Yuri Merkuryev

Simulation-Based Case Studies in Logistics: Education and Applied Research Yuri Merkuryev 2nd Printing. edition

Marc Notes: Includes bibliographical references and index. Jacket Description/Back: Simulation-based Case Studies in Logistics presents an intensive learning course on the application of simulation as a decision support tool to tackle complex logistic problems. The book describes and illustrates different approaches to developing simulation models at the right abstraction level to be used efficiently by engineers when dealing with strategic, tactical or operational decisions in logistic systems. 11 simulation-based case studies in logistics and supply chain management are discussed, based on the results of applied research, covering application areas such as production logistics, warehousing, transportation, material flow management, and hospital logistics. Simulation-based Case Studies in Logistics is an essential text for postgraduate engineering students and researchers working in the area of logistics modeling and simulation. Table of Contents: 1. Factory Railway System / A. Guasch, J. Figueras, P. Fonseca -- 1.1. Introduction -- 1.2. Aims of the Study -- 1.3. Description of the System -- 1.3.1. The Factory -- 1.3.2. Arrivals of Hot Coils -- 1.3.3. Hot-Coil Consumption -- 1.3.4. Railway System and Storage Operations -- 1.4. Modelling Methodology -- 1.5. Conceptual Model Building, Coding and Verification -- 1.5.1. Arrivals of Hot Coils -- 1.5.2. Storage Areas and Unloading of Ships and External Trains -- 1.5.3. Pickling Line -- 1.5.4. Scheduling -- 1.6. Experimentation -- 1.6.1. Initial Scenario -- 1.6.2. Second Scenario: Dampen the Harbour Arrival Peaks Using A1 -- 14 -- 1.6.3. Third Scenario: Dampen the Arrival Peaks and Use Storage Area A3 to Store 4,000 t of Scheduled Coils -- 1.6.4. Experimentation Overview -- 1.7. Conclusions -- 1.8. Questions -- 2. Manufacturing System Planning and Scheduling / G. Merkuryeva, N. Shires -- 2.1. Introduction -- 2.2. Problem Formulation -- 2.3. Modelling Approach -- 2.3.1. A High-Level Business/Manufacturing System Model -- 2.3.2. A Low-Level Anodising Process Stage Sub-Model -- 2.4. Experimentation -- 2.4.1. Planning Scenarios for Business Process Optimisation -- 2.4.2. Testing Sequencing Rules for Processing Production Orders -- 2.5. Conclusions -- 2.6. Questions -- 3. Supply Chain Dynamics / J-C. Hennet -- 3.1. Introduction -- 3.2. A Model of a Supply Chain System -- 3.2.1. Presentation of the Dynamic Model -- 3.2.2. Some Properties of the Model -- 3.2.3. A Five-Product Example -- 3.3. The Production and Ordering Policies -- 3.3.1. The Inventory-Based Policy -- 3.3.2. The Order-Based Policy -- 3.3.3. The Mean-Demand-Driven Policy -- 3.4. Numerical Results -- 3.4.1. The Inventory-Based Policy -- 3.4.2. The Order-Based Policy -- 3.4.3. The Mean-Demand-Driven Policy -- 3.4.4. Variance Analysis for the Three Policies -- 3.5. Supply Chain Dynamics in Practice -- 3.5.1. The Beer Game -- 3.5.2. Some Real Consequences of the Bullwhip Effect -- 3.6. Conclusion -- 3.7. Questions and Assignments -- 4. Pharmaceutical Distribution Network / H. Van Landeghem -- 4.1. The Pharmaceutical Distribution Network -- 4.2. Determining Transportation Modes and Associated Unit Costs -- 4.2.1. Inbound Transportation -- 4.2.2. Outbound Transportation -- 4.3. Lane Mode Optimisation Model Using MILP -- 4.3.1. MILP Model Formulation -- 4.3.2. Experimental Design and Results -- 4.4. Conclusions -- 4.5. Assignments -- 5. Hospital Resource Management / R. M. Aguilar Chinea, I. Castilla Rodriguez, R. C. Munoz Gonzalez -- 5.1. Introduction: Objectives -- 5.2. Conceptual Model -- 5.2.1. Patient Flow Model -- 5.3. Verification and Validation -- 5.4. Experimentation -- 5.4.1. Implementation of a Gynaecological Consultation: Sequential Flow -- 5.4.2. Implementation of Gynaecological Visits and Diagnostic Tests: Combined Sequential-Simultaneous Flow -- 5.5. Conclusions -- 5.6. Questions -- 6. Supply Chain Cyclic Planning and Optimisation / G. Merkuryeva, L. Napalkova -- 6.1. Introduction -- 6.2. Problem Definition -- 6.2.1. Assumptions -- 6.2.2. Objective Functions -- 6.2.3. Decision Variables -- 6.2.4. Constraints -- 6.2.5. Express Analysis -- 6.3. Simulation Model Description -- 6.4. Optimisation Methodology -- 6.4.1. Simulation-Based Optimisation Scheme -- 6.4.2. Optimisation Methods and Software Add-On -- 6.4.3. A Two-Phase Hybrid Optimisation Algorithm -- 6.5. Experimentation -- 6.5.1. Optimisation Scenario 1 -- 99 -- 6.5.2. Optimisation Scenario 2 -- 100 -- 6.5.3. Optimisation Scenario 3 -- 101 -- 6.5.4. Optimisation Scenario 4 -- 103 -- 6.6. Conclusions -- 6.7. Questions and Assignments -- Appendix -- 7. Flexible Manufacturing Systems / M. Angel Piera Eroles, M. Narciso Farias, R. Buil Gine -- 7.1. Introduction -- 7.2. Simulation Shortcomings to Improving FMS Performance -- 7.3. Managing Simulation Model Complexity -- 7.3.1. Petri Net Modelling Formalism -- 7.3.2. Reasons for Using Petri Nets -- 7.4. Coloured Petri Net Formalism -- 7.4.1. The Coverability Tree -- 7.5. System Description: a Flexible Manufacturing System -- 7.6. CPN Model -- 7.7. Results -- 7.8. Conclusions -- 7.9. Questions -- 8. Fresh-Food Supply Chain / A. Bruzzone, M. Massei, E. Bocca -- 8.1. Introduction -- 8.1.1. Fresh-Goods Processing -- 8.1.2. Logistics Solutions -- 8.2. Meat Distribution Simulator -- 8.2.1. Redistribution Algorithms -- 8.3. Fresh Fish: Definition of Delivery Processes -- 8.3.1. MARLIN Simulator -- 8.4. Conclusions -- 8.5. Questions -- 9. Warehouse Order Picking Process / Y. Merkuryev, A. Burinskiene, G. Merkuryeva -- 9.1. Introduction -- 9.2. Objectives of the Project -- 9.3. Description of Order Picking Process -- 9.3.1. Warehouse Layout -- 9.3.2. Storage Strategies -- 9.3.3. Customer Orders -- 9.3.4. Routing Methods in a Wide-Aisle Warehouse -- 9.4. Model Description and Instructions -- 9.4.1. Warehouse Layout and Location Names Database -- 9.4.2. Location Visit Identification Numbers Database -- 9.4.3. Pick Lists Database -- 9.4.4. Simulation Algorithm -- 9.5. Verification and Validation -- 9.6. Tasks for the Reader -- 9.7. Experiments -- 9.8. Concluding Remarks -- 9.9. Questions -- 10. Material Handling System / G. Neumann -- 10.1. Objectives of the Project -- 10.2. Description of the Material Handling System -- 10.2.1. System Functionality -- 10.2.2. System Structure and Boundaries -- 10.2.3. Pallet Flows -- 10.2.4. Process Control -- 10.3. System Analysis -- 10.4. Model Building -- 10.4.1. The DOSIMIS-3 Simulation Package -- 10.4.2. Model Structure -- 10.4.3. Model Parameters -- 10.5. Verification and Validation -- 10.6. Simulation Experiments -- 10.6.1. Objectives and Procedure -- 10.6.2. Simulation Results -- 10.7. Conclusions -- 10.8. Questions -- 11. Vessel Traffic in the Strait of Istanbul / O. S. Uluscu, B. Ozbas, T. Altiok, I. Or, A. O. Almaz -- 11.1. Introduction -- 11.2. Vessel Traffic in the Strait of Istanbul -- 11.3. Regulations -- 11.4. Literature on Modelling of Waterways -- 11.5. Modelling the Transit Vessel Traffic -- 11.5.1. Vessel Arrivals -- 11.5.2. Resources -- 11.5.3. Vessel Scheduling -- 11.5.4. Lane Structure -- 11.5.5. Simulation Model -- 11.5.6. Measures of Performance -- 11.5.7. Validation -- 11.5.8. Analysis of System Behaviour -- 11.6. Conclusions -- 11.7. Questions -- 12. Airport Logistics Operations / Miquel Angel Piera Eroles, J. Jose Ramos, E. Robayna -- 12.1. Introduction -- 12.1.1. The Current System -- 12.2. Main Airport Subsystems -- 12.2.1. Airport Operators -- 12.2.2. Air Traffic Controllers -- 12.2.3. Airlines and Ground Handling Segment -- 12.3. Collaborative Decision Approach Benefits -- 12.4. A Discrete-Event System Approach -- 12.5. Palma de Mallorca Airport: Check-In Assignment Sensibility -- 12.5.1. Delay Propagation in the Passenger Flow Area -- 12.5.2. Delay Propagation in the Passenger Transfer -- 12.6. Delay Propagation Simulation Model for Pushback Operations -- 12.7. Conclusions -- 12.8. Questions -- Subject Index. Biographical Note: Dr Yuri Merkuryev is full professor of the Institute of Information Technology and head of the Department of Modelling and Simulation at Riga Technical University. Dr Merkuryev is also president of the Latvian Simulation Society and director of the Latvian Centre of the McLeod Institute of Simulation Science. Dr Galina Merkuryeva is full professor of the Institute of Information Technology at the Department of Modelling and Simulation, Riga Technical University and honorary visiting professor of the University of Ljubljana, Slovenia, as well as vice-director of the Latvian Centre of the McLeod Institute of Simulation Science. Dr Merkuryeva has previously worked as an IT consultant for SIA Baldis and served as a technical expert on a project evaluation funded by the Latvian Ministry of Education and Science, and Ministry of Economics. Dr Miquel Angel Piera is a professor and director of Aeronautical Studies at the Universitat Autonoma de Barcelona (UAB) and director of the International Mediterranean & Latin American Council of Simulation. Dr Piera is also director of the McLeod Institute of Simulation Science and co-director of LogiSim, a technological transfer centre. Dr Antoni Guasch is professor of the Universitat Politecnica de Catalunya and co-director of LogiSim, a technological transfer centre."Publisher Marketing: This title presents an intensive learning course on the application of simulation as a decision support tool to tackle complex logistic problems. The book describes and illustrates different approaches to developing simulation models at the right abstraction level to be used efficiently by engineers.