Browsing by Author "Kang, Parminder"

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    Exploration of Lean Principals in Higher Educational Institutes – Based on Degree of Implementation and Indigence
    (International Journal of Scientific & Engineering Research, 2014-02) Kang, Parminder; Manyonge, Lawrence
    Increased competition and scarcity of resources in global markets has augmented the problems for manufacturing industry, which has forced organizations to adopt new tools and techniques in order to find the proactive solutions. In Past, manufacturing industry has exemplified that the key to survive in highly competitive and rapidly changing environments is to implement the more effective, proactive and long term solutions to a problem. Similar to the manufacturing organizations, higher educational institutes are facing similar challenges such as increased competitions, higher quality of service at competitive cost and variability in customer demand in terms of requested services. In fact, in terms of problem landscape, associated problem variables and goals for higher educational institutes are similar as of manufacturing industries. The only difference is the way these are interpreted and represented. Lean principles and waste used by the manufacturing organizations are used as basic building block for this research. The main objective of this paper is to illustrate the waste in higher educational institutes with respect to the three fundamental elements i.e. Students, Research and Staff. This can provide the basic framework for other process improvement implementations in higher educational institutes. Along this, the other most important aspect is the indigence to implement Lean based approaches and up to what extent as well as effectiveness of implementation in terms of invested time, resources and money. The scope of this paper is limited to interpret the waste in higher educational institutes, which can act as basic framework for other process improvements in the educational industry. This is step forward to implement Lean based structure to the higher educational institutes in order to maximize the revenue, throughput and customer satisfaction with high quality products and minimize the cost and waste, which one of the objective of any organization.
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    Improving Manufacturing Systems Using Integrated Discrete Event Simulation and Evolutionary Algorithms
    (De Montfort University, 2012) Kang, Parminder
    High variety and low volume manufacturing environment always been a challenge for organisations to maintain their overall performance especially because of the high level of variability induced by ever changing customer demand, high product variety, cycle times, routings and machine failures. All these factors consequences poor flow and degrade the overall organisational performance. For most of the organisations, therefore, process improvement has evidently become the core component for long term survival. The aim of this research here is to develop a methodology for automating operations in process improvement as a part of lean creative problem solving process. To achieve the stated aim, research here has investigated the job sequence and buffer management problem in high variety/low volume manufacturing environment, where lead time and total inventory holding cost are used as operational performance measures. The research here has introduced a novel approach through integration of genetic algorithms based multi-objective combinatorial optimisation and discrete event simulation modelling tool to investigate the effect of variability in high variety/low volume manufacturing by considering the effect of improvement of selected performance measures on each other. Also, proposed methodology works in an iterative manner and allows incorporating changes in different levels of variability. The proposed framework improves over exiting buffer management methodologies, for instance, overcoming the failure modes of drum-buffer-rope system and bringing in the aspect of automation. Also, integration of multi-objective combinatorial optimisation with discrete event simulation allows problem solvers and decision makers to select the solution according to the trade-off between selected performance measures.
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    Integration of Design of Experiments with Discrete event Simulation for Problem Identification
    (2010) Kang, Parminder; Khalil, R. A. (Riham A.); Stockton, David
    Lean is acknowledged as one of the key tools for manufacturing organizations and service industry to survive in the competitive environment. Lean approach focuses on continuous improvement by eliminating process waste. Lean creative problem solving plays a vital role in process improvement. One of main focus is the effect of Lean problem solving in production line performance measurements. The current research focuses on Lean creative problem solving techniques by examining the root cause of the problem. Lean creative problem solving method was developed by using simulation modelling techniques.
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    Integration of discrete event simulation with an automated problem identification.
    (IMECS– International Multi-Conference of Engineers and Computer Scientists, 2010-03) Khalil, R. A. (Riham A.); Kang, Parminder; Stockton, David
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    Job Sequence Optimisation Using Combinatorial Evolutionary Approach in High Variety/Low Volume Manufacturing Environment
    (International Journal of Scientific & Engineering Research, 2013-07-01) Kang, Parminder; Khalil, R. A. (Riham A.); Stockton, David
    Today’s manufacturing industry is been through unprecedented degree of change in terms of high variety and low volume, high value, global competition, shortened product life cycles, change is management strategies, increasing quality requirements and customer expectations and increased process complexity. As a result, in recent years organisations have adopted towards optimisation of the manufacturing operations in order to stay in competition, sustain their operational performance and maximise their economic benefits. This paper exemplifies a novel approach for development of combinatorial optimisation framework using evolutionary algorithms and Discrete Event Simulation modelling to determine the optimal job sequence by taking in account multiple organisational constraints. Simulation model used in this research represents the working area at Perkins Engines Limited. This may enable organisations to deal with such a highly diversified product portfolio without jeopardizing the benefits of an efficient flow-production. In the proposed methodology, two objectives used are manufacturing lead time and total inventory holding cost to measure the effectiveness of proposed solution. However, chosen objectives can be changed according to the organisational priorities.
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    Multi-Objective Optimization Approach Using Genetic Algorithms for Quick Response to Effects of Variability in Flow Manufacturing
    (Science and Information Society (SAI), 2012) Khalil, R. A. (Riham A.); Stockton, David; Kang, Parminder; Mukhongo, Lawrence
    This paper exemplifies a framework for development of multi-objective genetic algorithm based job sequencing method by taking account of multiple resource constraints. Along this, Theory of Constraints based Drum-Buffer-Rope methodology has been combined with genetic algorithm to exploit the system constraints. This paper introduces the Drum-Buffer-Rope to exploit the system constraints, which may affect the lead times, throughput and higher inventory holding costs. Multi-Objective genetic algorithm is introduced for job sequence optimization to minimize the lead times and total inventory holding cost, which includes problem encoding, chromosome representation, selection, genetic operators and fitness measurements, where Queuing times and Throughput are used as fitness measures. Along this, paper provides a brief comparison of proposed approach with other optimisation approaches. The algorithm generates a sequence to maximize the throughput and minimize the queuing time on bottleneck/Capacity Constraint Resource (CCR). Finally, Results are analysed to show the improvement by using current research framework.
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    A Multi-Objective Optimization Approach Using Genetic Algorithms to Reduce the Level of Variability from Flow Manufacturing
    (IEEE Press, 2012) Kang, Parminder; Khalil, R. A. (Riham A.); Stockton, David
    This paper exemplifies a framework for development of multi-objective genetic algorithm based job sequencing method by taking account of multiple resource constraints. Along this, Theory of Constraints based Drum-Buffer-Rope methodology has been combined with genetic algorithm to exploit the system constraints. This paper introduces the Drum- Buffer-Rope to exploit the system constraints, which may affect the lead times, hroughput and higher inventory holding costs. Multi-Objective genetic algorithm is introduced for job sequence optimization to minimize the lead times and total inventory holding cost, which includes problem encoding, chromosome representation, selection, genetic operators and fitness measurements, where Queuing times and Throughput are used as fitness measures. The algorithm generates a sequence to maximize the throughput and minimize the queuing time on bottleneck/Capacity Constraint Resource (CCR). Finally, Results are analyzed to show the improvement by using current research framework.
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    On semi-bent functions with Niho exponents.
    (Springer, 2012) He, Y.; Ma, W.; Kang, Parminder