Optimality of ( s , S ) policies in EOQ models with general cost structures

2017 ◽  
Vol 187 ◽  
pp. 216-228 ◽  
Author(s):  
Sandun Perera ◽  
Ganesh Janakiraman ◽  
Shun-Chen Niu
Keyword(s):  
Author(s):  
Pia Negro ◽  
Dorian Ridderskamp ◽  
Moritz Paul ◽  
Fabian Fehn ◽  
Heidrun Belzner ◽  
...  
Keyword(s):  

2020 ◽  
Vol 45 (4) ◽  
pp. 446-474
Author(s):  
Zuchao Shen ◽  
Benjamin Kelcey

Conventional optimal design frameworks consider a narrow range of sampling cost structures that thereby constrict their capacity to identify the most powerful and efficient designs. We relax several constraints of previous optimal design frameworks by allowing for variable sampling costs in cluster-randomized trials. The proposed framework introduces additional design considerations and has the potential to identify designs with more statistical power, even when some parameters are constrained due to immutable practical concerns. The results also suggest that the gains in efficiency introduced through the expanded framework are fairly robust to misspecifications of the expanded cost structure and concomitant design parameters (e.g., intraclass correlation coefficient). The proposed framework is implemented in the R package odr.


Author(s):  
Sri Satya Kanaka Nagendra Jayanty ◽  
William J. Sawaya ◽  
Michael D. Johnson

Engineers, policy makers, and managers have shown increasing interest in increasing the sustainability of products over their complete lifecycles and also from the ‘cradle to grave’ or from production to the disposal of each specific product. However, a significant amount of material is disposed of in landfills rather than being reused in some form. A sizeable proportion of the products being dumped in landfills consist of packaging materials for consumable products. Technological advances in plastics, packaging, cleaning, logistics, and new environmental awareness and understanding may have altered the cost structures surrounding the lifecycle use and disposal costs of many materials and products resulting in different cost-benefit trade-offs. An explicit and well-informed economic analysis of reusing certain containers might change current practices and results in significantly less waste disposal in landfills and in less consumption of resources for manufacturing packaging materials. This work presents a method for calculating the costs associated with a complete process of implementing a system to reuse plastic containers for food products. Specifically, the different relative costs of using a container and then either disposing of it in a landfill, recycling the material, or reconditioning the container for reuse and then reusing it are compared explicitly. Specific numbers and values are calculated for the case of plastic milk bottles to demonstrate the complicated interactions and the feasibility of such a strategy.


2015 ◽  
Vol 90 (6) ◽  
pp. 2305-2335 ◽  
Author(s):  
Martin Holzhacker ◽  
Ranjani Krishnan ◽  
Matthias D. Mahlendorf

ABSTRACT This paper extends prior literature on cost behavior by providing insights into how firms achieve changes to cost structure in response to two important risk drivers, i.e., demand uncertainty and financial risk. Using theory from labor economics, supply-chain management, and finance, we posit that demand uncertainty and financial risk influence cost management activities. Specifically, we argue that firms are likely to alter resource procurement choices to increase cost elasticity in response to these two risk drivers. We use data from California hospitals that allow for the calibration of three distinct resource procurement choices that increase cost elasticity: outsourcing, leasing of equipment, and hiring contract labor. Mediation analysis using 2,202 hospital year observations indicates that both demand uncertainty and financial risk influence cost elasticity. Importantly, these effects are mediated by the three aforementioned resource procurement choices. Overall, our findings support the view that firms make procurement choices to manage the risk associated with cost structures. Data Availability: Data used in this study are publicly available from the Office of Statewide Health Planning and Development (see: http://www.oshpd.ca.gov/). JEL Classifications: I18; M41.


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