Veena Adlakha

Attributes of Service Quality: The Consumers′ Perspective

Examines the assessment of service quality as perceived by consumers. Identifies and ranks the attributes for good and poor quality of five selected types of services. The five types of services considered in this study are physician service, retail banking, auto maintenance, college/university, and fast food. The findings indicate that consumers have well‐conceived ideas about service quality, and that certain quality attributes are considered important for most types of services. Interestingly, finds that some attributes seem to have effects similar to Herzberg′s hygiene factors, i.e. the absence of certain attributes may lead consumers to perceive service quality as poor. However, the presence of these attributes may not substantially improve the perceived quality of the service. Also suggests that most customers would be willing to trade some convenience for a price break, and that the behaviour, skill level and performance of service employees are key determinants of perceived quality of services. Th...

A SIMPLE HEURISTIC FOR SOLVING SMALL FIXED-CHARGE TRANSPORTATION PROBLEMS

The fixed-charge transportation problem (FCTP) is an extension of the classical transportation problem in which a fixed cost is incurred, independent of the amount transported, along with a variable cost that is proportional to the amount shipped. The introduction of fixed costs in addition to variable costs results in the objective function being a step function. Therefore, fixed-charge problems are usually solved using sophisticated analytical or computer software. This paper deviates from that approach. It presents a simple heuristic algorithm for the solution of small fixed-charge problems. We present numerical examples to illustrate applications of the proposed method.

A quick sufficient solution to the More-for-Less paradox in the transportation problem

In a transportation problem, is it possible to find a solution with less (or equal) cost than the optimal solution by shipping more units under the condition that at least the same amount is shipped from each supply point and to each market? This more-for-less analysis could be useful for managers in decisions such as increasing warehouse/plant capacity, or advertising efforts to increase demand at certain markets. In this paper we develop a sufficient condition to identify candidate markets and supply points. The method is easy to apply and can serve as an effective tool for managers in solving the more-for-less paradox for large transportation problems, by providing the user with an insight into the problem. The procedure developed in this paper can also be used as an affective alternate solution algorithm for solving certain transportation problems.

Solving transportation problems with mixed constraints

Abstract In this paper we provide a heuristic algorithm for solving transportation problems with mixed constraints and extend the algorithm to find a more-for-less (MFL) solution, if one exists. Though many transportation problems in real life have mixed constraints, these problems are not addressed in the literature because of the rigor required to solve these problems optimally. The proposed algorithm builds on the initial solution of the transportation problem.

A heuristic method for 'more-for-less' in distribution-related problems

A heuristic algorithm for shipping more for less in distribution-related problems is proposed. Usually these problems are formulated as transportation problems. Many distribution problems, however, can only be modelled as fixed-charge transportation problems. The more-for-less paradox occurs when it is possible to ship more total goods for less (or equal) total cost while shipping the same amount or more from each origin and to each destination and keeping all the shipping costs non-negative. The existing literature has demonstrated the practicality of identifying cases where the paradoxical situation exists. Though vital to decision making, these topics are usually not covered in business classes because of the rigour required to solve these problems optimally. The proposed algorithm builds on the optimal solution of a transportation problem. As such, it is easy to understand and apply, and can serve as an effective tool for managers and students alike for extending the solution to search for more-for-less opportunities in distribution problems. We present numerical examples to illustrate applications of the proposed method.

Managing cost uncertainties in transportation and assignment problems

In a fast changing global market, a manager is concerned with cost uncertainties of the cost matrix in transportation problems (TP) and assignment problems (AP).A time lag between the development and application of the model could cause cost parameters to assume different values when an optimal assignment is implemented. The manager might wish to determine the responsiveness of the current optimal solution to such uncertainties. A desirable tool is to construct a perturbation set (PS) of cost coeffcients which ensures the stability of an optimal solution under such uncertainties.

HEURISTIC ALGORITHMS FOR THE FIXED-CHARGE TRANSPORTATION PROBLEM

In a recent paper, Adlakha and Kowalski [Omega, 31, 205–211] present a heuristic algorithm for solving fixed-charge transportation problem with a fixed cost between an origin and destination pair which is independent of the amount transported, along with a variable cost that is proportional to the amount shipped. The method though simple is designed for only solving small problems and does not provide optimal solution in many instances as fixed costs increase. In this paper we present a series of robust algorithms to extend the applicability to larger size problems including those with large fixed costs. Using randomly generated test problems, we present computational experience for the proposed and comparable algorithms.

A note on the procedure MFL for a more-for-less solution in transportation problems

In a recent paper, Adlakha and Kowalski [A quick sufficient solution to the more-for-less paradox in the transportation problems. Omega, 1998;26:541-7] present a solution method for the more-for-less paradox for transportation problems. The method, though efficient, does not provide specific directions in some instances. In this note we modify the procedure to address issues raised by readers.

An alternative solution algorithm for certain transportation problems

This paper presents an algorithm consisting of two pre-processing techniques that go part way in solving certain transportation problems. The proposed algorithm approaches the problem in a novel way and consists of Procedures A and B. Procedure A searches for absolute cells, loads those with the maximum feasible amount, and reduces the TP matrix. In some cases, this preprocessing, Procedure A, might solve a transportation problem completely and yield the optimal solution. Procedure B searches for inverse absolute cells and eliminates those from further considerations. If enough of these cells are eliminated, the optimal solution is squeezed into the remaining cells and found by an analysis in the last step. Unlike other methods, the first solution is the optimal solution. The algorithm provides the user/student an insight into the problem and the ability to critically analyze the data. It helps to better understand the phenomena of alternative solutions. It is easy to apply and can serve as an effective p...

A Monte Carlo Technique with Quasirandom Points for the Stochastic Shortest Path Problem

SYNOPTIC ABSTRACTThis paper develops a simulation procedure for estimating the distribution function and other parameters of the shortest path length in stochastic activity networks. The method builds on a recently developed conditional Monte Carlo method which involves both the uniformly directed cutsets and the unique arcs while exploiting the presence of series. We extend the estimation beyond the distribution function to other parameters related to the shortest path through a stochastic network. The method uses quasirandom points in lieu of independent random points in an attempt to further improve the efficiency of estimators. The technique is illustrated using a Monte Carlo sampling experiment for a network with 15 arcs. Using an extensive experimental design, the results reveal that the use of quasirandom points greatly enhances the performance of the method.