• IE interfaces
• /
• v.14 no.4
• /
• pp.341-347
• /
• 2001

This paper describes a real-time manufacturing scheduling system on Internet using DBR(Drum-Buffer-Rope) scheduling method. We intend to change company-oriented manufacturing scheduling, which has been used at most manufacturing companies, to customer-oriented manufacturing scheduling. Customers can not only choose product kinds, quantities and order due dates, but also evaluate optimum order due date by themselves in real-time through internet and then the results will be converted into practical manufacturing scheduling. If the company cannot meet the customer order due date, it will offer reliable and accurate information to the customers by suggesting the earliest order due date. To evaluate the customer order due date in real time, companies should be able to estimate their accurate production capacity. This research uses Goldratt's DBR scheduling method to realize that function. The DBR scheduling does not recognize the production capacity of the whole company, but only of the constraint resources which have a great effect on the company throughput. Thus, it can improve the customer service level as well as the profit by performing more dynamic and reliable scheduling through Internet.

• Management Science and Financial Engineering
• /
• v.9 no.2
• /
• pp.47-68
• /
• 2003

We consider the customer order scheduling problem with job capacity restriction where the number of jobs in the shop at the same time is fixed. In the customer order scheduling problem, each job is part of some batch (customer order) and the composition of the jobs (product) in the batch is pre-specified. The objective function is associated with the completion time of the batches instead of the completion time of the jobs. We first summarize the known results for the general customer order scheduling problems. Then, we establish some new properties for the problems with job capacity restriction. For the case of unit processing time with the objective of minimizing makespan, we develop a polynomial-time optimal procedure for the two machine case. For the same problem with a variation of no batch alternation, we also develop a polynomial-time optimal procedure. Then, we show that the problems with the objectives of minimizing makespan and minimizing average batch completion time become NP-hard when there exist arbitrary number of machines. Finally, We propose optimal solution procedures for some special cases.

• Journal of Korean Institute of Industrial Engineers
• /
• v.29 no.4
• /
• pp.304-311
• /
• 2003

This paper considers a new variation of scheduling problems where jobs are dispatched in batches. The variation is the case where the batch sequence is fixed. The objective is to minimize the sum of the completion times of the batches. This simple environment has a variety of real world applications such as part kitting and customer order scheduling. We show that this problem is binary NP-complete when there exist two machines. For the same problem, we develop an optimal dynamic programming (DP) algorithm which runs in pseudo-polynomial time. We finally prove the optimality of the DP algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.05a
• /
• pp.615-619
• /
• 2004

This paper considers a variation of customer order scheduling problems. The variation is the case where machine-job assignment is fixed, and the objective is to minimize the sum of the completion times of the batches. In customer order scheduling problems, jobs are dispatched in batches. While a machine can process only one job at a time, multiple machines can simultaneously process jobs in a batch. We first establish a couple of lower bounds. Then, we develop a dynamic programming (DP) algorithm that runs in exponential time on the number of batches when there exist two machines. For the same problem with arbitrary number of machines, we present two simple heuristics, which use simple scheduling rules such as shortest batch first and shortest makespan batch first rules. Finally, we empirically evaluate the heuristics.

• Management Science and Financial Engineering
• /
• v.17 no.1
• /
• pp.95-116
• /
• 2011

This paper considers a flowshop scheduling problem where a customer orders multiple products (jobs) from a production facility. The objectives are to minimize makespan and to minimize the sum of order (batch) completion times. The order cannot be shipped unless all the products in the order are manufactured. This problem was motivated by numerous real world problems encountered by a variety of manufacturers. For the makespan objective, we develop an optimal solution procedure which runs in polynomial time. For the sum of order completion time objective, we establish the complexity of the problem including several special cases. Then, we introduce a simple heuristic and find an asymptotically tight worst case bound on relative error. Finally, we conclude the paper with some implications.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.19 no.39
• /
• pp.275-284
• /
• 1996

It can be made a definition that scheduling is a imposition of machinery and equipment to perform a collection of tasks. Ultimately scheduling is an assessment of taking order for which would be perform. So it is called "sequencing" in other words. In a job shop scheduling, the main object is to making delivery in accordance with the due date and order form customer, not to producing lots of quantity with minimizing mean flow time in a given time. Actually, in a company, they concentrate more in the delivery than minimizing the mean flow time. Therefore this paper suggest a new priority dispatching rule under consideration as below in a n/m job shop scheduling problem with due date. 1. handling/transportation time, 2. the size of customer order With this algorithm, we can make a scheduling for minimizing the tardiness of delivery which satisfy a goal of production.roduction.

• Management Science and Financial Engineering
• /
• v.11 no.2
• /
• pp.19-43
• /
• 2005

This paper considers a variation of the customer order scheduling problem, and the variation is the case where the machine-job assignment is fixed. We examine the parallel machine environment, and the objective is to minimize the sum of the completion times of the batches. While a machine can process only one job at a time, different machines can simultaneously process different jobs in a batch. The recognition version of this problem is known to be NP-complete in the strong sense even if there exist only two parallel machines. When there are an arbitrary number of parallel machines, we establish three lower bounds and develop a dynamic programming (DP) algorithm which runs in exponential time on the number of batches. We present two simple but intuitive heuristics, SB and GR, and find some special cases where SB and GR generate an optimal schedule. We also find worst case upper bounds on the relative error. For the case of the two parallel machines, we show that GR generates an optimal schedule when processing times of all batches are equal. Finally, the heuristics and the lower bounds are empirically evaluated.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.10a
• /
• pp.101-104
• /
• 2002

Leading companies have embraced the new economy with new and innovative BTO models. Instead of conventional company-oriented manufacturing scheduling, customer-oriented scheduling method attracts more and more attention. To evaluate the delivery of customer order in advance, the real production capacity as well as procurement lead time should be taken into account. This paper describes a quasi-real-time order delivery estimation system using TOC(Theory of Constraints) based scheduling method.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.33 no.1
• /
• pp.117-129
• /
• 2008

Make-to-order manufacturers with multiple plants at multiple sites need to have the ability to quickly determine which plant will produce which customer order to meet the due date and minimize the transportation cost from the plants to the customer. Balancing the work loads and minimizing setups and make-span are also of great concern. Solving such scheduling problems usually takes a long time. We propose a new approach, which we call 'preprocessing', for resolving such complex problems. In preprocessing scheme, a 'good' a priori schedule is prepared and maintained using unconfirmed order information. Upon the confirmation of orders. the preprocessed schedule is quickly modified to obtain the final schedule. We present a preprocessing solution algorithm for multi-site constraint scheduling problem (MSCSP) using genetic algorithm; and conduct computational experiments to evaluate the performance of the algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.32 no.3
• /
• pp.40-48
• /
• 2009

In this article, scheduling of a casting sequence is studied in a casting foundry which must deliver products according to the Just-in-time(JIT) production policy of a customer. When a foundry manufactures a variety of casts with an identical alloy simultaneously, it frequently faces the task of production scheduling. An optimal casting schedule should be emphasized in order to maximize the production rate and raw material efficiency under the constraints of limited resources; melting furnaces and operation time for a casting machine. To solve this practical problem-fulfilling the objectives of casting the assigned mixed orders for the highest raw material efficiency in a way specified by the customer's JIT schedule, we implement simple integer programming. A simulation to solve a real production problem in a typical casting plant proves that the proposed method provides a feasible solution with a high accuracy for a complex, multi-variable and multi-constraint optimization problem. Employing this simple methodology, a casting foundry having an automated casting machine can produce a mixed order of casts with a maximum furnace utilization within the due date, and provide them according to their customer's JIT inventory policy.