A Two-Phased Gradient Technique For Budget Allocation: An Application To Data Collection Budget Allocation In Efficiency Measurement Of DEA.

This paper presents a gradient based algorithm to solve the data collection budget allocation problem in efficiency measurement of DEA

A preliminary study on using Data Envelopment Analysis (DEA) in measuring supply chain efficiency

This paper illustrates the use of Data Envelopment Analysis (DEA) in measuring supply chain efficiency. Two models were developed – Technical Efficiency (TE) model and Cost Efficiency (CE) model. The information obtained enable managers to identify inefficient operations and take appropriate actions for improvement. The opportunity cost serves as a good reference on resource allocations. The models are further enhanced with scenario analysis to derive more meaningful insights for resources planning. The study proves the usefulness of DEA as a decision-making tool in supply chain. Future research could look into the possibility of modelling DEA in a stochastic supply chain environment

Dynamic lot-sizing problem with demand time windows and container-based transportation cost

10.1007/s11590-006-0042-1Optimization Letters2139-5

Genetic algorithms for the multiple-machine economic lot scheduling problem

International Journal of Advanced Manufacturing Technology4311-121251-1260IJAT

The economic lot scheduling problem under extended basic period and power-of-two policy

10.1007/s11590-009-0154-5Optimization Letters42157-17

DISCRETE EVENT SIMULATION MODEL FOR AIRLINE OPERATIONS: SIMAIR

SIMAIR is a C++ based research tool meant for the simulation of airline operations. It provides a means for devising and evaluating various airline recovery mechanisms to handle disruptions, and can also be used as a tool to evaluate the performance of a given schedule of operations. The performance of a given recovery mechanism can be quantified for research and evaluation purposes.

A two-echelon inventory optimization model with demand time window considerations

10.1007/s10898-004-6092-yJournal of Global Optimization304347-366JGOP