Smart Industry - Better Management

The ebook edition of this title is Open Access and freely available to read online. Smart industry requires better management. As industrial and production systems are future-proofed, becoming smart and interconnected through use of new manufacturing and product technologies, work is advancing on improving product needs, volume, timing, resource efficiency, and cost, optimally using supply chains. Presenting innovative, evidence-based, and cutting-edge case studies, with new conceptualizations and viewpoints on management, Smart Industry, Better Management explores concepts in product systems, use of cyber physical systems, digitization, interconnectivity, and new manufacturing and product technologies. Contributions to this volume highlight the high degree of flexibility in people management, production, including product needs, volume, timing, resource efficiency and cost in being able to finely adjust to customer needs and make full use of supply chains for value creation. Smart Industry, Better Management illustrates how industry can enabled by a more network-centric approach, making use of the value of information and the latest available proven manufacturing techniques

Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects

Models and algorithms for berth allocation problems in port terminals

Seaports play a key role in maritime commerce and the global market economy. Goods of different kinds are carried in specialized vessels whose handling requires ad hoc port facilities. Port terminals comprise the quays, infrastructures, and services dedicated to handling the inbound and outbound cargo carried on vessels. Increasing seaborne trade and ever-greater competition between port terminals to attract more traffic have prompted new studies aimed at improving their quality of service while reducing costs. Most terminals implement operational planning to achieve more efficient usage of resources, and this poses new combinatorial optimization problems which have attracted increasing attention from the Operations Research community. One of the most important problems confronted at the quayside is the efficient allocation of quay space to the vessels calling at the terminal over time, also known as the Berth Allocation Problem. A closely related problem arising in terminals that specialize in container handling concerns the efficient assignment of quay cranes to vessels, which, together with quay space planning, leads to the Berth Allocation and Quay Crane Assignment Problem. These problems are known to be especially hard to solve, and therefore require designing methods capable of attaining good solutions in reasonable computation times. This thesis studies different variants of these problems considering well-known and new real-world aspects, such as terminals with multiple quays or irregular layouts. Mathematical programming and metaheuristics techniques are extensively used to devise tailored solution methods. In particular, new integer linear models and heuristic algorithms are developed to deal with problem instances of a broad range of sizes representing real situations. These methods are evaluated and compared with other state-of-the-art proposals through various computational experiments on different benchmark sets of instances. The results obtained show that the integer models proposed lead to optimal solutions on small instances in short computation times, while the heuristic algorithms obtain good solutions to both small and large instances. Therefore, this study proves to be an effective contribution to the efforts aimed at improving port efficiency and provides useful insights to better tackle similar combinatorial optimization problems

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

An improved tabu search for airport gate assignment.

Kwan, Cheuk Lam.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 115-118).Abstract also in Chinese.Chapter 1 --- Introduction --- p.9Chapter 1.1 --- The Gate Assignment Problem --- p.9Chapter 1.2 --- Contributions --- p.10Chapter 1.3 --- Formulation of Gate Assignment Problem --- p.11Chapter 1.4 --- Organization of Thesis --- p.13Chapter 2 --- Literature Review --- p.15Chapter 2.1 --- Introduction --- p.15Chapter 2.2 --- Formulations of Gate Assignment Problems --- p.15Chapter 2.2.1 --- Static Gate Assignment Model --- p.16Chapter 2.2.1.1 --- Total Passenger Walking Distance --- p.17Chapter 2.2.1.2 --- Waiting Time --- p.20Chapter 2.2.1.3 --- Unassigned Flights --- p.21Chapter 2.2.2 --- Stochastic and Robust Gate Assignment Model --- p.22Chapter 2.2.2.1 --- Idle Time --- p.22Chapter 2.2.2.2 --- Buffer Time --- p.23Chapter 2.2.2.3 --- Flight Delays --- p.23Chapter 2.2.2.4 --- Gate Conflicts --- p.24Chapter 2.3 --- Solution Methodologies --- p.25Chapter 2.3.1 --- Expert System Approaches --- p.25Chapter 2.3.2 --- Optimization --- p.27Chapter 2.3.2.1 --- Exact Methods --- p.27Chapter 2.3.2.2 --- Heuristic Approaches --- p.28Chapter 2.3.2.3 --- Meta-Heuristics Approaches --- p.29Chapter 2.3.2.4 --- Tabu Search and Path Relinking --- p.31Chapter 2.4 --- Current Practice of Gate Assignment Problems --- p.32Chapter 2.5 --- Summary --- p.32Chapter 3 --- Tabu Search --- p.34Chapter 3.1 --- Introduction --- p.34Chapter 3.2 --- Mathematical Model --- p.34Chapter 3.3 --- Principles of Tabu Search --- p.36Chapter 3.4 --- Neighborhood Structures --- p.38Chapter 3.4.1 --- Insert Move --- p.38Chapter 3.4.2 --- Exchange Move --- p.39Chapter 3.5 --- Short Term Memory Structure --- p.41Chapter 3.6 --- Aspiration Criterion --- p.42Chapter 3.7 --- Intensification and Diversification Strategies --- p.43Chapter 3.8 --- Tabu Search Framework --- p.45Chapter 3.8.1 --- Initial Solution --- p.45Chapter 3.8.2 --- Tabu Search Algorithm --- p.46Chapter 3.9 --- Computational Studies --- p.52Chapter 3.9.1 --- Parameters Tuning --- p.52Chapter 3.9.1.1 --- Fine-tuning a Tabu Search Algorithm with Statistical Tests --- p.53Chapter 3.9.1.2 --- Tabu Tenure --- p.54Chapter 3.9.1.3 --- Move Selection Strategies --- p.56Chapter 3.9.1.4 --- Frequency of Exchange Moves --- p.59Chapter 3.9.2 --- Comparison the Fine-tuned TS with original TS --- p.62Chapter 3.10 --- Conclusions --- p.63Chapter 4 --- Path Relinking --- p.65Chapter 4.1 --- Introduction --- p.65Chapter 4.2 --- Principles of Path Relinking --- p.65Chapter 4.2.1 --- Example of Path Relinking --- p.66Chapter 4.3 --- Reference Set --- p.68Chapter 4.3.1 --- Two-Reference-Set Implementation --- p.71Chapter 4.3.1.1 --- Random Exchange Gate Move --- p.72Chapter 4.4 --- Initial and Guiding Solution --- p.73Chapter 4.5 --- Path-Building Process --- p.74Chapter 4.6 --- Tabu Search Framework with Path Relinking --- p.78Chapter 4.6.1 --- Computational Complexities --- p.82Chapter 4.7 --- Computational Studies --- p.82Chapter 4.7.1 --- Best Configuration for Path Relinking --- p.83Chapter 4.7.1.1 --- Reference Set Strategies and Initial and Guiding Criteria --- p.83Chapter 4.7.1.2 --- Frequency of Path Relinking --- p.86Chapter 4.7.1.3 --- Size of Volatile Reference Set --- p.87Chapter 4.7.1.4 --- Size of Non-volatile Reference Set --- p.89Chapter 4.7.2 --- Comparisons with Other Algorithms --- p.94Chapter 5 --- Case Study --- p.98Chapter 5.1 --- Introduction --- p.98Chapter 5.2 --- Airport Background --- p.98Chapter 5.2.1 --- Layout of ICN --- p.98Chapter 5.3 --- Data Preparation --- p.99Chapter 5.3.1 --- Passenger Data --- p.103Chapter 5.4 --- Computational Studies --- p.104Chapter 5.4.1 --- Experiments without Airline Preference --- p.104Chapter 5.4.2 --- Experiments with Airline Preference --- p.106Chapter 5.4.2.1 --- Formulation --- p.106Chapter 5.4.2.2 --- Results --- p.108Chapter 5.5 --- Conclusion --- p.111Chapter 6 --- Conclusion --- p.112Chapter 6.1 --- Summary of Achievement --- p.112Chapter 6.2 --- Future Developments --- p.113Bibliography --- p.115Appendix --- p.119Chapter 1. --- Friedman´ةs Test --- p.119Chapter 2. --- Wilcoxon's Signed Rank Test for Paired Observation --- p.120Chapter 3. --- Hybrid Simulated Annealing with Tabu Search Approach --- p.121Chapter 4. --- Arrival Flight Data of Incheon International Airport --- p.122Chapter 5. --- Departure Flight Data of Incheon International Airport --- p.13

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios