Simulation Modeling of Cross-Dock and Distribution Center Based Supply Chains

Companies are implementing new strategies to meet the customer requirements in terms of quality, timing, and cost. One of these strategies is cross-docking, which can be defined as the process of consolidating the products coming from different suppliers, but having the same destination, with minimal handling and almost no storage between loading and unloading of the goods. The purpose of this research is to investigate the benefits of having a cross-docking facility in a supply chain. In this research, we focus on developing discrete event simulation models using the opensource Java Simulation Library (JSL). Also, we work on augmenting an object-oriented library for simulating supply chains to include the modeling of cross-dock facilities. The modeling of a crossdock facility includes the receiving, staging/sorting, and load building activities. Because the operational performance of the inner workings of the cross-dock is not needed, detailed modeling of the resources within the cross-dock such as the number of workers, and pieces of equipment are not included in the modeling. However, the flow, time delays, and inventory aspects are modeled because the key emphasis is on how the cross-dock affects the performance of the supply chain. Simulation experiments are conducted to test the performance of the object-oriented library and to compare the performance of two multi-echelon inventory networks with and without crossdocking to identify the significant factors which affect the performance of the two types of supply chains

Optimal Inventory Control and Distribution Network Design of Multi-Echelon Supply Chains

Optimale Bestandskontrolle und Gestaltung von Vertriebsnetzen mehrstufiger Supply Chains Aufgrund von Global Sourcing, Outsourcing der Produktion und Versorgung weltweiter Kunden innerhalb eines komplexen Vertriebsnetzes, in welchem mehrere Anlagen durch verschiedene Aktivitäten miteinander vernetzt sind, haben die meisten Unternehmen heutzutage immer komplexere Supply Chain-Netzwerke in einer immer unbeständiger werdenden Geschäftsumgebung. Mehr beteiligte Unternehmen in der Wertschöpfungskette bedeuten mehr Knoten und Verbindungen im Netzwerk. Folglich bringt die Globalisierung Komplexität und neue Herausforderungen, obwohl Unternehmen immer stärker von globalen Supply Chains profitieren. In einer solchen Geschäftsumgebung müssen sich die Akteure innerhalb der Supply Chain (SC) auf die effiziente Verwaltung und Koordination des Materialflusses im mehrstufigen System fokussieren, um diesen Herausforderungen handhaben zu können. In vielen Fällen beinhaltet die Supply Chain eines Unternehmens unterschiedliche Entscheidungen auf verschiedenen Planungsebenen, wie der Anlagenstandort, die Bestände und die Verkehrsmittel. Jede dieser Entscheidungen spielt eine bedeutende Rolle hinsichtlich der Gesamtleistung und das Verhältnis zwischen ihnen kann nicht ignoriert werden. Allerdings wurden diese Entscheidungen meist einzeln untersucht. In den letzten Jahren haben zahlreiche Studien die Bedeutung der Integration von beteiligten Entscheidungen in Supply Chains hervorgehoben. In diesem Zusammenhang sollten Entscheidungen über Anlagenstandort, Bestand und Verkehrsmittel gemeinsam in einem Optimierungsproblem des Vertriebsnetzes betrachtet werden, um genauere Ergebnisse für das Gesamtsystem zu erzeugen. Darüber hinaus ist ein effektives Management des Materialflusses über die gesamte Lieferkette hinweg, aufgrund der dynamischen Umgebung mit mehreren Zielen, ein schwieriges Problem. Die Lösungsansätze, die in der Vergangenheit verwendet wurden, um Probleme mehrstufiger Supply Chains zu lösen, basierten auf herkömmlichen Verfahren unter der Verwendung von analytischen Techniken. Diese sind jedoch nicht ausreichend, um die Dynamiken in Lieferketten zu bewältigen, aufgrund ihrer Unfähigkeit, mit den komplexen Interaktionen zwischen den Akteuren der Supply Chain umzugehen und das stochastische Verhalten zu repräsentieren, das in vielen Problemen der realen Welt besteht. Die Simulationsmodellierung ist in letzter Zeit zu einem wichtigen Instrument geworden, da ein analytisches Modell nicht in der Lage ist, ein System abzubilden, das sowohl der Variabilität als auch der Komplexität unterliegt. Allerdings erfordern Simulationen umfangreiche Laufzeiten, um möglichst viele Lösungen zu bewerten und die optimale Lösung für ein definiertes Problem zu finden. Um mit dieser Schwierigkeit umzugehen, muss das Simulationsmodell in Optimierungsalgorithmen integriert werden. In Erwiderung auf die oben genannten Herausforderungen, ist eines der Hauptziele dieser Arbeit, ein Modell und ein Lösungsverfahren für die optimale Gestaltung von Vertriebsnetzwerken integrierter Supply Chains vorzuschlagen, das die Beziehung zwischen den Entscheidungen der verschiedenen Planungsebenen berücksichtigt. Die Problemstellung wird mithilfe von Zielfunktionen formuliert, um die Kundenabdeckung zu maximieren, den maximalen Abstand von den Anlagenstandorten zu den Bedarfspunkten zu minimieren oder die Gesamtkosten zu minimieren. Um die optimale Anzahl, Kapazität und Lage der Anlagen zu bestimmen, kommen der Nondominated Sorting Genetic Algorithm II (NSGA-II) und der Quantum-based Particle Swarm Optimization Algorithm (QPSO) zum Einsatz, um dieses Optimierungsproblem im Spannungsfeld verschiedener Ziele zu lösen. Aufgrund der Komplexität mehrstufiger Systeme und der zugrunde liegenden Unsicherheiten, wurde die Optimierung von Beständen über die gesamte Lieferkette hinweg zur wesentlichen Herausforderung, um die Kosten zu reduzieren und die Serviceanforderungen zu erfüllen. In diesem Zusammenhang ist das andere Ziel dieser Arbeit die Darstellung eines simulationsbasierten Optimierungs-Frameworks, in dem die Simulation, basierend auf der objektorientierten Programmierung, entwickelt wird und die Optimierung metaheuristische Techniken mit unterschiedlichen Kriterien, wie NSGA-II und MOPOSO, verwendet. Insbesondere das geplante Framework regt einen großen Nutzen an, sowohl für das Bestandsoptimierungsproblem in mehrstufigen Supply Chains, als auch für andere Logistikprobleme.Today, most companies have more complex supply chain networks in a more volatile business environment due to global sourcing, outsourcing of production and serving customers all over the world with a complex distribution network that has several facilities linked by various activities. More companies involved within the value chain, means more nodes and links in the network. Therefore, globalization brings complexities and new challenges as enterprises increasingly benefit from global supply chains. In such a business environment, Supply Chain (SC) members must focus on the efficient management and coordination of material flow in the multi-echelon system to handle with these challenges. In many cases, the supply chain of a company includes various decisions at different planning levels, such as facility location, inventory and transportation. Each of these decisions plays a significant role in the overall performance and the relationship between them cannot be ignored. However, these decisions have been mostly studied individually. In recent years, numerous studies have emphasized the importance of integrating the decisions involved in supply chains. In this context, facility location, inventory and transportation decisions should be jointly considered in an optimization problem of distribution network design to produce more accurate results for the whole system. Furthermore, effective management of material flow across a supply chain is a difficult problem due to the dynamic environment with multiple objectives. In the past, the majority of the solution approaches used to solve multi-echelon supply chain problems were based on conventional methods using analytical techniques. However, they are insufficient to cope with the SC dynamics because of the inability to handle to the complex interactions between the SC members and to represent stochastic behaviors existing in many real world problems. Simulation modeling has recently become a major tool since an analytical model is unable to formulate a system that is subject to both variability and complexity. However, simulations require extensive runtime to evaluate many feasible solutions and to find the optimal one for a defined problem. To deal with this problem, simulation model needs to be integrated in optimization algorithms. In response to the aforementioned challenges, one of the primary objectives of this thesis is to propose a model and solution method for the optimal distribution network design of an integrated supply chain that takes into account the relationship between decisions at the different levels of planning horizon. The problem is formulated with objective functions to maximize the customer coverage or minimize the maximal distance from the facilities to the demand points and minimize the total cost. In order to find optimal number, capacity and location of facilities, the Nondominated Sorting Genetic Algorithm II (NSGA-II) and Quantum-based Particle Swarm Optimization Algorithm (QPSO) are employed for solving this multiobjective optimization problem. Due to the complexities of multi-echelon system and the underlying uncertainty, optimizing inventories across the supply chain has become other major challenge to reduce the cost and to meet service requirements. In this context, the other aim of this thesis is to present a simulation-based optimization framework, in which the simulation is developed based on the object-oriented programming and the optimization utilizes multi-objective metaheuristic techniques, such as the well-known NSGA-II and MOPSO. In particular, the proposed framework suggests a great utility for the inventory optimization problem in multi-echelon supply chains, as well as for other logistics-related problems

Why do nonlinearities matter? The repercussions of linear assumptions on the dynamic behaviour of assemble-to-order systems

The hybrid assembly-to-order (ATO) supply chain, combining make-to-stock and make-to-order (MTS-MTO) production, separated by a customer order decoupling point (CODP), is well recognised in many sectors. Based on the well-established Inventory and Order Based Production Control Systems (the IOBPCS family), we develop a hybrid ATO system dynamics model and analytically study the impact of nonlinearities on its dynamic performance. Nonlinearities play an important, sometimes even a dominant, role in influencing the dynamic performance of supply chain systems. However, most IOBPCS based analytical studies assume supply chain systems are completely linear and thereby greatly limit the applicability of published results, making it difficult to fully explain and describe oscillations caused by internal factors. We address this gap by analytically exploring the non-negative order and capacity constraint nonlinearities present in an ATO system. By adopting nonlinear control engineering and simulation approaches, we reveal that, depending on the mean and amplitude of the demand, the non-negative order and capacity constraints in the ATO system may occur and their significant impact on system dynamics performance should be carefully considered. Failing to monitor non-negative order constraints may underestimate the mean level of inventory and overestimate the inventory recovery speed. Sub-assemblers may suffer increased inventory cost (i.e. the consequence of varying inventory levels and recovery speed) if capacity and non-negative order constraints are not considered at their production site. Future research should consider the optimal trade-off design between CODP inventory and capacity and the exploration of delivery lead-time dynamics

Discrete Event Simulations

Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself reflects the plurality that these points of view represent. The book embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into five groups. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor firmly believes that this book will be interesting for both beginners and practitioners in the area of DES

On the versatility of Little’s Law in operations management: a review and classification using vignettes

Despite Little’s Law being considered as one of the ‘laws’ of operations management, evidence of its application in an empirical context is diverse and diffuse. Hence, this paper aims to identify, classify and consolidate published empirical applications of Little’s Law in a systematic manner to better understand its versatility. This paper undertakes a systematic literature review of the databases of the five main publishers of operations management journals and snowball sampling for additional papers. A final sample of 128 empirical journal articles is identified and categorized. Tactical, medium-term decisions relating to capacity dynamics and operations re-engineering are the most popular categories. To give further insights into versatility, vignettes for each category are developed. The review and vignettes confirm Little’s Law as a highly relevant paradigm to operations management decisions due to its empirical versatility across levels, sectors and time domains. The paper suggests four factors to underline the empirical versatility of Little’s Law in operations management: applicability, utility, simplicity and visibility

Enabling the “Easy Button” for Broad, Parallel Optimization of Functions Evaluated by Simulation

Java Optimization by Simulation (JOBS) is presented: an open-source, object-oriented Java library designed to enable the study, research, and use of optimization for models evaluated by simulation. JOBS includes several novel design features that make it easy for a simulation modeler, without extensive expertise in optimization or parallel computation, to define an optimization model with deterministic and/or stochastic constraints, choose one or more metaheuristics to solve it and run, using massively parallel function evaluation to reduce wall-clock times. JOBS is supported by a new language independent, application programming interface (API) for remote simulation model evaluation and a serverless computing environment to provide massively parallel function evaluation, on demand. Dynamic loop scheduling methods are evaluated in the serverless environment with the opportunity for significant resource contention for master node computing power and network bandwidth. JOBS implements several population-based and single-solution improvement metaheuristics (solvers) for real, discrete, and mixed problems. The object-oriented design is extendible with classes that drastically reduce the amount of code required to implement a new solver and encourage re-use of solvers as building blocks for creating new multi-stage solvers or memetic algorithms