satisfy the rising requirements which result from the Fourth Industrial Revolution and to benefit from the consequent integration of the Internet of Things and Services, the system is designed to link the data of highly decentralized entities to virtual representatives. The goal0 is to mesh complex information and material flows as well as their interdependencies in order to achieve an integrated optimization of production and logistic processes. Due to the high dynamics, the domain of courier and express services provides one of the most challenging environments, in which a high amount of decentralized data and information has to be considered, updated, and processed continuously during operations. The chapter summarizes the state-of-the-art of agent-based approaches in transport logistics and describes the limitations for their application in Industry 4.0 processes. Next, it presents the developed dispAgent approach, the applied coordination and negotiation protocols for the synchronization in highly parallelized negotiations, as well as the solver which have been developed for the individual decision making of the autonomously acting agents. The system is evaluated on two established benchmarks for the Vehicle Routing Problem as well as by a case study with real-world data which was conducted in cooperation with our industrial partner.

The dispAgent Approach

This chapter presents the dipsAgent approach which optimizes the planning and control processes in transport logistics and allows handling domain-specific demands in complex and dynamic environments. The challenge is to preserve, extend, and optimize suitable concepts, components, and approaches in order to overcome the weaknesses described in Chapters 2−3 and to use multiagent-based autonomous processes in real-world applications.