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Visit the Taylor & Francis website at http://www.taylorandfrancis.com and the CRC Press website at http://www.crcpress.com. This book is dedicated to my dear husband Kadri, my lovely daughters Sıla and Maya, and my dear father İmdat, who has always been the inspiration of my life.
Preface
Editors
He earned BS and MS in industrial engineering from Middle East Technical University in 1982 and 1984, respectively. He is also on the editorial board of a number of scientific journals in the fields of industrial engineering and operations research. He is a member of the Institute of Industrial Engineering, Institute for Operations Research, Management Sciences and the Simulation Society.
He is also a member of the Council of Chairs of Academic Departments of Industrial Engineering (CIEADH) and various other professional and society committees. Roth Professor and Chair of the Department of Industrial Engineering at the University of Pittsburgh. He was previously Chairman of the Council of Heads of Academic Departments for Industrial Engineering (CIEADH) and also a member of the Board of Directors of the Institute of Industrial Engineers.
Bidanda is a fellow of the Institute of Industrial Engineers and is currently a commissioner on the ABET Engineering Accreditation Commission. In 2012, he received the John Imhoff Award for Global Excellence in Industrial Engineering from the American Society for Engineering Education.
Contributors
Ghorbani-Totkaleh Department of Industrial
Amin Nayeri
Sheikh Sajadieh Department of Industrial
Introduction
Introduction
In light of these observations, in this chapter we investigate tripartite logistic network optimization and provide a practical hybrid metaheuristic method. The model supports decision-making at the tactical level for daily planning and inventory management in the presence of demand variance. To deal with this problem, we extend our strategic approach to include some decisions at the operational level.
In particular, we address the multi-vehicle routing problem (M-VRP) while considering inventory management issues. By taking into account the dynamics of demand and warehouse stocks, we try to provide a practical approach that can innovatively solve everyday planning problems. Next, to examine some of the effects of demand variance on inventory levels, we conducted a parametric study on reorder points.
The ultimate goal of this study is to develop an integrated information and decision support system (DSS) that can dynamically manage relevant product resource and demand databases (see Figure 1.1). To realize this goal, additional work needs to be done, such as introducing variations of the basic idea and using parallel computing to increase the speed of finding solutions and improve information retrieval and visualization of results on a real map.
Problem Statements .1 Background of the Study
A recent review of articles published on supply chain management within the last decade has revealed a lack of models that capture dynamic aspects relevant to real-world applications and has emphasized the need for extensive research on this topic (Melo et al., 2009). Therefore, the number of studies is still growing due to the excellent advancements in both computer software and computer hardware. A popular problem studied at this level is called the vehicle routing problem (VRP; Yeun et al., 2008).
Recently, many researchers are interested in VRP with different collection and delivery configurations, as it is the most practical and appropriate way to consider reverse logistics (Min, 1989; Catay, 2010; Goksal et al., 2013). Instances of VRPSPD problem are common in the distribution system of bottled beverages, groceries, liquefied propane gas tanks, hotel laundry services, etc. Due to the difficulty of solving such problems, only small cases of VRP are solved to test its effectiveness. validate the approximations.
To solve the VRP in terms of the Ton-Kilo basis, we developed a hybrid approach consisting of a modified parsing method and modified tabu search (Shimizu, 2011b,c). Thus, it becomes necessary to resolve the inconsistency in cost accounting while facing the inherent rigidity of the problem.
Problem Formulation
At this level it is necessary to consider connections with both the upper (strategic) level and the lower (operational) level. In such problems, decisions about allocations to the depot are considered in addition to VRP. However, it is common to use the Ton-Kilo base at the strategic level and the Kilo base at the operational level.
Qj: Maximum capacity in warehouse j Sj: Maximum inventory in warehouse j Wv: Maximum capacity of vehicle v Index set.
DC J: Depot
- Daily Decision Associated with Inventory Conditions .1 Multilevel Approach Incorporating Vehicle-Routing Problem
- Numerical Experiments .1 Setup of Test Problem
- Prospects for Further Applications .1 Variants of the Modified Savings Method
- Conclusion
- Introduction
- Literature Review
- Proposed Solution Methodology
- Numerical Study
- Conclusion
- Introduction
- Model Development
- Model Analysis
- Numerical Experimentation
- Conclusions
- Motivation
- Related Work
- Model Description
- Solution Methodology
- Case Study
- Conclusions and Further Research Directions
- Introduction
- Route Selection Problem in the Arctic Region
- Methodology .1 Linguistic Variable
- GF-AHP Design and Application for Track Selection
- Conclusion
- Introduction
- Problem Definition
- Proposed Mathematical Models
- Computational Results
- Conclusion
- Introduction
- Quality Function Deployment
- DEMATEL Method
- Fusion of Fuzzy Information
- MCDM Model for Supplier Evaluation
- Case Study
- Conclusion
- Introduction and Problem Definition
- Literature Review
- Complexity Analysis
- Mathematical Model
As noted in Anon (n.d.), general RM practices are classified into quantity-based RM and price-based RM. By constraint set (4.7), the number of vessels assigned to a berth at a given time is limited to 1. This study is part of a research project funded by TUBITAK (The Scientific and Technological Research Council of Turkey): 1001— Program Support Program for scientific and technological research projects grant no.
