Total Usage Cost Research Articles

Evaluating and Comparing the Impact of an Enhanced Antimicrobial Stewardship Programme in a District Specialist Hospital: A Two-Year Retrospective Study

Antimicrobial stewardship (AMS) programme is established to optimise use of antibiotics and to contain antibiotic resistance. This single centre, cross sectional retrospective study aimed to evaluate and compare the impact of an enhanced AMS programme in 2019 with data obtained in 2018 before its implementation. Types of interventions made by the AMS team, acceptance rate of AMS recommendations, antibiotic usage (DDD/1000 patients-days) and expenditure (antibiotic usage cost, RM) of 14 antibiotics under national surveillance were reviewed. Our study demonstrated non-significant reduction in total antibiotic usage (mean 188.25 versus 183.94; p = 0.523). Nonetheless, significant decline in prescribing of cefoperazone either alone or in combination with sulbactam, ciprofloxacin and meropenem was observed. There was a significant reduction in total usage cost (mean RM80,070.39 versus RM70,858.81; 95% confidence interval (CI):1519.48, 16903.69; p = 0.022) contributed in part by decreased third generation cephalosporins, meropenem and ciprofloxacin prescriptions. During enhanced AMS period, total AMS cases (45 versus 358), frequency of rounds (12 versus 37) and ward pharmacist-initiated AMS interventions were increased. The most common intervention and recommendation encountered were inappropriate choice and de-escalation of antibiotic, respectively. There was an improvement in overall acceptance rate in 2019 (67% versus 78%; p = 0.081). In conclusion, the enhanced programme resulted in decreased overall antibiotic prescription and expenditure, besides greater acceptance of AMS recommendations.

Optimizing a warranty–based sustainable product service system using game theory

In recent years, the concepts of sustainability and product service system have been closely associated with each other. In today’s competitive markets, due to Extended Producer Responsibility and customer environmental awareness, producers shift from ‘product-seller’ towards ‘product-service provider’ for using the environmental and economic advantages of coupling a product with services. This paper proposes a novel approach to determine the optimal warranty period and the out-of-warranty replacement period, from the point of view of the producer and the customer to minimise the total cost of usage and end of life of product. As regards the fact that adopted strategies by producers and customers sometimes are in conflict and it affects choosing the optimal product usage period, a game theory model was developed in this study. Finally, a case study with data from chain of local notebook service centres was applied to demonstrate some practical aspects of the developed model.

Optimal selection of application loading on cloud services

There is a need for identifying computing power hours and storage utilisation along with total cost optimisation. The present paper focuses on optimal selection of application loading process on the cloud services considering relevant factors. Using this model, small companies that plan to develop applications and use cloud services may determine cost and optimal selection of service by taking into account its own as well as provider’s perspectives into consideration. The paper consists of four stages. First stage deals with the estimation of required computing power hours in the planned duration. Second stage relates to the calculation of storage capacity. Third stage corresponds to the formation of multi objective goal programme to prioritise computing power hours and storage utilisation requirements of applications and optimise total cost of usage. Finally, fourth stage deals with the mixed integer non-linear programming to minimise total cost considering other variable factors. For small application developers who cannot afford in-house IT infrastructure, we find an optimal framework for allocating number of applications on cloud services such as Infrastructure as a Service and Platform as a Service. For ease in planning, the user company can quickly decide corresponding number of applications at appropriate services, and at the same time can reduce overall usage cost. With the help of proposed method, the service provider may keep a suitable inventory of cores to provide backup computing power and storage capacity. This adds value to developers also, as company can plan for their operations corresponding to the business growth.

An Investigation of Sequential Sampling Method for Crossdocking Simulation Output Variance Reduction

This paper investigates the reduction of variance associated with a simulation output performance measure, using the Sequential Sampling method while applying minimum simulation replications, for a class of JIT (Just in Time) warehousing system called crossdocking. We initially used the Sequential Sampling method to attain a desired 95% confidence interval half width of ± 0.5 for our chosen performance measure (Total usage cost, given the mean maximum level of £157,000 and a mean minimum level of £149,000). From our results, we achieved a 95% confidence interval half width of ± 2.8 for our chosen performance measure (Total usage cost, with an average mean value of £115,000). However, the Sequential Sampling method requires a huge number of simulation replications to reduce variance for our simulation output value to the target level. Arena® (version 11) simulation software was used to conduct this study.

Optimal handling capacity at a berth

In a recent paper Wanhill [1] described a method for determining the number of berths that should be provided at a port so that the total usage cost would be minimized. However, in many situations a port will provide a single specialized berth to cater for a particular type of cargo; for example, the unloading of iron-ore at a steelworks or the discharge of oil at a general cargo port. The problem then is not one of deciding on the number of berths to be built but rather that of finding the optimal cargo handling capacity for the single berth available. A model is presented in this paper indicating how such a decision may be made, and two examples give approximate lower and upper bounds for the optimal capacity for many practical cases. A second paper will show how a theoretical model often has to be modified in the light of particular circumstances.