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Software reliability plays an important role in assuring the quality of a software. To ensure software reliability, the software is tested thoroughly during the testing phase. The time invested in the testing phase or the optimal software release time depends on the level of reliability to be achieved. There are two different concepts related to software reliability, viz., testing reliability and operational reliability. In this paper, we compare both types of software reliabilities to determine the optimal testing time of the software so as to minimize the total expected software maintenance cost. We consider a software has a number of clusters of modules, each having a different number of errors and a different failure rate. A hyperexponential model is employed for analyzing software reliability growth. Parameter estimation using the maximum likelihood estimation technique is also discussed. Numerical illustrations are taken to explore the effect of various parameters on reliability and maintenance cost. It is noticed that the operational reliability concept should be adopted for the software testing time problem.

For a repairable product that fails under warranty, the manufacturer can choose between repair and replacement. Many different warranty servicing strategies involving these two types of rectification action have already been studied. In this paper, we propose two new servicing strategies involving minimal repair, each with two parameters that are selected optimally to minimize expected warranty servicing costs. We develop models to obtain the optimal parameter values for each new strategy and then carry out a comparison with a selection of the existing strategies through a numerical example.

Offering warranty for a second-hand item stimulates the sales of the item, but at the same time, it accumulates additional warranty servicing cost. This additional cost can be reduced through actions that improve the reliability of the item, such as overhaul and upgrade. An upgrade action brings the second-hand item to an improved functional state and it effectively reduces the age of the item. In this paper, we propose a model aiming to determine the optimal upgrade action strategies that achieve a sensible trade-off between the cost of an upgrade action and the reduction of the expected warranty cost due to this action. A practical application case on electric drills is used to illustrate our findings.

The time of domination has ended. Competition is "in". To get prolonged huge profit in highly competitive markets, firms need some effective tools and techniques. An instrument for competitive advantage in such prevailing cut throat environment is product differentiation. The general differentiation factors can be the product or service offerings through the value added services or offerings through product extensions and one of the most important differentiating factors amongst all is product warranty. Warranty is seller assurance to a buyer that a product will carry out as stated and this promise works as a confidence for the buyer in purchasing the product. But servicing a warranty engages additional costs to the manufacturer and this has an effect on the profit levels. Warranty cost may be reduced by providing higher reliable product. The conflict existing in the context of software reliability, warranty period and selling price need to be considered jointly. In this paper, we develop an optimization model to determine the optimal price, time to stop testing under a given warranty period with constraint on reliability and sales of the software product so as to maximize the profit. Numerical examples have been done to illustrate the proposed model.

Accelerated life tests (ALTs) are used to make timely assessments of the life time distribution of highly reliable materials and components. Much of the previous work on ALTs has focused on constant-stress, step-stress, ramp-stress schemes and their various combinations. In the literature ramp-stress ALTs that have been formulated can be conducted when more than one test chambers are available; installation of which may prove to be costly. Even when one test chamber is used the stress rate remains constant throughout the duration of the experiment. It is not only necessary but imperative to examine if the test specimen is able to withstand differing stress conditions with the passage of time. This paper therefore, presents an optimal design of multi-objective modified ramp-stress ALT model with weighted goal programming approach. The modified ramp-stress uses one test chamber in place of the various chambers used in simple ramp-stress ALT thus saving experimental cost. With the market being increasingly competitive the emphasis today is on goal attainment with minimum deviations. Goal programming is a method to solve multi-objective problems that has applications in varied fields of engineering and operational research. The optimal plan consists in finding out relevant experimental variables, namely, stress rate, stress rate change point and warranty period, by using goal programming on weighted sum of variance of reliability function and expected warranty cost with pre-specified mission time under normal operating conditions. The Burr type XII life distribution and time-censored data have been used for the purpose. Burr type XII life distribution has been found appropriate for accelerated life testing experiments. The method developed has been explained using a numerical example and sensitivity analysis carried out. Comparative study has also been done to highlight the merits of the proposed model.

Technological advancements have affected each and every aspect of our day to day life. Over the last few decades our dependence on software products has increased immensely. At the same time, software technologies have been advancing at an electrifying pace to achieve new milestones. With this rapid development, competition between the developers is gaining new heights. In such a competitive business arena, customer satisfaction plays a vital role to decide on the survival of product in the market. So, it becomes essential for the firms to work on strategies of product portfolios to attract more customers in order to elevate profit levels. In this regard, firms are using different strategies to obtain a significant market share. An appropriate price becomes one of the most effective factors to influence customers demand. On the other hand, warranty length also acts as an attribute of quality and forms another factor that may sway the customer towards or away from the product. In a warranty contract, the seller or manufacturer promises for a free repair or renewal policy for failed products. In this paper, we have investigated the marketing and production problem for a software firm considering price and warranty as simultaneous dynamic decision variables under the condition of a dynamic demand. We have considered a demand function dependent upon price, warranty and cumulative sales. We have used optimal control theoretic approach for the proposed profit maximization model to derive optimal price and warranty policies. The results specify several optimal policies with which decision makers would gain insight into the consequence of their decisions. Further, genetic algorithm is used to find the optimal results. Numerical example is provided to illustrate the applicability of model.

In this paper, the optimum user’s maintenance strategy of a repairable system for free preventive maintenance (PM) warranty policy is proposed. This study considers predicted maintenance due to the system failure is likely occurring and requires repair during periodic maintenance time. Periodic maintenance can be classified as one of three types — imperfect PM, perfect PM and predicted maintenance. The probability that periodic maintenance is perfect PM or predicted maintenance depends on the number of imperfect maintenance operations conducted since the previous renewal cycle. The sellers offer free perfect and imperfect PM warranty. An optimal periodic maintenance time is determined by minimizing the total cost. Some special cases, implemented with machine learning and human learning, are given to demonstrate the feasibility of the proposed strategy. A numerical example is given.