Lecture Notes in Logistics Series Editors Uwe Clausen Flow & Logistics IML, Fraunhofer Institute for Material, Dortmund, Germany Michael ten Hompel and Logistics IML, Fraunhofer Institute for Material F, Dortmund, Germany Robert de Souza The Logistics Inst-Asia Pacific, National Univ of Singapore, Singapore, Singapore More information about this series at http://www.springer.com/series/11220 Editors Herbert Kotzab, Jürgen Pannek and Klaus-Dieter Thoben Dynamics in Logistics Proceedings of the 4th International Conference LDIC, 2014 Bremen, Germany 1st ed 2016 Editors Herbert Kotzab University of Bremen, Bremen, Germany Jürgen Pannek Universität Bremen, Bremen, Germany Klaus-Dieter Thoben Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany ISSN 2194-8917 e-ISSN 2194-8925 Lecture Notes in Logistics ISBN 978-3-319-23511-0 e-ISBN 978-3-319-23512-7 https://doi.org/10.1007/978-3-319-23512-7 Springer Cham Heidelberg New York Dordrecht London Library of Congress Control Number: 2015951763 © Springer International Publishing Switzerland 2016 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com) Preface Continuing in the footsteps of the three previous international conferences on Dynamics in Logistics, LDIC 2014 was the fourth event in this series to be held in Bremen (Germany) from February 10 to 14, 2014 The conference was accompanied by a “Doctoral Workshop” as well as the “InTraRegio International Dialog Event” and the “MAPDRIVER Kickoff Meeting” as satellite events Similar to its predecessors LDIC 2007, LDIC 2009, and LDIC 2012, the Bremen Research Cluster for Dynamics in Logistics (LogDynamics) of the University of Bremen organized the conference in cooperation with the Bremer Institut für Produktion und Logistik (BIBA), which is a scientific research institute affiliated to the University of Bremen The conference is concerned with the identification, analysis, and description of the dynamics of logistic processes and networks The spectrum reaches from the modeling and planning of processes over innovative methods like autonomous control and knowledge management to the new technologies provided by radio frequency identification, mobile communication, and networking The growing dynamic confronts the area of logistics with completely new challenges: it must become possible to rapidly and flexibly adapt logistic processes and networks to continuously changing conditions LDIC 2014 provided a venue for researchers from academia and industry interested in the advances in dynamics in logistics induced by new technologies and methods The conference addressed research in logistics from a wide range of fields including engineering, business administration, computer science, and mathematics The LDIC 2014 proceedings consist of 72 papers including 10 young researcher papers selected by a strong reviewing process The volume is organized into the following main areas: Shared Resources, Planning, and Control, Synchronization, Technology Application in Logistics, Transport and Green Logistics, Supply Chain Management, and Frameworks, Methodologies, and Tools There are many people whom we have to thank for their help in one or the other way For pleasant and fruitful collaboration we are grateful to the members of the program and organization committee: Michael Bourlakis, Cranfield (UK) Sergey Dashkovskiy, Erfurt (Germany) Neil A Duffie, Madison (Wisconsin, USA) Enzo M Frazzon, Florianópolis (Brazil) Michael Freitag, Bremen (Germany) Kai Furmans, Karlsruhe (Germany) David B Grant, Hull, Yorkshire (UK) Axel Hahn, Oldenburg (Germany) Bonghee Hong, Pusan (Korea) Alamgir Hossain, Newcastle upon Tyne (UK) Hamid Reza Karimi, Agder (Norway) Kap Hwan Kim, Pusan (Korea) Aseem Kinra, Copenhagen (Denmark) Matthias Klumpp, Essen (Germany) Antônio G.N Novaes, Florianópolis (Brazil) Kulwant S Pawar, Nottingham (UK) Marcus Seifert, Osnabrück (Germany) Alexander Smirnov, St Petersburg (Russia) Gyan Bahadur Thapa, Kathmandu (Nepal) Dieter Uckelmann, Stuttgart (Germany) Carrying the burden of countless reviewing hours, we wish to thank our secondary reviewers Jannicke Baalsrud Hauge, Till Becker, Tobias Buer, Matthias Busse, Jens Eschenbaecher, Stephanie Finke, Julia Funke, Rosa Garcia Sanchez, Carmelita Görg, Hans-Dietrich Haasis, Florian Harjes, Jens Heger, Jan Heitkötter, Otthein Herzog, Aleksandra Himstedt, Michael Hülsmann, Reiner Jedermann, Frank Kirchner, Herbert Kopfer, Hans-Jörg Kreowski, Thomas Landwehr, Walter Lang, Michael Lawo, Burkhard Lemper, Marco Lewandowski, Michael Lütjen, Rainer Malaka, Afshin Mehrsai, Jasmin Nehls, Jürgen Pannek, Moritz Rohde, Ingrid Rügge, Jörn Schönberger, Kristian Schopka, Xin Wang, Dirk Werthmann, Stefan Alexander Wiesner, and Jochen Zimmermann for their help in the selection process We are also grateful to Aleksandra Himstedt, Ingrid Rügge, Marco Lewandowski, and countless other colleagues and students for their support in the local organization and the technical assistance during the conference Special thanks go to Ingrid Rügge and Aleksandra Himstedt for organizing the “Doctoral Workshop” as well as the “InTraRegio International Dialog Event” and the “MAPDRIVER Kickoff Meeting.” Moreover, we would like to acknowledge the financial support by the BIBA, the Research Cluster for Dynamics in Logistics (LogDynamics), and the University of Bremen Finally, we appreciate the excellent cooperation with Springer-Verlag, which continuously supported us regarding the proceedings of all LDIC conferences Herbert Kotzab Jürgen Pannek Klaus-Dieter Thoben Bremen September 2015 Contents Part I Shared Resources, Planning and Control A Micro- and Macroeconomic View on Shared Resources in Logistics Jörn Schönberger, Herbert Kopfer and Herbert Kotzab The Regulation of Shared Resources—Impacts on the Logistics Sector Sören Brandt and Jochen Zimmermann Shared Transport Systems—A New Chance for Intermodal Freight Transport? Aline Monika Gefeller and Jörn Schönberger Application of Topological Network Measures in Manufacturing Systems Till Becker Optimization of a Factory Line Using Multi-Objective Evolutionary Algorithms Andrew Hardin, Jason Zutty, Gisele Bennett, Ningjian Huang and Gregory Rohling Managing the Life Cycle of IT-Based Inter-firm Resources in Production and Logistics Networks Jens Pưppelb, Michael Teucke, Dirk Werthmann and Michael Freitag Autonomous Control Strategy for High Precision Marking of Installation Drill Holes Using a Mobile Robot Jürgen Pannek, Tom Naundorf and Matthias Gerdts The Impact of Shortest-Path Searches on Dynamic Autonomous Transport Scheduling Max Gath, Otthein Herzog and Maximilian Vaske A Mathematical Dynamic Fuzzy Logic to Estimate the Average Throughput Time for a New Automated Full-Case Picking System Mohammed Ruzayqat, Valentine Obi and Bernd Noche Pilot Prototype of Autonomous Pallets and Employing Little’s Law for Routing Afshin Mehrsai, Hamid-Reza Karimi, Klaus-Dieter Thoben and Bernd Scholz-Reiter Toward a Comprehensive Approach to the Transformation of Logistic Models Hans-Jörg Kreowski, Marco Franke, Karl Hribernik, Sabine Kuske, Klaus-Dieter Thoben and Caro von Totth Savings Potential Through Autonomous Control in the Distribution of Rental Articles Florian Harjes and Bernd Scholz-Reiter Established Slack-Based Measure in Container Terminal for Risk Assessment Kasypi Mokhtar, Muhamamad Zaly Shah Muhammad Hussein, Khalid Samo and Ab Saman Abd Kader Improving Wind Turbine Maintenance Activities by Learning from Various Information Flows Available Through the Wind Turbine Life Cycle Elaheh Gholamzadeh Nabati and Klaus Dieter Thoben Empty Container Management—The Case of Hinterland Stephanie Finke Part II Synchronization Synchronization in Vehicle Routing: Benders’ Decomposition for the Home Health Care Routing and Scheduling Problem Dorota Slawa Mankowska Heterogeneity of Velocity in Vehicle Routing—Insights from Initial Experiments Jörn Schönberger and Herbert Kopfer New Design of a Truck Load Network Andy Apfelstädt and Matthias Gather Costs and Travel Times of Cooperative Networks in Full Truck Load Logistics Sergey N Dashkovskiy and Bernd Nieberding Optimizing Mixed Storage and Re-Marshalling Plans Yeong Su Choi and Kap Hwan Kim Container Flows and Truck Routes in Inland Container Transportation Julia Funke and Herbert Kopfer Application of Semi-Markov Drift Processes to Logistical Systems Modeling and Optimization Mykhaylo Ya Postan An Agent-Based Approach to Multi-criteria Process Optimization in In-House Logistics Christoph Greulich Part III Technology Application in Logistics Machine-to-Machine Sensor Data Multiplexing Using LTE-Advanced Relay Node for Logistics Farhan Ahmad, Safdar Nawaz Khan Marwat, Yasir Zaki, Yasir Mehmood and Carmelita Görg Impact of Machine-to-Machine Traffic on LTE Data Traffic Performance Yasir Mehmood, Thomas Pötsch, Safdar Nawaz Khan Marwat, Farhan Ahmad, Carmelita Görg and Imran Rashid Dynamic Temperature Control in the Distribution of Perishable Food Dynamic Temperature Control in the Distribution of Perishable Food Antonio G.N Novaes, Orlando F Lima Jr, Carolina C Carvalho and Edson T Bez RFID-Enabled Real-Time Dynamic Operations and Material Flow Control in Lean Manufacturing Muawia Ramadan, Mohammed Alnahhal and Bernd Noche Applying Product-Integrated RFID Transponders for Tracking Vehicles Across the Automotive Life Cycle Florian Peppel, Martin Müller, Miguel Silveira, Lars Thoroe, Malte Schmidt and Michael Schenk Airflow Simulation Inside Reefer Containers Safir Issa and Walter Lang Cloud-Based Platform for Collaborative Design of Decentralized Controlled Material Flow Systems in Facility Logistics Orthodoxos Kipouridis, Moritz Roidl, Willibald A Günthner and Michael Ten Hompel Preactive Maintenance—A Modernized Approach for Efficient Operation of Offshore Wind Turbines Stephan Oelker, Marco Lewandowski, Abderrahim Ait Alla and Klaus-Dieter Thoben Eco- and Cost-Efficient Personal E-mobility in Europe—An Innovative Concept for the Informational Synchronization Between E-vehicle users and the Smart Grid of the Future Using NFC Technology Antonio Lotito, Jan Heitkötter, Moritz Quandt, Thies Beinke, Michele Pastorelli and Maurizio Fantino Food Traceability Chain Supported by the Ebbits IoT Middleware Karol Furdik, Ferry Pramudianto, Matts Ahlsén, Peter Rosengren, Peeter Kool, Song Zhenyu, Paolo Brizzi, Marek Paralic and Alexander Schneider A BCI System Classification Technique Using Median Filtering and Wavelet Transform Muhammad Zeeshan Baig, Yasir Mehmood and Yasar Ayaz Interaction Mechanism of Humans in a Cyber-Physical Environment Marco Franke, Bogdan-Constantin Pirvu, Dennis Lappe, Bala-Constantin Zamfirescu, Marius Veigt, Konstantin Klein, Karl Hribernik, Klaus-Dieter Thoben and Matthias Loskyll The Influential Factors for Application of the Electric Commercial Vehicle in the Urban Freight Transport Molin Wang and Klaus-Dieter Thoben Modeling the Impact of Drivers’ Behavior on Energy Efficiency of Medium Duty Electric Vehicles Tessa T Taefi Part IV Transport and Green Logistics Green Bullwhip Effect Cost Simulation in Distribution Networks Matthias Klumpp, Nihat Engin Toklu, Vassilis Papapanagiotou, Roberto Montemanni and Luca Maria Gambardella Challenges and Solutions Toward Green Logistics Under EU-Emission Trading Scheme Fang Li, Hans-Dietrich Haasis and Irina Dovbischuk Economic Ship Travel Speed and Consequences for Operating Strategies of Container Shipping Companies Timm Gudehus and Herbert Kotzab A Five-Step Approach for Dynamic Collaborative Transportation Planning on Hard Time Horizon Kristian Schopka, Xin Wang and Herbert Kopfer On Using Collaborative Networked Organizations in International Outbound Logistics Kim Jansson, Iris Karvonen and Aino Vaittinen Application of the Adapted SCOR Model to the Leather Industry: An Ethiopian Case Study Fasika Bete Georgise, Klaus-Dieter Thoben and Marcus Seifert Operational Supply Chain Planning Method for Integrating Spare Parts Supply Chains and Intelligent Maintenance Systems Eduardo Francisco Israel, Enzo Morosini Frazzon, Ann-Kristin Cordes, Bernd Hellingrath and André Albrecht Lopes Macro-institutional Complexity in Logistics: The Case of Eastern Europe Frederic Wessel, Aseem Kinra and Herbert Kotzab Collaborative Carry-Out Process for Empty Containers Between Truck Companies and a Port Terminal Sanghyuk Yi, Bernd Scholz-Reiter and Kap Hwan Kim Optimization of Container Multimodal Transport Service Based on Segmented Procurement Hualong Yang and Di Liu Comparative Analysis of European Examples of Freight Electric Vehicles Schemes—A Systematic Case Study Approach with Examples from Denmark, Germany, the Netherlands, Sweden and the UK Tessa T Taefi, Jochen Kreutzfeldt, Tobias Held, Rob Konings, Richard Kotter, Sara Lilley, Hanna Baster, Nadia Green, Michael Stie Laugesen, Stefan Jacobsson, Martin Borgqvist and Camilla Nyquist the maximum one was 1.4 % For individual stations, the increases were from to 3.15 % So the effect is very small This is because any shortage in a station inventory will most probably be compensated in the next route where the stations with the unsatisfied demand have the priority to be satisfied in that next route In the case that K is less than the average demand of the stations, regular kanban performance is deteriorated The utilization levels of the stations were found in the case in which C = 60, µ = 3, B = 15, and K = (less than the average demand) 96.00, 95.62, 94.47, 83.63, and 77.68 % for the stations from to 5, respectively If these stations belong to the same assembly line, the utilization of this assembly line is based on the lowest one which is 77.68 % So it will be better if these stations have the same average utilization to increase the utilization of the assembly line In the previous example, utilization probabilities for the five stations in time-adjusted electronic kanban were around 90 % which is the same result found from Eq (6) Theoretically, regular kanban can achieve the same results if we use parts plus full bins in initial inventory and if we set initial inventory levels to the right values, but this is not easy in the practice because in this case kanban must be put in kanban post when a certain part in the bin is consumed Conclusion In this study, we differentiate between push and pull systems for material flow using milk run trains In pull system, we investigate using regular kanban, electronic kanban, and AEK Push system was found to be effective if the variance of parts consumption is not high On the other hand, regular kanban was found to be effective if there is sufficient train capacity Based on its line-side inventory performance, electronic kanban is the best choice even if there is sufficient train capacity In the case of problems in train capacity, AEK is the best choice These results were obtained analytically and based on simulation This study presents for the first time such comparison between these material flow control systems Future research can investigate analytically the difference between regular kanban and electronic kanban based on their line-side inventory performance References Alnahhal M, Noche B (2013) Efficient material flow in mixed model assembly lines SpringerPlus 2(1):1–12 [Crossref] Álvarez R, Calvo R, Peña MM, Domingo R (2009) Redesigning an assembly line through lean manufacturing tools Int J Adv Manuf Technol 43:949–958 [Crossref] Baudin M (2004) Lean logistics: the nuts and bolts of delivering materials and goods Productivity Press, New York Bozer Y, Ciemnoczolowski D (2013) Performance evaluation of small-batch container delivery systems used in lean manufacturing— Part 1: system stability and distribution of container starts Int J Prod Res 51(2):555–567 [Crossref] Ciemnoczolowski D, Bozer Y (2013) Performance evaluation of small-batch container delivery systems used in lean manufacturing— Part 2: number of Kanban and workstation starvation Int J Prod Res 51(2):568–581 [Crossref] Domingo R, Alvarez R, Peña MM, Calvo R (2007) Materials flow improvement in a lean assembly line: a case study Assembly Autom 27:141–147 [Crossref] Droste M, Deuse J (2011) A planning approach for in-plant milk run processes to optimize material provision in assembly systems In: Proceedings of the 4th International conference on changeable, agile, reconfigurable and virtual production (CARV2011), Montreal Emde S, Boysen N (2012) Optimally routing and scheduling tow trains for JIT supply of mixed-model assembly lines Eur J Oper Res 217:287–299 [MathSciNet][MATH] Emde S, Fliedner M, Boysen N (2012) Optimally loading tow trains for JIT-supply of mixed-model assembly lines IIE Trans 44:121–135 [Crossref][MATH] Faccio M, Gamberi M, Persona A (2013a) Kanban number optimization in a supermarket warehouse feeding a mixed-model assembly system Int J Prod Res 51(10):2997–3017 [Crossref] Faccio M, Gamberi M, Persona A, Regattieri A, Sgarbossa F (2013b) Design and simulation of assembly line feeding systems in the automotive sector using supermarket, kanbans and tow trains: a general framework J Manag Control 24(2):187–208 [Crossref] Golz J, Gujjula R, Günther H-O, Rinderer S, Ziegler M (2011) Part feeding at high-variant mixed-model assembly lines Flex Serv Manuf J 24:119–141 [Crossref] Satoglu SI, Sahin IE (2012) Design of a just-in-time periodic material supply system for the assembly lines and an application in electronics industry Int J Adv Manuf Technol doi:10.1007/s00170-012-4171-7 © Springer International Publishing Switzerland 2016 Herbert Kotzab, Jürgen Pannek and Klaus-Dieter Thoben (eds.), Dynamics in Logistics, Lecture Notes in Logistics, https://doi.org/10.1007/978-3-319-23512-7_71 Resource of Genius Loci in Tourism Galina Sergeevna Sologubova1 (1) Economy and Management in Tourism, St Petersburg State University of Economics, Saint Petersburg, Russia Galina Sergeevna Sologubova Email: en-consalt@mail.ru Abstract The subject of the report is substantiation of the definition “Destination” in terms of pseudo-touristic space and the use of the coordinate model, the equilibrium model of the center of mass, the method of the optimization of the objective function, and the method of relative preference to define the expected (cost-effective) geographical location of the tourist center with distinctive meanings of “heritage.” Keywords Destination – Pseudo-touristic space – The equilibrium model of the center of mass – Coordinate’s model – The method of relative preference The Author’s Idea The choice of location is a strategically important task—to ensure an efficient life activity, including effectiveness of economic management The obvious multicriteriality of the choice of a place (costs, risks, demand satisfaction, profit, environmental damage), however, is reduced to the priority of such condition, as is the speed of response The number of investments in major projects of territories development indicates a trend of business dependence on the breadth of the territorial coverage, ensuring the timely and quick delivery of any proposal The increase in the number of infrastructure of distributed centers exponentially reduces the response time That means the creation of new centers is advisable; therefore, necessary to ensure the economic efficiency of this process The idea of the author is to offer the development of tourist destinations adulterated meanings in the territories which not necessarily have to have valuable recreational resources or are historically established routes of people and ideas movement, but belongs to the new intersection of these flows or, on the contrary, neglected, abandoned, littered with garbage, uninhabited, remote, and deaf towns There is a need to choose the place and determine the number of potential centers of tourist interest and to compare and select the best option of dislocation The solution must meet the limitations and requirements of the law of demand and supply, economic efficiency, balance of power recreational resource, infrastructure in the host destination, and potential demand For substantiation of economic efficiency of choice dislocation of a new tourist destination, econometrics offer the coordinate model, equilibrium model of the center of mass, the method of the optimization of the objective function, and the method of relative preferences Calculation algorithm is simple and intuitive The reliability of mathematical results is confirmed by the historicity of the choice of place of the genius loci, a famous ancient good spirit, linking the intellectual, spiritual, and emotional phenomena with their material environment Approaches to the Choice of Location Administrative maps demonstrate fields’ influence of cities on the surrounding territories The boundaries of identified areas subject landscape heterogeneity, the beds of the rivers and the coastal lines of the seas, the historically established relations and trade routes Ideal distribution of fields of influence (Voronoi diagrams, 1850) to bind to geographically dispersed customers to service centers can be achieved, if the road network does not play a large value, such as mobile cellular operators In the case of a dense road network, its heterogeneity can be neglected, constituting private algorithms Voronoi diagrams The actual distribution of the field of influence of cities depends on the resistance of the environmental movement of flows (material, informational, financial, flows of people, services, knowledge) The idea of the field of influence found an interesting continuation with respect to the problem of search of the optimal position of the objects in the supply chain According to the physical analogy, each of the cities is the center of attraction and has a certain weight (consumer potential) In the models of commercial attraction based on the gravitational analogy, the tasks use zoning consumers and their subsequent fixing of the trading point Model Reilly (1929) used two points of attraction for the problem of zoning the market The Reilly’s model laid the assumption that the demand for goods and services is directly proportional to the number of population in the city and is inversely proportional to the square of the distance from the consumer to the city In the Christaller model (1933), the role of the city is interpreted as a place of centralized supply of goods and services of the surrounding countryside (villages and other towns) The scale of the city —the center of effectively organized trade, according to Christaller is determined by four factors: (1) the level of economic development, (2) the number of working-age population, (3) the economic distance, determined by transport availability and cost, and (4) the frequency of shopping, determined by the importance and closeness The Huff model (Huff retail, 1963) defines the search of the perfect position through many pre-set locations, taking into account the costs in time and money of the consumer on the road to trading points, proportional to the distance and speed of the delivery To solve this optimization problem, two methods of calculating distances are used: (1) along the shortest path between two points on the plane (Euclidean distance); and (2) the streets of the city with rectangular quarters (Manhattan distance) An alternative gravity model is the approach, in which the optimal location for center of gravity corresponds to the point that minimizes the value of multiplication of the mass of the transported cargoes on distance of transportation (task Weber, 1903) Based on this approach, the Chopra’ model (Chopra, 2000) as a criterion for decision, uses the criterion of minimizing the total costs in the supply chain Chopra distributed total costs by categories: vehicles, real estate, stocks, and personnel The method of balance of costs (1) correlated with the method of balance of moments (2) (1) (2) Thus, using economic metrics (Table 1) in the gravitational analogy, it is possible to make up the equations of the balance at the point of “indifference” and calculate an equilibrium coefficient, which characterizes the business situation, to model scenarios of optimizing space (Zaitsev 2011) Table The designations that identify criteria of efficiency of a tourist’s destination dislocation The name of the criterion Designation, identifier of Z Number of planned arrivals p (pers.) The population of the regions suppliers N (thous pers.) Attractiveness (expert preference) U (score) The distance to the point of destination L (km) Loading thoroughfares//transport support G = pL (pers km) The duration of the journey//speed of transportation t = L/v (h) The costs of the tourist transport services S = C L L + C t L/v (rub.) The costs of transportation S = C L L (rub.) The costs of the work of transport vehicles S = C g G (rub.) The costs associated with time in the way S = C t t (rub.) The income for one person of the population in the location of suppliers or specific solvent demand q (rub./pers.) Turnover of goods in the places of dislocation of tourist destinations Q = q N i , (rub.) The capacity of the tourism infrastructure of the settlement A (number of beds in the collective accommodation) The throughput of the railway stations and ports (passenger traffic) R (people per hour) Where C L unit transport costs—tariff for transportation (rub./km), C g specific costs for operation and maintenance of vehicles (rub./h); C t unit value of time in transit (rub./h) The methods mentioned above can visualize an approach, oriented on a system of restrictions, which is exclusively economic in nature In reality, there is objective landscape, social, environmental, and other restrictions that reduce the number of iterations—for n possible points determined, there are n−1 geographic configurations (Malyarenko 2010) Approaches to the Definition of Tourist Space—Destination Tourist space is considered as a part of the geographical environment in the aggregate of natural and anthropogenic elements and their interconnections, which formed the real solvent demand and a system offering a variety of services for tourist consumption Structuring of the tourist area includes a selection of not only individual tourist-recreational areas, but also of individual subjects of the tourist market as centers of demand The territorialspatial division is carried out not on the basis of geographical zoning, but on the basis of a concentration and specialization of tourist services (Dergachoff 2003) The territorial-spatial boundaries are formed under the influence of the economic laws of supply and demand, as a result of the number of partial geographic overlay markets of recreational territories, the coincidence of the centers of donor investments, regions of the labor-supplying The cores of such geoeconomic systems become a destination (Fig 1) Fig a The administrative approach—the core of the geoeconomics system is a recreational area, forming around the touristrecreational system b The approach on the basis of scientific criteria of recreational geography and economic mechanisms of the formation of supply and demand—the core of the geoeconomics system is the destination Tourist-recreation centers and destinations are (1) the places of residence of the population, which is engaged in various sectors of the economy and (2) economic centers or regions, around spaces that depend on them economically and administratively Their spatial system that was formed historically, on the map is displayed as the network of industry centers, corresponding transport communications The subordinated spaces represent economic areas as a result of the zoning allocated recreational zones, which form the touristrecreational system around economic centers The recipient regions and their cores turn into tourist destinations The axiological nature of constant “destination” is disclosed in the heritage (Zorin 2000) Exactly, the heritage is a semantic side of tourism Meanings are formed in space and time, i.e., they possess the properties of historicity and location: the future, the present, and the past through the stratums of the ability of a person to the perception of the environment—consciousness, knowledge, memory, opinions, wishes, hopes Subjectivity of perception determines the scope and content of space: the pseudo-illusory; quasi-imaginary; personal-comfortable, secluded; virtual–unreal reality Man’s perception gives the destination false ideas—inventions Fiction becomes subject to demand, substituting the traditional meanings of natural-climatic and cultural and historical heritages The heritage of the fictional space focuses the attention of the traveler to: (1) landscape (the appearance of the country, the object—the memorial of nature, man-made monument) and (2) the game (simulation of processes with replacement of elements on the axis of time); (3) the theme (functional objectivity) The active development of thematic tourism, which specializes in the materialization of adulterated meanings and the events, is the tendency of the last years Not a historical city or natural and geographical attractions determine the spatial selectivity of the tourist flows, although they are often used as territorial anchors, and the newly created objects, to meet the demands of the modern consumer—planetariums, water parks, Lego City, the country of Santa Claus, shopping and entertainment venues become the centers of tourism demand—destinations Good Relations with Genius Loci Planning and design of new destinations is carried out on the basis of an assessment of a complex of factors: the tourist potential of the region, the level of competition in it, the investment climate, administrative support, socio-demographic characteristics, etc Initiation of destination with the adulterated meanings imply, above other things, the selection of the best locations from the point of view of tourists and the investors’ and minor advantageous positions in the real geographical space Good relations with the genius loci—the genius of the place are a combination of common sense, observation, intuition, and mathematical standard perception of space That is why, when choosing the location of the thematic (false sense) destinations, the greatest attention is paid to the issues of transport accessibility, including price, and distance from regional centers—potential donors of tourist flow The lower the total costs, the higher the economic result of tourist business And therefore, the option of choice will be effective In practice, the design of tourist destinations is dominated by decisions and tasks from the marketing point of view (surveys, financial-economic and comparative analyses) The verbal and heuristic character of the models is in a high degree subjective and focused on statistical reports— information of the past periods Application of simulation of coordinate models, based on GIS technologies, allows substantiating the multicriteria, the different measures (Table 1) selecting the location of the point of destination on the map and providing a greater objectivity of the project solution Modern econometrics offers several ways for a solution of such tasks: (1) coordinate model— positioning; (2) the model of choice for the costs; and (3) multicriteria model, taking into account factors of preference The diversity of conditions of economic activities in tourism and objectives of the niche optimization allow us to use the entire arsenal of modern science to substantiate and make the best possible decisions For example, a coordinate model can be used to define not only the optimal location of the object of tourism demand, but also a number of attractive facilities and their capacity The model of choice for the costs is applicable to the problems of alternatives evaluating In conditions of uncertainty and the multifactor nature of its decision-making, it is simple enough and effective, to use the method of relative preferences An Example of Task Solving Selection The task: substantiate the location of Kamyshin city, Volgograd region as optimal for organization of tourist destination on the territory of the Russian Federation, including Saratov, Voronezh, Volgograd, and Astrakhan region Prospective concepts of destination are: (1) environmental, ecological space; (2) adulterated meanings; (3) recreation; and (4) yacht tourism The solution of the problem involves three stages The first stage includes analysis of the tourism potential of the selected region and the city of Kamyshin, the methods used are positioning on the plane, center of mass, and optimization effectiveness As a result of zoning of the territory and identification of the settlements with the assessment of the number (N) and the solvency of the population (q) in each of n possible points Terms of selection of settlements are: (1) the presence of the route, (2) the journey time is not more than h, (3) the population over 100,000 people The population in Kamyshin city is 128,000 people The share of income of the population by 2012 amounted to 18,000 rubles Location of the settlements on the plane is determined by the method of combining maps with grid coordinates The coordinates of the settlements (X i , Y i ), the income and population (q i , N i ), respectively, are the source data for compiling the balance equation by the method of the center of mass, the economic meaning to determine the equilibrium of the system of costs of tourists from different cities, which are the suppliers of solvent demand for tourist products of Kamyshin city From the balance equations of optimal coordinates of the destination can be calculated by the formulas (3) (3) where Z i is a criterion of efficiency of the decisions, connected with the distance from the destination to the ith settlement (L i ), time in a way (t i ) and costs to travel (S i ) The demand for tourism services is directly proportional to the population size and its solvency and it is inversely proportional to the square of the distance that tourists need to overcome and the costs associated with transportation and time in the way of (4) (4) where N i —is the number of the population of the city—the supplier of tourist flow; q i —average per capita income of the city supplier of tourist flow; —it is the distance from the destination to the ith of the settlement; —tourist cost related to the payment of the transport services and costs in a way According to calculations, the optimal location of the tourist destination has the following coordinates on the X-axis = 103.07; on the axis Y = 203.05 The coordinates of the city of Kamyshin in the diagram correspond to the values on the X-axis = 120; on the axis Y = 165 Deviations in the values of coordinates determine the area equal to 22 km and the travel time of 40 The nearest settlement, corresponding to the calculated coordinates,—Petrov Val, the population of 12,000 people, the average per capita income is 15,000 rubles a month, that does not satisfy the system limitation of decision-making The introduced restrictions system requires the balance of potential consumer demand and capacity of the local infrastructure for tourists’ reception Calculating result of the potential tourism demand in 11 cities of the selected regions of the Russian Federation outlined the probable number of arrivals to 1,410,732 people a year, which will draw 23,927,140 rubles into the economy of the city Analysis of recreational resources and infrastructure potential of the city of Kamyshin showed compliance to demand for tourist services According to the economic meaning of the balance equations of moments, the location of Kamyshin is best for the development of tourist destination, receiving tourist’s flow from the territories of Voronezh, Saratov, Volgograd, and Astrakhan regions The second stage considers alternative solutions dislocation of a tourist destination in the indicated region: the placement of not one, but m tourist destinations, formation of optimal distribution of the demand for tourist products or designing tourist routes with the transit destinations (2 or more intermediate center of tourist interest) The task was solved in the framework of the coordinate model of objects disposition and optimization of the objective function—minimization of expenditures of a tourist (5) (5) The tourist’ costs represent the sum of costs: S 1—to travel from the town to the transit destination (6), S 2—the travel from the transit destination to the target one (7) and S 3—to stay in transit destination (8) (6) (7) (8) where Д - the number of days of stay The second stage determines the number, capacity and location of centers of tourist demand Coordinate tags correlate with the location of the cities Saratov, Kamyshin and Volgograd, which can be considered as objects of tourist demand in the real-time mode, i.e., they already possess attractors and infrastructure Resulting from the analysis and calculation of the objective function information on the geoeconomic condition of the territory can be used for its development For example, design of the infrastructure system of service of tourists: camping, motels, gas and motor-car repair stations, mobile points of food and beverages, trade centers, fairs, etc Taking into account the experience of geomarketing technologies and using the values of the real coordinates of the existing cities, we can continue problem solution of the demand’s distribution and the routes’ design At the third stage, applying the method of relative preferences, choose a specific place for the organization of a new tourist destination: from m possible variants of the decision on the basis of n factors influencing the choice (Tables 2, 3) Table Analysis of factors influencing on the choice of location Factors The cost Designation Dimension Inverse value Significance X1 Thous rubles 1/X State and prospects X Score X2 The tourist resource X Score X3 Table Initial data for calculation by the method of relative preferences Option selection X X2X3 Jirnovsk 6078.35 2 Kamyshin 5820.15 3 Nikolaevsk 6938.1 Comparing pairs of variants of decisions on each of the factors and recording these comparisons in the form of preference relations we obtain n matrices (B 1, B 2, B 3) of order m (number of factors) and n weight vectors G k = {g ki } that forms the aggregate weight matrix of solutions U = (G 1, G 2, G 3) (Table 4) Table Aggregation matrix, the final decision United matrix U scales options factors B –B Weight, g Weight, g Weight, g Weight, g City Solution Options Options Options Factors V= U × g 0.34 0.33 0.25 0.25 Jirnovsk 0.36 0.5 0.38 0.35 Kamyshin 0.42 0.3 0.17 0.38 0.4 Nikolaevsk 0.29 0.30 The final solution of the problem of choice is a vector of weight options V, defined as the product of matrices (9) (9) The greatest value of R = max (v 1, v 2, v 3) corresponds to the best variant of the decision (in the sense of preferences under uncertainty) In this example, the maximum value of preferences corresponds with Kamyshin city Conclusion The genius loci = Intuition + Observation + Mathematical Standard perception of space + Common sense Intuition: 1569 year—an attempt to combine the Volga river with the Don of the Turkish Sultan Selim; 1697 connection of seas according to the plan of Peter the great; 1942—the construction of the Volga belt road to supply the troops that participated in the battle of Stalingrad Observation: the analysis of the landscape in Kamyshin town, as an object of heritage and recreational resource, revealed a significant number of paleobotanical, geomorphologic, geological monuments Among them are mountain Ears, Stolbiches, Karavaies (loaves)—huge round (in diameter they reach 4–6 m) boulders, ravines and beams—beds of ancient rivers The unique lake Elton, spring river Ilovlya, Medveditsa, Kamyshinka, freshwater keys are unique balneological resource destination (Baranov 1952) Recreational potential of the river Volga is not restricted to water rides, a pronounced continental climate provides an always hot, dry summer, which contributes to the development of beach rest and medical tourism with the readings of the lung, skin, neurological pathologies Mathematical standard perception of space: the coordinates of the optimal location correspond to the locality Petrov Val (22 km North of Kamyshin); the number of tourist arrivals may be 1,410,732 people a year, that will draw 23,927,140 rubles into the economy of the city; the adequacy of resource destination Kamyshin introduced a system of restrictions (the aggregate room Fund with deficit and the total passenger traffic of all transport dominants with excess); the dislocation of Saratov and Volgograd complementarily in relation to the destination Kamyshin; the largest weighting factor (0, 42), which characterizes the multivariate analysis the different measures values, determines the maximum value of preferences to Kamyshin city Common sense It is obvious that location of a tourist destination between the cities of Kamyshin and Petrov Val meets the requirements of the comprehensive efficiency: corresponds to the balance of costs, has sufficient human resources, has recreational and semantic potential, characterized by transport availability, and the potential for development of new transport communications—the possibility of building a new river passenger port and yacht port The contribution of the author: (1) developed the idea of the target application of GIS technologies in tourism and (2) proposed a model of use References Baranov VI (1952) What they say sandstones Kamyshin and Sands Ergeni The Regional Publishing House, Stalingrad Calculation of distances (2012) Auto Trans info http://ati.su/Trace/ Accessed 29 Nov 2012 Dergachoff VA (2003) The global geo-Economics (transformation of the world economic space) Monograph.: ИПPЭЭИ of national Academy of Sciences, Odessa Malyarenko I (2010) The genius of the place of the supply chain Management consulting http://www.i-bakery.ru/ Accessed 21 Jan 2013 Routing and calculation of distances (2012) Toplogistic Prof http://www.toplogistic.ru/314.html Accessed 28 Nov 2012 The number of the population cities of Russia (2012)The Reference system, Russia http://www.ru.all.biz/guide-population Accessed 29 Dec 2012 Zaitsev EI (2011) Logistics and supply chain management CBM project: advanced information technology in logistics http://eiz.engec. ru/ Accessed June 2013 Zorin IV (2000) Axiological space as the subject area of pedagogy of tourism Theory and practice of physical culture no © Springer International Publishing Switzerland 2016 Herbert Kotzab, Jürgen Pannek and Klaus-Dieter Thoben (eds.), Dynamics in Logistics, Lecture Notes in Logistics, https://doi.org/10.1007/978-3-319-23512-7_72 The Usage of Social Media Text Data for the Demand Forecasting in the Fashion Industry Samaneh Beheshti-Kashi1, and Klaus-Dieter Thoben1 (1) BIBA-Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Hochschulring 20, 28359 Bremen, Germany (2) International Graduate School for Dynamics in Logistics (IGS), University of Bremen, Hochschulring 20, 28359 Bremen, Germany Samaneh Beheshti-Kashi (Corresponding author) Email: bek@biba.uni-bremen.de Klaus-Dieter Thoben Email: tho@biba.uni-bremen.de Abstract The fashion industry faces different challenges in the field of demand forecasting Factors such as long delivery times in contrast to short selling periods requires precise demand figures in order to place accurate production plans This paper presents firstly the idiosyncrasies of the fashion industry and shows current fashion forecasting approaches Then, the idea of applying social media text data within the demand forecasting process is presented by showing works of integrating user generated content in different application fields Following the research question on the predictive value of social media text data for the fashion industry, the research objective and the methodology are formulated in a last step Keywords Demand forecasting – Apparel industry – Social media – Communities Introduction and Problem Description The apparel industry often deals with stock out or overstocked inventories which result into high losses for companies Especially, this industry is characterized through high impulse purchases and, most buying decisions are made at the POS Therefore, the availability of a product is highly crucial for the companies’ success (Nenni et al 2013) While companies require accurate information about future demands, mostly this information is not present, since the demand is influenced by variant factors such as changing weather conditions, competition, holidays as well as the general economic situation (Thomassey 2010) In addition, fashion trends are very short and approximately 95 % of fashion items of a collection will be replaced in the following season Consequently, companies face a lack of historical sales data for future items (Thomassey 2014) Volatile consumer demands and high product varieties in color and sizes are additional idiosyncrasies (Christopher et al 2004) While most production plants are located in Asian countries such as China, Bangladesh or Taiwan, the target region for these products are european countries (Mostard et al 2011) Due to this fact time- to- market has been compared to the short selling period of a fashion product for a long time Therefore, production of successful products is rarely possible (Fissahn 2001) In order to be time efficient companies also fly the products to Europe, which is however, related to high costs (Hoyndorff et al 2010) Consequently, accurate forecasts are crucial since production decisions are often due before exact demand figures are known Due to the described factors and the lack of historical data, traditional forecasting methods are difficult to be applied and therefore, new approaches have to be considered Fashion Sales Forecasting and the Predictive Power of Online Chatter For forecasting of sales data statistical techniques such as exponential smoothing, ARIMA, Box and Jenkins model, regression models or Holt Winters model are often applied However, due to the idiosyncrasies of the fashion industry and requirement of historical data these methods can be hardly adopted by apparel companies (Thomassey 2014) Nevertheless, a large number of commercial software often applies these techniques for their predictions (Jain 2007), although most sales experts use these forecasts only as a baseline for their own estimations (Thomassey 2014) Recently, advanced forecasting methods such as extreme learning machine (ELM) algorithms have been introduced (Sun et al 2008) Wong and Guo (2010) base their model on the ELM and propose a hybrid intelligent model for mid-term forecasts for fashion retailer In the work of Au et al (2008) evolutionary neural networks (ENN) show promising results especially in the case of noisy data Other authors use further soft computing techniques such as fuzzy logic (Thomassey 2010) These works focus on the application of different techniques In contrast, Mostard et al (2011) show a different approach by considering pre-order demand information The present paper addresses the described challenges by integrating customers’ opinions in the forecasting process With the rise of the Web 2.0 and the emerging social media applications the ordinary user obtained a new role: He is an active and producing entity and not purely consuming For this role literature introduced the term producer (Bruns 2006) Especially fashion is a widely discussed topic in the communities and many fashion blogs publish different fashion related topics Kaplan and Haenlein (2010) define Social Media as group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Content Various authors have examined the relationship from online chatter to real world outcomes and the predictive power of such user generated content For instance, Asur and Huberman (2010) focused on movie box-office revenues and Twitter data and showed a strong correlation between the online data and the real future rank of a movie Dhar and Chang (2009) suggest that user generated content is a good indicator for future sales of online music sales However, they emphasize the consideration of also other influencing factors Further research focuses on exploring sentiments from Twitter data and examining potential correlations to the value of the Dow Jones Industrial Average (Bollen et al 2011) Likewise Twitter posts were used to investigate the platforms role in predicting the outcome of future elections (Tumasjan et al 2010) A further research stream is the usage of search keywords for prediction Google Flu trends estimates influenza distributions based on search keywords related to the topic influenza (Google 2014) Goel et al (2010) focus on entertainment goods and assume that consumers interested in a specific movie or game might also search for it They conclude that search-based predictions are domain specific and other domains should be considered in further research This paper intent to integrate both described research streams Research Objective The objective of the research is to examine the applicability of the integration of data, which is published online by ordinary user in the fashion demand forecasting process At the one hand social media applications have to be focused in order to analyze their relation to fashion products and to be able to identify factors, which are identifiers for future trends On the other hand sales data of fashion companies should be examined In addition, the current handling of fashion companies with social media applications and content will be examined After analyzing these different aspects and finding out effects and relationships between them, then a solution on how these data might be integrated in the demand forecasting process for fashion products will be derived Research Methodology Following the research question on the predictive value of social media text data for the fashion industry several perspectives have to be considered A corpus has to be generated from different social media applications, which will be done by the mean of web mining methods For the preprocessing step of the text, different text mining methods have to be applied In a following step, sentiment analysis and opinion mining methods will serve for analysis purposes These results will be the basis for examining correlations to real sales data A case study approach will serve as the main method for a requirement analysis based on fashion companies for an adequate integration of social media data in real life demand forecasting processes After reviewing the literature regarding fashion forecasting as well as the existing theories on the impact of social media on real world outcomes the different cases will be selected Expert interviews and online questionnaires will serve for the data collection and be the ground for analysis purposes References Asur S, Huberman BA (2010) Predicting the future with social media In: IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, vol 1, pp 492–499 Au KF, Choi TM, Yu Y (2008) Fashion retail forecasting by evolutionary neural networks Int J Prod Econ 114:615–630 [Crossref] Bollen J, Maoa H, Zengb X (2011) Twitter mood predicts the stock market J Comput Sci 2:2–8 [Crossref] Bruns A (2006) Towards produsage: futures for user-led content production In: Sudweeks F, Hrachovec H, Ess C (eds) Cultural attitudes towards communication and technology 2006, 28 June–1 July Tartu, Estonia Christopher M, Lowson R, Peck H (2004) Creating agile supply chains in the fashion industry Int J Retail Distrib Manage 32(8):367–376 [Crossref] Dhar V, & Chang EA (2009) Does chatter matter? The impact of user-generated content on music sales J Interact Mark 23(4):300– 307 http://linkinghub.elsevier.com/retrieve/pii/S1094996809000723 Fissahn J (2001) Marktorientierte Beschaffung in der Bekleidungsindustrie Dissertation, Münster University Goel S, Hofman JM, Lahaie S, Pennock DM, Watts DJ (2010) What can search predict Working paper Google (2014) Google Trends http://www.google.org/flutrends/about/how.html Accessed 10 Mar 2014 Hoyndorff K, Hülsmann S, Spee D, ten Hompel M (2010) Fashion logistics Grundlagen über Prozesse und IT entlang der Supply Chain, Munich, Huss-Verlag GmbH Jain CL (2007) Benchmarking forecasting software and systems J Bus Forecast Methods Syst 26(4):30–33 Kaplan AM, Haenlein M (2010) Users of the world, unite! the challenges and opportunities of social media Bus Horiz 53:59–68 [Crossref] Mostard J, Teunter R, de Koster R (2011) Forecasting demand for single period products: a case study in the apparel industry Eur J Oper Res 211:139–147 [Crossref] Nenni ME, Giustiniano L, Pirolo L (2013) Demand forecasting in the fashion industry: a review Int J Eng Bus Manage 5(37):1–6 Sun ZL, Choi TM, Au KF, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing Decis Support Syst 46(1):411–419 [Crossref] Thomassey S (2010) Sales forecasting in clothing industry: the key success factor of the supply chain management Int J Prod Econ 128(2):470–483 [Crossref] Thomassey S (2014) Sales forecasting in apparel and fashion industry: a review In: Choi TM, Hui CL, Yu Y (eds) Intelligent fashion forecasting systems Springer, Berlin Tumasjan A, Sprenger TO, Sandner FG, Welpe IM (2010) Predicting elections with twitter: what 140 characters reveal about political sentiment In: Proceedings of the fourth international AAAI conference on weblogs and social media, pp 178–185 Wong W, Guo ZX (2010) A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm Int J Prod Econ 128(2):614–624 [Crossref] ... Klaus-Dieter Thoben Dynamics in Logistics Proceedings of the 4th International Conference LDIC, 2014 Bremen, Germany 1st ed 2016 Editors Herbert Kotzab University of Bremen, Bremen, Germany Jürgen... research in logistics from a wide range of fields including engineering, business administration, computer science, and mathematics The LDIC 2014 proceedings consist of 72 papers including 10 young... from the almost free allocation in the years 2005– 2012 In contrast to the emissions trading scheme used in the U.S., the EU ETS offers the option of a decentralised influence and refinement of the