Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems

Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (1/4)

Technical report about sustainable urban freight solutions, part 1 of

Implications of additive manufacturing on supply chain and logistics

Additive manufacturing (AM) technology has attracted the interest of industrial professionals and researchers in the last years. This interest lies primarily in understanding the trends, benefits, and implications of AM technology on supply chain (SC) and logistics, as it requires reconfiguring the supply chain based on a distributed manufacturing strategy, closer to the consumer market, with shorter lead times and less raw materials. It still is an emerging field, and needs further study. Therefore, a better understanding of main trends will contribute to the dissemination of knowledge about AM technology and its consolidation. This article seeks to investigate the implications of AM, as an advanced manufacturing model, on SC and logistics. A four-step research method was used to develop a systematic literature review and a bibliometric analysis on the AM implications in SC and logistics. The main implications of AM on SC and logistics were classified in seven key issues gathered as result of the literature review. Additionally, bibliometric study allowed understanding researches major trends in this field. The key aspects highlighted and characterized as major implications of AM on SC and logistic are: supply chain complexity reduction; more flexible logistics and inventory management; better spreading and popularization of mass customization; decentralization of manufacturing; greater design freedom and rapid prototyping; increasing of resource efficiency and sustainability, and the need to have clearly defined legal and safety aspects

Operational knowledge management:identification of knowledge objects, operation methods, and goals and means for the support function

Though much has been written about knowledge management, this field has not been described extensively from an operational management perspective. Consequently, knowledge management seems difficult to implement at the operational levels of the organisations. To solve this problem, the abstract notion of knowledge is translated in operational knowledge objects. These objects are the input and output of two operation methods: (1) transformation or learning; and (2) knowledge logistics. The article describes several activities of these operation methods, and gives a classification of operational goals and means for the operations support function. The author concludes with mentioning challenges for the field of operational knowledge managemen

Short Line Railroads and Municipal Land Use Planning, Policy, and Regulation

This research puts forth an examination of the relationship between municipal planning and short line freight railroads. Methodologically, it employs a content analysis framework that explores local master plans and zoning bylaws for the presence of concepts relevant to short line railroads. A historically omitted topic, the railroads are found to be frequently omitted from plans, often conflicting with civic and recreational interests despite their increasingly efficient ability, economic and environmental, to service numerous industries. Zoning bylaws show a disfavor to these entities, and at times may exceed their authority. Moreover, they may create physical and legal limitations to new, rail-sustained industry, as well as the rehabilitation of former industrial clusters. Findings related to regulatory preemption, transportation and land use policy, corridor conversion, and shifting land use patterns are presented. Consequentially, daunting implications may resonate for both the railroad and municipalities. Recommendations encompass municipal, regional, and state policy, as well as opportunities for multi-agency collaboration, economic development initiatives, and revised regulatory structures

2005 : Part-Time Programmes

Book containing map of DIT main locations, frequently asked questions, key contacts and full listing of part-time programmes on offer at DIT 2005

Handbook of Computational Intelligence in Manufacturing and Production Management

Artificial intelligence (AI) is simply a way of providing a computer or a machine to think intelligently like human beings. Since human intelligence is a complex abstraction, scientists have only recently began to understand and make certain assumptions on how people think and to apply these assumptions in order to design AI programs. It is a vast knowledge base discipline that covers reasoning, machine learning, planning, intelligent search, and perception building. Traditional AI had the limitations to meet the increasing demand of search, optimization, and machine learning in the areas of large, biological, and commercial database information systems and management of factory automation for different industries such as power, automobile, aerospace, and chemical plants. The drawbacks of classical AI became more pronounced due to successive failures of the decade long Japanese project on fifth generation computing machines. The limitation of traditional AI gave rise to development of new computational methods in various applications of engineering and management problems. As a result, these computational techniques emerged as a new discipline called computational intelligence (CI)