Sanjoy Paul is currently working as a Senior Lecturer in operations and supply chain management at UTS Business School. Prior joining to UTS as a lecturer in 2017, he also served RMIT University, Australia and BUET, Bangladesh as an academic staff. He has published more than 50 articles in top-tier journals and conferences including European Journal of Operational Research, International Journal of Production Economics, Computers and Operations Research, International Journal of Production Research, Annals of Operations Research, Journal of Management in Engineering, Journal of Cleaner Production, Computers and Industrial Engineering, Journal of Retailing and Consumer Services, Journal of Intelligent Manufacturing and so on. He is also an active reviewer of many reputed journals. His research interest includes supply chain risk management, modelling, applied operations research, and intelligent decision making.
Dr Paul has received several awards in his career, including ASOR Rising Star Award, Excellence in Early Career Research Award from UTS Business School, the Stephen Fester prize for most outstanding thesis from UNSW, high impact publications award for publishing articles in top-tier journals, and several university scholarships for outstanding results in undergraduate and postgraduate level.
Dr Paul is a member of the Australian Society for Operations Research (ASOR) and Australasian Supply Chain Institute (ASCI). He is also a committee member for the industry risk committee of ASCI.
2019 - Associate Editor, Modern Supply Chain Research and Applications, Emerald
2019 - 20, Guest Editor, Sustainability, MDPI
2019 - 20 Guest Editor, Modern Supply Chain Research and Applications, Emerald
2019 - 20 Guest Editor, International Journal of Shipping and Transport Logistics, Inderscience
2019 Member, Industry Risk Committee, Australasian Supply Chain Institute (ASCI)
2019- Editorial Board Member, Journal of International Logistics and Trade
2019 Invited Speaker, International Conference on Business Analytics and Operations Research, 14-16 June, India
2018 ASOR Rising Star Award (a national award), The Australian Society for Operations Research (ASOR)
2018 Excellence in Early Career Research Award, UTS Business School
2017 -18 Guest Editor, Global Journal of Flexible Systems Management, Springer
2016 Program committee member, ASOR conference
2015 The Stephen Fester Prize for the most outstanding PhD thesis from UNSW Canberra
2015 The high impact publications award from UNSW Canberra
Member, Australian Society for Operations Research
Member, Australasian Supply Chain Institute
Received recognition of outstanding Reviewer of many academic journals
Can supervise: YES
Supply chain risk and disturbance management
Sustainable supply chain
Modelling and simulation
Applied operations research
Intelligent decision making
Multi-criteria decision making
Strategic supply chain management
Supply chain complexity and risk management
Operations and supply chain management
Enterprise risk management
Quantitative decision making
Paul, SK, Asian, S, Goh, M & Torabi, SA 2019, 'Managing sudden transportation disruptions in supply chains under delivery delay and quantity loss', Annals of Operations Research, vol. 273, no. 1-2, pp. 783-814.View/Download from: UTS OPUS or Publisher's site
Paul, S, Sarker, R, Essam, D & Lee, PT-W 2019, 'A Mathematical Modelling Approach for Managing Sudden Disturbances in a Three-Tier Manufacturing Supply Chain', Annals of Operations Research, pp. 1-37.View/Download from: UTS OPUS or Publisher's site
Moktadir, MA, Ali, SM, Paul, SK & Shukla, N 2019, 'Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh', Computers and Industrial Engineering, vol. 128, pp. 1063-1075.View/Download from: UTS OPUS or Publisher's site
Recently, big data (BD) has attracted researchers and practitioners due to its potential usefulness in decision-making processes. Big data analytics (BDA) is becoming increasingly popular among manufacturing companies as it helps gain insights and make decisions based on BD. However, there many barriers to the adoption of BDA in manufacturing supply chains. It is therefore necessary for manufacturing companies to identify and examine the nature of each barrier. Previous studies have mostly built conceptual frameworks for BDA in a given situation and have ignored examining the nature of the barriers to BDA. Due to the significance of both BD and BDA, this research aims to identify and examine the critical barriers to the adoption of BDA in manufacturing supply chains in the context of Bangladesh. This research explores the existing body of knowledge by examining these barriers using a Delphi-based analytic hierarchy process (AHP). Data were obtained from five Bangladeshi manufacturing companies. The findings of this research are as follows: (i) data-related barriers are most important, (ii) technology-related barriers are second, and (iii) the five most important components of these barriers are (a) lack of infrastructure, (b) complexity of data integration, (c) data privacy, (d) lack of availability of BDA tools and (e) high cost of investment. The findings can assist industrial managers to understand the actual nature of the barriers and potential benefits of using BDA and to make policy regarding BDA adoption in manufacturing supply chains. A sensitivity analysis was carried out to justify the robustness of the barrier rankings.
Paul, SK, Sarker, R & Essam, D 2018, 'A reactive mitigation approach for managing supply disruption in a three-tier supply chain', Journal of Intelligent Manufacturing, vol. 29, no. 7, pp. 1581-1597.View/Download from: UTS OPUS or Publisher's site
Lu, D, Ding, Y, Asian, S & Paul, SK 2018, 'From Supply Chain Integration to Operational Performance: The Moderating Effect of Market Uncertainty', Global Journal of Flexible Systems Management, vol. 19, no. 1, pp. 3-20.View/Download from: UTS OPUS or Publisher's site
This research examines the moderating effect of market uncertainty on the causal effects from supply chain integration to operational performance of a typical supply chain. Based on an extensive and critical literature review, two exploratory conceptual hypotheses have been developed for the nonlinear relationship between the supply chain integration and operational performance of the original equipment manufacturer, and how may that relationship be moderated by a specific construct of market uncertainty. Empirical survey instrument has been designed and applied to gather the data from a wide spectrum of automotive industry in China. Confirmative factor analysis and threshold regression analysis were used as the primary research methodology to test the hypotheses. We find strong support to the hypotheses from the empirical evidence, which leads to the finding that the relationship between the supply chain integration and operational performance is 'nonlinear', and the 'nonlinearity' can be significantly moderated by the market uncertainty as one of the key environmental factors for the supply chain. This study extends the current literature by contributing for the first time the discussion of an analytical model that represents the causal effects from supply chain integration to its operational performance with respect to the market uncertainty as a moderating factor.
Ali, SM, Rahman, MH, Tumpa, TJ, Moghul Rifat, AA & Paul, SK 2018, 'Examining price and service competition among retailers in a supply chain under potential demand disruption', Journal of Retailing and Consumer Services, vol. 40, pp. 40-47.View/Download from: UTS OPUS or Publisher's site
Moktadir, MA, Rahman, T, Rahman, H, Ali, SM & Paul, SK 2018, 'Drivers to sustainable manufacturing practices and circular economy: a perspective of leather industries in Bangladesh', Journal of Cleaner Production, vol. 174, pp. 1366-1380.View/Download from: UTS OPUS or Publisher's site
Paul, SK & Rahman, S 2018, 'A quantitative and simulation model for managing sudden supply delay with fuzzy demand and safety stock', International Journal of Production Research, vol. 56, no. 13, pp. 4377-4395.View/Download from: UTS OPUS or Publisher's site
In this paper, a recovery model is developed for managing sudden supply delays that affect retailers' economic order quantity model. For this, a mathematical model is developed that considers fuzzy demand and safety stock, and generates a recovery plan for a finite future period immediately after a sudden supply delay. An efficient heuristic solution is developed that generates the recovery plan after a sudden supply delay. An experiment with scenario-based analysis is conducted to test our heuristic and to analyse the results. To assess the quality and consistency of solutions, the performance of the proposed heuristic is compared with the performance of the generalised reduced gradient method, which is widely applied in constrained mathematical programming. A simulation model is also designed to bring the recovery model closer to real-world processes. Several numerical examples are presented and a sensitivity analysis is performed to demonstrate the effects of various parameters on the performance of the heuristic method. The results show that safety stock plays an important role in recovery from sudden supply delays, and there is a trade-off between backorder and lost sales costs in the recovery plan. With the help of the proposed model, supply chain decision-makers can make accurate and prompt decision regarding recovery plans in case of sudden supply delay.
Moktadir, MA, Ali, SM, Rajesh, R & Paul, SK 2018, 'Modeling the interrelationships among barriers to sustainable supply chain management in leather industry', Journal of Cleaner Production, vol. 181, pp. 631-651.View/Download from: UTS OPUS or Publisher's site
Ahsan, K & Paul, SK 2018, 'Procurement issues in donor-funded international development projects', Journal of Management in Engineering, vol. 34, no. 6, pp. 1-13.View/Download from: UTS OPUS or Publisher's site
This study investigated the critical procurement challenges faced by international development (ID) projects in Bangladesh. Initially, a framework of challenges was developed via literature review. We then ranked the importance of these challenges and categorized them based on interview data and analytical hierarchy processing analysis. Interviews were conducted with procurement experts from three major ID project stakeholder groups: donor organizations, host country government policymakers, and project implementation units. The most important categories of challenges were those related to project management capacity/capability, and ethics. More specifically, the challenges deemed most important were those related to improper project planning, undue practices in procurement implementation, government bureaucracy and interference in procurement, and inexperienced procurement staff. This paper contributes to the ID project procurement literature by identifying the critical challenges to procurement, which differ from those of other project-related areas. The findings may assist the multibillion-dollar ID project procurement industry in Bangladesh by highlighting the major issues that require effective management by all stakeholders. Ultimately, this may improve procurement outcomes and overall project performance
Agarwal, R, Chowdhury, M & Paul, SK 2018, 'The Future of Manufacturing Global Value Chains, Smart Specialization and Flexibility!', Global Journal of Flexible Systems Management.View/Download from: UTS OPUS
Paul, SK, Sarker, R & Essam, D 2017, 'A quantitative model for disruption mitigation in a supply chain', European Journal of Operational Research, vol. 257, no. 3, pp. 881-895.View/Download from: UTS OPUS or Publisher's site
Paul, SK, Sarker, R & Essam, D 2016, 'MANAGING RISK AND DISRUPTION IN PRODUCTION-INVENTORY AND SUPPLY CHAIN SYSTEMS: A REVIEW', JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION, vol. 12, no. 3, pp. 1009-1029.View/Download from: UTS OPUS or Publisher's site
Paul, SK, Sarker, R & Essam, D 2015, 'Managing disruption in an imperfect production-inventory system', COMPUTERS & INDUSTRIAL ENGINEERING, vol. 84, pp. 101-112.View/Download from: UTS OPUS or Publisher's site
Paul, SK 2015, 'Supplier selection for managing supply risks in supply chain: a fuzzy approach', INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, vol. 79, no. 1-4, pp. 657-664.View/Download from: UTS OPUS or Publisher's site
Hasan, MM, Shohag, MAS, Azeem, A & Paul, SK 2015, 'Multiple criteria supplier selection: A fuzzy approach', International Journal of Logistics Systems and Management, vol. 20, no. 4, pp. 429-446.View/Download from: UTS OPUS or Publisher's site
Copyright © 2015 Inderscience Enterprises Ltd. A company must purchase a lot of diverse components and raw materials from different upstream suppliers to manufacture or assemble its products. However, it is not only a very complicated and perplexing task to select outstanding suppliers for decision-makers of strategic purchasing, but also it involves uncertainty and produces erroneous results while considering single criteria. For this reason, the decision-makers of strategic purchasing greatly require an efficient, valid and fair tool to assist them in selecting appropriate suppliers forthwith. This paper proposes a supplier selection model for any kind of company by using MATLAB fuzzy logic toolbox to help the purchasing department in selecting the most appropriate supplier. The main task in the proposed model involves determining the numerical score for different suppliers considering their respective performance in various qualitative and quantitative evaluation criteria and then selecting the best supplier having highest score. Fuzzy control is used to determine the best supplier by calculating the score in selected evaluation criteria which are provided in numerical values for the convenience of calculation.
Paul, SK, Azeem, A & Ghosh, AK 2015, 'Application of adaptive neuro-fuzzy inference system and artificial neural network in inventory level forecasting', International Journal of Business Information Systems, vol. 18, no. 3, pp. 268-284.View/Download from: UTS OPUS or Publisher's site
Copyright © 2015 Inderscience Enterprises Ltd. Determining optimum level of inventory is very important for any organisation which depends on various factors. In this research, six main factors have been considered as input parameters and the inventory level has been considered as the single output for this inventory management problem. Price of raw material, demand of raw material, holding cost, setup cost, supplier's reliability and lead time are considered as input parameters. An adaptive neuro-fuzzy inference system (ANFIS) has been applied as the artificial intelligence technique for modelling the inventory problem. ANFIS results have been compared with results from another artificial intelligence technique, artificial neural network (ANN), to validate the output results. Performance of both methods has been shown regarding different error measures. Comparison clearly shows the superiority of ANFIS results over ANN results and thus makes ANFIS a better choice for inventory level forecasting.
Paul, SK, Sarker, R & Essam, D 2015, 'A disruption recovery plan in a three-stage production-inventory system', COMPUTERS & OPERATIONS RESEARCH, vol. 57, pp. 60-72.View/Download from: UTS OPUS or Publisher's site
Paul, SK, Sarker, R & Essam, D 2014, 'Managing real-time demand fluctuation under a supplier-retailer coordinated system', INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, vol. 158, pp. 231-243.View/Download from: UTS OPUS or Publisher's site
Paul, SK, Azeem, A, Sarker, R & Essam, D 2014, 'Development of a production inventory model with uncertainty and reliability considerations', OPTIMIZATION AND ENGINEERING, vol. 15, no. 3, pp. 697-720.View/Download from: UTS OPUS or Publisher's site
Sultana, I, Ahmed, I, Chowdhury, AH & Paul, SK 2014, 'Economic design of X¯ control chart using genetic algorithm and simulated annealing algorithm', International Journal of Productivity and Quality Management, vol. 14, no. 3, pp. 352-372.View/Download from: UTS OPUS or Publisher's site
© 2014 Inderscience Enterprises Ltd. Control charts are very popular for monitoring production processes and designed economically to achieve minimum quality costs. This paper focuses on evaluating the performance of genetic algorithm (GA) and simulated annealing algorithm (SAA) in economical design of X¯ control chart. The performances of GA and SAA is demonstrated through a numerical example and the results were compared with Montgomery (1982). To outperform Montgomery's approach the paper dealt with the same example and demonstrate its utility. Duncan model of single assignable cause without taking into account process improvement and statistical properties is adopted to formulate the cost minimising equation and the computation is achieved through Simpson's one-third approximation rule. A comparison between the performance of GA and SAA is also exhibited in this paper. Copyright
Paul, SK, Sarker, R & Essam, D 2014, 'Real time disruption management for a two-stage batch production-inventory system with reliability considerations', EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, vol. 237, no. 1, pp. 113-128.View/Download from: UTS OPUS or Publisher's site
Ahmed, I, Sultana, I, Paul, SK & Azeem, A 2014, 'Performance evaluation of control chart for multiple assignable causes using genetic algorithm', INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, vol. 70, no. 9-12, pp. 1889-1902.View/Download from: UTS OPUS or Publisher's site
Al Masud, MA, Paul, SK & Azeem, A 2014, 'Optimisation of a production inventory model with reliability considerations', International Journal of Logistics Systems and Management, vol. 17, no. 1, pp. 22-45.View/Download from: UTS OPUS or Publisher's site
In this paper, a production inventory model with reliability of production process is developed to minimise total inventory cost. Production, setup, holding, inspection, depreciation, rejection and backorder cost are considered to develop the model. The economic production lot size and the reliability of the production process along with the production period are the decision variables and total cost per cycle is the objective function which is to be minimised. A meta-heuristic particle swarm optimisation (PSO) algorithm is applied to solve the unconstrained non-integer non-linear form of objective function. Some numerical examples have been presented to explain the model. The results obtained from PSO algorithm are compared with results obtained from genetic algorithm (GA) applying on the same inventory model. Comparison clearly shows the superiority of PSO results over GA results thus makes PSO a better choice for this kind of modelling. Copyright © 2014 Inderscience Enterprises Ltd.
Latif, HH, Paul, SK & Azeem, A 2014, 'Ordering policy in a supply chain with adaptive neuro-fuzzy inference system demand forecasting', International Journal of Management Science and Engineering Management, vol. 9, no. 2, pp. 114-124.View/Download from: UTS OPUS or Publisher's site
Ahmed, I, Sultana, I, Paul, SK & Azeem, A 2013, 'Employee performance evaluation: A fuzzy approach', International Journal of Productivity and Performance Management, vol. 62, no. 7, pp. 718-734.View/Download from: UTS OPUS or Publisher's site
Purpose: Managers encounter many decisions that require the simultaneous use of different types of data in their decision-making process. A critical decision area for managers is the performance evaluation of personnel, whether individually or as a member of a team. Performance evaluation is critically essential for the effective management of the human resource of an organization and evaluation of staff that help develop individuals, improve organizational performance, and feed into business planning. Design/methodology/approach: Performance evaluations require and often involve disparate types of information that are vague, incomplete, objective, and subjective. This paper proposes a performance evaluation system of employees considering various performance evaluation criteria using fuzzy logic. The main task in the proposed approach involves determining the performance indices of employees considering their respective performance in various qualitative and quantitative evaluation criteria and then selecting the best employee who holds highest performance index comparing all the indices. Findings: A model is developed for any kind of organization where performance evaluation is significantly important for staff motivation, attitude and behavior development, communicating and aligning individual and organizational aims, and fostering positive relationships between management and staff. Fuzzy control is used to determine the overall performance index by combining results of the performance in selected criteria and provided it in numerical values which will undoubtedly ensure convenience of the concerned human resource personnel during performance rating calculation. Originality/value: This is the first time, a performance evaluation model is developed using fuzzy approach for any kind of organization where performance evaluation is significantly important for staff motivation, attitude and behavior development, communicating and aligning individual and organizati...
Mahbub, N, Paul, SK & Azeem, A 2013, 'A neural approach to product demand forecasting', International Journal of Industrial and Systems Engineering, vol. 15, no. 1, pp. 1-18.View/Download from: UTS OPUS or Publisher's site
This paper develops an artificial neural network (ANN) model to forecast the optimum demand as a function of time of the year, festival period, promotional programmes, holidays, number of advertisements, cost of advertisements, number of workers and availability. The model selects a feed-forward back-propagation ANN with 13 hidden neurons in one hidden layer as the optimum network. The model is validated with a furniture product data of a renowned furniture company. The model has also been compared with a statistical linear model named Brown's double smoothing model which is normally used by furniture companies. It is observed that ANN model performs much better than the linear model. Overall, the proposed model can be applied for forecasting optimum demand level of furniture products in any furniture company within a competitive business environment. Copyright © 2013 Inderscience Enterprises Ltd.
Zahin, S, Latif, HH, Paul, SK & Azeem, A 2013, 'A comparative analysis of power demand forecasting with artificial intelligence and traditional approach', International Journal of Business Information Systems, vol. 13, no. 3, pp. 359-380.View/Download from: UTS OPUS or Publisher's site
Power demand forecasting is a significant factor in the planning and economic and secure operation of modern power system. This research work has compared different forecasting techniques and opted to find out better technique in context of power generation, which varies rapidly from time to time. The dataset has been generated from yearly demand of electricity of Bangladesh for last five years. Year, irrigation season, temperature and rainfall amount have been considered as input parameters where as single output is demand of load in adaptive neuro-fuzzy inference system (ANFIS). Another artificial intelligence technique, artificial neural network (ANN) has been used to validate the output results. The best suited traditional technique for forecasting power generation is seasonal forecasting. Seasonal forecasting is also used to compare with ANFIS and ANN to find out better technique. The result of experiment indicates that ANFIS is superior method to tackle forecasting of power generation from different error measures. Copyright © 2013 Inderscience Enterprises Ltd.
Islam, MN, Paul, SK & Azeem, A 2013, 'Fuzzy optimisation of multi-objective job shop scheduling based on inventory information', International Journal of Services and Operations Management, vol. 15, no. 2, pp. 123-139.View/Download from: UTS OPUS or Publisher's site
Job shop scheduling problems are one of the oldest combinatorial optimisation problems being studied. In this paper, fuzzy processing times of operations and fuzzy due dates of jobs are considered to incorporate fuzziness in the problem. Percentage of inventory consumption and profit earned form the orders are also considered in this fuzzy multi-objective job shop scheduling problem. Fuzzy inference system (FIS) is used to calculate the job weights based on the percentage of inventory consumption for a particular job and profit can be earned from the jobs. Average weighted tardiness, number of tardy jobs, total flow time and idle times of machines are considered as objectives which should be minimised. In this paper, genetic algorithm (GA) is used as a heuristic technique with specially encoded chromosomes that denotes the complete schedule of the jobs. A local search technique, simulated annealing (SA) is also used to compare the results obtained in two different methods. Different problem sizes has been tested and the fitness function values and computation times of the problems for each method is compared. © 2013 Inderscience Enterprises Ltd.
Paul, SK 2013, 'Sustainable sequencing of N jobs on one machine: A fuzzy approach', International Journal of Services and Operations Management, vol. 15, no. 1, pp. 44-57.View/Download from: UTS OPUS or Publisher's site
Sequencing of jobs on one machine is a very common problem in scheduling. Several factors have to be taken into consideration to make the sequencing more realistic. In this paper, a fuzzy inference system is developed to tackle the uncertainty of variables in a sequencing problem. Arrival order, processing time, due date, slack time remaining, critical ratio, queue ratio and slack time remaining per operation, are considered as input variables and priority of jobs is considered as output variable. Multiple objectives are fulfilled as priority is obtained from the aggregated optimised result of individual rule developed in a rule editor. A job with higher priority is given more preferences in sequencing. MATLAB fuzzy logic toolbox is used to develop the model. A numerical example is presented to explain the approach. Copyright © 2013 Inderscience Enterprises Ltd.
Bhattacharjee, B, Azeem, A, Ali, SM & Paul, SK 2012, 'Controller based on runge-kutta method', International Journal of Computer Aided Engineering and Technology, vol. 4, no. 5, pp. 445-464.View/Download from: UTS OPUS or Publisher's site
The parametric interpolators of modern CNC machines use Taylor's series approximation to generate successive parameter values for the calculation of x, y, z coordinates of tool positions. In order to achieve greater accuracy, higher order derivatives are required at every sampling period which complicates the calculation for contours represented by NURBS curve. In addition, this method calculates the chordal error in a given segment through estimation of the curvature neglecting a fraction of the error. In order to avoid calculating higher derivatives and make the calculations simpler, this paper proposes the classical fourth-order Runge-Kutta (RK) method for the determination of successive tool positions requiring the calculation of the first derivatives only. Furthermore, a method of estimating the chordal error on the average value of parameters at the end points of a given curve segment is proposed here that does not require the calculation of curvature at every segment. Finally, a variable feedrate interpolation scheme is designed combining the RK method of parameter calculation and the proposed method of chordal error calculation. Results show that reduced chordal error and feedrate fluctuations are achievable with the proposed interpolator compared to the conventional interpolator based on Taylor's approximation with higher order terms. Copyright © 2012 INDerscience Enterprises Ltd.
Zaman, T, Paul, SK & Azeem, A 2012, 'Sustainable operator assignment in an assembly line using genetic algorithm', INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, vol. 50, no. 18, pp. 5077-5084.View/Download from: UTS OPUS or Publisher's site
Ali, SM, Paul, SK, Azeem, A & Ahsan, K 2011, 'Forecasting of optimum raw material inventory level using artificial neural network', International Journal of Operations and Quantitative Management, vol. 17, no. 4, pp. 333-348.View/Download from: UTS OPUS
This paper develops an artificial neural network (ANN) model to forecast the optimum level of raw materials inventory as a function of product demand, manufacturing lead-time, supplier reliability, material holding cost, and material cost. The model selects a feed-forward back-propagation ANN with twelve hidden neurons as the optimum network. We test the model with pharmaceutical company data. The results show that the model can be useful to forecast raw material inventory level in response to different parameters. We also compare the model with fuzzy inference system (FIS) and simple economic order quantity (EOQ). It can be seen that ANN model outperforms others. Overall, the model can be applied for forecasting of raw materials inventory for any manufacturing enterprise in a competitive business environment.
Paul, SK & Azeem, A 2011, 'An artificial neural network model for optimization of finished goods inventory', International Journal of Industrial Engineering Computations, vol. 2, no. 2, pp. 431-438.View/Download from: UTS OPUS or Publisher's site
In this paper, an artificial neural network (ANN) model is developed to determine the optimum level of finished goods inventory as a function of product demand, setup, holding, and material costs. The model selects a feed-forward back-propagation ANN with four inputs, ten hidden neurons and one output as the optimum network. The model is tested with a manufacturing industry data and the results indicate that the model can be used to forecast finished goods inventory level in response to the model parameters. Overall, the model can be applied for optimization of finished goods inventory for any manufacturing enterprise in a competitive business environment. © 2011Growing Science Ltd. All rights reserved.
Paul, SK & Azeem, A 2010, 'Minimization of work-in-process inventory in hybrid flow shop scheduling using fuzzy logic', International Journal of Industrial Engineering : Theory Applications and Practice, vol. 17, no. 2, pp. 115-127.
This paper addresses the Hybrid Flow Shop (HFS) scheduling problems to minimize the total work-in-process inventory. Job scheduling problems are one of the oldest and real world combinational optimization problems. It is multi objective and complex in nature. There exist some criteria that must be taken into consideration when evaluating the quality of the proposed schedule. Consideration of job and machine reliability is very important during assignment of jobs in each stage to get realistic hybrid flow shop schedule. In this paper, flow shop problem concerns the sequencing of a given number of jobs through a series of machines in the exact same order on all machines with the aim to satisfy a set of constraint as much as possible and optimize a set of objectives. Fuzzy sets and logic can be used to tackle uncertainties inherent in actual flow shop scheduling problems. Fuzzy due dates, cost over time and profit rate result the job priority and to determine the machine priority processing time of each machine is considered. MATLAB fuzzy tool box is used to calculate the priorities of jobs and machines at different stages. Finally, jobs are assigned into machines based on a grouping and sequencing algorithm that minimizes the total work-in-process inventory. © INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING.
Paul, SK & Azeem, A 2010, 'Selection of the optimal number of shifts in fuzzy environment: Manufacturing company's facility application', Journal of Industrial Engineering and Management, vol. 3, no. 1, pp. 54-67.View/Download from: Publisher's site
This paper addresses the selection of optimal shift numbers considering inventory information, customer requirements and machine reliability using fuzzy logic. Number of shift is one of the most important criteria for the production planners to minimize the production costs and is essential for appropriate production planning. The main task involves optimizing the shift periods considering constraints of raw material, due date, demand, finished goods inventory and machine breakdown. A model is developed for any kind of manufacturing company where shift periods affect company's profit and cost. Fuzzy control is used to optimize the number of shifts under the constraints of raw material, due date, demand, finished goods inventory and machine breakdown. MATLAB Fuzzy Logic Tool Box is used to develop the model. © Journal of Industrial Engineering and Management, 2010.
Paul, SK, Sarker, R & Essam, D 2013, 'A disruption recovery model in a production-inventory system with demand uncertainty and process reliability', Springer, Germany, pp. 511-522.View/Download from: UTS OPUS or Publisher's site
This paper develops a risk management tool for a productioninventory system that involves an imperfect production process and faces production disruption and demand uncertainty. In this paper, the demand uncertainty is represented as fuzzy variable and the imperfectness is expressed as process reliability. To deal with the production scheduling in this environment, a non-linear constrained optimization model has been formulated with an objective of maximizing the graded mean integration value (GMIV) of the total expected profit. The model is applied to solve the production-inventory problem with single as well as multiple disruptions on a real time basis that basically revises the production quantity in each cycle in the recovery time window. We propose a genetic algorithm (GA) based heuristic to solve the model and obtain an optimal recovery plan. A numerical example is presented to explain usefulness of the developed model. © 2013 IFIP International Federation for Information Processing.
Paul, SK & Rahman, S 2016, 'A Recovery Model for Sudden Supply Delay with Demand Uncertainty and Safety Stock', Proceedings of the Australian Society for Operations Research Conference, Australian Society for Operations Research Conference, Springer International Publishing, Canberra, Australia, pp. 243-257.View/Download from: UTS OPUS or Publisher's site
In this paper, a recovery model is developed for managing sudden supply delays that affect retailers' Economic Order Quantity (EOQ) model. For this, a mathematical model is developed that considers demand uncertainty and safety stock, and generates a recovery plan for a finite future period immediately after a sudden supply delay. Solving recovery problems involve high commercial software costs, and their solutions are complex. Therefore, an efficient heuristic solution is developed that generates the recovery plan after a sudden supply delay. An experiment is conducted to test the proposed approach. To assess the quality and consistency of solutions, the performance of the proposed heuristic is compared with the performance of the Generalized Reduced Gradient (GRG) method, which is widely applied in constrained mathematical programming. Several numerical examples are presented and a sensitivity analysis is performed to demonstrate the effects of various parameters on the performance of the heuristic method. The results show that safety stock plays an important role in recovery from sudden supply delays, and there is a trade-off between backorder and lost sales costs in the recovery plan.
Rathore, A, Agarwal, R, Bajada, C & Paul, S 2017, 'Japan's technology management legacy impacting its IoT leadership', Production and Operations Management Society (POMS) 2017 International Conference, MGSm Sydney.View/Download from: UTS OPUS
Abstract: The objective of this study is to identify the factors in traditional Japanese corporate management style which are impacting Japan's leadership in IoT on global platform. To-date, Japan has 18% of worldwide share of IoT patents assigned. Fujitsu is the only Japanese company at 8th place in world's top ten patents assignee ranking. Top places are filled by US, Chinese, Korean and European companies (Trappey, A. 2016). Japan envisioned the concept of an 'Intelligent Object Network' TRON (The Real-time Operating system Nucleus), an open real-time operating system kernel - similar to IoT - as one of the Tokyo University's objectives as far as back in 1987 (Sakamura J, 2015). However, Japan simply let Germany initiate 'Industry 4.0' policy and standards while allowing the United States to lead the IIC (Industrial Internet Consortium) despite years of experience and lead in embedded systems and high level proficiency in ubiquitous computing. Exploratory research revealed that management-related factors such as catch-up and mass production roll out policies were the largest inhibitors to setting international software standards whereas local focus related policies were considered major hindrances for Japanese gadgets to succeed worldwide. Local focus of gadgets and unwillingness of management into development of open source IoT software were found to be interlinked that Japan is not leading Industry 4.0. Institutional arrange¬ments of Japan's catch-up system in most key industries are the primary cause of Japan's software firms' competitive weak¬ness. The very arrangements that help explain Japan's success in steel, machine tools, semiconductors, and computer hardware are found to be the source of its weakness in computer software (Anchordoguy, M 1999). Insularity is a long-standing problem in Japan, often referred to as the "Galapagos Syndrome." Products are highly evolved but don't survive well beyond the water's edge (Pesek, W 2013). Many Japanese gadgets ...
Paul, SK, Sarker, RA & Essam, DL 2014, 'Managing supply disruption in a three-tier supply chain with multiple suppliers and retailers', IEEE International Conference on Industrial Engineering and Engineering Management, IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE, Bandar Sunway, Malaysia, pp. 194-198.View/Download from: UTS OPUS or Publisher's site
© 2014 IEEE. In this paper, a supply disruption management model is introduced in a three-tier supply chain with multiple suppliers and retailers, where the system may face sudden disruption in its raw material supply. At first, we formulated a mathematical model for ideal conditions and then reformulated it to revise the supply, production and delivery plan after the occurrence of a disruption, for a future period, to recover from the disruption. Here, the objective is to minimize the total cost during the recovery time window while being subject to supply, capacity, demand, and delivery constraints. We have also proposed an efficient heuristic to solve the model and the results have been compared, with another established solution approach, for a good number of randomly generated test problems. The comparison showed the consistent performance of our developed heuristic. This paper also presents some numerical examples to explain the usefulness of the proposed approach.
Paul, SK, Sarker, R & Essam, D 2013, 'A production inventory model with disruption and reliability considerations', Proceedings of International Conference on Computers and Industrial Engineering, CIE, International Conference on Computers and Industrial Engineering (CIE), Curran Associates, Hong Kong, China, pp. 291-304.View/Download from: UTS OPUS or Publisher's site
In this paper, a single stage batch production-inventory system is introduced. For it, the production can face either a sudden or multiple disruptions, for a certain period of time. The model also considers production process reliability because production environment are often imperfect. The problem is formulated as a non-linear constrained optimization problem that considers production capacity, demand, delivery and transportation constraints while attempting to maximize the total profit in the disruption recovery window. The model is also applied to solve production systems with single or multiple disruptions. The production quantity in each cycle in the recovery window is revised after every disruption, for as long as disruptions take place in the system, while considering the effect of all dependent disruptions. The model is solved by using both a pattern search and a genetic algorithm based solution approach. The results are also compared. Both a numerical example and a sensitivity analysis are presented to explain the model.
Paul, SK & Azeem, A 2009, 'Defects Identification and Analysis of a Pharmaceutical Product Using Pareto and Cause-Effect Analysis', 8th International Conference on Mechanical Engineering, Dhaka, Bangladesh, pp. 1-6.
Jannat, S, Khaled, AA & Paul, SK 2010, 'Optimal Solution for Multi-Objective Facility Layout Problem Using Genetic Algorithm', International Conference on Industrial Engineering and Operations Management, Dhaka, Bangladesh, pp. 751-756.