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Authors Brief Biographies Authors Brief Biographies Thomas Hanne received master’s degrees in Economics and Computer Science, and a Ph.D. in Economics. From 1999 to 2007 he worked at the Fraunhofer Institute for Industrial Mathematics (ITWM) as senior scientist. Since then he is Professor for Information Systems at the University of Applied Sciences and Arts Northwest- ern Switzerland and Head of Competence Center Systems Engineering since 2012. Thomas Hanne is author of about 80 journal and conference articles and editor of several journals and special issues. His current research interests include multicriteria decision analysis, evolutionary algorithms, metaheuristics, optimiza- tion, simulation, logistics, and supply chain management. Rolf Dornberger is the head of the Institute for Information Systems, School of Business, University of Applied Sciences and Arts Northwestern Switzerland FHNW (since 2007) and the head of the competence centers New Trends & Innovation (since 2013) and Technology, Organization & People (since 2014) and was head of the competence center Systems Engineering (2006–2010). In 2002, he was appointed associate professor and, in 2003, full professor for Infor- mation Systems at the University of Applied Sciences and Arts Northwestern Switzerland FHNW or rather at its predecessor the University of Applied Sciences Solothurn Switzerland. Additionally, he was a part-time lecturer and visiting professor at the University of Stuttgart and the University of Applied Sciences Zurich. Before returning to academy, he worked in industry in different manage- ment positions as a consultant, IT officer and senior researcher in different engi- neering, technology and IT companies in the field of power generation systems and IT solutions for the airline business. He holds a Ph.D. (1998) and a Diploma degree in Aerospace Engineering (1994). His current research interests include computa- tional intelligence, optimization, innovation and technology management, and new trends and innovations. © Springer International Publishing Switzerland 2017 171 T. Hanne, R. Dornberger, Computational Intelligence in Logistics and Supply Chain Management, International Series in Operations Research & Management Science 244, DOI 10.1007/978-3-319-40722-7 Index A Cooper’s heuristic, 136, 137, 140 Advanced Interactive Multidimensional Coordinated uncapacitated lot-sizing Modeling System (AIMMS), 156 problem, 82 A Mathematical Programming Language Council of Supply Chain Management (AMPL), 155 Professionals, 4, 8 Ant colony optimization, 18, 32, 34–35, 55, 57, Covariance matrix adaptation, 27 63, 64, 84, 85, 93, 116, 138, 143, CPLEX Optimization Studio, 85, 93, 154–156, 144, 166 165 multiple ant colony system, 134 Crossdocking, 67, 144 APS software, 161, 162, 164, 165 Crossover, 17, 23, 24, 28, 30, 51–55, 57, 63, Arrival time, 66, 100, 101, 110 113, 114, 127, 132, 137, 140 Artificial bee colony algorithm, 32, 56, 130 cycle crossover, 54, 115 Artificial immune systems, 18, 19, 21, 36 distance-preserving crossover, 57 Automated guided vehicles, 2, 7 job-based order crossover, 115 Automated storage/retrieval systems (AS/RS), linear order crossover, 115 86, 88, 163, 164 order-based crossover, 54, 115 order crossover, 53, 113–115 partial schedule exchange crossover, 115 B partially mapped crossover, 28, 53, 115 Bees algorithm, 18, 32 position-based crossover, 54, 115 Bill of materials, 81, 160 precedence preserving order-based Bionic engineering, 15, 16, 26 crossover, 114 Business software, 160, 167 sequential constructive crossover, 54 subsequence exchange crossover, 115 substring exchange crossover, 115 C Cube-per-order index, 87 Capacitated facility location problem, Cuckoo search (CS), 56 133–134, 140 Capacitated multi-facility Weber problem, 138–141 D Capacitated vehicle routing problem, 57, 58, Deadline, 100 62, 85 Differential evolution, 18, 23, 31, 112 Completion time, 100, 105, 106, 108 Dijkstra’s algorithm, 45 Container, 7, 167 Discrete particle swarm optimization, 34 © Springer International Publishing Switzerland 2017 173 T. Hanne, R. Dornberger, Computational Intelligence in Logistics and Supply Chain Management, International Series in Operations Research & Management Science 244, DOI 10.1007/978-3-319-40722-7 174 Index Due date, 100, 101 H Dynamic scheduling, 103 Harmony search, 18, 23, 31, 130 History of logistics, 4, 5 Holding costs, 75–77, 88, 89, 91 E Hopfield networks, 57 Earliness, 101, 102 Hotframe, 158 Economic order quantity, 76, 77 Hub location problems, 144 Elastic net, 57 Hybrid metaheuristics, 23, 30, 57, 63, 64, 84, Encoding, 24, 27, 28, 51, 99, 111, 112, 114, 85, 87, 134, 137, 140, 158 116, 127, 132, 137 ERP software, 160 Euclidean metrics, 47, 136, 141 I Evolution strategies, 18, 23, 26–27, 30, 31 Intermodal transport, 7 Evolutionary algorithms, 11, 18, 22–32, 51–55, Inventory routing, 73, 88–93 64, 93, 111–113, 127, 137, 138, 140 Inventory-related costs, 8, 9 Evolutionary computation, 16–19, 22–23, Iterated local search, 38, 57, 143 31–32, 57 Evolutionary programming, 18, 23, 30 J Job shop scheduling, 104–108, 111, 115, 116 F flexible job shop scheduling, 106, 107, Facility location problems, 122, 124, 125, 128, 111, 116 131, 132, 137, 139, 140, 142, 143, Just-in-time production, 75, 76 145, 146 Firefly algorithm, 32, 56 Fitness, 20, 23–29, 31, 33, 34, 57, 132, 137 L Flow shop scheduling, 104, 106, 107, 111, 116 Lead time, 74, 78, 79 hybrid flow shop scheduling, 107 Learning classifier systems, 18, 23 FreeMat, 159 Liberalization, 6 Functions of a company, 1 Linear sum assignment problem, 44, 45 Fuzzy logic, 17, 19, 36 Lin-Kernighan heuristic, 51, 55 Fuzzy modelling, 16, 36, 49, 50, 62, 93, 110, Local search, 23, 30, 31, 37–39, 54, 57, 63, 64, 122, 126, 139, 140, 162 112, 115, 126, 127, 130, 132–134, 137, 143, 146, 158, 165, 166 Location-allocation problem, 135 G Location routing problems, 141–143 Gantt diagram, 100, 105 Logistics costs, 8–10 General capacitated lot-sizing problem, 82 Lot-sizing problems, 73, 77, 79–85 General Algebraic Modeling System (GAMS), 156 Genetic algorithms, 18, 23, 27–31, 57, 60, 63, M 64, 84, 85, 87, 112, 115, 116, 127, Machine learning, 15 132–134, 137, 138, 140, 141, 143, 144, Makespan, 101, 105–107, 111, 115 162, 165, 166 Manufacturing resource planning, 161 Genetic programming, 18, 23, 29, 30, 32 Maple, 155 Geographic information systems, 121 Material requirement planning, 160, 161, 163 Global Positioning System (GPS), 7, 46 Mathematica, 155 Glowworm swarm optimization, 32 Matheuristics, 23, 143 Grammatical evolution, 29 MATLAB, 155, 156, 159 Greedy randomized adaptive search procedure, Maximal covering location criterion, 122 39, 55, 63, 84, 85, 134, 140, 144 Maximum inventory levels, 90, 92 Gross domestic product (GDP), 6–9 Memetic algorithms, 18, 23, 30–31, 38, 54, 56, Gurobi, 154, 156 63, 64, 84, 113, 128 Index 175 Meta-genetic programming, 29 P Metaheuristics, 11, 15, 19, 21, 23, 37–39, 50, Packing, 166–167 51, 54–56, 60, 62–64, 67, 79, 80, 83–85, ParadisEO, 158 87, 93, 99, 110, 112, 115, 116, 126–130, Particle swarm optimization, 18, 32–34, 55, 63, 132–134, 136–138, 140, 142–146, 84, 113, 132, 133 157–159, 165–167 Path relinking, 63, 85, 127, 134, 144 MOSEK, 154 p-center problem, 128–130 Multicommodity-flow problem, 67 Perceptron, 35 Multicriteria location problems, 121–123 Pheromone, 34, 55, 134, 143 Multi-echelon inventory, 93 Pickup and delivery problem, 65 Multi-echelon location models, 145 p-median problem, 124–130, 142 Multi-item capacitated lot-sizing problem, Processing time, 100, 104–106, 111 80–82 Production coefficients, 81, 82 Multi-level uncapacitated lot-sizing problem, Production factor, 1 81, 82 Multiobjective optimization, 22, 30, 158 Multistart local search, 38 R Mutation, 11, 17, 23–30, 51, 52, 54, 63, 112, R, 159 114, 115, 132, 137 Radio-frequency identification (RFID), 7 hypermutation, 127 Random key encoding, 51, 84, 112, 113 precedence preserving shift mutation, 114, Recombination, 11, 23–25, 27, 29, 53, 54, 115 112–114, 127 Reinforcement learning, 18, 36–37 Reorder point, 78, 79 N Rostering, 108, 109 Nearest neighbor, 50 Network design, 145 Network flow problems, 43, 67 S Neural networks, 17, 19, 21, 22, 35–36, 55–56 Safety stocks, 74, 78 Non-dominated solutions, 22, 30 Scatter search, 127, 130, 134, 159 Non-dominated sorting genetic algorithm, 30, Scheduling 93 dependencies, 100, 104, 105, 108 Non-dominated sorting genetic algorithm II, earliest due date-scheduling, 104 30, 93 offline scheduling, 103 Non-preemptive scheduling, 102, 105, 107 online scheduling, 103 NP-hard, 21, 47, 50, 62, 67, 80–83, 100, 105, resource constrained project scheduling, 106, 108, 110, 125, 126, 129, 132, 133, 108 136, 146 Scilab, 159 Selection, 11, 17, 23–27, 30, 36, 122, 134, 140, 143, 145 O Self-organizing maps, 19, 35, 56, 64, 138 Object-oriented programming, 24, 25 Setup costs, 76, 80–82, 122, 124, 130, 131, Octave, 159 134, 140 Open shop scheduling, 104, 107, 108, 110, 112 Shortest path, 11, 43, 45, 47, 48, 123 OpenDINO, 159 Shortest processing time rule, 104 OpenOpal, 158, 159 Simulated annealing, 18, 37, 38, 55–57, 63, 84, Optimization software, 85, 153–157, 159, 85, 87, 116, 127, 130, 133, 134, 138, 160, 167 140, 142–145, 158, 165 OptimJ, 156 Single link shipping problem, 88 OptQuest, 159 Single-item capacitated lot-sizing problem, Order picking, 85–87, 163 79–81 Order-up-to level inventory policy, 91, 92 Soft computing, 15 Start time, 100 176 Index Stock location assignment problem, 87 U Stock-outs, 9 Uncapacitated facility location problem, Storage locations, 73, 85–88 130–133 Strength Pareto
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