Generation of stocking and relocation recommendations for last-in first-out storages with customized shipping preferences

Abweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in engl. SpracheIn dieser Arbeit werden unterschiedliche Ansätze zum Generieren von Ein- und Umlagervorschlägen in einem Lager mit einer Last-In First-Out Strategie vorgestellt. Das Ziel besteht darin, den Zeitaufwand der Auslagerungen, in Betracht auf eine gegebene Lagerstrategie, möglichst gering zu halten. Eine einfache und zeitsparende Auslagerung charakterisiert sich dadurch, dass die Verladung der Ware ohne aufwändige Umräumungen vollzogen werden kann. Dies kann erreicht werden, indem die Ware bei der Einlagerung so im Lager platziert wird, dass auf die entsprechenden auszulagernden Waren ein direkter Zugriff gegeben ist. Dafür ist ein System entwickelt worden, das, aufgrund von Informationen über die Waren und den Lagerzustand, eine im Hinblick auf die Auslagerung optimale Einlagerung liefert. Zusätzlich wird die Möglichkeit geboten, durch Umlagerungen des Lagerinhalts, vorhandene Problematiken bezüglich der Auslagerung aufzuheben, sodass wiederum die Auslagerzeiten minimiert werden. Um dies umzusetzen wurde eine Funktion modelliert, die unter Berücksichtigung der Problematiken hinsichtlich der Auslagerungen, eine Bewertung des Lagerzustands vornimmt, welche bei den Ein- und Umlagerungen minimiert wird. Die Einlagerung selbst wird mittels Greedy Verfahren realisiert, während Umlagervorschläge unter anderem mit Verfahren basierend auf Lokaler Suche (engl. local search) und Variabler Nachbarschaftsabstieg (engl. variable neighborhood descent) berechnet werden.Die entwickelten Methoden konnten anhand von Echtweltdaten getestet werden, da der im Zuge der Arbeit entstandene Prototyp inzwischen in einem Lager eines Papierherstellers im Einsatz ist. Es wird gezeigt, dass die Einführung der Einlagerungsstrategie zu einem erheblich besseren Lagerzustand führt, und dadurch die Auslagerung der Ware effizienter und somit kostengünstiger durchführbar ist.In this thesis we present different approaches to generate stocking and relocation recommendations to use in a Last-In First-Out storage.Our goal is to minimize the time required for stock removal using a given stocking strategy. A simple, time efficient stock removal is characterized by the possibility to retrieve goods for shipment without having to perform time consuming restocking operations and can be achieved by stocking the goods in a way that always allows direct access to the wares might be shipped shortly. To achieve this, we developed a system that takes into account information about the current storage situation, incoming goods and future shipments and generates a stocking suggestion that results in a storage structure optimal for stock removal. In addition, we offer the possibility to generate recommendations for restructuring the given storage that, when applied, resolve existing conflicts regarding stock removal which also minimizes shipping time.The main idea of the implementation is to model a function that evaluates the storage structure taking into account conflicts regarding stock removal. Using our approach, this function is minimized at every stocking and restocking operation. To determine which storage bin should be used for stocking incoming goods, our approach makes use of greedy methods while restocking suggestions are constructed using local search and variable neighborhood descent algorithms.The methods presented in this thesis are applied in one of the warehouses of a paper manufacturing company. We show that introducing the strategies presented in this work has led to a hugely improved storage structure in said warehouse that allows much quicker shipping times and at the same time a higher average filling level of the storage.8

A digital twin based decision support system for dynamic vehicle routing problems

International audienceThe research on transportation problems is strongly motivated by real problems. One important industry is the courier, express, and parcel (CEP) market. Recent developments are the growth of the CEP market, the increasing expectations in customer services, and the new technological advances like tracking information, data storage and mobile communication. Modern decision support systems (DSS) are therefore expected to provide real-time decision-making and require fast reactions to changes in the system. Although literature of dynamic vehicle routing problems (DVRPs) exists, further challenges have to be considered when dealing with a real-time DSS. Thus, besides the development of sophisticated optimization approaches, it is essential to provide a concept representing the real world and its dynamic behaviors.In this work, we outline a digital twin based DSS with simulation capabilities for DVRPs. We present various aspects which must be considered in a real-world DSS, in order to provide high-quality solutions within short response times. We show the implementation of a background optimization task and discuss a solution for synchronizing the current system state with the planned routes. Then, we demonstrate the advantages of integrating a simulation model into the DSS. Most importantly, this allows extensive analysis of the optimization algorithm performance in advance and facilitates systematic evaluation and algorithm tuning under simulated real-world conditions. Additionally, we show how the simulation can be used to support a human decision maker during day-to-day operation. Finally, the connection to the digital twin and its advantages are demonstrated by an example application

A restricted dynamic programming algorithm for the dial-a-ride problem

International audienceIn this paper, a restricted dynamic programming algorithm for the static multiple vehicle dial-a-ride problem is presented. Passengers have to be transported between pickup and delivery locations, while minimizing travel distances, respecting time window, user ride time and route duration constraints. We report preliminary results for benchmark instances which provide promising results. Algorithmic extensions and a hybrid metaheuristic are considered as pathways for future work

Dynamic programming based metaheuristics for the dial-a-ride problem

International audienceThe organization of a specialized transportation system to perform transports for elderly and handicapped people is usually modeled as dial-a-ride problem. Users place transportation requests with specified pickup and delivery locations and times. The requests have to be completed under user inconvenience considerations by a specified fleet of vehicles. In the dial-a-ride problem, the aim is to minimize the total travel times respecting the given time windows, the maximum user ride times, and the vehicle restrictions. This paper introduces a dynamic programming algorithm for the dial-a-ride problem and demonstrates its effective application in (hybrid) metaheuristic approaches. Compared to most of the works presented in literature, this approach does not make use of any (commercial) solver. We present an exact dynamic programming algorithm and a dynamic programming based metaheuristic, which restricts the considered solution space. Then, we propose a hybrid metaheuristic algorithm which integrates the dynamic programming based algorithms into a large neighborhood framework. The algorithms are tested on a given set of benchmark instances from the literature and compared to a state-of-the-art hybrid large neighborhood search approach

A survey on dynamic and stochastic vehicle routing problems

International audienceResearch on dynamic and stochastic vehicle routing problems received increasing interest in the last decade. It considers a novel problem class, aiming at an appropriate handling of dynamic events combined with the incorporation of stochastic information about possible future events. This survey summarizes recent literature in this area. Besides the classification according to the available stochastic information, a new classification based on the point in time where substantial computational effort for determining decisions or decision policies arises, is introduced. Furthermore, the difference in solution quality is analyzed between approaches which consider either purely dynamic or stochastic problems compared to those which consider both, stochastic and dynamic aspects. A graphical representation demonstrates the strength of the reviewed approaches incorporating dynamic and stochastic information. The survey also gives an overview on the intensity of research for the different problem classes and its benefit in recent years. Finally, guidelines and promising directions for further research are presented

Comparison of anticipatory algorithms for a dial-a-ride problem

International audienceProgress in digitalization opens opportunities to capture accurate transportation logistics data and provide advanced decision support, which leads into the question of how to efficiently exploit this progress in order to improve solution quality in transportation services. Here we address this issue in the context of a dynamic and stochastic patient transportation problem, where besides considering new events, we also incorporate stochastic information about future events. We propose different anticipatory algorithms and investigate which algorithm performs best according to the given settings in a real-world application. We therefore address different types of dynamic events, appropriate response times, and the synchronization of real-world data with the plan. In order to test and analyze how the algorithms behave and perform, we apply the concept of a digital twin. The implemented anticipatory algorithms compared here are a sample scenario planning approach and two waiting strategies. The question of the value of more sophisticated algorithms compared to algorithms with less computational effort is investigated. The experimental results show that solution quality benefits from incorporating information about future requests, and that simple waiting strategies prove most suitable for such a highly dynamic environment. We find that in highly stochastic environments, a rescheduling should be done whenever a new event occurs, whereas in less stochastic environments it is better to let the optimization engine run a bit longer and not start reoptimization after every new event

Cooperative container trucking – System, model and solution

This work considers a multi-resource routing problem (MRRP) where drayage orders need to be completed with trucks and trailers from multiple cooperating carriers. The problem contains multiple challenging aspects including a complex pricing model and the need to coordinate cooperating vehicles of several carriers. While the cooperation aspect of the problem can make the overall drayage operation more efficient, it also introduces additional requirements, most notably a cooperation model which respects the individual preferences of carrier companies and customers. In this paper, we first present an efficient way to model this problem and describe a system that collects and analyzes the required data. We then introduce a solution method based on a variable neighborhood search (VNS) metaheuristic, describe our solution representation and discuss the cost function which is specially tailored to the resource sharing aspect of the problem. Finally, we present a scenario-based evaluation of the system based on real-world data and discuss the results