UTILIZING DOMAIN-SPECIFIC INFORMATION FOR THE OPTIMIZATION OF LOGISTICS NETWORKS

Continuously maintaining a logistics network (LNW) in good condition is a challenging task for decision makers. For purposes of improving an LNW’s performance, promising actions need to be identified, such as the centralization of a stock keeping unit (SKU). In order to support the decision maker, the authors have developed a logistics assistance system (LAS) based on discrete-event simulation. With an increasing size of the LNW, the response time of such an LAS increases exponentially. In this paper, the authors present an approach for utilizing domain-specific information to guide the search for promising actions and, therefore, reduce the LAS’s response time. The given examples show that the LAS’s response time can be decreased. For example, the approach reduces the number of iterations needed by an evolutionary algorithm to converge.

[1]  Markus Rabe,et al.  Utilizing Domain-Specific Information in Decision Support for Logistics Networks , 2018, LDIC.

[2]  P. Baker,et al.  The Handbook of Logistics and Distribution Management , 2001 .

[3]  Wilhelm Dangelmaier,et al.  Combining Simulation with Heuristics to solve Stochastic Routing and Scheduling Problems , 2013 .

[4]  Thomas Bäck,et al.  Evolutionary Algorithms: The Role of Mutation and Recombination , 2000 .

[5]  Giulio Zotteri,et al.  Introduction to Distribution Logistics , 2013 .

[6]  Susanne Hertz.  Advances In Evolutionary Algorithms Theory Design And Practice , 2016 .

[7]  Athakorn Kengpol.  Design of a decision support system to evaluate logistics distribution network in Greater Mekong Subregion Countries , 2008 .

[8]  Bernd Hellingrath,et al.  Logistische Assistenzsysteme - Entscheidungsunterstützung für eine robuste Logistik , 2008 .

[9]  M. Schwind,et al.  A Reinforcement Learning Approach for Supply Chain Management , 2022 .

[10]  Tim Sheard,et al.  A software engineering experiment in software component generation , 1996, Proceedings of IEEE 18th International Conference on Software Engineering.

[11]  Ulrike Beißert,et al.  Introduction OTD-NET and LAS: Order-to-delivery network simulation and decision support systems in complex production and logistics networks , 2013, 2013 Winter Simulations Conference (WSC).

[12]  Arie van Deursen,et al.  Domain-specific languages: an annotated bibliography , 2000, SIGP.

[13]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[14]  Rafael Radkowski,et al.  Unified Modeling Language to Enhance the Specification of Discrete Event Systems for Virtual Reality Applications , 2011 .

[15]  Gianpaolo Ghiani,et al.  Introduction to Logistics Systems Management , 2013 .

[16]  Markus Rabe,et al.  Method to model actions for discrete-event simulations of logistics networks , 2017, 2017 Winter Simulation Conference (WSC).

[17]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[18]  Omid Bozorg-Haddad,et al.  Meta-Heuristic and Evolutionary Algorithms for Engineering Optimization , 2017 .

[19]  Jon Louis Bentley,et al.  Programming pearls: little languages , 1986, CACM.

[20]  P. Read Montague,et al.  Reinforcement Learning: An Introduction , 2005, IEEE Transactions on Neural Networks.

[21]  Andreas Reinholz,et al.  Assistenzsysteme für die Entscheidungsunterstützung , 2009 .

[22]  E. Bykov,et al.  Decision Support for Gasoline Tanker Logistics with BPsim.DSS , 2015 .

[23]  Bernhard Rumpe,et al.  Meaningful modeling: what's the semantics of "semantics"? , 2004, Computer.

[24]  Michael Toth,et al.  Logistic assistance systems for collaborative supply chain planning , 2011, Int. J. Simul. Process. Model..