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This study considers a two-stage supply chain (SC) consisting of a single supplier and a manufacturer. When the manufacturer introduces a new product to the market, both the manufacturer and supplier should install production capacity in advance. Since capacity building often takes a long time, the demand is uncertain at the time of capacity decision making. The supplier often makes a conservative decision on capacity building to avoid possible capital risks due to excess capacity, which leads to the so-called double marginalization problem. Various risk-sharing supply contracts have recently been studied in academia to overcome the double marginalization problem. However, most existing studies ignore the bargaining power of each SC member and capacity investment. This study aims to fill the research gap by including capacity investment and bargaining power in the supply contract process. A capacity cost-sharing (CCS) contract is introduced in which the manufacturer shares the supplier’s capacity investment risk. We investigate how to set the contract parameters in the CCS contract to coordinate the supply chain. It is found that the wholesale price and manufacturer’s CCS ratio are negatively proportional to each other, and the manufacturer’s expected profit increases as the CCS ratio (wholesale price) increases (decreases) in the coordinated CCS contract. We show that there exists a CCS contract leading to a coordinated supply chain for a specific range of bargaining power. We also present a new CCS contract for a supply chain with a risk-averse supplier. A numerical illustration is provided to clarify how the contract parameters are determined and to examine the effect of the contract parameters on SC performance. Managerial implications and possible future work are discussed.

The widespread outbreak of the novel coronavirus disease COVID-19 has posed an enormous threat to global public health. A different set of policy interventions has been implemented to mitigate the spread in most countries. While the use of personal protective equipment and social distancing has been specifically emphasized, South Korea has deployed massive testing and contact-tracing program from the early stage of the outbreak. This study aims at investigating the effectiveness of testing and contact-tracing to counter the spread of infectious diseases. Based on the SEICR (susceptible-exposed-infectious-confirmed-recovered) model, an agent-based simulation model is developed to represent the behavior of disease spreading with the consideration of testing and contact-tracing in place. Designed experiments are conducted to verify the effects of testing and contact tracing on the peak number of infections. It has been observed that testing combined with contact tracing may lower the peak infections to a great extent, and it can thus be avoided for the hospital bed capacity to be overwhelmed by infected patients. It is implied that an adequate capability of testing and contact-tracing may enable us to become better prepared for an impending risk of infectious diseases.

A bucket-brigade is a way of transporting items where items are passed from one person to the next. The operation of the bucket brigade imitates the cooperative behavior of ants when brood, food or other resources are moved. Koo (2009) presented a bucket brigade-based assembly cell where each worker follows a simple rule: perform assembly operations on a product until the next worker downstream takes it over; then go back to the previous worker upstream to take over a new assembly job. In this way, the flow line is self-balanced without any predetermined job assignment. However, there are some productivity losses related to hand-off and blocking. This paper examines the hand-off and blocking losses and presents a new bucket brigade-based assembly cell where working areas for each assembler is restricted with the help of buffer interfaces. Simulation experiments are used to validate the performance of the new assembly system.

In this paper, we investigate the relationship between Internet search trends and stock market. Under the assumption that investors may use Internet search engine to obtain information for companies of their interests before taking actual investment actions, the relationship between the changes on Internet search volume and the fluctuation of trading volume as well as stock price of a company is analyzed with actual market data. A search trend investment strategy that reflects the changes on Internet search volume is applied to large enterprises’ group and to small and medium enterprises’ (SMEs) group, and the correlation between profit rate and trading volume is analyzed for each company group. Our search trend investment strategy has outperformed average stock market returns in both KOSPI and KOSDAQ markets during the seven-year study period (2007～2013). It is also shown that search trend investment strategy is more effective to SMEs than to large enterprises. The relationship between changes on Internet search volume and stock trading volume is stronger at SMEs than at large enterprises.

Pyung-Hoi KooDepartment of Systems Management and Engineering, Pukyong National UniversityThis paper provides a real-time loading strategy for batch processing machines in which a number of jobs are simultaneously processed as a batch. The batch processing machines can be seen in both manufacturing indus-tries (e.g., semiconductor, automobile and metal working) and service industries (transportation vehicles, mail shipment and theme park). This paper focuses on batch processing machines in semiconductor manufacturing. We present a look-ahead loading strategy for tardiness minimization where future arrivals and due dates are taken into consideration. Simulation tests are performed on the presented strategy and some existing loading heuristics under various production settings with different traffic intensities and forecasting errors. Experimental results show that our strategy provides the performance of good quality.

Together with soaring interest on Big Data, now there are vigor ous reports that unearth various social values lying underneath those data from a number of application areas. Among those reports many are using such data as Internet search histories from Google site, social relationships from Facebook, and transactional or locational traces collected from various ubiquitous devices. Many of those researches, howe ver, are conducted based on the data sets that are accumulated over the North American and European areas, which means that direct interpretation and application of social values exhibited by those researches to the other are as like Korea can be a disturbing task.This research has started from a validation study against Korean environment of the former paper which says an investment strategy that exploits up and down of Google sear ch volume on a carefully selected set of terms shows high market performance. A huge difference between North Americ an and Korean environment can be eye witnessed via the distinction in profit rates that are exhibited by the corresponding set of search terms. Two sets of search terms actually presented low correlation in their profit rates over two financial markets. Even in an experiment which compares the profit rates with two different investment periods with the same set of search terms showed no such meaningful result that outperforms the market average. With all these results, we cautiously conclude that establishing an investment strategy that exploits Internet search volume ove r a specified word set needs more conscious approach.

Batch processing machines process several jobs simultaneously as a batch. When a batch machine completes the service of a batch and there is at least one product waiting in queue, a realtime control decision is made to decide whether to start a job with a partial batch or wait until future job arrivals occur. This paper provides a review of research works on real-time control strategies of batch processing machines in semiconductor manufacturing. We relate the research works with some distinct characteristics of semiconductor manufacturing and discuss the issues for future works.

In the capacity constrained manufacturing systems where multiple product types are manufactured, the products are often produced in lots. Although the lot production may increase the system throughput by reducing changeover times, it may also increase production lead time because each item in a large lot has a long waiting time. Hence, a production manager should consider both throughput and lead time at the same time when deciding production lot sizes. This paper, which is an extension to the previous work done in Koo et al. (2007) that assumes homogeneous setup times, addresses a lot sizing problem in the system with multiple product types and unequal setup times. We develop a non-linear optimization model for simultaneous determination of throughput rate and lot size for each product. Since this optimization model cannot be solved analytically, we propose a heuristic solution procedure by analyzing the characteristics of the problem. Some numerical examples are presented to validate the proposed model, and finally the performance of the heuristic procedure is evaluated by comparison with the results of simulation experiments.

In manufacturing systems, there often exists a bottleneck machine whose capacity is equal to or less than the market demand. Any idle or waste time at the bottleneck machine directly impacts the output of the entire plant because it results in a loss of throughput. In order to maximize the capacity utilization by less setup losses at the bottleneck machine, the parts are often produced in batches. Traditionally, most batch sizing decisions are made based on the economic order quantity model where setup and inventory holding costs are considered. This paper presents an alternative method to determine batch size at a bottleneck machine. We present a new objective function and cost factors for batch sizing and investigate queuing and throughput models. A linear search algorithm is introduced to find the optimal throughput rate and batch size at the same time. Numerical examples are examined to see how the batching algorithm works.

Motivated by a bottleneck operation in a multi-layer ceramic capacitor production line, we study a scheduling problem of batch processing machine in which a number of jobs are processed simultaneously as a batch. The performance measures considered include makespan, total completion time, and total weighted completion time. We first present a new simple integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems. However, since the problem is NP-hard and the size of a real problem is very large, we propose a number of heuristic algorithms and design a hybrid genetic algorithm to solve practical big-size problems in a reasonable computational time. To verify performance of the algorithms, we compare them with lower bounds for the problems. From the results of these computational experiments the heuristic algorithms including the genetic algorithm show different performances for the three problems.

Semiconductor wafer fabrication lines can be characterized by re-entrant product flow, long production lead-time, large variety of production processes, and large capital investment. These distinctive characteristics make the flow control in the fab very complicated. Throughput rate and lead-time are among the most important performance measures. The throughput rate is usually determined by a bottleneck resource, and the lead-time depends on the machine utilization level and the amount of variability in the system. Due to the high efficiency of material handling and reduced particles, automated material handling systems such as automatic guided vehicles (AGVs), overhead hoist transporters (OHTs), and overhead shuttles (OHSs) are being widely used in wafer fabrication lines (wafer fabs) instead of human operators. Although a material handling system itself is seldom a bottleneck of production in a fab, it is important for that to effectively support the bottleneck machines to maximize the throughput and reduce production lead-time. This paper presents a vehicle dispatching procedure based on the concept of theory of constraints, in which vehicle dispatching decisions are made to utilize the bottleneck machines at the maximum level. Simulation experiments have been performed to compare the proposed vehicle dispatching procedure with existing ones under different levels of machine utilization, vehicle utilization, and local buffer capacity.

Motivated by a bottleneck operation in an MLCC (multi-layer ceramic capacitor) production line, we study the scheduling problem of parallel batch processing machines in which a number of jobs can be processed simultaneously in a machine as a batch. Volumes of the jobs are different from each other and each job belongs to the family in which all jobs have the same processing time. In this situation, we analyse three kinds of problems whose performance measures are makespan, total completion time, and total weighted completion time, respectively. Since these problems are known to be NP-hard, we propose a number of heuristics and design genetic algorithms for the problems. Through some computational experiments, we evaluate the performances of the heuristic algorithms proposed, including the genetic algorithms for each of three problems.

As manufacturing systems have grown in size and complexity, material flow control has become one of the key issues for system efficiency, and determination of the number of vehicles required is an important issue in the design of the AGV (automatic guided vehicle) systems for automated material flow control. In an AGV system, a part issues a delivery request for its transportation, and then an empty vehicle is assigned based on a pre-determined vehicle selection rule and provides delivery service. This research presents a fleet sizing procedure for an AGV system with multiple pickup and delivery stations. A queueing model is used to estimate part waiting times. The fleet sizing procedure estimates the minimum number of vehicles needed to ensure a predefined part waiting time limit. While most stochastic models assume first-come-first-served or random vehicle selection rules for the selection of an empty vehicle, this model considers such additional rules as the nearest vehicle selection rule, which is the most popular among all vehicle selection rules. The performance of the proposed model is examined through computational experiments.

The design and evaluation of AGV-based material handling systems are highly complex because of the randomness and the large number of variables involved. Vehicle travel time is a fundamental parameter for solving various flexible manufacturing system (FMS) design problems. This article presents stochastic vehicle travel time models for AGV-based material handling systems with emphasis on the empty travel times of vehicles. Various vehicle dispatching rules examined here include the nearest vehicle selection rule and the longest idle vehicle selection rule. A simulation experiment is used to evaluate and demonstrate the presented models.

As machine tools become more versatile, single-stage multimachine systems (SSMS) have emerged in which all the operations of a part are performed on a single machine. In this system, parts stay on a machine all through their operations while tools are delivered to the machines as requested. The performance of the system is then restricted by the tooling conditions. This paper addresses the problem of how many tools should be maintained in an SSMS under a tool sharing environment. An open queueing network-based approach is proposed which not only determines tool requirement levels but also predicts the tool waiting time given the tooling capacity. The tool life and tool delivery are taken into consideration in determining the tool requirement levels. A simulation study is performed to evaluate the proposed approach.

This paper addresses a tooling problem in automated manufacturing systems under dynamic tool assignment policy. In this type of systems, while parts stay on a machine all through its operations, the tools required (but not available on the machine) are delivered in time for requirements. The system performance is restricted to a great extent by tooling constraints. This paper presents an analytical method to determine tool requirement levels under dynamic production environment. The appropriate tool duplication levels are determined by trading off tool duplication levels and tool waiting time. Simulation studies are used to verify the performance of the proposed analytical model.

This paper describes how the theory of automated manufacturing cell and industrial robotics has been applied to the design and operation of a multirobot cell. The manufacturing cell is installed at Purdue University and consists of a PLC (Programmable Logic Controller), two industrial robots, two machine stations (for grinding and washing), and a conveyor. The design and control issues of the implemented manufacturing cell include the design of control architecture, synchronous and asynchronous sequence controls, deadlock prevention by Petri-net analysis, off-line operation plan by robot time-and-motion study, and error recovery. The application of the theoretical tools in this cell has proven effective in achieving the efficiency and reliability advantages of multirobotic operations.

In this paper, we describe the spreadsheet modeling of manufacturing systems which are represented by multi-server and multi-product open networks. The spreadsheet software is characterized by easy data manipulation, on-screen numerical and visual feedback, fast calculation (what-if analysis), and its availability. So far, most spreadsheet models of this type have been used to solve static and deterministic problems in manufacturing systems, ignoring most of the uncertainties. We propose here that a spreadsheet can be implemented for capturing not only static features but also stochastic behaviour of manufacturing systems. The experiments show that the results from the spreadsheet model are reasonably close to those from the other existing approaches. The proposed spreadsheet model could be applied by an engineer to the modeling of manufacturing systems, especially for a first-cut design stage, utilizing his/her own spreadsheet software.

One of the major elements in improving efficiency of healthcare services is patient flow. Patients require a variety of healthcare resources as they receive healthcare services. Poor management of patient flow results in long waiting time of patients, under/over utilization of medical resources, low quality of care and high healthcare cost. This article addresses patient flow problems at a Gastrointestinal endoscopy unit. We attempt to analyze the main factors that contribute to the inefficient patient flow and process bottlenecks and to propose efficient patient scheduling and staff allocation alternatives to increase the system efficiency. Simulation models are used to examine various healthcare operational strategies. One of the major elements in improving efficiency of healthcare services is patient flow. Patients require a variety of healthcare resources as they receive healthcare services. Poor management of patient flow results in long waiting time of patients, under/over utilization of medical resources, low quality of care and high healthcare cost. This article addresses patient flow problems at a Gastrointestinal endoscopy unit. We attempt to analyze the main factors that contribute to the inefficient patient flow and process bottlenecks and to propose efficient patient scheduling and staff allocation alternatives to increase the system efficiency. Simulation models are used to examine various healthcare operational strategies.

This paper considers a transporter scheduling problem under dynamic block transportation environment in shipbuilding. In dynamic situations, there exist the addition, cancellation or change of block transportation requirements, sudden breakdowns, and maintenance of transporters. Some blocks are available to be picked up at a specific time during the planning horizon while some other blocks need to be delivered before a specific time. These requirements cause two penalty times: 1) delay times incurred when a block is picked up after a required start time, and 2) tardy times incurred when a block shipment is completed after a required delivery time. The blocks are located at different areas in the shipyard and transported by transporters. The objective of this paper is to propose a heuristic algorithm based on a network flow model which minimize the weighted sum of empty transporter travel times, delay times, and tardy times. Also, a rolling scheduling algorithm is proposed for dynamic block transportation environment. The performance of the proposed heuristic algorithms are evaluated through a simulation experiment.

In common manufacturing systems, there exist bottleneck machines that limit the capacity of the entire system. This paper addresses a batch sizing problem at the bottleneck machines in which various product types are processed. Koo et al. (2006) have presented a batch sizing model at single-product bottleneck machines. This paper is an extension to the previous research work where multiple products are taken into explicit considerations in batch sizing. We introduce a linear search algorithm to find optimal throughput rate and batch size at the same time. Numerical examples are provided to see how the proposed method works.

This paper addresses the work assignment problems in assembly cells. The assembly cells are known to be more flexible and productive than the traditional assembly line. To maximize the utilization of resources in assembly cells, it is important to have the line balanced. This paper presents a dynamic work assignment method where each worker performs assembly operations on a product until the next worker downstream takes it over. This pull-type assignment method balances the assembly cell in an autonomous way. The performance of the new assignment method is examined and compared with existing assignment methods.

This paper considers a transporter scheduling problem under dynamic block transportation environment in shipbuilding. In dynamic situations, there exist the addition or cancellation of block transportation requirements. The transportation of the blocks in the shipyard has some distinct characteristics. Some blocks are available to be picked up at a specific time during the planning horizon while some other blocks need to be delivered before a specific time. These requirements cause two penally times: 1) delay time incurred when a block is picked up after a required start time, and 2) tardy time incurred when a block shipment is completed after the required delivery time. The blocks are located at different areas in the shipyard. The objective of this paper is to propose heuristic algorithms which minimize the weighted sum of empty transporter travel times, delay times, and tardy times. Four heuristic algorithms for transporter scheduling are proposed and their performance is evaluated through computational experiments.

Busan is one of the busiest seaports in the world where millions of containers are handled every year. The space of the container terminal at the port is so limited that several small container yards are scattered in the city. Containers are frequently transported between the container terminal and container yards, which may cause tremendous traffic problems. The competitiveness of the container terminal may seriously be aggravated due to the increase in logistics costs. Thus, there exist growing needs for developing an efficient fleet management tool to resolve this situation. This paper proposes a new fleet management procedure based on a heuristic tabu search algorithm in a container transportation system. The proposed procedure is aimed at simultaneously finding the minimum fleet size required and travel route for each vehicle while satisfying all the transportation requirements within the planning horizon. The transportation system under consideration is static in that all the transportation requirements are predetermined at the beginning of the planning horizon. The proposed procedure consists of two phases: In phase one, an optimization model is constructed to obtain a fleet planning with minimum vehicle travel time and to provide a lower bound on the fleet size. In phase two, a tabu search based procedure is presented to construct a vehicle routing with the least number of vehicles. The performance of the procedure is evaluated and compared with two existing methods through computational experiments.