Dynamic Promotional Resource Allocation for Segment Specific and Spectrum Effect of Promotion for a Product Line Incorporating Repeat Purchase Behavior

Promotion is an essential element in the marketing mix. It is used by businesses to inform, influence and persuade customers to adopt the products and services they offer. Without promotion, business would be stagnant and lack substantial growth because the brands would have low visibility in the market. Moreover, today’s vast and assorted markets comprise of customers with different needs and varied behavior. So it is rarely possible for companies to satisfy all customers by treating them alike. Thus there arises a need to divide the market into segments having customers with similar traits/characteristics. After identifying appropriate market segments, firms can design differentiated promotional campaigns for each segment. At the same time there can be a mass market promotional campaign that reaches different segments with a fixed spectrum. Also since promotional effort resources are limited, one must use them judiciously. In this paper, we formulate mathematical programming problem under repeat purchase scenario, which optimally allocates mass promotional effort resources and differentiated promotional effort resources across the segments dynamically in order to maximize the overall sales obtained from multiple products of a product line under budgetary and minimum sales aspiration level constraint on each product under consideration in each segment. The planning horizon is divided into multi periods, the adoption pattern of each product in each segment is observed in every subinterval and accordingly promotional effort allocations are determined for the next period till we reach the end of planning period. The optimization model has been further extended to incorporate minimum aspiration level constraints on total sales for each product under consideration from all the segments taken together. The non linear programming problem so formulated is solved using differential evolution approach. A numerical example has been discussed to illustrate applicability of the model.

Optimal Sprint Length Determination for Agile-Based Software Development

A carefully planned software development process helps in maintaining the quality of the software.In today’s scenario the primitive software development models have been replaced by the Agile based models like SCRUM,KANBAN,LEAN,etc.Although,every framework has its own boon,the reason for widespread acceptance of the agile-based approach is its evolutionary nature that permits change in the path of software development.The development process occurs in iterative and incremental cycles called sprints.In SCRUM,which is one of the most widely used agile-based software development modeling framework;the sprint length is fixed throughout the process wherein;it is usually taken to be 1–4 weeks.But in practical application,the sprint length should be altered intuitively as per the requirement.To overcome this limitation,in this paper,a methodical work has been presented that determines the optimal sprint length based on two varied and yet connected attributes;the cost incurred and the work intensity required.The approach defines the number of tasks performed in each sprint along with the corresponding cost incurred in performing those tasks.Multi-attribute utility theory(MAUT),a multi-criterion decision making approach,has been utilized to find the required trade-off between two attributes under consideration.The proposed modeling framework has been validated using real life data set.With the use of the model,the optimal sprint for each sprint could be evaluated which was much shorter than the original length.Thus,the results obtained validate the proposal of a dynamic sprint length that can be determined before the start of each sprint.The structure would help in cost as well as time savings for a firm.

Reliability Growth Modeling and Optimal Release Policy Under Fuzzy Environment of an N-version Programming System Incorporating the Effect of Fault Removal Efficiency

Failure of a safety critical system can lead to big losses. Very high software reliability is required for automating the working of systems such as aircraft controller and nuclear reactor controller software systems. Fault-tolerant softwares are used to increase the overall reliability of software systems. Fault tolerance is achieved using the fault-tolerant schemes such as fault recovery （recovery block scheme）, fault masking （N-version programming （NVP）） or a combination of both （Hybrid scheme）. These softwares incorporate the ability of system survival even on a failure. Many researchers in the field of software engineering have done excellent work to study the reliability of fault-tolerant systems. Most of them consider the stable system reliability. Few attempts have been made in reliability modeling to study the reliability growth for an NVP system. Recently, a model was proposed to analyze the reliability growth of an NVP system incorporating the effect of fault removal efficiency. In this model, a proportion of the number of failures is assumed to be a measure of fault generation while an appropriate measure of fault generation should be the proportion of faults removed. In this paper, we first propose a testing efficiency model incorporating the effect of imperfect fault debugging and error generation. Using this model, a software reliability growth model （SRGM） is developed to model the reliability growth of an NVP system. The proposed model is useful for practical applications and can provide the measures of debugging effectiveness and additional workload or skilled professional required. It is very important for a developer to determine the optimal release time of the software to improve its performance in terms of competition and cost. In this paper, we also formulate the optimal software release time problem for a 3VP system under fuzzy environment and discuss a the fuzzy optimization technique for solving the problem with a numerical illustration.

Selecting Best Software Vulnerability Scanner Using Intuitionistic Fuzzy Set TOPSIS

Software developers endeavor to build their products with the least number of bugs.Despite this,many vulnerabilities are detected in software that threatens its integrity.Various automated software i.e.,vulnerability scanners,are available in the market which helps detect and manage vulnerabilities in a computer,application,or a network.Hence,the choice of an appropriate vulnerability scanner is crucial to ensure efficient vulnerability management.The current work serves a dual purpose,first,to identify the key factors which affect the vulnerability discovery process in a network.The second,is to rank the popular vulnerability scanners based on the identified attributes.This will aid the firm in determining the best scanner for them considering multiple aspects.The multi-criterion decision making based ranking approach has been discussed using the Intuitionistic Fuzzy set(IFS)and Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS)to rank the various scanners.Using IFS TOPSIS,the opinion of a whole group could be simultaneously considered in the vulnerability scanner selection.In this study,five popular vulnerability scanners,namely,Nessus,Fsecure Radar,Greenbone,Qualys,and Nexpose have been considered.The inputs of industry specialists i.e.,people who deal in software security and vulnerability management process have been taken for the ranking process.Using the proposed methodology,a hierarchical classification of the various vulnerability scanners could be achieved.The clear enumeration of the steps allows for easy adaptability of the model to varied situations.This study will help product developers become aware of the needs of the market and design better scanners.And from the user’s point of view,it will help the system administrators in deciding which scanner to deploy depending on the company’s needs and preferences.The current work is the first to use a Multi Criterion Group Decision Making technique in vulnerability scanner selection.

Operationalizing Adolescent Health Services at Primary Health Care Level in India: Processes, Challenges and Outputs

Background: Ministry of Health, Government of India developed the Adolescent Reproductive and Sexual Health (ARSH) strategy and operationalized adolescent health services up to district and sub-district hospital level. Objectives: To operationalize adolescent health services at primary health care level in a block of Maharashtra;assess impact of need based interventions on quality of services;and understand potential for scalability in the state. Methods: Adolescent and Youth friendly centers were established at primary health care settings and interventions such as health system strengthening, sensitizing gatekeepers, involving Accredited Social Health Activist (ASHAs), developing inter and intra-sectoral linkages, improving monitoring and evaluation were tested. Results: Over a period of 2009-2014, there was a steady increase in the number of clients attending the Adolescent and Youth Friendly Health Centers (A&YFHCs). Attitude of providers to address adolescents’ needs improved significantly. Successful interventions were networking with schools, colleges and Non Government Organization (NGOs), linkages with HIV program and Integrated Child Development Services (ICDS), and involvement of ASHAs. Conclusions: The study demonstrates that although health system has the primary responsibility of addressing health issues among adolescents;it has limitations in terms of its reach to adolescents and generating demand for services. There is a need to network with education sector, ICDS, NGOs working for adolescent health and development to work as a team and address the multifaceted needs of the adolescents. Such a strategy will be crucial while implementing the recently launched Rashtriya Kishor Swasthya Karyakram— the new national adolescent health programme in India.

Adaptation of ant supercolony behavior to solve route assignment problem in integers

Purpose-Until now,the algorithms used to compute an equilibrate route assignment do not return an integer solution.This disagreement constitutes a non-negligible drawback.In fact,it is shown in the literature that a fractional solution is not a good approximation of the integer one.The purpose of this paper is to find an integer route assignment.Design/methodology/approach-The static route assignment problem is modeled as an asymmetric network congestion game.Then,an algorithm inspired from ant supercolony behavior is constructed,in order to compute an approximation of the Pure Nash Equilibrium(PNE)of the considered game.Several variants of the algorithm,which differ by their initializing steps and/or the kind of the provided algorithm information,are proposed.Findings-An evaluation of these variants over different networks is conduced and the obtained results are encouraging.Indeed,the adaptation of ant supercolony behavior to solve the problem under consideration shows interesting results,since most of the algorithm’s variants returned high-quality approximation of PNE in more than 91 percent of the treated networks.Originality/value-The asymmetric network congestion game is used to model route assignment problem.An algorithm with several variants inspired from ant supercolony behavior is developed.Unlike the classical ant colony algorithms where there is one nest,herein,several nests are considered.The deposit pheromone of an ant from a given nest is useful for the ants of the other nests.

Optimal advertising duration for profit maximization

The current research elucidates the advertising scheme of automotive innovation by incorporating the various stages of the product life cycle.The study proposes an empirical model for the automotive industry to evaluate a time-point known as a switch-point or a take-off point at which firms should modify the advertising and sales promotion strategies to boost sales volume.The problem applies a time-series innovation diffusion model wherein adoption rate changes when a product enters a growth stage and then again when the company stops the advertising campaign in the maturity stage.The present paper develops a profit maximization problem,which optimizes the overall advertising duration and advertising take-off point.A numerical illustration is provided using the actual sales data of automobile industries,and sensitivity analysis is further performed to validate the effect of critical parameters on the optimization problem.

An innovation diffusion model for consumer durables with three parameters

Strategic innovation diffusion converts newly created knowledge into increasing a firm’s value primarily through innovative product offerings.In this paper,we present a time-based adoption pattern with pricing and promotional expenditure as a three-dimensional innovation diffusion model(3D-IDM).In our proposed 3D-IDM,we assume that value of the product plays a crucial role of being the major driver of diffusion,and is classified into the following three main factors:(1)continuation time of the product in the market–representing goodwill of the product;(2)price of the product–indicating consumers’buying behaviour;and(3)marketing efforts of the firm.A special form of the Cobb–Douglas production function is used to design the three-dimensional framework.An empirical study is performed on number of consumer-durable sales data to validate and compare the proposed model.Various performance measures are treated uniquely using the Mahalanobis distance-based approach(DBA)to determine the relative strength of each model.