Adnan Hassan | Universiti Teknologi Malaysia - UTM (original) (raw)

Papers by Adnan Hassan

Symmetry, 2021

Monitoring manufacturing process variation remains challenging, especially within a rapid and aut... more Monitoring manufacturing process variation remains challenging, especially within a rapid and automated manufacturing environment. Problematic and unstable processes may produce distinct time series patterns that could be associated with assignable causes for diagnosis purpose. Various machine learning classification techniques such as artificial neural network (ANN), classification and regression tree (CART), and fuzzy inference system have been proposed to enhance the capability of traditional Shewhart control chart for process monitoring and diagnosis. ANN classifiers are often opaque to the user with limited interpretability on the classification procedures. However, fuzzy inference system and CART are more transparent, and the internal steps are more comprehensible to users. There have been limited works comparing these two techniques in the control chart pattern recognition (CCPR) domain. As such, the aim of this paper is to demonstrate the development of fuzzy heuristics and ...

Journal of Humanitarian Logistics and Supply Chain Management

Purpose In recent years, the balanced scorecard (BSC) has received considerable interest among pr... more Purpose In recent years, the balanced scorecard (BSC) has received considerable interest among practitioners for managing their organization’s performance. Unfortunately existing BSC frameworks, particularly for humanitarian supply chains, lack causal relationships among performance indicators, actions, and outcomes. They are not able to provide a dynamic perspective of the organization with factors that drive the organization’s behavior toward its mission. Lack of conceptual references seems to hinder the development of a performance measurement system toward this direction. The paper aims to discuss these issues. Design/methodology/approach The authors formulate the interdependencies among key performance indicators (KPIs) in terms of cause-and-effect relationships based on published case studies reported in international journals from 1996 to 2017. Findings This paper aims to identify the conceptual interdependencies among KPIs and represent them in the form of a conceptual model...

SSRN Electronic Journal

In recent years, the Balanced Scorecard (BSC) has received considerable interest among practition... more In recent years, the Balanced Scorecard (BSC) has received considerable interest among practitioners for managing their organization's performance. Unfortunately existing BSC frameworks, particularly for humanitarian supply chains, lack causal relationships among performance indicators, actions, and outcomes. They are not able to provide a dynamic perspective of the organization with factors that drive the organization's behavior towards its mission. Lack of conceptual references seems to hinder the development of a performance measurement system towards this direction. Design/methodology/approach We formulate the interdependencies among KPIs in terms of cause-and-effect relationships based on published case studies reported in international journals from 1996 to 2017. Findings This paper aims to identify the conceptual interdependencies among key performance indicators (KPIs) and represent them in the form of a conceptual model. Research limitations/implications The study is solely based on relevant existing literature. Therefore further practical research is needed to validate the interdependencies of performance indicators. Practical implications The proposed conceptual model provides the structure of a Dynamic Balanced Scorecard (DBSC) in the humanitarian supply chain and should serve as a starting reference for the development of a practical DBSC model. The conceptual framework proposed in this paper aims to facilitate further research in developing a DBSC for humanitarian organizations. Originality/value Existing BSC frameworks do not provide a dynamic perspective of the organization. The proposed conceptual framework is a useful reference for further work in developing a DBSC for humanitarian organizations.

International Journal of Services and Operations Management, 2017

Neural Computing and Applications, 2017

Just-In-Time (JIT) is a popular philosophy in many industrial practices. The concept of JIT in ea... more Just-In-Time (JIT) is a popular philosophy in many industrial practices. The concept of JIT in early studies concerned with improving operational efficiency and waste minimization. In recent decades, however, JIT principles have also connected to logistics efficiency particularly for distribution of raw materials and finished goods. In the literature, several attempts have been made to optimize JIT logistics networks. On the one hand, most studies have typically focused on deterministic and small-scale problems which have been solved by exact algorithms. On the other hand, when large-scale problems were considered and usually were solved by metaheuristics algorithms, uncertainty sources and fine-tuning of the metaheuristics parameters were generally ignored. In this paper, we develop a mixed-integer linear optimization model to investigate a large-scale JIT logistics problem with 15 different sizes. To deal with different uncertainty sources, the customers demand and suppliers’ capacity as the two main sources of uncertainty in practice are considered as triangular fuzzy parameters. The proposed model aims to minimize total logistics cost including costs of transportation, inventory holding and backorders. A particle swarm optimization algorithm is applied to solve the problem, and its results are then validated by a harmony search algorithm. Both algorithms parameters are tuned using response surface methodology and Taguchi method. Finally, the conclusion and some directions for future research are proposed.

Advanced Materials Research, Feb 6, 2014

This paper presents a preliminary work on a development of dynamic job shop scheduling model. The... more This paper presents a preliminary work on a development of dynamic job shop scheduling model. The motivation of the study comes from an urgent need for practical procedures to enable easier and accurate feedback at operational level particularly related to job shop in small and medium-sized companies. A spreadsheet-based scheduling template is formulated and modeled using Microsoft Excel. A job shop benchmark case study available in OR-Library has been chosen to demonstrate the applicability of the basic model. The preliminary result indicating that the proposed spreadsheet model needs further refinement through incorporation of dynamic factors to be obtained from industrial practitioners.

The basic goal of this paper is to create an alternative based analysis in a reliability model. D... more The basic goal of this paper is to create an alternative based analysis in a reliability model. Despite the vast amount of studies on reliability analysis and related techniques, the evaluation of the importance of different alternatives is less investigated. In order to fill this gap, this research was carried out to systematically investigate the result of setting three different scenarios on a reliability model. A model of reliability system was proposed based on redundancy allocation problems. There are certain limitations (Weight and Cost)of proposed model. In particular, three different scenarios (the results of Failure Rate preference, Weight preference and Cost Preference) was presented. This investigation operated based on the assumption of allocating only one redundant component for the subsystems, and the main goal of this investigation is to determine what would be the results when lowest possible Weight, lowest possible Cost and lowest possible failure rate is needed.

Universiti Teknologi Malaysia Institutional Repository is powered by EPrints 3 which is developed... more Universiti Teknologi Malaysia Institutional Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.

Journal of Optimization, 2016

Scheduling is considered as an important topic in production management and combinatorial optimiz... more Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications. The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex andNP-hard problems. This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan. In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules. In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation. A machine based precedence preserving order-based crossover was proposed to generate the ...

Computers & Industrial Engineering, 2016

Products distribution and transportation is one of the largest sources of CO2 emission in supply ... more Products distribution and transportation is one of the largest sources of CO2 emission in supply chains. To date, a number of researchers have argued that intensive transportation activities through popular distribution strategies such as Just-In-Time (JIT) could significantly increase carbon emissions within logistics chains. However, a systematic understanding of how JIT distribution affects carbon emissions is still lacking in current literature. In this study, we develop a bi-objective optimization model for a carbon-capped JIT distribution of multiple products in a multi-period and multi-echelon distribution network. The aims are to jointly minimize total logistics cost and to minimize the maximum carbon quota allowed per period (carbon cap). The considered problem is investigated under three different carbon emission constraints namely periodic, cumulative and global. Since the studied problem is NP-Hard, a non-dominated sorting genetic algorithm-II (NSGA-II) is developed and its parameters are tuned by Taguchi method. For further quality improvement of the developed solution approach, a novel local search approach called modified firefly algorithm incorporates NSGA-II. Different sizes of the problem are considered to compare the performances of the proposed hybrid NSGA-II and the classical one. Finally, the results are presented along with some policy and managerial insights. For policy makers, the findings show the impact of varying the carbon emission cap on total cost and total emissions under JIT distribution concept. From managerial perspectives, we analyze the relationships between average inventory holding and backlog level per period which can assist mangers to identify critical decisions for JIT distribution of products in carbon-capped environment.

Classification of Shewhart X-Bar Control Chart Patterns 93 5 CLASSIFICATION OF SHEWHART X-BAR CON... more Classification of Shewhart X-Bar Control Chart Patterns 93 5 CLASSIFICATION OF SHEWHART X-BAR CONTROL CHART PATTERNS USING ARTIFICIAL NEURAL NETWORK Adnan Hassan 5. 1 INTRODUCTION Control charts were introduced by Shewhart in 1924 and remain ...

IOP Conference Series: Materials Science and Engineering, 2016

European Journal of Scientific Research, Jan 6, 2010

Control chart pattern recognition has become an active area of research since late 1980s. Much pr... more Control chart pattern recognition has become an active area of research since late 1980s. Much progress has been made, in which there are trends to heighten the performance of artificial neural network (ANN)-based control chart pattern recognition schemes through feature-based and wavelet-denoise input representation techniques, and through modular and integrated recognizer designs. There is also a trend to enhance it's capability for monitoring and diagnosing multivariate process shifts. However, there is a lack of literature providing a critical review on the issues associated to such advances. The purpose of this paper is to highlight research direction, as well as to present a summary of some updated issues in the development of ANN-based control chart pattern recognition schemes as being addressed by the frontiers in this area. The issues highlighted in this paper are highly related to input data and process patterns, input representation, recognizer design and training, and multivariate process monitoring and diagnosis. Such issues could be useful for new researchers as a starting point to facilitate further improvement in this area.

Although most job shop scheduling problems are concerning dynamic demand and stochastic processin... more Although most job shop scheduling problems are concerning dynamic demand and stochastic processing time, the majority of existing scheduling techniques are based on static demand and deterministic processing time. As a consequence, the solutions obtained from the traditional scheduling technique are ineffective wherever changes occur to the system. This project profile reports on the development of an improved model for solving dynamic job shop scheduling problem. The model refers to as a decision support tool (DST) was developed using discrete event simulation, artificial neural network, and database techniques. An integrated reference model for guiding in design and development of the DST for dynamic job shop scheduling is presented. The experimental results provide evidence that the prototype DST system is promising and able to effectively satisfy multiple scheduling objectives. The DST system provides alternative recommended schedules aimed for practitioners with minimum knowled...

Development in computing technology has motivated researchers to explore the application of patte... more Development in computing technology has motivated researchers to explore the application of pattern recognition to classify the statistical process control (SPC) chart patterns. Most of the existing SPC chart pattern recognition systems are limited to standalone systems. This research attempts to enhance the existing control chart pattern recognition into a web-enabled system. This web-based control chart pattern recognition system aims to provide information sharing and to enable user to access it at anytime from anywhere through Internet. The paper highlights design consideration for the development of such systems. A preliminary framework being investigated by the authors are discussed.

Various artificial neural networks (ANN)-based pattern recognition schemes have been developed fo... more Various artificial neural networks (ANN)-based pattern recognition schemes have been developed for monitoring and diagnosis of mean shift variations in bivariate processes. These schemes generally perform better in classifying mean shifts and provide more diagnosis information compared to the traditional multivariate statistical process control (MSPC) charts. However, some disadvantages in terms of reference multivariate/bivariate patterns and excess false alarms may restrict attention on the scopes and development in this area. Therefore, this paper aims to investigate two-stages monitoring and diagnosis for some reference bivariate correlated patterns using an integrated multivariate exponentially weighted moving average (MEWMA)-ANN. Feature-based input representation was applied into an ANN training towards improving discrimination capability between bivariate normal and bivariate mean shift patterns. Besides comparable diagnosis performance, the proposed scheme has resulted in b...

Automated recognition of control chart patterns for monitoring and diagnosing process quality has... more Automated recognition of control chart patterns for monitoring and diagnosing process quality has been an active area of research since the last 20 years. An artificial neural network (ANN) based models with back-propagation algorithm was known to have resulted the promising recognition accuracy. However, the performance of an ANN depends on a proper selection of the design parameters. In this paper, full factorial design of experiment (DOE) was utilized in investigating several parameters that influence the recognition accuracy of an ANN. This systematic method provided an optimal ANN design with satisfied recognition accuracy.

Journal of King Saud University - Computer and Information Sciences, 2012

Various artificial neural networks (ANN)-based pattern recognition schemes have been developed fo... more Various artificial neural networks (ANN)-based pattern recognition schemes have been developed for monitoring and diagnosis of bivariate process variation in mean shifts. In comparison with the traditional multivariate statistical process control (MSPC) charts, these advanced schemes generally perform better in identifying process mean shifts and provide more effective information towards diagnosing the root causes. However, it seemly less effective for multivariate quality control (MQC) application due to disadvantages in reference bivariate patterns and imbalanced monitoring performance. To achieve 'balanced monitoring and accurate diagnosis', this study proposes an integrated multivariate exponentially weighted moving average (MEWMA)-ANN scheme for two-stages monitoring and diagnosis of some reference bivariate patterns. Raw data and statistical features input representations were applied into training of the Synergistic-ANN recognizer for improving patterns discrimination capability. The proposed scheme has resulted in better monitoring-diagnosis performances with smaller false alarm, quick mean shift detection and higher diagnosis accuracy compared to the basic scheme.

Although most job shop scheduling problems are concerning dynamic demand and stochastic processin... more Although most job shop scheduling problems are concerning dynamic demand and stochastic processing time, the majority of existing scheduling techniques are based on static demand and deterministic processing time. As a consequence, the solutions obtained from the traditional scheduling technique are ineffective wherever changes occur to the system. The Decision Support Tool (DST) for dynamic job shop scheduling was developed. The components of DST, namely, the knowledge-base, database , rule base, and graphical user interface were integrated. It provides alternative recommended schedules to be selected by practitioners with minimum knowledge in job shop scheduling. Validation of DST shows that the performance of DST is promising and able to provide comparable results as when using discrete event simulation. This paper demonstrates the capability of the DST to handling changes in demand, i.e. job cancellation. The demonstration suggests that the proposed DST can effectively handle job cancellation during early, middle or late time instances.

Symmetry, 2021

Journal of Humanitarian Logistics and Supply Chain Management

SSRN Electronic Journal

International Journal of Services and Operations Management, 2017

Neural Computing and Applications, 2017

Advanced Materials Research, Feb 6, 2014

Journal of Optimization, 2016

Computers & Industrial Engineering, 2016

IOP Conference Series: Materials Science and Engineering, 2016

European Journal of Scientific Research, Jan 6, 2010

Journal of King Saud University - Computer and Information Sciences, 2012

Various artificial neural networks (ANN)-based pattern recognition schemes have been developed fo... more Various artificial neural networks (ANN)-based pattern recognition schemes have been developed for monitoring and diagnosis of bivariate process variation in mean shifts. In comparison with the traditional multivariate statistical process control (MSPC) charts, these advanced schemes generally perform better in identifying process mean shifts and provide more effective information towards diagnosing the root causes. However, it seemly less effective for multivariate quality control (MQC) application due to disadvantages in reference bivariate patterns and imbalanced monitoring performance. To achieve 'balanced monitoring and accurate diagnosis', this study proposes an integrated multivariate exponentially weighted moving average (MEWMA)-ANN scheme for two-stages monitoring and diagnosis of some reference bivariate patterns. Raw data and statistical features input representations were applied into training of the Synergistic-ANN recognizer for improving patterns discrimination capability. The proposed scheme has resulted in better monitoring-diagnosis performances with smaller false alarm, quick mean shift detection and higher diagnosis accuracy compared to the basic scheme.

Although most job shop scheduling problems are concerning dynamic demand and stochastic processin... more Although most job shop scheduling problems are concerning dynamic demand and stochastic processing time, the majority of existing scheduling techniques are based on static demand and deterministic processing time. As a consequence, the solutions obtained from the traditional scheduling technique are ineffective wherever changes occur to the system. The Decision Support Tool (DST) for dynamic job shop scheduling was developed. The components of DST, namely, the knowledge-base, database , rule base, and graphical user interface were integrated. It provides alternative recommended schedules to be selected by practitioners with minimum knowledge in job shop scheduling. Validation of DST shows that the performance of DST is promising and able to provide comparable results as when using discrete event simulation. This paper demonstrates the capability of the DST to handling changes in demand, i.e. job cancellation. The demonstration suggests that the proposed DST can effectively handle job cancellation during early, middle or late time instances.