Warehouse manpower planning strategies in times of financial crisis: evidence from logistics service providers and retailers in the Netherlands

The economic crisis that is experienced in Europe has a large influence on transport and logistics companies. Since turnover typically drops strongly during a crisis, companies try to reduce costs in order to survive. The study reported in this paper has investigated how manpower planning in warehouses has been used to counter effects of the crisis and what the results are of the measures taken. A survey was carried out among warehouses run by retailers and logistics service providers. The results of the survey show that there is a significant relation between a decrease in turnover and the four investigated manpower planning strategies. Furthermore, the study shows that the most effective manpower planning strategies are flexible planning of employees and balancing the workload. Hence, the study concludes that in particular better operational planning is a key strategy to counter the effects of the financial crisis, which is an important insight for the management of warehouses

Overcoming the risk of inaction from emissions uncertainty in smallholder agriculture

The potential for improving productivity and increasing the resilience of smallholder agriculture, while also contributing to climate change mitigation, has recently received considerable political attention (Beddington et al 2012). Financial support for improving smallholder agriculture could come from performance-based funding including sale of carbon credits or certified commodities, payments for ecosystem services, and nationally appropriate mitigation action (NAMA) budgets, as well as more traditional sources of development and environment finance. Monitoring the greenhouse gas fluxes associated with changes to agricultural practice is needed for performance-based mitigation funding, and efforts are underway to develop tools to quantify mitigation achieved and assess trade-offs and synergies between mitigation and other livelihood and environmental priorities (Olander 2012)

Virtualno Comptonovo raspršenje na niskim energijama i poopćene polarizivosti nukleona

Virtual Compton scattering (VCS) γ ∗p → γp at low CM energy gives access to the generalized polarizabilities of the nucleon. These observables generalize the concept of electromagnetic polarizabilities to the case of a virtual photon. Dedicated VCS experiments have been performed at MAMI, Jefferson Lab and MIT-Bates. The experimental status is reviewed, including methods of analysis and physical results. The measurement of absolute (ep → epγ) cross sections allows the extraction of the two unpolarized VCS structure functions PLL − PTT/ǫ and PLT, which are combinations of the generalized polarizabilities of the proton. Future prospects in the field of VCS at low energy are also presented.Virtualno Comptonovo raspršenje (VCR) γ ∗p → γp na niskim energijama u CMS pruža mogućnost određivanja poopćenih polarizivosti nukleona. Te opservable poopćuju pojam elektromagnetske polarizivosti za slučaj virtualnih fotona. Usmjerena mjerenja VCR načinjena su u MAMI, Jefferson Labu i u MIT-Batesu. Dajemo pregled mjerenja, te metoda analize podataka i ishode. Mjerenja apsolutnih udarnih presjeka reakcije (ep→epγ) dozvoljava izvođenje dviju strukturnih funkcija VCR bez polarizacije PLL − PTT/ǫ i PLT, koje su kombinacije poopćenih polarizivosti protona. Predstavljaju se izgledi budućih istraživanja VCR a niskim energijama

The behaviour of repeat visitors to museums: Review and empirical findings

This study presents a theoretical and operational framework for analysing repeat visit to museums. Starting from the literature on repeat visit in tourism, the specificities of these cultural attractions are made explicit through a review of theoretical and applied works. Consistently with previous contributors, the paper suggests that the analysis of actual past behaviours has to be preferred to the one of attitudes. The application of proper econometric models is also remarked in order to put into account individual profiles. Information coming from three techniques is then used in an integrated way in order to provide a more comprehensive view of the phenomenon. Evidence from an ad hoc survey suggests the necessity to give a greater attention to perceived cultural value during the visit, promoting cultural events during the week and addressed to children, and taking care of those visitors that come from far places also through an integrated tourist supply. © 2013 Springer Science+Business Media Dordrecht

The Inborn Errors of Immunity-Virtual Consultation System Platform in Service for the Italian Primary Immunodeficiency Network: Results from the Validation Phase

Purpose: Inborn errors of immunity (IEI) represent a heterogeneous group of rare genetically determined diseases. In some cases, patients present with complex or atypical phenotypes, not fulfilling the accepted diagnostic criteria for IEI and, thus, at high risk of misdiagnosis or diagnostic delay. This study aimed to validate a platform that, through the opinion of immunologist experts, improves the diagnostic process and the level of care of patients with atypical/complex IEI. Methods: Here, we describe the functioning of the IEI-Virtual Consultation System (VCS), an innovative platform created by the Italian Immunodeficiency Network (IPINet). Results: In the validation phase, from January 2020 to June 2021, 68 cases were entered on the IEI-VCS platform. A final diagnosis was achieved in 35/68 cases (51%, 95% CI 38.7 to 64.2). In 22 out of 35 solved cases, the diagnosis was confirmed by genetic analysis. In 3/35 cases, a diagnosis of secondary immunodeficiency was made. In the remaining 10 cases, an unequivocal clinical and immunological diagnosis was obtained, even though not substantiated by genetic analysis. Conclusion: From our preliminary study, the VCS represents an innovative and useful system to improve the diagnostic process of patients with complex unsolved IEI disorders, with benefits both in terms of reduction of time of diagnosis and access to the required therapies. These results may help the functioning of other international platforms for the management of complex cases

Sorotipos virais de dengue identificados em crianças de Manaus, Estado do Amazonas, 2008

INTRODUCÃO: A dengue é uma arbovirose que vem causando sérios problemas de saúde pública, em regiões tropicais e subtropicais do planeta. MÉTODOS: Neste estudo, foram investigadas amostras de sangue de crianças, através da RT-PCR, com o intuito de se identificar sorotipos do vírus dengue nessa população infantil, em Manaus/AM, durante o ano de 2008. RESULTADOS: O DENV-3 foi o único sorotipo viral identificado. CONCLUSÕES: No presente estudo, 83% das crianças analisadas apresentaram resultado negativo para dengue através do RT-PCR sugerindo a ocorrência de outras doenças febris que necessitam ser esclarecidas

Guidelines for the deployment and implementation of manufacturing scheduling systems

It has frequently been stated that there exists a gap between production scheduling theory and practice. In order to put theoretical findings into practice, advances in scheduling models and solution procedures should be embedded into a piece of software - a scheduling system - in companies. This results in a process that entails (1) determining its functional features, and (2) adopting a successful strategy for its development and deployment. In this paper we address the latter question and review the related literature in order to identify descriptions and recommendations of the main aspects to be taken into account when developing such systems. These issues are then discussed and classified, resulting in a set of guidelines that can help practitioners during the process of developing and deploying a scheduling system. In addition, identification of these issues can provide some insights to drive theoretical scheduling research towards those topics more in demand by practitioners, and thus help to close the aforementioned gap.Framiñan Torres, JM.; Ruiz García, R. (2012). Guidelines for the deployment and implementation of manufacturing scheduling systems. International Journal of Production Research. 50(7):1799-1812. doi:10.1080/00207543.2011.564670S17991812507Baek, D. H. (1999). A visualized human-computer interactive approach to job shop scheduling. International Journal of Computer Integrated Manufacturing, 12(1), 75-83. doi:10.1080/095119299130489Comesaña Benavides, J. A., & Carlos Prado, J. (2002). Creating an expert system for detailed scheduling. International Journal of Operations & Production Management, 22(7), 806-819. doi:10.1108/01443570210433562Bensana, E. 1986. An expert-system approach to industrial job-shop scheduling. In: Proceedings of the 1986 IEEE international conference on robotics and automation. 1986. Vol. 3, pp.1645–1650.Berglund, M., & Karltun, J. (2007). Human, technological and organizational aspects influencing the production scheduling process. International Journal of Production Economics, 110(1-2), 160-174. doi:10.1016/j.ijpe.2007.02.024Besbes, W., Teghem, J., & Loukil, T. (2010). Scheduling hybrid flow shop problem with non-fixed availability constraints. European J. of Industrial Engineering, 4(4), 413. doi:10.1504/ejie.2010.035652Bhattacharyya, S., & Koehler, G. J. (1998). Learning by Objectives for Adaptive Shop-Floor Scheduling. Decision Sciences, 29(2), 347-375. doi:10.1111/j.1540-5915.1998.tb01580.xBitran, G. R., & Tirupati, D. (1988). OR Practice—Development and Implementation of a Scheduling System for a Wafer Fabrication Facility. Operations Research, 36(3), 377-395. doi:10.1287/opre.36.3.377Buxey, G. (1989). Production scheduling: Practice and theory. European Journal of Operational Research, 39(1), 17-31. doi:10.1016/0377-2217(89)90349-4Chen, J.-F. (2004). Unrelated parallel machine scheduling with secondary resource constraints. The International Journal of Advanced Manufacturing Technology, 26(3), 285-292. doi:10.1007/s00170-003-1622-1Collinot, A., Le Pape, C., & Pinoteau, G. (1988). SONIA: A knowledge-based scheduling system. Artificial Intelligence in Engineering, 3(2), 86-94. doi:10.1016/0954-1810(88)90024-6Cowling, P. (2003). A flexible decision support system for steel hot rolling mill scheduling. Computers & Industrial Engineering, 45(2), 307-321. doi:10.1016/s0360-8352(03)00038-xDudek, R. A., Panwalkar, S. S., & Smith, M. L. (1992). The Lessons of Flowshop Scheduling Research. Operations Research, 40(1), 7-13. doi:10.1287/opre.40.1.7Dumond, E. J. (2005). Understanding and using the capabilities of finite scheduling. Industrial Management & Data Systems, 105(4), 506-526. doi:10.1108/02635570510592398Fox, M. S., & Smith, S. F. (1984). ISIS?a knowledge-based system for factory scheduling. Expert Systems, 1(1), 25-49. doi:10.1111/j.1468-0394.1984.tb00424.xFraminan, J. M., & Ruiz, R. (2010). Architecture of manufacturing scheduling systems: Literature review and an integrated proposal. European Journal of Operational Research, 205(2), 237-246. doi:10.1016/j.ejor.2009.09.026Freed, T., Doerr, K. H., & Chang, T. (2007). In-house development of scheduling decision support systems: case study for scheduling semiconductor device test operations. International Journal of Production Research, 45(21), 5075-5093. doi:10.1080/00207540600818351Gao, C and Tang, L. 2008. A decision support system for color-coating line in steel industry. In: Proceedings of the IEEE international conference on automation and logistics, ICAL 2008. 2008. pp.1463–1468.Grant, T. J. (1986). Lessons for O.R. from A.I.: A Scheduling Case Study. Journal of the Operational Research Society, 37(1), 41-57. doi:10.1057/jors.1986.7Graves, S. C. (1981). A Review of Production Scheduling. Operations Research, 29(4), 646-675. doi:10.1287/opre.29.4.646HALSALL, D. N., MUHLEMANN, A. P., & PRICE, D. H. R. (1994). A review of production planning and scheduling in smaller manufacturing companies in the UK. Production Planning & Control, 5(5), 485-493. doi:10.1080/09537289408919520Higgins, P. G. (1996). Interaction in hybrid intelligent scheduling. International Journal of Human Factors in Manufacturing, 6(3), 185-203. doi:10.1002/(sici)1522-7111(199622)6:33.0.co;2-6Kanet, J. J., & Adelsberger, H. H. (1987). Expert systems in production scheduling. European Journal of Operational Research, 29(1), 51-59. doi:10.1016/0377-2217(87)90192-5Kathawala, Y., & Allen, W. R. (1993). Expert Systems and Job Shop Scheduling. International Journal of Operations & Production Management, 13(2), 23-35. doi:10.1108/01443579310025286Kerr, R. M. (1992). Expert systems in production scheduling: Lessons from a failed implementation. Journal of Systems and Software, 19(2), 123-130. doi:10.1016/0164-1212(92)90063-pKnolmayer, G., Mertens, P., & Zeier, A. (2002). Supply Chain Management Based on SAP Systems. doi:10.1007/978-3-540-24816-3Leachman, R. C., Benson, R. F., Liu, C., & Raar, D. J. (1996). IMPReSS: An Automated Production-Planning and Delivery-Quotation System at Harris Corporation—Semiconductor Sector. Interfaces, 26(1), 6-37. doi:10.1287/inte.26.1.6MACCARTHY, B. L., & LIU, J. (1993). Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. International Journal of Production Research, 31(1), 59-79. doi:10.1080/00207549308956713McKay, K. N., & Black, G. W. (2007). The evolution of a production planning system: A 10-year case study. Computers in Industry, 58(8-9), 756-771. doi:10.1016/j.compind.2007.02.002McKay, K. N., Safayeni, F. R., & Buzacott, J. A. (1988). Job-Shop Scheduling Theory: What Is Relevant? Interfaces, 18(4), 84-90. doi:10.1287/inte.18.4.84McKay, K. N., Morton, T. E., Ramnath, P., & Wang, J. (2000). ?Aversion dynamics? scheduling when the system changes. Journal of Scheduling, 3(2), 71-88. doi:10.1002/(sici)1099-1425(200003/04)3:23.0.co;2-0MCKAY, K., PINEDO, M., & WEBSTER, S. (2009). PRACTICE-FOCUSED RESEARCH ISSUES FOR SCHEDULING SYSTEMS*. Production and Operations Management, 11(2), 249-258. doi:10.1111/j.1937-5956.2002.tb00494.xMissbauer, H., Hauber, W., & Stadler, W. (2009). A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry. International Journal of Production Research, 47(15), 4147-4172. doi:10.1080/00207540801950136Numao, M and Morishita, S. 1989. A scheduling environment for steel-making processes. In: Proceedings of the 5th conference on artificial intelligence applications. 1989. pp.279–286.Olhager, J., & Rapp, B. (1995). Operations Research Techniques in Manufacturing Planning and Control Systems. International Transactions in Operational Research, 2(1), 29-43. doi:10.1111/j.1475-3995.1995.tb00003.xPerez-Gonzalez, P., & Framinan, J. M. (2009). Scheduling permutation flowshops with initial availability constraint: Analysis of solutions and constructive heuristics. Computers & Operations Research, 36(10), 2866-2876. doi:10.1016/j.cor.2008.12.018Pinedo, M., & Yen, B. P.-C. (1997). Annals of Operations Research, 70, 359-378. doi:10.1023/a:1018986524234Portougal, V., & Robb, D. J. (2000). Production Scheduling Theory: Just Where Is It Applicable? Interfaces, 30(6), 64-76. doi:10.1287/inte.30.6.64.11623Reisman, A., Kumar, A., & Motwani, J. (1997). Flowshop scheduling/sequencing research: a statistical review of the literature, 1952-1994. IEEE Transactions on Engineering Management, 44(3), 316-329. doi:10.1109/17.618173Steffen, MS. 1986. A survey of artificial intelligence-based scheduling systems. In: Proceedings of the fall industrial engineering conference. 1986.Storer, R. H., Wu, S. D., & Vaccari, R. (1992). New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling. Management Science, 38(10), 1495-1509. doi:10.1287/mnsc.38.10.1495Tang, L., & Wang, G. (2008). Decision support system for the batching problems of steelmaking and continuous-casting production. Omega, 36(6), 976-991. doi:10.1016/j.omega.2007.11.002T’kindt, V., Billaut, J.-C., Bouquard, J.-L., Lenté, C., Martineau, P., Néron, E., … Tacquard, C. (2005). The e-OCEA project: towards an Internet decision system for scheduling problems. Decision Support Systems, 40(2), 329-337. doi:10.1016/j.dss.2004.04.001Wiers, VCS. 1997. Human–computer interaction in production scheduling: Analysis and design of decision support systems for production scheduling tasks. Ph.D. Thesis, Technische Universiteit Eindhoven, NetherlandsWiers, V. C. S. (2002). A case study on the integration of APS and ERP in a steel processing plant. Production Planning & Control, 13(6), 552-560. doi:10.1080/09537280210160321Wiers, V. C. S., & Van Der Schaaf, T. W. (1997). A framework for decision support in production scheduling tasks. Production Planning & Control, 8(6), 533-544. doi:10.1080/095372897234876Zhang, L., Krishnamurthy, A., Malmborg, C. J., & Heragu, S. S. (2009). Variance-based approximations of transaction waiting times in autonomous vehicle storage and retrieval systems. European J. of Industrial Engineering, 3(2), 146. doi:10.1504/ejie.2009.02360

Deficiency of 25-Hydroxyvitamin D and Dyslipidemia in Indian Subjects

Background. Vitamin D deficiency is widespread throughout the world. Several reports have incriminated vitamin D deficiency as the cause of rickets, osteomalacia, and other chronic diseases. Recent studies have suggested a possible link between deficiency of 25-hydroxyvitamin D and dyslipidemia. Aim. To investigate the association between 25-hydroxyvitamin D deficiency and dyslipidemia in Indian subjects. Methodology. We recruited 150 asymptomatic consecutive subjects from patients' attendees at the Departments of Neurology and Medicine in Yashoda Hospital, Hyderabad, India. Study period was from October 2011 to March 2012. All subjects underwent 25-hydroxyvitamin D assay by chemiluminescent microparticle immunoassay, fasting blood sugar and lipid profile, calcium, phosphorus, alkaline phosphatase, and C-reactive protein (CRP). Results. Out of 150 subjects, men were 82 (54.6%), and mean age was 49.4 (±15.6) years. Among risk factors, hypertension was noted in 63/150 (42%), 25-hydroxyvitamin D deficiency in 59/150 (39.3%), diabetes in 45/150 (30%), dyslipidemia in 60 (40%), smoking in 35/150 (23.3%), and alcoholism in 27/150 (18%). Deficiency of 25-hydroxyvitamin D was significantly associated with dyslipidemia ( = 0.0001), mean serum glucose ( = 0.0002) mean CRP ( = 0.04), and mean alkaline phosphatase ( = 0.01). Multivariate analysis showed that 25-hydroxyvitamin D deficiency was independently associated with dyslipidemia (odds ratio: 1.9; 95% CI : 1.1-3.5). Conclusions. We found that deficiency of 25-hydroxyvitamin D was independently associated with dyslipidemia in Indian subjects