The objective of this paper is to apply the Translog Stochastic Frontier production model (SFA) and Data Envelopment Analysis (DEA) to estimate efficiencies over time and the Total Factor Productivity (TFP) growth rate for Bangladeshi rice crops (Aus, Aman and Boro) throughout the most recent data available comprising the period 1989–2008. Results indicate that technical efficiency was observed as higher for Boro among the three types of rice, but the overall technical efficiency of rice production was found around 50%. Although positive changes exist in TFP for the sample analyzed, the average growth rate of TFP for rice production was estimated at almost the same levels for both Translog SFA with half normal distribution and DEA. Estimated TFP from SFA is forecasted with ARIMA (2, 0, 0) model. ARIMA (1, 0, 0) model is used to forecast TFP of Aman from DEA estimation.

Banking system plays an important role in the economic development of any country. Domestic banks, which are the main components of the banking system, have to be efficient; otherwise, they may create obstacle in the process of development in any economy. This study examines the technical efficiency of the Malaysian domestic banks listed in the Kuala Lumpur Stock Exchange (KLSE) market over the period 2005–2010. A parametric approach, Stochastic Frontier Approach (SFA), is used in this analysis. The findings show that Malaysian domestic banks have exhibited an average overall efficiency of 94 percent, implying that sample banks have wasted an average of 6 percent of their inputs. Among the banks, RHBCAP is found to be highly efficient with a score of 0.986 and PBBANK is noted to have the lowest efficiency with a score of 0.918. The results also show that the level of efficiency has increased during the period of study, and that the technical efficiency effect has fluctuated considerably over time.

The proper estimation of pedestrian speed-flow-density relationships is of vital importance, because such relationships play an important role in developing useful tools for analysing and improving pedestrian facilities in terms of efficiency and safety. One of the major problems with previous macroscopic studies of pedestrian flow characteristics is that the relationships were established based on a model with specification errors that had been estimated by ordinary least squares (OLS). Thus, the validity of the relationships and conclusions drawn from those studies is open to question and should be examined further. In this study, pedestrian speed-flow-density relationships in Dhaka, Bangladesh, are estimated using a weighted regression method. The flows and speeds generated by the derived flow-density and speed-flow relationships based on the weighted regression method and the OLS method, separately, are compared with empirical values. The root mean square error is used as an evaluation criterion. In addition, the pedestrian characteristics of Dhaka are compared with those of other studies. The results indicate the existence of a probable bias in previous studies and an improvement in predictive power with the use of the weighted regression method. Pedestrian flows on the sidewalks in Dhaka have some particular characteristics that are not similar to the uninterrupted pedestrian flows in other countries. Since the weighted regression estimation techniques can mitigate a part of the OLS bias, such techniques could be incorporated in simulation packages to predict pedestrian flows and speeds as well as to design and analyse the capacity of a pedestrian facility precisely. The study also recommends refraining from the direct adoption of foreign design and parameters for pedestrian facilities in Dhaka.

This paper presents a multiobjective winner determination combinatorial auction mechanism for transportation carriers to present multiple transport lanes and bundle the lanes as packet bids to the shippers for the purposes of ocean freight. This then allows the carriers to maximize their network of resources and pass some of the cost savings onto the shipper. Specifically, we formulate three multi-objective optimization models (weighted objective model, preemptive goal programming, and compromise programming) under three criteria of cost, marketplace fairness, and the marketplace confidence in determining the winning packages. We develop solutions on the three models and perform a sensitivity analysis to show the options the shipper can use depending on the existing conditions at the point of awarding the transport lanes.

M/G/C/C state dependent queuing networks consider service rates as a function of the number of residing entities (e.g., pedestrians, vehicles, and products). However, modeling such dynamic rates is not supported in modern Discrete Simulation System (DES) software. We designed an approach to cater this limitation and used it to construct the M/G/C/C state-dependent queuing model in Arena software. Using the model, we have evaluated and analyzed the impacts of various arrival rates to the throughput, the blocking probability, the expected service time and the expected number of entities in a complex network topology. Results indicated that there is a range of arrival rates for each network where the simulation results fluctuate drastically across replications and this causes the simulation results and analytical results exhibit discrepancies. Detail results that show how tally the simulation results and the analytical results in both abstract and graphical forms and some scientific justifications for these have been documented and discussed.

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Data envelopment analysis (DEA) as introduced by Charnes et al [3] is a linear programming technique that has widely been used to evaluate the relative efficiency of a set of homogenous decision making units (DMUs). In many real applications, the input-output variables cannot be precisely measured. This is particularly important in assessing efficiency of DMUs using DEA, since the efficiency score of inefficient DMUs are very sensitive to possible data errors. Hence, several approaches have been proposed to deal with imprecise data. Perhaps the most popular fuzzy DEA model is based on -cut. One drawback of the -cut approach is that it cannot include all information about uncertainty. This paper aims to introduce an alternative linear programming model that can include some uncertainty information from the intervals within the α-cut approach. We introduce the concept of "local α-level" to develop a multiobjective linear programming to measure the efficiency of DMUs under uncertainty. An example is given to illustrate the use of this method.

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