Efficiency evaluation of Greek commercial banks using DEA

The purpose of this work is to evaluate the efficiency of the biggest commercial banks that operated in Greece at the financial year 2009. The method used is Data Envelopment Analysis. Each bank is modelled as a linear system with multiple inputs and outputs. The innovation of the paper refers to the choice of data and the use of a combination of the intermediation approach and the Sealey and Lindley (1977) approach. The data used was derived from the Hellenic Banks Association and from the balance sheets of its members. These data include the interest expenses, fixed assets, deposits etc. To estimate the relative efficiency of the chosen DMU’s the MS Excel add-in program xIDEA 2.1 is employed. The results indicate several inefficiencies that may not have direct relation to the profitability of such institutions. But, these inefficiencies indicate the vulnerability of the Greek banking system and its potential to ask for help from the FSF (Financial Stability Fund)

Genetic Algorithm Approach for the Inventory Routing Problem with Backlogging

We consider a multi-period inventory-routing problem where a vendor serves multiple geographically dispersed customers who receive units of a single product from a depot with adequate supply. The class of problems arising from the combination of distribution and inventory management decisions is perhaps the most striking example of this concept and is known as the inventory routing problem (IRP). In this category of problems, the inventory routing problem with backlogs (IRPwB) deals with determining inventory level, backlogging and vehicle routing decisions from a single depot to a set of n customers over a specific number of time periods, using a fleet of homogenous vehicles. The aim is to minimise the average daily cost for the planning period, while ensuring that inventory level capacity constraints are not violated. We first develop an Integer Programming model to provide an accurate description of the problem and in a second phase a Genetic Algorithm (GA) with suitably designed genetic operators, is employed in order to obtain near optimal solutions

A Queueing Network Application to a Telecommunications Distributed System

The purpose of this paper is to present and solve a particular class of a telecommunications related Process Allocation Problem. The problem deals with the allocation of processes to a network of processors with the aim to minimize a "trade off"objective function composed of (a) the queueing delays overhead which is formed in the underlying queueing network. (b)the communication costs incurred between processes residing on different processors.Various application constraints are also taken into account. A cost function id first constructed to reflect the queueing delay "felt" by a subscriber and a simulated annealing algorithm is then used to minimize the trade off objective function

ASSIGNMENT OF DISTRIBUTED PROCESSING SOFTWARE: A COMPARATIVE STUDY

A major issue of the operation of distributed systems is the problem of allocating a number of processes to a network of processors, with the aim of fully utilising their potential and flexibility. This paper presents a solution to the process allocation problem from a mathematical programming point of view, employing two heuristic algorithms . The first one is an adaptation of the simulated annealing heuristic algorithm, while the second one is based on an iterative improvement procedure. The characteristics of both heuristics are briefly examined, and in the sequel both algorithms are tested on a set of random problems having characteristics similar to a real-world problem

Ranking Alternative Production Scenarios Using Super-Efficiency Analysis

The modern, particularly competitive and demanding operational environment has led many companies to a continuous effort for implementing techniques and evaluating alternative production scenarios, which will allow them to optimise their production processes and reduce their cost. In this study, a consumer goods manufacturing company was selected to implement modern optimisation techniques in its production processes and then to evaluate the efficiency of potential changes on its operation as well as to record the problems and difficulties arising in such a case. Data Envelopment Analysis, a linear programming based technique was employed to evaluate the efficiency of twelve alternative production layout scenarios. Those scenarios were created through the application of advanced Group Technology techniques and some basic indices/characteristics were attached to each one of those layouts. Results indicated that more than one of these scenarios can be effective. An additional analysis for ranking those scenarios was conducted using the super-efficiency model. According to the results of this study, nine of the proposed scenarios are efficient and thus significant improvements can be achieved in the system’s performance, without actually changing its basic production parameters. It is concluded that both the results of the evaluation and the experience gained during the implementation phase, can be very useful for supporting the goals and decisions of the company

Application of Simulated Annealing in Improving the Performance of Stereolithography

Effective utilisation of Stereolithography (SL) mainly relies on orienting and packing parts optimally on the fabrication platform of the machine, so to achieve maximum space utilisation and minimum build time, without of course compromising surface quality. The present work focuses on an effective way to pack parts optimally on the fabrication platform of SL machine. Due to technical constrains set by SL technology, the original 3-D packing problem is simplified by one dimension by projecting each one of the parts on the build platform (x-y plane) and packing their projections instead of the actual parts themselves. In order to solve the resulting 2-D packing problem a heuristic method has been adopted. The heuristic method consists of a Simulated Annealing algorithm employing a polynomial-time cooling schedule and a new improved placement rule

ASSIGNMENT OF DISTRIBUTED PROCESSING SOFTWARE: A COMPARATIVE STUDY

A major issue of the operation of distributed systems is the problem of allocating a number of processes to a network of processors, with the aim of fully utilising their potential and flexibility. This paper presents a solution to the process allocation problem from a mathematical programming point of view, employing two heuristic algorithms . The first one is an adaptation of the simulated annealing heuristic algorithm, while the second one is based on an iterative improvement procedure. The characteristics of both heuristics are briefly examined, and in the sequel both algorithms are tested on a set of random problems having characteristics similar to a real-world problem

A heuristic approach for an inventory routing problem with backorder decisions

A multi-period inventory-routing problem is considered where a vendor serves multiple geographically dispersed customers who receive units of a single product from a depot, with adequate supply, using a capacitated vehicle. The class of problems arising from the combination of routing and inventory management decisions is known as the inventory routing problem (IRP). In this category of problems, the inventory routing problem with backorders (IRPB) deals with determining inventory levels when backorders are allowed. The aim is to minimise the total cost for the planning period, comprising of holding cost, transportation and backorder penalty cost while ensuring that inventory level capacity constraints are not violated. An Integer Programming model is first developed to provide an accurate description of the problem and then a Genetic Algorithm (GA) with suitably designed genetic operators is employed in order to obtain near optimal solutions. Computational results are presented to demonstrate the effectiveness of the proposed procedure

Efficiency evaluation of hydroelectric power plants using data envelopment analysis

The purpose of this paper is to evaluate the efficiency of a network of hydroelectric power plants using the Data Envelopment Analysis approach. The network is modelled as a linear system with multiple inputs and outputs. As inputs one could consider, for instance, the age of a plant, the total number of hours that a plant is in operation during each year, etc. As outputs the model considers the electrical energy delivered per year, the number of hours that the plant is not in operation, etc. The proposed approach does not only evaluate each plant relative to the other ones, but it also ‘produces’ policy making scenarios that would enable plant managers to improve the plant’s operational characteristics. Computational results based on real-world data are presented and discussed. Relationships between efficiency scores and various inputs/outputs are also investigated and some interesting trends are identified

Genetic Algorithm Approach for the Inventory Routing Problem with Backlogging

We consider a multi-period inventory-routing problem where a vendor serves multiple geographically dispersed customers who receive units of a single product from a depot with adequate supply. The class of problems arising from the combination of distribution and inventory management decisions is perhaps the most striking example of this concept and is known as the inventory routing problem (IRP). In this category of problems, the inventory routing problem with backlogs (IRPwB) deals with determining inventory level, backlogging and vehicle routing decisions from a single depot to a set of n customers over a specific number of time periods, using a fleet of homogenous vehicles. The aim is to minimise the average daily cost for the planning period, while ensuring that inventory level capacity constraints are not violated. We first develop an Integer Programming model to provide an accurate description of the problem and in a second phase a Genetic Algorithm (GA) with suitably designed genetic operators, is employed in order to obtain near optimal solutions