This study focuses on the formation of input release lots in a semiconductor wafer fabrication facility. After the order-lot pegging process assigns lots in the fab to orders and calculates the required quantity of wafers for each product type to meet customers’ orders, the decisions on the formation of input release lots should be made to minimize the production costs of the release lots. Since the number of lots being processed in the wafer fab directly is related to the productivity of the wafer fab, the input lot formation is crucial process to reduce the production costs as well as to improve the efficiency of the wafer fab. Here, the input lot formation occurs before every shift begins in the semiconductor wafer fab. When input quantities (of wafers) for product types are given from results of the order-lot pegging process, lots to be released into the wafer fab should be formed satisfying the lot size requirements. Here, the production cost of a homogeneous lot of the same type of product is less than that of a heterogeneous lot that will be split into the number of lots according to their product types after passing the branch point during the wafer fabrication process. Also, more production cost occurs if a lot becomes more heterogeneous. We developed a multi-dimensional dynamic programming algorithm for the input lot formation problem and showed how to apply the algorithm to solve the problem optimally with an example problem instance. It is necessary to reduce the number of states at each stage in the DP algorithm for practical use. Also, we can apply the proposed DP algorithm together with lot release rules such as CONWIP and UNIFORM.

This paper suggests a procedure to define business process improvement (BPI) projects with analysis results based on the cause-and-effect chain. The procedure developed in this paper focuses on eliminating root causes of business problems resulted from abnormal events occurred in business process executions. First, we develop three criteria used to make clusters of the root causes where a cluster of root causes will be eliminated together by a BPI project defined based on the cluster. Second, we develop a method to formulate desired expectations from the BPI project. Also, we suggest a method to calculate the relative importance of the BPI projects that help a BPI organization determine priorities of them. We illustrate the procedure and the methods with some examples for the domestic mail delivery process in the postal service industry.

In this study, we develop a method for analyzing business process based on the event-driven process chain (EPC) model. The method consists of five stages such as identifying abnormal events, finding causes for the abnormal events and problems caused by the abnormal events, making cause-and-effect chains, drawing root-cause map, and defining improvement areas. We illustrate how to apply the method with some examples for the domestic registered mail delivery process.

Max-plus algebra is a nonlinear system made of two operations, maximization (max) and additions (plus), that are corresponding to the addition and multiplication in the traditional algebra, in respect. This algebraic method can be applied to many types of discrete event systems showing the state transition with the maximization and addition operations. Robotic cell with predetermined cyclic schedule is one of such systems. We consider a robotic cell that consists of a single robot and several isolated processing modules. We discuss steady state analysis of such robot cell when all the parts have the processing order as a flow shop. To model timing behavior of a robotic cell, we develop a max-plus algebraic system. Given a particular robot transfer sequence, we prove the unique existence of the stationary cyclic behavior and identify the schedule. Furthermore we suggest max-plus algebraic way to develop a robust schedule which endures the outside processing variability. Finally, a monitoring and control system for such robotic cell based on the max-plus algebraic model is also proposed.

Lot-order assignment is the process of assigning items in lots being processed in a production facility to orders to meet due-dates of the orders. In this study, we consider the lot-order assignment problem (LOAP) with the objective of minimizing total tardiness of the orders with distinct due dates. We address similarity relationships between the LOAP and the single machine total tardiness scheduling problem (SMTTSP) and suggest priority rules for the LOAP based on those for the SMTTSP. Performances of the priority rules are compared with each other and with that of the commercial optimization software package in computational experiments.

In this study, we develop a methodology for business process improvement (BPI) based on the event-driven process chain (EPC) model. The methodology consists of six stages for BPI such as identifying abnormal events, finding causes for the abnormal events and problems caused by the abnormal events, making cause and effect chains, drawing root-cause map, scoping business processes for improvement, and defining process improvement projects. We illustrate how to apply the methodology with some examples for the domestic registered mail delivery process.

We consider a problem of presort and loading of commercial bulk mails in a mailing service. Here, presort is the process by which a mailer prepares mail so that it is sorted to at least the finest extent required by the mailing service provider for the (discounted) price claimed. The problem is formulated as a special type of transportation problem. To solve industrial-sized problems, we develop an efficient heuristic algorithm and perform experimental tests on randomly generated problem instances. Results of the tests show that mailers can save mailing cost much more when they use small-sized mail trays with less frequent mailings. Also, large-sized mailers can obtain much more cost saving than small-sized mailers. In addition, cost saving effect is influenced by delivery area distribution of mails and fluctuation of mailing demand.