Purpose -The purpose of this paper is to address the inefficiency in resource allocation for disaster relief procurement operations. It presents a holistic and reconfigurable procurement auctions-based framework which includes the announcement construction, bid construction and bid evaluation phases. Design/methodology/approach -The holistic framework is developed in a way that auctioneers and bidders compete amongst each other in multiple rounds of the procurement auction. Humanitarian organization in disaster locations are considered as auctioneers (buyers) and suppliers are considered as bidders. Findings -Unique system parameters (e.g. announcement options, priority of items, bidder strategies, etc.) are introduced to represent the disaster relief environment in a practical way. The framework is verified by simulation and optimization techniques using the system characteristics of the disaster relief environment as an input. Based on the parameters and their values, behavioural changes of auctioneers and suppliers are observed. Originality/value -Combining the three phases of procurement auctions is unique both in the auction literature and in the disaster relief research, and it helps the humanitarian organizations supply the immediate and long-term requirements in the disaster location more efficiently.

Efficient allocation and utilization of staff resources is an important issue facing emergency department (ED) administrators. Increased pressure from competition, heath care reform, reimbursement difficulties, and rising heath care costs are primarily responsible for the high level of interest in this, and other ED operating efficiency issues. This paper discusses the use of computer simulation to test alternative ED attending physicianstaffing schedules and to analyze the corresponding impacts on patient throughput and resource utilization. The simulation model can also be used to help identify process inefficiencies and to evaluate the effects of staffing, layout, resource, and patient flow changes on system performance without disturbing the actual system. The development of this model was based on the

This paper investigates the application of multi-attribute utility theory (MAUT) to aid in the decision-making process when performing a benchmarking gap analysis. Multi-attribute utility theory was selected to identify the overall best-in-class performer for performance metrics involving inventory record accuracy within a public sector warehouse. A traditional benchmarking analysis was conducted on 21 industry warehouse participants to determine industry best practices for the six critical warehouse metrics of picking and inventory accuracy, storage speed, inventory and picking tolerance, and order cycle time. A gap analysis was performed on the critical metrics and the absolute best-in-class was used to measure performance gaps for each metric. The gap analysis results were then compared to the MAUT utility values, and a sensitivity analysis was performed to compare the two methods. The results indicate that an approach based on MAUT was advantageous in its ability to consider all critical metrics and define a best overall performer for these data. An approach based on MAUT allowed the assignment of priorities and analyzed the subjectivity for these decisions. MAUT added robustness to the decision-making process and provided a framework to identify one performer as best across all critical metrics.

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