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Author: search.filters.author.Masuchun, WiboonHas files: No

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    Integrating m-machine scheduling into MRP
    (2010-06-01)
    Masuchun, Ruedee
    ;
    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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    Impact of time and costs when determining batch size in a supply chain
    (2009-11-01)
    Masuchun, Ruedee
    ;
    Tirasesth, Kitti
    ;
    Thepmanee, Teerawat
    ;
    Masuchun, Wiboon
    ;
    Shi, Yan
    This paper addresses planning a set of jobs on a sequence of machines considering the possibility of producing and delivery in batches either to other stages for further processing or to customers in a supply chain network. Determining batch size at each stage unavoidably is of importance at this point. Using large batch size can put away some costs but time is wasted as trade-off. Small batch size can speed up the process but certain costs are increasing. To represent both time and costs aspects, a nonlinear integer mathematical model with the objective function of minimizing both time and costs simultaneously is studied and applied to determine (production) batch size to match up the flow of production at manufacturer with the demand required at retailers and appro- priate means of shipment (transfer batch size). In view of the fact that time and costs are different in units; this paper furthermore investigates the relationship between them through varying coefficients and parameters. Since this problem is classically NP-hard at each stage of supply chain; the acceptable production and transfer batch sizes are obtained using the advantage of random search approach. The numerical illustrations and results substantiate that the model proposed is useful to simultaneously consider both time and costs when determining batch size at each stage in a supply chain network and it is not necessary to use the same batch size when manufacturing and transferring. Moreover, we found that the conversion factor is statistically robust to the objective function value when the ratio of the production time over the setup time is not high. © 2009 ISSN.
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    Integrating production scheduling and Material Requirements Planning
    (2009-09-01)
    Masuchun, Ruedee
    ;
    Masuchun, Wiboon
    ;
    Thepmanee, Teerawat
    This paper addresses a technique to integrate the production scheduling andMaterial Requirements Planning (MRP). Generally, generating MRP considers onlyBill of Materials (BOM) not capacity constraints and operating sequences leadingto an in-feasible production plan when scheduling is actually performed next onshop-floor. That is why most researchers recently turn attention to executingboth simultaneously. Our technique uses the advantage of an integer linearprogramming model to plan and schedule at once so that the capacity constraintsand operating sequences are not ignored. The objective function of the proposedmodel includes both planning and scheduling purposes that is to minimize totalinventory costs and order's tardiness. Significant and inevitable concerns whenseparately generating MRP and schedule are all included in the model throughseveral constraints. Numerical results verify that this model can be used tosimultaneously generate reasonable MRP as well as feasible and optimal schedule.ICIC International © 2009.
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    System for supply network management
    (2006-12-01)
    Masuchun, Wiboon
    ;
    Davis, Steve
    ;
    Rangsaritratsamee, Ruedee
    Strategies and algorithms for operational planning and control are quite important to successful operations of a supply network. Implementation of a strategy requires substantial information system support, but few detailed designs of such systems have appeared in the literature. We designed an information system for a centralized management concept that could handle any type of strategy or algorithm. We developed algorithms for two different strategies deemed important by researchers and practitioners, push and pull, and implemented them in system modules. This information system performed well in simulations of the management of operations of an example six-stage supply network. This system provides an example for operational development as well as a platform for laboratory experiments. © Springer Science + Business Media, LLC 2006.