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The allowable time approach of the uncertain task for three U-shaped lines with the minimum workstations
(2020-02-01)
Varnasilpin, Suthep
;
Masuchun, Ruedee
UL (U-shaped Line) reduces the waste material in a manufacturing process. This paper presents the application of three U-shaped Lines with the minimum workstations which consists of UL-1, UL-2, and UL-3. The uncertain task of three U-shaped Lines determines the time as an interval. Three U-shaped Lines formulate the model with a binary integer linear programming. Each uncertain task reduces the normal time to the optimism time. The normal time reduction relates to the allowable time inversely. This paper indicates the procedure of the normal time reduction that applies MATLAB R2014a. The allowable time of UL-1, UL-2, and UL-3 for each uncertain task is 1.5 times of the optimistic time, 1.2 times of the optimistic time, and 1 time of the most optimistic time, respectively. For the utility of this paper, the uncertain task adjusts the finish time which does not affect the modification of the minimum number of workstations.
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Cost and reliability analyses for the vehicle routing problem
(2017-11-03)
Janjarassuk, Udom
;
Masuchun, Ruedee
The vehicle routing problem (VRP) is one of the central problems in logistics and supply chain management. In this paper, we present a simulation approach for cost and reliability analyses for the vehicle routing problem with stochastic demands. The objective of this study is to provide a method in evaluating the reliability and the associated cost of a VRP solution by using simulation. First, a deterministic variant of the capacitated vehicle routing problem (CVRP) is solved by using an ant colony optimization algorithm. Then the solution is evaluated under stochastic demands for the incurred cost and reliability. The reliability of a solution is estimated based on the number of route failures where customers' demands exceed the remaining capacity of the vehicles. When a failure occurs, a recourse action must be taken to fulfill the customer's requirement, which in turn increases the operating cost. We provide experiments for the proposed method by using standard benchmark problems for the CVRP. Computational results are given for the estimates by using a 95% confidence interval (CI) with 1% accuracy.
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A simple approach for identifying an optimal service level for minimizing schedule nervousness under non-stationary stochastic demand
(2017-04-01)
Hasachoo, Narat
;
Masuchun, Ruedee
Schedule Nervousness occurs when there is a revision in a planned schedule due to an uncertainty in demand. This is because in reality, a future demand is not a deterministic with, a stationary trend, but a non-stationary stochastic demand. A higher inventory service level may lower the probability that actual demand will exceed the inventory, but expected total cost, also inclined as the service level increased. So the objective of this paper is to propose a simple approach for identifying an optimal service level between expected total cost, and nervousness. First, the cost, of operating in a nervousness-five operation was revealed by solving a mixed integer linear programming under static-dynamic uncertainty in a determined set of service levels. The second part provides a proposal of a new simple formulation for identifying an optimal service level between expected total cost, and nervousness based on the findings in the first part. Finally, a similar approach was taken for an experiment, by using a case study in one of the most, nervousness sensitive industries, the airline catering industry. The results were found to be satisfied in both, generated data and the case study.
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An ant colony optimization method for the capacitated vehicle routing problem with stochastic demands
(2017-02-21)
Janjarassuk, Udom
;
Masuchun, Ruedee
The ant colony optimization (ACO) method has been extensively studied recently for solving many combinatorial optimization problems. In this paper, an ant colony optimization method for solving the vehicle routing problem with stochastic demands is presented. The 2-opt local search is employed within the ACO algorithm to improve the qualities of the solutions. Simulation technique is used for the estimate of the expected cost under stochastic demands. Computational results are also reported.
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Factors affecting schedule nervousness in the production operations of airline catering industry
(2016-01-18)
Hasachoo, Narat
;
Masuchun, Ruedee
The airline catering industry is one of the most complex operational systems since caterers need to serve an average of one hundred thousand meals daily with the challenge of unavailability of exact order numbers right up until departure time. Typically, an optimally planned operation schedule is issued that seeks to utilize resources usage, e.g. manpower and machines. Due to the existence of schedule nervousness, operations schedules are unavoidably sub-optimally executed. The aim of this research was to identify operations-related factors that affected schedule nervousness in the production operations of an airline catering company in Thailand. Total data of 5,572 orders from two planning horizons was collected for analysis. Nervousness was quantified by the revision in initial planned schedule and tested with operation-related factors for the relationship. Results showed that forecasting inaccuracy has a positive correlation with schedule nervousness, while an occurrence of buffer stock has a negative relation.
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Schedule nervousness in production operations of an airline catering company: The challenge of an effective demand response program
(2016-01-01)
Hasachoo, Narat
;
Masuchun, Ruedee
Demand Response (DR), also known as electric load-shifting program, is a different way of shaping energy demand with the goal to reduce electricity costs for an organization. Extensive prior research studies have demonstrated the effectiveness of the action of DR through the use of operations research methodology e.g. efficient production scheduling. In the real world, schedulers typically issue an optimally planned operations schedule that takes into account the demands for manpower, machinery and even electricity profiles. However, due to the occurrence of schedule nervousness, the operations schedules are inevitably suboptimally executed. In order to restore the optimal state, schedulers could alternatively issue a rescheduling plan and thereby it is imperative that planners identify the existence of the nervousness. This paper attempts to identify the schedule nervousness and its impact on the DR program at shop-floor scheduling in the production operations of an airline catering company. The study findings, which have proved the existence of scheduling nervousness in this industry and its impact over DR program, are expected to contribute to future research on the development of more efficient demand response plans as well as rescheduling plans by integrating the pattern of schedule nervousness found in this work. © IEOM Society International.
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Automatic SPRT module calibration using automated low thermal matrix scanners
(2011-08-12)
Thepmanee, Teerawat
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Namsirilert, Sumetha
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Pongswatd, Sawai
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Masuchun, Ruedee
In general, the process of the SPRT module calibration takes long time and requires all employees participation in the calibration process, and it also requires an expert for calculation of results and measurement uncertainty. All of these waste a vast amount of working time. Therefore, this paper provides the automatic SPRT module calibration process which can reduce the steps of calibration, resulting in process time reduction and the automatic process doesn't require the expert to calculate results and measurement uncertainty. © 2011 IEEE.
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Using genetic algorithm to manage and schedule temperature sensor calibration
(2011-01-01)
Thepmanee, Teerawat
;
Masuchun, Ruedee
Currently, generating a schedule to calibrate temperature sensors for both laboratory calibration and field calibration are very time-consuming and inefficient, especially when several temperature sensors await for being calibrated and the due dates are required to be specified in advance. General rule of thumb used is first-come-first-served but the due date is difficult to determine in advance. Moreover, overview of which standards will be required and, if required, at what temperatures each of them should be set to cannot be globally visualized. Another method is to calibrate those temperature sensors requiring same temperature together but the experience is strictly required to avoid overwhelming calculation time. This paper presents the application of scheduling technique using genetic algorithm to schedule temperature sensor calibration. The fitness function is to minimize the time required to calibrate all temperature sensors. Our method takes little time to generate good schedule while the due date of each temperature sensor can be at once stated and the global view of required standards can be anticipated for further preparation. Moreover, our method is easy to use and suitable for anyone without the experience in scheduling temperature sensors. © 2011 ISSN.
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Integrating m-machine scheduling into MRP
(2010-06-01)
Masuchun, Ruedee
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Masuchun, Wiboon
;
Thepmanee, Teerawat
This paper presents an approach to integrate the m-machine productionscheduling into Material Requirements Planning (MRP). In general, generating MRPconsiders only Bill of Materials (BOM) and, ignores capacity constraints and,operating sequences; therefore, a, production plan is unachievable whenscheduling is actually performed next on shop-floor. That is why recent researchhas focused on executing both MRP and, scheduling simultaneously. Our approachuses an integer linear programming model to plan and, schedule concurrently tolook right through the capacity constraints and, operating sequences. Theobjective function of the proposed, model considers both planning and,scheduling purposes that is to minimize total inventory costs and, order'stardiness. All significant and inevitable concerns when separately generatingMRP and schedule are incorporated, with the model through several constraints.Numerical results show that this model can be used, to simultaneously generatereasonable MRP as well as feasible and, optimal m-machine production schedule.ICIC International © 2010.
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Integrating m-Machine scheduling into MRP
(2009-12-01)
Masuchun, Ruedee
;
Masuchun, Wiboon
;
Thepmanee, Teerawat
This paper presents an approach to integrate the m-machine production scheduling into Material Requirements Planning (MRP). In general, generating MRP considers only Bill of Materials (BOM) and ignores capacity constraints and operating sequences; therefore, a production plan is unachievable when scheduling is actually performed next on shop-floor. That is why recent research has focused on executing both MRP and scheduling simultaneously. Our approach uses an integer linear programming model to plan and schedule concurrently to look right through the capacity constraints and operating sequences. The objective function of the proposed model considers both planning and scheduling purposes that is to minimize total inventory costs and order's tardiness. All significant and inevitable concerns when separately generating MRP and schedule are incorporated with the model through several constraints. Numerical results show that this model can be used to simultaneously generate reasonable MRP as well as feasible and optimal m-machine production schedule. © 2009 IEEE.

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