{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:29:33Z","timestamp":1778603373425,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T00:00:00Z","timestamp":1567987200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T00:00:00Z","timestamp":1567987200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Syst Assur Eng Manag"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s13198-019-00879-6","type":"journal-article","created":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T13:04:31Z","timestamp":1568034271000},"page":"481-493","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["An intelligent algorithm for final product demand forecasting in pharmaceutical units"],"prefix":"10.1007","volume":"11","author":[{"given":"Mohsen Sadegh","family":"Amalnick","sequence":"first","affiliation":[]},{"given":"Naser","family":"Habibifar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1498-7507","authenticated-orcid":false,"given":"Mahdi","family":"Hamid","sequence":"additional","affiliation":[]},{"given":"Mahdi","family":"Bastan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"879_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.aap.2015.11.007","volume":"87","author":"A Azadeh","year":"2016","unstructured":"Azadeh A, Zarrin M, Hamid M (2016) A novel framework for improvement of road accidents considering decision-making styles of drivers in a large metropolitan area. Accid Anal Prev 87:17\u201333","journal-title":"Accid Anal Prev"},{"key":"879_CR2","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.ijepes.2016.03.012","volume":"82","author":"S Barak","year":"2016","unstructured":"Barak S, Sadegh SS (2016) Forecasting energy consumption using ensemble ARIMA\u2013ANFIS hybrid algorithm. Int J Electr Power Energy Syst 82:92\u2013104","journal-title":"Int J Electr Power Energy Syst"},{"key":"879_CR3","first-page":"8887","volume":"75","author":"S Benkachcha","year":"2013","unstructured":"Benkachcha S, Benhra J, El Hassani H (2013) Causal Method and Time Series Forecasting model based on Artificial Neural Network. Int J Comput Appl 75:8887","journal-title":"Int J Comput Appl"},{"key":"879_CR4","unstructured":"Bholowalia P, Kumar A (2014) EBK-means: a clustering technique based on elbow method and k-means in WSN. Int J Comput Appl:105"},{"key":"879_CR5","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470","volume-title":"Classification and regression trees","author":"L Breiman","year":"2017","unstructured":"Breiman L (2017) Classification and regression trees. Routledge, Abingdon"},{"key":"879_CR6","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.rser.2017.07.032","volume":"81","author":"A Buga\u0142a","year":"2018","unstructured":"Buga\u0142a A, Zaborowicz M, Boniecki P, Janczak D, Koszela K, Czeka\u0142a W, Lewicki A (2018) Short-term forecast of generation of electric energy in photovoltaic systems. Renew Sustain Energy Rev 81:306\u2013312","journal-title":"Renew Sustain Energy Rev"},{"key":"879_CR7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.17858\/jmisci.06816","volume":"2","author":"G Candan","year":"2014","unstructured":"Candan G, Taskin MF, Yazgan H (2014) Demand forecasting in pharmaceutical industry using neuro-fuzzy approach. J Manag Inf Sci 2:41\u201349","journal-title":"J Manag Inf Sci"},{"key":"879_CR8","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1016\/j.ejor.2006.12.004","volume":"184","author":"R Carbonneau","year":"2008","unstructured":"Carbonneau R, Laframboise K, Vahidov R (2008) Application of machine learning techniques for supply chain demand forecasting. Eur J Oper Res 184:1140\u20131154","journal-title":"Eur J Oper Res"},{"key":"879_CR9","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/0377-2217(78)90138-8","volume":"2","author":"A Charnes","year":"1978","unstructured":"Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429\u2013444","journal-title":"Eur J Oper Res"},{"key":"879_CR10","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.ijforecast.2012.09.002","volume":"29","author":"A Davydenko","year":"2013","unstructured":"Davydenko A, Fildes R (2013) Measuring forecasting accuracy: the case of judgmental adjustments to SKU-level demand forecasts. Int J Forecast 29:510\u2013522","journal-title":"Int J Forecast"},{"key":"879_CR11","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6:182\u2013197","journal-title":"IEEE Trans Evolut Comput"},{"key":"879_CR12","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/TPWRS.2015.2390132","volume":"31","author":"N Ding","year":"2016","unstructured":"Ding N, Benoit C, Foggia G, B\u00e9sanger Y, Wurtz F (2016) Neural network-based model design for short-term load forecast in distribution systems. IEEE Trans Power Syst 31:72\u201381","journal-title":"IEEE Trans Power Syst"},{"key":"879_CR13","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.renene.2016.10.030","volume":"102","author":"Q Dong","year":"2017","unstructured":"Dong Q, Sun Y, Li P (2017) A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: a case study of wind farms in China. Renew Energy 102:241\u2013257","journal-title":"Renew Energy"},{"key":"879_CR14","doi-asserted-by":"publisher","first-page":"6697","DOI":"10.1016\/j.eswa.2008.08.058","volume":"36","author":"T Efendigil","year":"2009","unstructured":"Efendigil T, \u00d6n\u00fct S, Kahraman C (2009) A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: a comparative analysis. Expert Syst Appl 36:6697\u20136707","journal-title":"Expert Syst Appl"},{"key":"879_CR15","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s11063-008-9085-x","volume":"28","author":"TA Ferreira","year":"2008","unstructured":"Ferreira TA, Vasconcelos GC, Adeodato PJ (2008) A new intelligent system methodology for time series forecasting with artificial neural networks. Neural Process Lett 28:113\u2013129","journal-title":"Neural Process Lett"},{"key":"879_CR17","unstructured":"Ghousi R, Mehrani S, Momeni M, Anjomshoaa S (2012) Application of data mining techniques in drug consumption forecasting to help pharmaceutical industry production planning. In: Proceedings of the 2012 international conference on industrial engineering and operations management, pp 1162\u20131167"},{"key":"879_CR18","doi-asserted-by":"crossref","unstructured":"Habibifar N, Hamid M, Bastan M, Taher Azar A (2019) Performance optimization of a pharmaceutical production line by integrated simulation and data envelopment analysis. Int J Simul Process Model. In press","DOI":"10.1504\/IJSPM.2019.103587"},{"key":"879_CR19","first-page":"98","volume":"11","author":"M Hamid","year":"2018","unstructured":"Hamid M, Barzinpour F, Hamid M, Mirzamohammadi S (2018a) A multi-objective mathematical model for nurse scheduling problem with hybrid DEA and augmented \u03b5-constraint method: a case study. J Ind Syst Eng 11:98\u2013108","journal-title":"J Ind Syst Eng"},{"key":"879_CR20","doi-asserted-by":"publisher","first-page":"117","DOI":"10.22068\/ijiepr.29.2.117","volume":"29","author":"M Hamid","year":"2018","unstructured":"Hamid M, Hamid M, Nasiri MM, Ebrahimnia M (2018b) Improvement of operating room performance using a multi-objective mathematical model and data envelopment analysis: a case study. Int J Ind Eng Prod Res 29:117\u2013132. \nhttps:\/\/doi.org\/10.22068\/ijiepr.29.2.117","journal-title":"Int J Ind Eng Prod Res"},{"key":"879_CR21","volume-title":"Data mining: concepts and techniques","author":"J Han","year":"2011","unstructured":"Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam"},{"key":"879_CR22","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1108\/IJLM-04-2017-0088","volume":"29","author":"E Hofmann","year":"2018","unstructured":"Hofmann E, Rutschmann E (2018) Big data analytics and demand forecasting in supply chains: a conceptual analysis. Int J Logist Manag 29:739\u2013766","journal-title":"Int J Logist Manag"},{"key":"879_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-48318-9","volume-title":"Multiple attribute decision making methods and applications","author":"C Hwang","year":"1981","unstructured":"Hwang C, Yoon K (1981) Multiple attribute decision making methods and applications. Springer, New York"},{"key":"879_CR24","first-page":"43","volume":"13","author":"AG Ivakhnenko","year":"1968","unstructured":"Ivakhnenko AG (1968) The group method of data handling-a rival of the method of stochastic approximation Soviet Automatic. Control 13:43\u201355","journal-title":"Control"},{"key":"879_CR25","doi-asserted-by":"crossref","unstructured":"Jain A, Srinivas E, Rauta R (2009) Short term load forecasting using fuzzy adaptive inference and similarity. In: NaBIC, Berlin, 2009. IEEE, pp 1743\u20131748","DOI":"10.1109\/NABIC.2009.5393627"},{"key":"879_CR26","unstructured":"Jamili A, Hamid M, Gharoun H, Khoshnoudi R (2018) Developing a comprehensive and multi-objective mathematical model for university course timetabling problem: a real case study. In: Conference: proceedings of the international conference on industrial engineering and operations management,Paris, France, 2018. pp 2108, 2119"},{"key":"879_CR27","volume-title":"Improving demand forecasting practices in the industrial context","author":"A Kerkk\u00e4nen","year":"2010","unstructured":"Kerkk\u00e4nen A (2010) Improving demand forecasting practices in the industrial context. Lappeenranta University of Technology, Lappeenranta"},{"key":"879_CR28","doi-asserted-by":"crossref","unstructured":"Khosravi A, Nahavandi S, Creighton D (2011) Short term load forecasting using interval type-2 fuzzy logic systems. In: IEEE international conference on fuzzy systems (FUZZ-IEEE 2011), Taipei, 2011. IEEE, pp 502\u2013508","DOI":"10.1109\/FUZZY.2011.6007450"},{"key":"879_CR29","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1016\/j.ijforecast.2015.12.003","volume":"32","author":"S Kim","year":"2016","unstructured":"Kim S, Kim H (2016) A new metric of absolute percentage error for intermittent demand forecasts. Int J Forecast 32:669\u2013679","journal-title":"Int J Forecast"},{"key":"879_CR30","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.cie.2016.02.013","volume":"99","author":"C-Y Lee","year":"2016","unstructured":"Lee C-Y, Chiang M-C (2016) Aggregate demand forecast with small data and robust capacity decision in TFT-LCD manufacturing. Comput Ind Eng 99:415\u2013422","journal-title":"Comput Ind Eng"},{"key":"879_CR31","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.enbuild.2015.11.056","volume":"111","author":"M Macas","year":"2016","unstructured":"Macas M et al (2016) The role of data sample size and dimensionality in neural network based forecasting of building heating related variables. Energy Build 111:299\u2013310","journal-title":"Energy Build"},{"key":"879_CR32","volume-title":"Sales forecasting management: a demand management approach","author":"JT Mentzer","year":"2004","unstructured":"Mentzer JT, Moon MA (2004) Sales forecasting management: a demand management approach. Sage, Thousand Oaks"},{"key":"879_CR33","doi-asserted-by":"crossref","unstructured":"Mishra S, Patra SK (2008) Short term load forecasting using neural network trained with genetic algorithm & particle swarm optimization. In: First international conference on emerging trends in engineering and technology,IEEE, pp 606\u2013611","DOI":"10.1109\/ICETET.2008.94"},{"key":"879_CR34","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.neucom.2017.08.017","volume":"273","author":"K Muralitharan","year":"2018","unstructured":"Muralitharan K, Sakthivel R, Vishnuvarthan R (2018) Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing 273:199\u2013208","journal-title":"Neurocomputing"},{"key":"879_CR35","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1016\/j.ifacol.2015.06.353","volume":"48","author":"PW Murray","year":"2015","unstructured":"Murray PW, Agard B, Barajas MA (2015) Forecasting Supply Chain Demand by Clustering Customers. IFAC-PapersOnLine 48:1834\u20131839","journal-title":"IFAC-PapersOnLine"},{"key":"879_CR36","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1016\/j.eswa.2009.08.019","volume":"37","author":"D Niu","year":"2010","unstructured":"Niu D, Wang Y, Wu DD (2010) Power load forecasting using support vector machine and ant colony optimization. Expert Syst Appl 37:2531\u20132539","journal-title":"Expert Syst Appl"},{"key":"879_CR37","unstructured":"Perea RG, Poyato EC, Montesinos P, D\u00edaz JAR (2018) Optimisation of water demand forecasting by artificial intelligence with short data sets Biosystems Engineering In press"},{"key":"879_CR38","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1016\/j.rser.2015.04.065","volume":"50","author":"MQ Raza","year":"2015","unstructured":"Raza MQ, Khosravi A (2015) A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew Sustain Energy Rev 50:1352\u20131372","journal-title":"Renew Sustain Energy Rev"},{"key":"879_CR39","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.energy.2015.03.084","volume":"85","author":"J Szoplik","year":"2015","unstructured":"Szoplik J (2015) Forecasting of natural gas consumption with artificial neural networks. Energy 85:208\u2013220","journal-title":"Energy"},{"key":"879_CR40","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.enbuild.2012.10.023","volume":"56","author":"F Ta\u015fp\u0131nar","year":"2013","unstructured":"Ta\u015fp\u0131nar F, Celebi N, Tutkun N (2013) Forecasting of daily natural gas consumption on regional basis in Turkey using various computational methods. Energy Build 56:23\u201331","journal-title":"Energy Build"},{"key":"879_CR41","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.apenergy.2012.01.010","volume":"94","author":"J Wang","year":"2012","unstructured":"Wang J, Li L, Niu D, Tan Z (2012) An annual load forecasting model based on support vector regression with differential evolution algorithm. Appl Energy 94:65\u201370","journal-title":"Appl Energy"},{"key":"879_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.enconman.2013.02.010","volume":"70","author":"J Wu","year":"2013","unstructured":"Wu J, Wang J, Lu H, Dong Y, Lu X (2013) Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model. Energy Convers Manag 70:1\u20139","journal-title":"Energy Convers Manag"},{"key":"879_CR43","first-page":"287","volume":"11","author":"R Yazdanparast","year":"2018","unstructured":"Yazdanparast R, Hamid M, Azadeh A, Keramati A (2018) An intelligent algorithm for optimization of resource allocation problem by considering human error in an emergency. J Ind Syst Eng 11:287\u2013309","journal-title":"J Ind Syst Eng"},{"key":"879_CR44","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.energy.2017.03.094","volume":"127","author":"Y-R Zeng","year":"2017","unstructured":"Zeng Y-R, Zeng Y, Choi B, Wang L (2017) Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. Energy 127:381\u2013396","journal-title":"Energy"}],"container-title":["International Journal of System Assurance Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-019-00879-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13198-019-00879-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-019-00879-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,7]],"date-time":"2020-09-07T23:49:17Z","timestamp":1599522557000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13198-019-00879-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,9]]},"references-count":43,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["879"],"URL":"https:\/\/doi.org\/10.1007\/s13198-019-00879-6","relation":{},"ISSN":["0975-6809","0976-4348"],"issn-type":[{"value":"0975-6809","type":"print"},{"value":"0976-4348","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,9]]},"assertion":[{"value":"4 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}