M. Mathirajan

A mathematical model for supply demand matching in a low carbon electricity system

Abstract In this study, the research problem of “optimally supply electricity from the existing supply options and considering non-supply options as well as future supply options in the presence of low carbon technology and emission policy interventions to minimize the overall economic costs and emission costs” is addressed. The problem is addressed in the supplyside management aspect of electricity planning. Three mathematical models are created step-by-step to propose a complete Mixed-integer Linear Programming (MILP) model for the defined research problem. A LINGO set code has been developed to generate the proposed MILP model for any given data. For validating the model, a suitable data is collected from the Karnataka Electricity System. Using this data, the proposed three models are generated and solved using LINGO. The results obtained on these three models are analyzed.

A mathematical formulation for low carbon electricity planning in the presence of technology and policy interventions

To address climate change and resource related constraints, there is a need for integrating Renewable Energy (RE) along with conventional non-renewable sources to achieve a low carbon electricity system. But in many instances, there is insufficient renewable energy capacity installed in the system. For optimally matching the supply-demand in an electricity utility with numerous supply-side management (SSM) alternatives of electricity generation, we explore various RE technology interventions along with RPO (Renewable Purchase Obligation) and REC (Renewable Energy Certificate) policy interventions. For solving this problem, a Linear Programming (LP) model to obtain an optimal mix of supply, and minimum cost and emission is developed. A LINGO set code is developed to generate the LP model for any given data. The proposed model is validated using the data obtained from Karnataka Electricity System.

Identifying Criteria for Continuous Evaluation of Software Engineers for Reward and Recognition: An Exploratory Research

Reward and Recognition R&R should be given to employees in a timely manner, based on continuous evaluation of their performance. Success of an R&R process lies in clear and well defined criteria for continuous evaluation of employee performance. Often such criteria are decided by the organization with no input from the employees. The purpose of this paper is to use qualitative research methods to explore and identify the criteria to be used for continuous employee performance evaluation for R&R in Information Technology organizations, from the perspectives of software engineers SEs and project managers PMs. Exploratory research was conducted in two phases. In Phase I, unstructured interviews are used to elicit information from 7 SEs. Caselets are prepared based on these interviews and 19 criteria are identified. In Phase II, the criteria identified in Phase I are confirmed using content analysis of semi-structured interviews, conducted on relatively larger group of SEs in stage 1 and PMs in stage 2. Additionally, 12 criteria are also identified in Phase II. Collectively 31 criteria are identified. The proposed criteria set is expected to comprehensively cover the SE performance on a continuous basis in various dimensions to award R&R.

Low-Carbon Planning in A Resource-Constrained Electricity System: A Case Study from India

India’s electricity generation system is faced with three critical challenges—energy and financial resource constraints, growing emissions of greenhouse gases, and meeting the ever-increasing and dynamic demand for electricity. A probable solution to these issues is to take judicious decisions with respect to adoption of low-carbon and renewable energy sources and technologies, and optimal management of existing installed capacity to minimize the demand–supply gap. Simultaneously, addressing these challenges results in a complex decision-making problem. We present here a mathematical model to solve this problem. The model integrates renewable energy resources, targets related to abatement of carbon emissions, and effective management of existing installed capacity with an objective to match the supply and demand for electricity dynamically while attempting to minimize emissions, emission costs, and the overall supply costs. The model is validated using the data from Karnataka state electricity system through four scenarios. The results show that the share of renewable electricity generation increases with a renewable energy penalty cost and renewable energy demand specified by an obligation to purchase it. Similarly, cost-effective reduction in emissions and increase in the share of renewable electricity generation happen simultaneously in the presence of both renewable energy and emission penalty costs but without emission reduction targets.

A conceptual simulation framework for the performance assessment of lot release policies

This paper presents a conceptual simulation framework (CSF) for the assessment of lot release policies on selected performance metrics, such as cycle time, WIP level and throughput rate. The development of a CSF is derived from the analysis of the semiconductor wafer fabrication, which is perhaps one of the most complex manufacturing processes found today. Due to economic efficiency and increased complexity, simulation emerges as a powerful technique that can capture and analyse such complex systems. The CSF which is developed here is expected to improve the productivity of simulation analyses and furthermore, it reduces the preparing time for input data and errors.

A conceptual multi-dimensional evaluation model for new product portfolio management — Using hybrid fuzzy model of AHP-DEA

Even a best performing industry requires a major improvement in project selection methods as nearly half of initial new product development ideas occur informally or without a specific goals. In an industry, for efficient selection of projects it requires trade-off among maximizing revenues, optimizing resources and minimizing risks. The objective of this paper, is to study and identify an efficient metrics to measure respective evaluative dimension; and develop a conceptual model for selection and evaluation of a set of projects for New Product Portfolio Management (NPPM). In this paper, a hybrid fuzzy model of AHP-DEA is developed. This model assist decision maker, to select the right set of projects with higher development potential to maximize profitability and minimize the associated risk.

Modeling of Scheduling Batch Processor in Discrete Parts Manufacturing

Processing using batch processors (BPs) is common in many manufacturing environments. Generally in a manufacturing environment, BPs are bottleneck operations due to their longer processing time. Batch processing also involves many complexities like compatibility of jobs that can be processed, sizes of different jobs, capacity constraint of the processor, and so on. Considering all these factors, proper scheduling of BPs is important. This chapter focuses on scheduling of BPs in three real life applications, namely, automobile gear manufacturing, semiconductor manufacturing, and steel casting industries. Integer programming models are formulated for the problems under consideration. These models will help the decision makers to understand the problems better and hence work toward appropriate solutions for the problems.