Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

Advances in Supply Chain Management Decision Support Systems: Potential for Improving Decision Support Catalysed by Semantic Interoperability between Systems

Globalization has catapulted ‘cycle time’ as a key indicator of operational efficiency [1] in processes such as supply chain management (SCM). Systems automation holds the promise to augment the ability of supply chain operations or supply networks to rapidly adapt to changes, with minimal human intervention, under ideal conditions. Business communities are emerging as loose federations or organization of networks that may evolve to act as infomediaries in global SCM. These changes, although sluggish, are likely to impact process knowledge and in turn may be stimulated or inhibited by the availability or lack of process interoperability, respectively. The latter will determine operational efficiencies of supply chains. Currently “community of systems” or organization of networks (aligned by industry or business focus) contribute minimally in SCM decisions because true collaboration remains elusive. Convergence and maturity of multiple advances offers the potential for a paradigm shift in interoperability. It may evolve hand-in-hand with [a] the gradual adoption of the semantic web [2] with concomitant development of ontological frameworks, [b] increase in use of multi-agent systems and [c] advent of ubiquitous computing enabling near real-time access to identification of objects and analytics [4]. This paper examines some of these complex trends and related technologies. Irrespective of the characteristics of information systems, the development of various industry-contributed ontologies for knowledge and decision layers, may spur self-organizing networks of business communities and systems to increase their ability to sense and respond, more profitably, through better enterprise and extraprise exchange. In order to transform this vision into reality, systems automation must be weaned from the syntactic web and integrated with the organic growth of the semantic web. Understanding of process semantics and incorporation of intelligent agents with access to ubiquitous near real-time data “bus” are pillars for “intelligent” evolution of decision support systems. Software as infrastructure may integrate plethora of agent colonies through improved architectures (such as, service oriented architecture or SOA) and business communities aligned by industry or service focus may emerge as hubs of such agent empires. However, the feasibility of the path from exciting “pilots” in specific areas toward an informed convergence of systemic real-world implementation remains unclear and fraught with hurdles related to gaps in knowledge transfer from experts in academia to real-world practitioners. The value of interoperability between systems that may catalyse real-time intelligent decision support is further compromised by the lack of clarity of approach and tools. The latter offers significant opportunities for development of tools that may segue to innovative solutions approach. A critical mass of such solutions may spawn the necessary systems architecture for intelligent interoperability, essential for sustainable profitability and productivity in an intensely competitive global economy. This paper addresses some of these issues, tools and solutions that may have broad applicability in several operations including the management of adaptive supply-demand networks [7]

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

Intelligent techniques for context-aware systems

Nowadays, with advances in communication technologies, researches are focused in the fields of designing new devices with increasing capabilities, implanting software frameworks or middleware to make these devices interoperable. Building better human interfaces is a challenging task and the adoption of Artificial Intelligence (AI) techniques to the process help associating semantic meaning to devices which makes possible the gesture recognition and voice recognition. This thesis is mainly concerned with the open problem in context-aware systems: the evaluation of these systems in Ambient Intelligence (AmI) environments. With regard to this issue, we argue that due to highly dynamic properties of the AmI environments, it should exist a methodology for evaluating these systems taking into account the type of scenarios. However in order to support with a solid ground for that discussion, some elements are to be discussed as well. In particular, we: • use a commercial platform that allows us to design and manage the contextual information of context- aware systems by means of a context manager included in the architecture; • analyze the formal representation of this contextual information by means of a knowledge based system (KBS); • discuss the possible methodologies to be used for modelling knowledge in KBS and our approach; • give reasons why intelligent agents is a valid technique to be applied to systems in AmI environments; • propose a generic multi-agent system (MAS) architecture that can be applied to a large class of envisaged AmI applications; • propose a multimodal user interface and its integration with our MAS; • propose an evaluation methodology for context-aware systems in AmI scenarios. The formulation of the above mentioned elements became necessary as this thesis was developed. The lack of an evaluation methodology for context-aware systems in AmI environments, where so many issues to be covered, took us to the main objective of this thesis. In this regard: • we provide an updated and exhaustive state-of-the-art of this matter; • examine the properties and characteristics of AmI scenarios; • put forward an evaluation methodology and experimentally test our methodology in AmI scenarios. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------La Inteligencia Ambiental y los entornos inteligentes hacen hincapié en una mayor facilidad de uso, soporte de servicios más eficientes, el apoderamiento de los usuarios, y el apoyo a las interacciones humanas. En esta visión, las personas estarán rodeadas de interfaces inteligentes e intuitivas incrustados en objetos cotidianos que nos rodean y los sistemas desarrollados para este ambiente deberán reconocer y responder a la presencia de individuos de una manera invisible y transparente a ellos. Esta tesis se centra principalmente en el problema abierto en los sistemas sensibles al contexto: la evaluación de estos sistemas en los entornos de Inteligencia Ambiental. Con respecto a este tema, se argumenta que debido a las propiedades altamente dinámica de los entornos de inteligencia ambiental, debería existir una metodología para la evaluación de estos sistemas, teniendo en cuenta el tipo de escenarios. Sin embargo, con el fin de apoyar con una base sólida para la discusión, algunos elementos deben ser discutidos también. En particular, nosotros: • Usamos una plataforma comercial que nos permite diseñar y gestionar la información contextual de los sistemas sensibles al contexto a través de un gestor de contexto incluido en la arquitectura; • Analizamos la representación formal de esta información contextual a través de un sistema basado en el conocimiento (SBC); • Discutimos las posibles metodologías que se utilizarán para el modelado del conocimiento en SBC y nuestra aproximación y propuesta; • Discutimos las razones del por qué los agentes inteligentes son una técnica válida para ser aplicada a los sistemas en entornos inteligencia ambiental; • Proponemos un sistema multi-agente (SMA), con una arquitectura genérica que se puede aplicar a una gran clase de aplicaciones de inteligencia ambiental; • Proponemos una interfaz de usuario multimodales y su integración con nuestro SMA; • Proponemos una metodología de evaluación de los sistemas sensibles al contexto en los escenarios de inteligencia ambiental. La formulación de los elementos antes mencionados se hizo necesaria en la medida que esta tesis se ha desarrollado. La falta de una metodología de evaluación de los sistemas sensibles al contexto en entornos de inteligencia ambiental, donde existen tantos temas a tratar, nos llevó al objetivo principal de esta tesis. En este sentido, en esta tesis: • Proporcionamos un estado del arte actualizado y exhaustivo de este asunto; • Examinamos las propiedades y características de los escenarios de inteligencia ambiental; • Proponemos una metodología de evaluación para este tipo de sistemas y experimentalmente probamos nuestra metodología en diversos escenarios de inteligencia ambiental

Multimodal, intermodal and terminals

The chapter looks at rail freight terminals, rail--sea interfaces, in particular, as part of a multimodal, or integrated transportation network. Terminals are key infrastructure for linking individual transport modes and governing and managing their interchange in a manner that creates a seamless and sustainable transportation system. Therefore, their performance is critical for maximising transport efficiency and modes integration. This chapter focused on how to measure the operational performance of rail freight terminals in a framework of integrated transportation network. In an increasingly competitive and commercialised world, there is an increasing demand to be able to rank transport options and routes in some way. Drawing on new material, this talk attempts to outline possible methods for how to measure the performance of rail terminals. It focuses on the identification of suitable methods to assess performance by key indicators. Intermodality demands for going beyond safeguarding the individual modes to ensuring the security of the intermodal inter-faces (terminals), the nodes that link and integrate passenger and freight flows. That demands for an integrated holistic approach built on the collaboration between international, national organisations and operators. The study put emphasis on the security challenges and threats to freight transport generally and in rail-sea interfaces more specifically. It moves onto the regulations already governing security in rail-sea interfaces. Finally, it focus on the role that infrastructure planning can play in improving security and offer some conclusions and recommendations for the futur

Model based test suite minimization using metaheuristics

Software testing is one of the most widely used methods for quality assurance and fault detection purposes. However, it is one of the most expensive, tedious and time consuming activities in software development life cycle. Code-based and specification-based testing has been going on for almost four decades. Model-based testing (MBT) is a relatively new approach to software testing where the software models as opposed to other artifacts (i.e. source code) are used as primary source of test cases. Models are simplified representation of a software system and are cheaper to execute than the original or deployed system. The main objective of the research presented in this thesis is the development of a framework for improving the efficiency and effectiveness of test suites generated from UML models. It focuses on three activities: transformation of Activity Diagram (AD) model into Colored Petri Net (CPN) model, generation and evaluation of AD based test suite and optimization of AD based test suite. Unified Modeling Language (UML) is a de facto standard for software system analysis and design. UML models can be categorized into structural and behavioral models. AD is a behavioral type of UML model and since major revision in UML version 2.x it has a new Petri Nets like semantics. It has wide application scope including embedded, workflow and web-service systems. For this reason this thesis concentrates on AD models. Informal semantics of UML generally and AD specially is a major challenge in the development of UML based verification and validation tools. One solution to this challenge is transforming a UML model into an executable formal model. In the thesis, a three step transformation methodology is proposed for resolving ambiguities in an AD model and then transforming it into a CPN representation which is a well known formal language with extensive tool support. Test case generation is one of the most critical and labor intensive activities in testing processes. The flow oriented semantic of AD suits modeling both sequential and concurrent systems. The thesis presented a novel technique to generate test cases from AD using a stochastic algorithm. In order to determine if the generated test suite is adequate, two test suite adequacy analysis techniques based on structural coverage and mutation have been proposed. In terms of structural coverage, two separate coverage criteria are also proposed to evaluate the adequacy of the test suite from both perspectives, sequential and concurrent. Mutation analysis is a fault-based technique to determine if the test suite is adequate for detecting particular types of faults. Four categories of mutation operators are defined to seed specific faults into the mutant model. Another focus of thesis is to improve the test suite efficiency without compromising its effectiveness. One way of achieving this is identifying and removing the redundant test cases. It has been shown that the test suite minimization by removing redundant test cases is a combinatorial optimization problem. An evolutionary computation based test suite minimization technique is developed to address the test suite minimization problem and its performance is empirically compared with other well known heuristic algorithms. Additionally, statistical analysis is performed to characterize the fitness landscape of test suite minimization problems. The proposed test suite minimization solution is extended to include multi-objective minimization. As the redundancy is contextual, different criteria and their combination can significantly change the solution test suite. Therefore, the last part of the thesis describes an investigation into multi-objective test suite minimization and optimization algorithms. The proposed framework is demonstrated and evaluated using prototype tools and case study models. Empirical results have shown that the techniques developed within the framework are effective in model based test suite generation and optimizatio