Contingencies are unexpected crises or events that cause a major threat to the safety, security and well-being of a certain population. This research effort builds upon the work on contingency logistics reliability models by Miman (2008) who extended the preliminary work conducted by Thomas (2004) that provides the modeling approach which takes a mission success orientation and focuses on the ability to recover from or prevent a contingency logistics failure. Miman (2008) proposes the sustainability model of a contingency logistics network using the concept of selective maintenance. This problem, once formulated, is a non-convex, non-linear, non-separable, multi-dimensional, discrete knapsack problem. These problems are known to be NP hard. Therefore, one needs to explore heuristic solutions in search of robust and effective solution approaches. He developed a memetic algorithm, GAFTS, and proposed this for identifying the best set of maintenance actions to sustain the contingency logistics network. Besides, he used Physical Programming, a multi criteria optimization procedure, to exploit a network manager’s preference toward the numerous criteria (reliability, cost, time, resource utilization etc...) judiciously. This research effort continues the exploration of heuristic techniques for the sustainability model developed by Miman (2008) and develops a hybrid heuristics technique, EDGASA, incooperating simulating annealing (SA) procedure with genetic algorithm (GA). Comparisons of EDGASA with GA and SA reveal that it outperforms in terms of average reliability, best reliability and worst reliability found at an expense of increased solution time. One of the contributions of this study is a multi-objective modeling approach developed based on utopia distance that aims at minimizing the weighted distance between a solution to the ideal point that could be achieved. The study fills some of the voids in the contingency logistics networks’ solution and modeling and highlights potential studies by applying the hybrid heuristic developed as well as multiobjective modeling approach proposed to other problems.

We propose a novel meta-approach to support collaborative multi-objective supplier selection and order allocation (SSOA) decisions by integrating multi-criteria decision analysis and linear programming (LP).

Leading the way in current thinking on environmental logistics, Green Logistics provides a unique insight on the environmental impacts of logistics and the actions that companies and governments can take to deal with them. It is written by leading researchers in the field and provides a comprehensive view of the subject for students, managers and policy-makers. Fully updated, the 3rd edition of Green Logistics has a more global perspective than previous editions. It introduces new contributors and international case studies that illustrate the impact of green logistics in practice. There is a new chapter on the links between green logistics and corporate social responsibility and a series of postscripts examining the effects of new developments, such as 3D printing, distribution by drone, the physical internet and the concept of peak freight. Other key topics examined include: carbon auditing of supply chains; transferring freight to greener transport modes; reducing the environmental impact of warehousing; improving the energy efficiency of freight transport; making city logistics more environmentally sustainable; reverse logistics for the management of waste; role of government in promoting sustainable logistics. The 3rd edition of Green Logistics includes indispensable online supporting materials, including graphics, tables, chapter summaries, and guidelines for lecturers.

Optimierung mit mehreren Zielen, evolutionäre Algorithmen: Dieses Buch wendet sich vorrangig an Einsteiger, denn es werden kaum Vorkenntnisse vorausgesetzt. Geboten werden alle notwendigen Grundlagen, um die Theorie auf Probleme der Ingenieurtechnik, der Vorhersage und der Planung anzuwenden. Der Autor gibt auch einen Ausblick auf Forschungsaufgaben der Zukunft.

The third edition of this textbook comprehensively discusses global supply chain and operations management (SCOM), combining value creation networks and interacting processes. It focuses on operational roles within networks and presents the quantitative and organizational methods needed to plan and control the material, information, and financial flows in supply chains. Each chapter begins with an introductory case study, while numerous examples from various industries and services help to illustrate the key concepts. The book explains how to design operations and supply networks and how to incorporate suppliers and customers. It examines how to balance supply and demand, a core aspect of tactical planning, before turning to the allocation of resources to meet customer needs. In addition, the book presents state-of-the-art research reflecting the lessons learned from the COVID-19 pandemic, and emerging, fast-paced developments in the digitalization of supply chain and operations management. Providing readers with a working knowledge of global supply chain and operations management, with a focus on bridging the gap between theory and practice, this textbook can be used in core, specialized, and advanced classes alike. It is intended for a broad range of students and professionals in supply chain and operations management.

Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.

Optimization of Power System Operation, 2nd Edition, offers a practical, hands-on guide to theoretical developments and to the application of advanced optimization methods to realistic electric power engineering problems. The book includes: New chapter on Application of Renewable Energy, and a new chapter on Operation of Smart Grid New topics include wheeling model, multi-area wheeling, and the total transfer capability computation in multiple areas Continues to provide engineers and academics with a complete picture of the optimization of techniques used in modern power system operation