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Publisher: Springer-Verlag (Total: 2351 journals)

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Showing 1 - 200 of 2351 Journals sorted alphabetically
3D Printing in Medicine     Open Access   (Followers: 1)
3D Research     Hybrid Journal   (Followers: 21, SJR: 0.222, CiteScore: 1)
4OR: A Quarterly J. of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.825, CiteScore: 1)
AAPS J.     Hybrid Journal   (Followers: 22, SJR: 1.118, CiteScore: 4)
AAPS PharmSciTech     Hybrid Journal   (Followers: 7, SJR: 0.752, CiteScore: 3)
Abdominal Imaging     Hybrid Journal   (Followers: 15, SJR: 0.866, CiteScore: 2)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 4, SJR: 0.439, CiteScore: 0)
Academic Psychiatry     Full-text available via subscription   (Followers: 26, SJR: 0.53, CiteScore: 1)
Academic Questions     Hybrid Journal   (Followers: 8, SJR: 0.106, CiteScore: 0)
Accreditation and Quality Assurance: J. for Quality, Comparability and Reliability in Chemical Measurement     Hybrid Journal   (Followers: 27, SJR: 0.316, CiteScore: 1)
Acoustical Physics     Hybrid Journal   (Followers: 11, SJR: 0.359, CiteScore: 1)
Acoustics Australia     Hybrid Journal   (SJR: 0.232, CiteScore: 1)
Acta Analytica     Hybrid Journal   (Followers: 7, SJR: 0.367, CiteScore: 0)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1, SJR: 0.675, CiteScore: 1)
Acta Biotheoretica     Hybrid Journal   (Followers: 4, SJR: 0.284, CiteScore: 1)
Acta Diabetologica     Hybrid Journal   (Followers: 18, SJR: 1.587, CiteScore: 3)
Acta Endoscopica     Hybrid Journal   (Followers: 1)
acta ethologica     Hybrid Journal   (Followers: 4, SJR: 0.769, CiteScore: 1)
Acta Geochimica     Hybrid Journal   (Followers: 7, SJR: 0.24, CiteScore: 1)
Acta Geodaetica et Geophysica     Hybrid Journal   (Followers: 2, SJR: 0.305, CiteScore: 1)
Acta Geophysica     Hybrid Journal   (Followers: 10, SJR: 0.312, CiteScore: 1)
Acta Geotechnica     Hybrid Journal   (Followers: 7, SJR: 1.588, CiteScore: 3)
Acta Informatica     Hybrid Journal   (Followers: 5, SJR: 0.517, CiteScore: 1)
Acta Mathematica     Hybrid Journal   (Followers: 12, SJR: 7.066, CiteScore: 3)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2, SJR: 0.452, CiteScore: 1)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6, SJR: 0.379, CiteScore: 1)
Acta Mathematica Vietnamica     Hybrid Journal   (SJR: 0.27, CiteScore: 0)
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal   (SJR: 0.208, CiteScore: 0)
Acta Mechanica     Hybrid Journal   (Followers: 21, SJR: 1.04, CiteScore: 2)
Acta Mechanica Sinica     Hybrid Journal   (Followers: 5, SJR: 0.607, CiteScore: 2)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 7, SJR: 0.576, CiteScore: 2)
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.638, CiteScore: 1)
Acta Neurochirurgica     Hybrid Journal   (Followers: 6, SJR: 0.822, CiteScore: 2)
Acta Neurologica Belgica     Hybrid Journal   (Followers: 1, SJR: 0.376, CiteScore: 1)
Acta Neuropathologica     Hybrid Journal   (Followers: 4, SJR: 7.589, CiteScore: 12)
Acta Oceanologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.334, CiteScore: 1)
Acta Physiologiae Plantarum     Hybrid Journal   (Followers: 2, SJR: 0.574, CiteScore: 2)
Acta Politica     Hybrid Journal   (Followers: 15, SJR: 0.605, CiteScore: 1)
Activitas Nervosa Superior     Hybrid Journal   (SJR: 0.147, CiteScore: 0)
adhäsion KLEBEN & DICHTEN     Hybrid Journal   (Followers: 8, SJR: 0.103, CiteScore: 0)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 23, SJR: 0.72, CiteScore: 2)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 9)
Administration and Policy in Mental Health and Mental Health Services Research     Partially Free   (Followers: 16, SJR: 1.005, CiteScore: 2)
Adsorption     Hybrid Journal   (Followers: 4, SJR: 0.703, CiteScore: 2)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4, SJR: 0.698, CiteScore: 1)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 37, SJR: 0.956, CiteScore: 2)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19, SJR: 0.812, CiteScore: 1)
Advances in Contraception     Hybrid Journal   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 55, SJR: 1.09, CiteScore: 1)
Advances in Gerontology     Partially Free   (Followers: 8, SJR: 0.144, CiteScore: 0)
Advances in Health Sciences Education     Hybrid Journal   (Followers: 28, SJR: 1.64, CiteScore: 2)
Advances in Manufacturing     Hybrid Journal   (Followers: 4, SJR: 0.475, CiteScore: 2)
Advances in Polymer Science     Hybrid Journal   (Followers: 45, SJR: 1.04, CiteScore: 3)
Advances in Therapy     Hybrid Journal   (Followers: 5, SJR: 1.075, CiteScore: 3)
Aegean Review of the Law of the Sea and Maritime Law     Hybrid Journal   (Followers: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2, SJR: 0.517, CiteScore: 1)
Aerobiologia     Hybrid Journal   (Followers: 3, SJR: 0.673, CiteScore: 2)
Aesthetic Plastic Surgery     Hybrid Journal   (Followers: 9, SJR: 0.825, CiteScore: 1)
African Archaeological Review     Hybrid Journal   (Followers: 17, SJR: 0.862, CiteScore: 1)
Afrika Matematika     Hybrid Journal   (Followers: 1, SJR: 0.235, CiteScore: 0)
AGE     Hybrid Journal   (Followers: 7)
Ageing Intl.     Hybrid Journal   (Followers: 7, SJR: 0.39, CiteScore: 1)
Aggiornamenti CIO     Hybrid Journal   (Followers: 1)
Aging Clinical and Experimental Research     Hybrid Journal   (Followers: 3, SJR: 0.67, CiteScore: 2)
Agricultural Research     Hybrid Journal   (Followers: 6, SJR: 0.276, CiteScore: 1)
Agriculture and Human Values     Hybrid Journal   (Followers: 14, SJR: 1.173, CiteScore: 3)
Agroforestry Systems     Hybrid Journal   (Followers: 20, SJR: 0.663, CiteScore: 1)
Agronomy for Sustainable Development     Hybrid Journal   (Followers: 12, SJR: 1.864, CiteScore: 6)
AI & Society     Hybrid Journal   (Followers: 8, SJR: 0.227, CiteScore: 1)
AIDS and Behavior     Hybrid Journal   (Followers: 14, SJR: 1.792, CiteScore: 3)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 4, SJR: 0.862, CiteScore: 3)
Akupunktur & Aurikulomedizin     Full-text available via subscription   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 6, SJR: 0.531, CiteScore: 0)
Algebra Universalis     Hybrid Journal   (Followers: 2, SJR: 0.583, CiteScore: 1)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1, SJR: 1.095, CiteScore: 1)
Algorithmica     Hybrid Journal   (Followers: 9, SJR: 0.56, CiteScore: 1)
Allergo J.     Full-text available via subscription   (Followers: 1, SJR: 0.234, CiteScore: 0)
Allergo J. Intl.     Hybrid Journal   (Followers: 2)
Alpine Botany     Hybrid Journal   (Followers: 5, SJR: 1.11, CiteScore: 3)
ALTEX : Alternatives to Animal Experimentation     Open Access   (Followers: 3)
AMBIO     Hybrid Journal   (Followers: 10, SJR: 1.569, CiteScore: 4)
American J. of Cardiovascular Drugs     Hybrid Journal   (Followers: 16, SJR: 0.951, CiteScore: 3)
American J. of Community Psychology     Hybrid Journal   (Followers: 29, SJR: 1.329, CiteScore: 2)
American J. of Criminal Justice     Hybrid Journal   (Followers: 8, SJR: 0.772, CiteScore: 1)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 16, SJR: 0.46, CiteScore: 1)
American J. of Dance Therapy     Hybrid Journal   (Followers: 4, SJR: 0.181, CiteScore: 0)
American J. of Potato Research     Hybrid Journal   (Followers: 2, SJR: 0.611, CiteScore: 1)
American J. of Psychoanalysis     Hybrid Journal   (Followers: 21, SJR: 0.314, CiteScore: 0)
American Sociologist     Hybrid Journal   (Followers: 12, SJR: 0.35, CiteScore: 0)
Amino Acids     Hybrid Journal   (Followers: 8, SJR: 1.135, CiteScore: 3)
AMS Review     Partially Free   (Followers: 4)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7, SJR: 0.211, CiteScore: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 5, SJR: 0.536, CiteScore: 1)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Analysis of Verbal Behavior     Hybrid Journal   (Followers: 5)
Analytical and Bioanalytical Chemistry     Hybrid Journal   (Followers: 32, SJR: 0.978, CiteScore: 3)
Anatomical Science Intl.     Hybrid Journal   (Followers: 3, SJR: 0.367, CiteScore: 1)
Angewandte Schmerztherapie und Palliativmedizin     Hybrid Journal  
Angiogenesis     Hybrid Journal   (Followers: 3, SJR: 2.177, CiteScore: 5)
Animal Cognition     Hybrid Journal   (Followers: 19, SJR: 1.389, CiteScore: 3)
Annales françaises de médecine d'urgence     Hybrid Journal   (Followers: 1, SJR: 0.192, CiteScore: 0)
Annales Henri Poincaré     Hybrid Journal   (Followers: 3, SJR: 1.097, CiteScore: 2)
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4, SJR: 0.438, CiteScore: 0)
Annali dell'Universita di Ferrara     Hybrid Journal   (SJR: 0.429, CiteScore: 0)
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1, SJR: 1.197, CiteScore: 1)
Annals of Biomedical Engineering     Hybrid Journal   (Followers: 18, SJR: 1.042, CiteScore: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 4, SJR: 0.932, CiteScore: 1)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Dyslexia     Hybrid Journal   (Followers: 10, SJR: 0.85, CiteScore: 2)
Annals of Finance     Hybrid Journal   (Followers: 30, SJR: 0.579, CiteScore: 1)
Annals of Forest Science     Hybrid Journal   (Followers: 7, SJR: 0.986, CiteScore: 2)
Annals of Global Analysis and Geometry     Hybrid Journal   (Followers: 1, SJR: 1.228, CiteScore: 1)
Annals of Hematology     Hybrid Journal   (Followers: 15, SJR: 1.043, CiteScore: 2)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 13, SJR: 0.413, CiteScore: 1)
Annals of Microbiology     Hybrid Journal   (Followers: 11, SJR: 0.479, CiteScore: 2)
Annals of Nuclear Medicine     Hybrid Journal   (Followers: 4, SJR: 0.687, CiteScore: 2)
Annals of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.943, CiteScore: 2)
Annals of Ophthalmology     Hybrid Journal   (Followers: 12)
Annals of Regional Science     Hybrid Journal   (Followers: 7, SJR: 0.614, CiteScore: 1)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of Solid and Structural Mechanics     Hybrid Journal   (Followers: 9, SJR: 0.239, CiteScore: 1)
Annals of Surgical Oncology     Hybrid Journal   (Followers: 13, SJR: 1.986, CiteScore: 4)
Annals of Telecommunications     Hybrid Journal   (Followers: 9, SJR: 0.223, CiteScore: 1)
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1, SJR: 1.495, CiteScore: 1)
Antonie van Leeuwenhoek     Hybrid Journal   (Followers: 5, SJR: 0.834, CiteScore: 2)
Apidologie     Hybrid Journal   (Followers: 4, SJR: 1.22, CiteScore: 3)
APOPTOSIS     Hybrid Journal   (Followers: 8, SJR: 1.424, CiteScore: 4)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2, SJR: 0.294, CiteScore: 1)
Applications of Mathematics     Hybrid Journal   (Followers: 2, SJR: 0.602, CiteScore: 1)
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 43, SJR: 0.571, CiteScore: 2)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 17, SJR: 0.21, CiteScore: 1)
Applied Cancer Research     Open Access  
Applied Categorical Structures     Hybrid Journal   (Followers: 2, SJR: 0.49, CiteScore: 0)
Applied Composite Materials     Hybrid Journal   (Followers: 49, SJR: 0.58, CiteScore: 2)
Applied Entomology and Zoology     Partially Free   (Followers: 4, SJR: 0.422, CiteScore: 1)
Applied Geomatics     Hybrid Journal   (Followers: 3, SJR: 0.733, CiteScore: 3)
Applied Geophysics     Hybrid Journal   (Followers: 8, SJR: 0.488, CiteScore: 1)
Applied Intelligence     Hybrid Journal   (Followers: 12, SJR: 0.6, CiteScore: 2)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4, SJR: 0.319, CiteScore: 1)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 9, SJR: 0.886, CiteScore: 1)
Applied Mathematics - A J. of Chinese Universities     Hybrid Journal   (SJR: 0.17, CiteScore: 0)
Applied Mathematics and Mechanics     Hybrid Journal   (Followers: 5, SJR: 0.461, CiteScore: 1)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 65, SJR: 1.182, CiteScore: 4)
Applied Physics A     Hybrid Journal   (Followers: 10, SJR: 0.481, CiteScore: 2)
Applied Physics B: Lasers and Optics     Hybrid Journal   (Followers: 24, SJR: 0.74, CiteScore: 2)
Applied Psychophysiology and Biofeedback     Hybrid Journal   (Followers: 8, SJR: 0.519, CiteScore: 2)
Applied Research in Quality of Life     Hybrid Journal   (Followers: 12, SJR: 0.316, CiteScore: 1)
Applied Solar Energy     Hybrid Journal   (Followers: 18, SJR: 0.225, CiteScore: 0)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5, SJR: 0.542, CiteScore: 1)
Aquaculture Intl.     Hybrid Journal   (Followers: 24, SJR: 0.591, CiteScore: 2)
Aquarium Sciences and Conservation     Hybrid Journal   (Followers: 2)
Aquatic Ecology     Hybrid Journal   (Followers: 34, SJR: 0.656, CiteScore: 2)
Aquatic Geochemistry     Hybrid Journal   (Followers: 4, SJR: 0.591, CiteScore: 1)
Aquatic Sciences     Hybrid Journal   (Followers: 13, SJR: 1.109, CiteScore: 3)
Arabian J. for Science and Engineering     Hybrid Journal   (Followers: 5, SJR: 0.303, CiteScore: 1)
Arabian J. of Geosciences     Hybrid Journal   (Followers: 2, SJR: 0.319, CiteScore: 1)
Archaeological and Anthropological Sciences     Hybrid Journal   (Followers: 21, SJR: 1.052, CiteScore: 2)
Archaeologies     Hybrid Journal   (Followers: 12, SJR: 0.224, CiteScore: 0)
Archiv der Mathematik     Hybrid Journal   (Followers: 1, SJR: 0.725, CiteScore: 1)
Archival Science     Hybrid Journal   (Followers: 62, SJR: 0.745, CiteScore: 2)
Archive for History of Exact Sciences     Hybrid Journal   (Followers: 7, SJR: 0.186, CiteScore: 1)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3, SJR: 0.909, CiteScore: 1)
Archive for Rational Mechanics and Analysis     Hybrid Journal   (SJR: 3.93, CiteScore: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5, SJR: 0.79, CiteScore: 2)
Archives and Museum Informatics     Hybrid Journal   (Followers: 144, SJR: 0.101, CiteScore: 0)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5, SJR: 1.41, CiteScore: 5)
Archives of Dermatological Research     Hybrid Journal   (Followers: 7, SJR: 1.006, CiteScore: 2)
Archives of Environmental Contamination and Toxicology     Hybrid Journal   (Followers: 14, SJR: 0.773, CiteScore: 2)
Archives of Gynecology and Obstetrics     Hybrid Journal   (Followers: 16, SJR: 0.956, CiteScore: 2)
Archives of Microbiology     Hybrid Journal   (Followers: 8, SJR: 0.644, CiteScore: 2)
Archives of Orthopaedic and Trauma Surgery     Hybrid Journal   (Followers: 8, SJR: 1.146, CiteScore: 2)
Archives of Osteoporosis     Hybrid Journal   (Followers: 2, SJR: 0.71, CiteScore: 2)
Archives of Sexual Behavior     Hybrid Journal   (Followers: 10, SJR: 1.493, CiteScore: 3)
Archives of Toxicology     Hybrid Journal   (Followers: 17, SJR: 1.541, CiteScore: 5)
Archives of Virology     Hybrid Journal   (Followers: 5, SJR: 0.973, CiteScore: 2)
Archives of Women's Mental Health     Hybrid Journal   (Followers: 14, SJR: 1.274, CiteScore: 3)
Archivio di Ortopedia e Reumatologia     Hybrid Journal  
Archivum Immunologiae et Therapiae Experimentalis     Hybrid Journal   (Followers: 2, SJR: 0.946, CiteScore: 3)
ArgoSpine News & J.     Hybrid Journal  
Argumentation     Hybrid Journal   (Followers: 6, SJR: 0.349, CiteScore: 1)
Arid Ecosystems     Hybrid Journal   (Followers: 2, SJR: 0.2, CiteScore: 0)
Arkiv för Matematik     Hybrid Journal   (Followers: 1, SJR: 0.766, CiteScore: 1)
Arnold Mathematical J.     Hybrid Journal   (Followers: 1, SJR: 0.355, CiteScore: 0)
Arthropod-Plant Interactions     Hybrid Journal   (Followers: 2, SJR: 0.839, CiteScore: 2)
Arthroskopie     Hybrid Journal   (Followers: 1, SJR: 0.131, CiteScore: 0)
Artificial Intelligence and Law     Hybrid Journal   (Followers: 11, SJR: 0.937, CiteScore: 2)
Artificial Intelligence Review     Hybrid Journal   (Followers: 16, SJR: 0.833, CiteScore: 4)
Artificial Life and Robotics     Hybrid Journal   (Followers: 9, SJR: 0.226, CiteScore: 0)
Asia Europe J.     Hybrid Journal   (Followers: 5, SJR: 0.504, CiteScore: 1)
Asia Pacific Education Review     Hybrid Journal   (Followers: 12, SJR: 0.479, CiteScore: 1)
Asia Pacific J. of Management     Hybrid Journal   (Followers: 16, SJR: 1.185, CiteScore: 2)
Asia-Pacific Education Researcher     Hybrid Journal   (Followers: 12, SJR: 0.353, CiteScore: 1)
Asia-Pacific Financial Markets     Hybrid Journal   (Followers: 2, SJR: 0.187, CiteScore: 0)
Asia-Pacific J. of Atmospheric Sciences     Hybrid Journal   (Followers: 19, SJR: 0.855, CiteScore: 1)
Asian Business & Management     Hybrid Journal   (Followers: 9, SJR: 0.378, CiteScore: 1)
Asian J. of Business Ethics     Hybrid Journal   (Followers: 9)
Asian J. of Criminology     Hybrid Journal   (Followers: 6, SJR: 0.543, CiteScore: 1)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 3, SJR: 0.548, CiteScore: 1)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5, SJR: 0.183, CiteScore: 0)
ästhetische dermatologie & kosmetologie     Full-text available via subscription  

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Journal Cover
Annals of Operations Research
Journal Prestige (SJR): 0.943
Citation Impact (citeScore): 2
Number of Followers: 10  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1572-9338 - ISSN (Online) 0254-5330
Published by Springer-Verlag Homepage  [2351 journals]
  • Big data analytics in operations and supply chain management
    • Authors: Samuel Fosso Wamba; Angappa Gunasekaran; Rameshwar Dubey; Eric W. T. Ngai
      Pages: 1 - 4
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-018-3024-7
      Issue No: Vol. 270, No. 1-2 (2018)
  • A cloud based job sequencing with sequence-dependent setup for sheet metal
    • Authors: Yashar Ahmadov; Petri Helo
      Pages: 5 - 24
      Abstract: This paper presents a prototype system of sheet metal processing machinery which collects production order data, passes current information to cloud based centralized job scheduling for setup time reduction and updates the production calendar accordingly. A centralized cloud service can collect and analyse production order data for machines and suggest optimized schedules. This paper explores the application of sequencing algorithms in the sheet metal forming industry, which faces sequence-dependent changeover times on single machine systems. We analyse the effectiveness of using such algorithms in the reduction of total setup times. We describe alternative models: Clustering, Nearest Neighbourhood and Travelling Salesman Problem, and then apply them to real data obtained from a manufacturing company, as well as to randomly generated data sets. Based on the prototype implementation clustering algorithm was proposed for actual implementation. Sequence-dependency increases the complexity of the scheduling problems; thus, effective approaches are required to solve them. The algorithms proposed in this paper provide efficient solutions to these types of sequencing problems.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2304-3
      Issue No: Vol. 270, No. 1-2 (2018)
  • Big data initiatives in retail environments: Linking service process
           perceptions to shopping outcomes
    • Authors: John A. Aloysius; Hartmut Hoehle; Soheil Goodarzi; Viswanath Venkatesh
      Pages: 25 - 51
      Abstract: Given the enormous amount of data created through customers’ transactions in retail stores, it comes as no surprise that retailers are actively seeking initiatives to leverage big data and offer their customers superior services that provide mutual, previously unattainable benefits. Nonetheless, fulfilment of such a strategic aim requires customers to adopt and embrace emerging technology-driven services. Exploring customers’ perceptions of such big data initiatives in retail environments, we develop a model examining the effects of technology enablers and privacy concerns on critical shopping outcomes including repatronage intentions, store image, and intention to use medium in the context of recently identified service configurations. We conduct an exploratory study to understand customers’ reactions toward emerging shopping scenarios and to enhance our survey instrument and then conduct an online survey (n = 442) to test our model. We found that customers’ usefulness perceptions of emerging services positively affected their intentions to use medium, and that their privacy concerns about the amounts of personal information, being collected through emerging services, negatively affected their repatronage intentions and store image. We discuss the implications of our work for research and practice.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2276-3
      Issue No: Vol. 270, No. 1-2 (2018)
  • Data laboratory for supply chain response models during epidemic outbreaks
    • Authors: Azrah A. Anparasan; Miguel A. Lejeune
      Pages: 53 - 64
      Abstract: Disasters in developing countries tremendously affect the economy and long-term development. Recent years have seen an increase in epidemic outbreaks in countries like Haiti and in West Africa. However, there seems to be a lack of decision support to address epidemic outbreak challenges in developing countries compared to their developed counterparts. The lack of data to implement such models is a potential reason. This paper presents a data set that will permit to develop data-driven allocation models and policies for an epidemic outbreak in a developing country. The data set is for the cholera epidemic that occurred in the aftermath of the 2010 earthquake in Haiti. The detailed time-series patient data is intended to facilitate the development and evaluation of multi-period supply chain models that support emergency health response, allocate medical resources and staff, and design coordination mechanisms among humanitarian stakeholders. We also provide a simple model to illustrate how the data can be utilized to develop a basic epidemic outbreak response model. The data set will be made available online for researchers interested in developing models in this field.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-017-2462-y
      Issue No: Vol. 270, No. 1-2 (2018)
  • A framework for investigating optimization of service parts performance
           with big data
    • Authors: Christopher A. Boone; Benjamin T. Hazen; Joseph B. Skipper; Robert E. Overstreet
      Pages: 65 - 74
      Abstract: As national economies continue to evolve across the globe, businesses are increasing their capacity to not only generate new products and deliver them to customers, but also to increase levels of after-sales service. One major component of after-sale service involves service parts management. However, service parts businesses are typically seen as add-ons to existing business models, and are not well integrated with primary businesses. Consequently, many service parts operations are managed using ad-hoc practices that are often subordinated to primary businesses. Early research in this area has been instrumental in assisting organizations to begin optimizing some aspects of service parts management. However, performance goals for service parts management are often ill-defined. Further, because these service parts businesses are often subordinated to primary businesses within a firm, the use of newer big data applications to help manage these processes is almost completely absent. Herein, we develop a framework that seeks to define service parts performance goals for the purpose of outlining where scholars and practitioners can further examine where, how, and why big data applications can be employed to enhance service parts management performance.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2314-1
      Issue No: Vol. 270, No. 1-2 (2018)
  • Big data-driven fuzzy cognitive map for prioritising IT service
           procurement in the public sector
    • Authors: Youngseok Choi; Habin Lee; Zahir Irani
      Pages: 75 - 104
      Abstract: The prevalence of big data is starting to spread across the public and private sectors however, an impediment to its widespread adoption orientates around a lack of appropriate big data analytics (BDA) and resulting skills to exploit the full potential of big data availability. In this paper, we propose a novel BDA to contribute towards this void, using a fuzzy cognitive map (FCM) approach that will enhance decision-making thus prioritising IT service procurement in the public sector. This is achieved through the development of decision models that capture the strengths of both data analytics and the established intuitive qualitative approach. By taking advantages of both data analytics and FCM, the proposed approach captures the strength of data-driven decision-making and intuitive model-driven decision modelling. This approach is then validated through a decision-making case regarding IT service procurement in public sector, which is the fundamental step of IT infrastructure supply for publics in a regional government in the Russia federation. The analysis result for the given decision-making problem is then evaluated by decision makers and e-government expertise to confirm the applicability of the proposed BDA. In doing so, demonstrating the value of this approach in contributing towards robust public decision-making regarding IT service procurement.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2281-6
      Issue No: Vol. 270, No. 1-2 (2018)
  • An SBM-DEA model with parallel computing design for environmental
           efficiency evaluation in the big data context: a transportation system
    • Authors: Jun-Fei Chu; Jie Wu; Ma-Lin Song
      Pages: 105 - 124
      Abstract: In the big data context, decision makers usually face the problem of evaluating environmental efficiencies of a massive number of decision making units (DMUs) using the data envelopment analysis (DEA) method. However, standard implementations of the traditional DEA calculation process will consume much time when the data set is very large. To eliminate this limitation of DEA applied to big data, firstly, the slacks-based measure (SBM) model is extended considering undesirable outputs and the variable returns to scale (VRS) assumption for environmental efficiency evaluation of the DMUs. Then, an approach comprised of two algorithms is proposed for environmental efficiency evaluation when the number of DMUs is massive. The set of DMUs is partitioned into subsets, a technique which facilitates the application of a parallel computing mechanism. Algorithm 1 can be used for identifying the environment efficient DMUs in any DMU set. Further, Algorithm 2 (a parallel computing algorithm) shows how to use the proposed model and Algorithm 1 in parallel to find the environmental efficiencies of all DMUs. A simulation shows that the parallel computing design helps to significantly reduce calculation time when completing environmental efficiency evaluation tasks with large data sets, compared with using the traditional calculation processes. Finally, the proposed approach is applied to do environmental efficiency analysis of transportation systems.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2264-7
      Issue No: Vol. 270, No. 1-2 (2018)
  • Sell to whom' Firm’s green production in competition facing
           market segmentation
    • Authors: Shaofu Du; Wenzhi Tang; Jiajia Zhao; Tengfei Nie
      Pages: 125 - 154
      Abstract: When substantial numbers of consumers claim to be “green”, firms face the choice of whether to develop green products which are more environmental than their traditional counterparts. In many cases, consumers may differ in their willingness-to-pay for the green products that firms should determine which segment to sell the green products to. This paper examines the role of costs, consumer’s green segmentation, and competition in firm’s green production decisions. We find that the cost conditions for green production is relaxed in competition cases compared with the monopolist case. Under competition, the traditional firm would possibly to defend his market share via decreasing the traditional product’s price, which leading to an equilibrium that green products are sold to green segment solely. And we show that in some cases, both traditional and green firms can benefit from a large green segment ratio and consumer’s premium differentiation.Big data contains huge value through which we can better understand consumers. Based on big data technologies development, consumers can be accurate segmented with improved indexes of green premium and segment ratio, thus these conclusions can provide guidance to green production in practice.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2291-4
      Issue No: Vol. 270, No. 1-2 (2018)
  • Pricing and bargaining strategy of e-retail under hybrid operational
    • Authors: Shuihua Han; Yufang Fu; Bin Cao; Zongwei Luo
      Pages: 179 - 200
      Abstract: Dual-channel, as a significant retail strategy, has got more and more attention for academia and industry. While most literature focus on the conflicts between traditional channel and online channel, there are few works consider the conflicts of online retail channels. This paper focuses on the pricing and bargaining strategy of manufacturer and e-retailer under hybrid operational patterns which are adopted by e-commerce platforms. The operational patterns are divided into two types: other-organization e-pattern, such as Amazon, and self-organization e-pattern, such as Alibaba. We consider the commission charge which is collected by self-organization e-platform; and the analysis reveals that a fixed commission only has an effect on the total profit of manufacturer, but a variable commission would influence the wholesale price of other-organization e-platform and e-retail prices of both e-platforms, respectively. The results also suggest that, the wholesale price and the e-retail price are both affected by the service quality and this effect is also influenced by the variable commission. In addition, we also discuss the possibility of the manufacturer and e-retailer adjust their pricing strategy based on big data implementation.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2214-4
      Issue No: Vol. 270, No. 1-2 (2018)
  • Back in business: operations research in support of big data analytics for
           operations and supply chain management
    • Authors: Benjamin T. Hazen; Joseph B. Skipper; Christopher A. Boone; Raymond R. Hill
      Pages: 201 - 211
      Abstract: Few topics have generated more discourse in recent years than big data analytics. Given their knowledge of analytical and mathematical methods, operations research (OR) scholars would seem well poised to take a lead role in this discussion. Unfortunately, some have suggested there is a misalignment between the work of OR scholars and the needs of practicing managers, especially those in the field of operations and supply chain management where data-driven decision-making is a key component of most job descriptions. In this paper, we attempt to address this misalignment. We examine both applied and scholarly applications of OR-based big data analytical tools and techniques within an operations and supply chain management context to highlight their future potential in this domain. This paper contributes by providing suggestions for scholars, educators, and practitioners that aid to illustrate how OR can be instrumental in solving big data analytics problems in support of operations and supply chain management.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2226-0
      Issue No: Vol. 270, No. 1-2 (2018)
  • Investment decision-making and coordination of a three-stage supply chain
           considering Data Company in the Big Data era
    • Authors: Pan Liu; Shu-ping Yi
      Pages: 255 - 271
      Abstract: In the Big Data era, Data Company as the Big Data information (BDI) supplier should be included in a supply chain. To research the investment decision-making problems of BDI and its effects on supply chain coordination, a three-stage supply chain with one manufacturer, one retailer, and one Data Company was chosen. Meanwhile, considering the manufacturer contained the internal BDI and the external BDI, four benefit models about BDI investment were proposed and analyzed in decentralized and centralized supply chains. Meanwhile, a revenue sharing contract was used to coordinate the decentralized supply chain after investing in BDI. Findings: (1) the Big Data investment threshold of the Data Company was determined by the cost improvement coefficient, meanwhile, Data Company’s benefit was influenced by the consumer preference information conversion coefficient. (2) Whether the manufacturer was suitable to invest in BDI, it was influenced by the cost improvement coefficient. (3) When revenue sharing coefficient could meet a certain range, the revenue sharing contract could make the supply chain coordinate. Moreover, the benefits of supply chain members were same after coordination.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-018-2783-5
      Issue No: Vol. 270, No. 1-2 (2018)
  • Industry 4.0 and the circular economy: a proposed research agenda and
           original roadmap for sustainable operations
    • Authors: Ana Beatriz Lopes de Sousa Jabbour; Charbel Jose Chiappetta Jabbour; Moacir Godinho Filho; David Roubaud
      Pages: 273 - 286
      Abstract: This work makes a case for the integration of the increasingly popular and largely separate topics of Industry 4.0 and the circular economy (CE). The paper extends the state-of-the-art literature by proposing a pioneering roadmap to enhance the application of CE principles in organisations by means of Industry 4.0 approaches. Advanced and digital manufacturing technologies are able to unlock the circularity of resources within supply chains; however, the connection between CE and Industry 4.0 has not so far been explored. This article therefore contributes to the literature by unveiling how different Industry 4.0 technologies could underpin CE strategies, and to organisations by addressing those technologies as a basis for sustainable operations management decision-making. The main results of this work are: (a) a discussion on the mutually beneficial relationship between Industry 4.0 and the CE; (b) an in-depth understanding of the potential contributions of smart production technologies to the ReSOLVE model of CE business models; (c) a research agenda for future studies on the integration between Industry 4.0 and CE principles based on the most relevant management theories.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-018-2772-8
      Issue No: Vol. 270, No. 1-2 (2018)
  • Data mining and predictive analytics applications for the delivery of
           healthcare services: a systematic literature review
    • Authors: M. M. Malik; S. Abdallah; M. Ala’raj
      Pages: 287 - 312
      Abstract: With the widespread use of healthcare information systems commonly known as electronic health records, there is significant scope for improving the way healthcare is delivered by resorting to the power of big data. This has made data mining and predictive analytics an important tool for healthcare decision making. The literature has reported attempts for knowledge discovery from the big data to improve the delivery of healthcare services, however, there appears no attempt for assessing and synthesizing the available information on how the big data phenomenon has contributed to better outcomes for the delivery of healthcare services. This paper aims to achieve this by systematically reviewing the existing body of knowledge to categorize and evaluate the reported studies on healthcare operations and data mining frameworks. The outcome of this study is useful as a reference for the practitioners and as a research platform for the academia.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2393-z
      Issue No: Vol. 270, No. 1-2 (2018)
  • Use of twitter data for waste minimisation in beef supply chain
    • Authors: Nishikant Mishra; Akshit Singh
      Pages: 337 - 359
      Abstract: Approximately one third of the food produced is discarded or lost, which accounts for 1.3 billion tons per annum. The waste is being generated throughout the supply chain viz. farmers, wholesalers/processors, logistics, retailers and consumers. The majority of waste occurs at the interface of retailers and consumers. Many global retailers are making efforts to extract intelligence from customer’s complaints left at retail store to backtrack their supply chain to mitigate the waste. However, majority of the customers don’t leave the complaints in the store because of various reasons like inconvenience, lack of time, distance, ignorance etc. In current digital world, consumers are active on social media and express their sentiments, thoughts, and opinions about a particular product freely. For example, on an average, 45,000 tweets are tweeted daily related to beef products to express their likes and dislikes. These tweets are large in volume, scattered and unstructured in nature. In this study, twitter data is utilised to develop waste minimization strategies by backtracking the supply chain. The execution process of proposed framework is demonstrated for beef supply chain. The proposed model is generic enough and can be applied to other domains as well.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2303-4
      Issue No: Vol. 270, No. 1-2 (2018)
  • Creating a marketing strategy in healthcare industry: a holistic data
           analytic approach
    • Authors: Asil Oztekin
      Pages: 361 - 382
      Abstract: This study aims to assist marketing managers in identifying locations in which to host peer-to-peer educational events for healthcare professionals (HCPs) throughout the country using data analytics. These events would allow physicians and other HCPs to engage with their peers and learn about the most up-to-date clinical data and research from worldwide known Key Opinion Leaders. Decision making power in the healthcare industry is beginning to grow and fragment into numerous drivers. There are increasingly more variables, which affect marketing initiatives, and hence marketing managers are challenged to find the right methodology to place large investments and resources in the correct market segment. 3400 observations were collected from several sources including: The National Institute of Infant Nutrition monthly survey, Nielsen Consumer Behavior Data Reports, Congressional Budget Office Core Based Statistical Areas, US Census 2010 SF2 File, ZCTA Population and account information from the sales force. There were 17 input variables considered in this current analysis. The variables included; Return on Investment rank, total dollars of distribution margin, hospital influence rate, mother’s decision rate, healthcare professional decision rate, total investment, and competitive market share. The results from the data analytic models indicate that the most accurate classifier was the support vector machines followed by artificial neural networks and decision trees respectively. Marketing managers can flexibly utilize the proposed data analytic methodology proposed here to assist in identifying their target market. With the deployment of data analytics, marketing managers may now begin to sort through the large and complex data they gather and enhance their analyses of key target markets.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-017-2493-4
      Issue No: Vol. 270, No. 1-2 (2018)
  • Big data in humanitarian supply chain networks: a resource dependence
    • Authors: Sameer Prasad; Rimi Zakaria; Nezih Altay
      Pages: 383 - 413
      Abstract: Humanitarian operations in developing world settings present a particularly rich opportunity for examining the use of big data analytics. Focal non-governmental organizations (NGOs) often synchronize the delivery of services in a supply chain fashion by aligning recipient community needs with resources from various stakeholders (nodes). In this research, we develop a resource dependence model connecting big data analytics to superior humanitarian outcomes by means of a case study (qualitative) of twelve humanitarian value streams. Specifically, we identify the nodes in the network that can exert power on the focal NGOs based upon the respective resources being provided to ensure that sufficient big data is being created. In addition, we are able to identify how the type of data attribute, i.e., volume, velocity, veracity, value, and variety, relates to different forms of humanitarian interventions (e.g., education, healthcare, land reform, disaster relief, etc.). Finally, we identify how the various types of data attributes affect humanitarian outcomes in terms of deliverables, lead-times, cost, and propagation. This research presents evidence of important linkages between the developmental body of knowledge and that of resource dependence theory (RDT) and big data analytics. In addition, we are able to generalize RDT assumptions from the multi-tiered supply chains to distributed networks. The prescriptive nature of the findings can be used by donor agencies and focal NGOs to design interventions and collect the necessary data to facilitate superior humanitarian outcomes.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2280-7
      Issue No: Vol. 270, No. 1-2 (2018)
  • Cold chain configuration design: location-allocation decision-making using
           coordination, value deterioration, and big data approximation
    • Authors: Adarsh Kumar Singh; Nachiappan Subramanian; Kulwant Singh Pawar; Ruibin Bai
      Pages: 433 - 457
      Abstract: The study proposes a cold chain location-allocation configuration decision model for shippers and customers by considering value deterioration and coordination by using big data approximation. Value deterioration is assessed in terms of limited shelf life, opportunity cost, and units of product transportation. In this study, a customer can be defined as a member of any cold chain, such as cold warehouse stores, retailers, and last mile service providers. Each customer only manages products that are in a certain stage of the product life cycle, which is referred to as the expected shelf life. Because of the geographical dispersion of customers and their unpredictable demands as well as the varying shelf life of products, complexity is another challenge in a cold chain. Improved coordination between shippers and customers is expected to reduce this complexity, and this is introduced in the model as a longitudinal factor for service distance requirement. We use big data information that reflects geospatial attributes of location to derive the real feasible distance between shippers and customers. We formulate the cold chain location-allocation decision problem as a mixed integer linear programming problem, which is solved using the CPLEX solver. The proposed decision model increases efficiency, adequately equates supply and demand, and reduces wastage. Our study encourages managers to ship full truck load consignments, to be aware of uneven allocation based on proximity, and to supervise heterogeneous product allocation according to storage requirements.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2332-z
      Issue No: Vol. 270, No. 1-2 (2018)
  • Environmental performance evaluation with big data: theories and methods
    • Authors: Ma-Lin Song; Ron Fisher; Jian-Lin Wang; Lian-Biao Cui
      Pages: 459 - 472
      Abstract: Traditional theories and methods for comprehensive environmental performance evaluation are challenged by the appearance of big data because of its large quantity, high velocity, and high diversity, even though big data is defective in accuracy and stability. In this paper, we first review the literature on environmental performance evaluation, including evaluation theories, the methods of data envelopment analysis, and the technologies and applications of life cycle assessment and the ecological footprint. Then, we present the theories and technologies regarding big data and the opportunities and applications for these in related areas, followed by a discussion on problems and challenges. The latest advances in environmental management based on big data technologies are summarized. Finally, conclusions are put forward that the feasibility, reliability, and stability of existing theories and methodologies should be thoroughly validated before they can be successfully applied to evaluate environmental performance in practice and provide scientific basis and guidance to formulate environmental protection policies.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2158-8
      Issue No: Vol. 270, No. 1-2 (2018)
  • How to check correctness of total interpretive structural models'
    • Authors: Sushil
      Pages: 473 - 487
      Abstract: Interpretive structural modelling (ISM) has been further interpreted in the form of total interpretive structural modelling (TISM). These are graphical models that represent the hierarchical relationships and help in better and precise conceptualization and theory building. ISM only interprets the nodes in a digraph, but TISM interprets both nodes and links. The errors observed in applications of ISM and TISM reported in past have acted as motivation for this paper to provide checks and guidelines for correctness of total interpretive structural models. The paper first gives an overview of past applications of TISM. The process of TISM is first outlined and then the guidelines and thumb rules are provided to check the correctness of TISM at each step. Some typical errors in TISM models and their modifications are discussed to help future modellers to translate their ill-structured mental models into sound theoretical models. A discussion on usefulness of TISM for big data analytics for theory building is provided and future directions of research are outlined.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2312-3
      Issue No: Vol. 270, No. 1-2 (2018)
  • Integrating big data analytic and hybrid firefly-chaotic simulated
           annealing approach for facility layout problem
    • Authors: Akash Tayal; Surya Prakash Singh
      Pages: 489 - 514
      Abstract: Manufacturing industries have become larger, diverse and the factors affecting a facility layout design have grown rapidly. Handling and evaluating these large set of criteria (factors) is difficult in designing and solving facility layout problems. These factors and uncertainties have a large impact on manufacturing time, manufacturing cost, product quality and delivery performance. In order to operate efficiently, these facilities should adapt to these variations over multiple time periods and this must be addressed while designing an optimal layout. This paper proposes a novel integrated framework by combining Big Data Analtics and Hybrid meta-heuristic approach to design an optimal facility layout under stochastic demand over multiple periods. Firstly, factors affecting a facility layout design are identified. The survey is conducted to generate data reflecting 3V’s of Big Data. Secondly, a reduced set of factors are obtained using Big Data Analytics. These reduced set of factors are considered to mathematically model a weighted aggregate objective for Multi-objective Stochastic Dynamic Facility Layout Problem (MO-SDFLP). Hybrid Meta-heuristic based on Firefly (FA) and Chaotic simulated annealing is used to solve the MO-SDFLP. To show the working methodology of proposed integrated framework an exemplary case is presented.
      PubDate: 2018-11-01
      DOI: 10.1007/s10479-016-2237-x
      Issue No: Vol. 270, No. 1-2 (2018)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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