This paper considers a joint problem for blood inventory planning at hospitals and blood delivery planning from blood centers to hospitals, in order to alleviate the blood service imbalance between big and small hospitals being occurred in practice. The joint problem is to determine delivery timing, delivery quantity, delivery means such as medical drones and legacy blood vehicles, and inventory level to minimize inventory and delivery costs while satisfying hospitals’ blood demand over a planning horizon. This problem is formulated as a mixed integer programming model by considering practical constraints such as blood lifespan and drone specification. To solve the problem, this paper employs a Lagrangian relaxation technique and suggests a time efficient Lagrangian heuristic algorithm. The performance of the suggested heuristic is evaluated by conducting computational experiments on randomly-generated problem instances, which are generated by mimicking the real data of Korean Red Cross in Seoul and other reliable sources. The results of computational experiments show that the suggested heuristic obtains near-optimal solutions in a shorter amount of time. In addition, we discuss the effect of changes in the length of blood lifespan, the number of planning periods, the number of hospitals, and drone specifications on the performance of the suggested Lagrangian heuristic.

Meta-heuristic algorithms have been developed to efficiently solve difficult problems and obtain a global optimal solution. A common feature mimics phenomenon occurring in nature and reliably improves the solution through repetition. And at the same time, the probability is used to deviate from the regional optimal solution and approach the global optimal solution. This study compares the algorithm created based on the above common points with existed SA and HS to show advantages in time and accuracy of results. Existing algorithms have problems of low accuracy, high memory, long runtime, and ignorance. In a two-variable polynomial, the existing algorithms show that the memory increases and the accuracy decrease. In order to improve the accuracy, the new algorithm increases the number of initial inputs and increases the efficiency of the search by introducing a direction using vectors. And, in order to solve the optimization problem, the results of the last experiment were learned to show the learning effect in the next experiment. The new algorithm found a solution in a short time under the experimental conditions of long iteration counts using a two-variable polynomial and showed high accuracy. And, it shows that the learning effect is effective in repeated experiments.

Due to increasing awareness on the treatment of end-of-use/life products, disassembly has been a fast-growing research area of interest for many researchers over recent decades. This paper introduces a novel lot-sizing problem that has not been studied in the literature, which is the service-parts lot-sizing with disassembly option. The disassembly option implies that the demands of service parts can be fulfilled by newly manufactured parts, but also by disassembled parts. The disassembled parts are the ones recovered after the disassembly of end-of-use/life products. The objective of the considered problem is to maximize the total profit, i.e., the revenue of selling the service parts minus the total cost of the fixed setup, production, disassembly, inventory holding, and disposal over a planning horizon. This paper proves that the single-period version of the considered problem is NP-hard and suggests a heuristic by combining a simulated annealing algorithm and a linear-programming relaxation. Computational experiment results show that the heuristic generates near-optimal solutions within reasonable computation time, which implies that the heuristic is a viable optimization tool for the service parts inventory management. In addition, sensitivity analyses indicate that deciding an appropriate price of disassembled parts and an appropriate collection amount of EOLs are very important for sustainable service parts systems.

This study pursues to solve a batch of nonlinear parameter estimation (NPE) problems where a model interpreting the independent and the dependent variables is given and fixed but corresponding data sets vary. Specifically, we assume that the model does not have an explicit form and the discrepancy between a value from a data set and a corresponding value from the model is unknown. Due to the complexity of the problem, one may prefer to use heuristic algorithms rather than gradient-based algorithms, but the performance of the heuristic algorithms depends on their initial settings. In this study, we suggest two schemes to improve the performance of heuristic algorithms to solve the target problem. Most of all, we apply a Bayesian optimization to find the best parameters of the heuristic algorithm for solving the first NPE problem of the batch and apply the parameters of the heuristic algorithm for solving other NPE problems. Besides, we save a list of simulation outputs obtained from the Bayesian optimization and then use the list to construct the initial population set of the heuristic algorithm. The suggested schemes were tested in two simulation studies and were applied to a real example of measuring the critical dimensions of a 2-dimensional high-aspect-ratio structure of a wafer in semiconductor manufacturing.

To understand the genesis of tropical cyclones (TC), we computed TC genesis probability (GPr) by partitioning a highly localized genesis frequency (GFq) into nearby grid boxes in proportion to the spatial coherence of genesis potential index (GPI). From the analysis of TCs simulated by the Seoul National University Atmosphere Model Version 0 and the observed TCs, it was shown that GPr reasonably converges to GFq when averaged over a long-term period in a decent grid size, supporting its validity as a proxy representing a true TC GPr. The composite anomalies of the gridded GPr in association with the Asia summer monsoon, El Nino-Southern Oscillation (ENSO), and the Madden-Julian Oscillation (MJO) are much less noisy than those of GFq, and consequently are better interpretable. In summary, GPr converges to GFq, varies more smoothly than GFq, represents the spatiotemporal variations of GFq better than GPI, and depicts GFq with greater spatial details than other spatially smoothed GFqs.

글로벌 경제 침체 속에서 기업은 날로 높아져 가는 소비자들의 수요를 만족하기 위하여 납기 대응 그리고 LB(Line Balance, 라인편성효율) 향상과 제조원가의 절감을 위한 생산성 향상은 중요한 개선 항목이다. 따라서 본 연구에서는 자동차 물류 중 조달물류를 대상으로 하여 불출자의 로드밸런스율을 증대할 수 있는 휴리스틱 알고리즘 개발에 대하여 연구를 진행함으로써 1차 목표 값을 적용하였을 load balancing율은 45.6%에서 91.7%로 개선 된 것을 확인할 수 있었다.

This research focuses on a scheduling problem in the semiconductor probing facility. Probing facility is composed of identical parallel machines and the parallel machines form three workstations for the tests with different recipes. Each machine can be set to three different tests and sequence-dependent setup times are required between operations due to temperature and probe card loading/unloading. Precedence relationship exists between three tests of each wafer lot. The scheduling problem for the probing facility is a parallel machine scheduling problem with precedence relationship and sequence dependent setup time. We develop heuristic algorithm to minimize makespan for the scheduling problem and numerical experiments are conducted to evaluate the performance.

Monte-Carlo Tree Search (MCTS) is a best-first search algorithm to evaluate states of the game tree in game playing, and has been successfully applied to various games, especially to the game of Go. Upper Confidence Bounds for Trees (UCT), which is a variant of MCTS, uses the UCB1 formula as selection policy, and balances exploitation and exploration of the states. Rapid Action-Value Estimation (RAVE), which is a All-Moves-As-First (AMAF) heuristic, treats all moves in a simulation as the first move, and therefore updates the statistics of all children of the root node. In this paper, we evaluate the performance of RAVE and UCT playing against each other in the game of Tic-Tac-Toe. The experimental results show that the first player RAVE is much inferior to the second player UCT (13.0±0.7%); on the other hand, the first player UCT is far superior to RAVE (99.9±0.1%).

Companies are pursuing the management of small quantity batch production or JIT(Just-in-time) system for improving the delivery response and LOB(Line Balancing) in order to satisfy consumers’ increasing demands in the current global economic recession. And in order to improve the growth of production for reducing manufacturing cost, improvements of the Load Balancing have become an important reformation factor. Thus this paper is aimed at warehouse which procures materials on the assembly line in procurement logistics of automotive logistics and proceed with research on heuristic algorithm development which can increase the Load Balancing of workers. As a result of this study, when applied the primary target value, it was verified that the whole workers decreased from 28 to 24. Furthermore, when specified the secondary target value and applied algorithm once more, it was verified that the Load Balance Ratio was improved from 44.96% to 91.7%.

Artillery fire power due to effectiveness which is hard to predict well-planned and surprising attack can give a fear and shock to the personnel and is a very core weapon system and takes a critical role in wartime. Therefore in order to maximize operational effectiveness, Army required protecting artillery and takes a quick attack action through rapid construction of artillery’s positions. The artillery use artillery’s position to prevent exposure by moving to other position frequently. They have to move and construct at new artillery’s positions quickly against exposing existed place by foe’s recognition. These positions should be built by not manpower but engineering construction equipment. Because artillery positions have to protect human and artillery equipment well and build quickly. Military engineering battalion have lots of construction equipment which include excavator, loader, dozer, combat multi-purposed excavator, armored combat earthmover dump truck and so on. So they have to decide to optimal number of Team combining these equipments and determine construction sequence of artillery’s position in operational plan. In this research, we propose to decide number of Team efficiently and allocate required construction’s positions for each Team under constraints of limited equipments and time. To do so, we develop efficient heuristic method which can give near optimal solution and be applied to various situation including commander’s intention, artillery position’s priority or grouping etc. This heuristic can support quick and flexible construction plan of artillery positions not only for using various composition’s equipment to organize Teams but also for changing quantity of positions.

Maritime transport is now regarded as one of the main contributors to global climate change by virtue of its CO2 emissions. Meanwhile, slow steaming, i.e., slower ship speed, has become a common practice in the maritime industry so as to lower CO2 emissions and reduce bunker fuel consumption. The practice raised various operational decision issues in terms of shipping companies: how much ship speed is, how much to bunker the fuel, and at which port to bunker. In this context, this study addresses an operation problem in a shipping companies, which is the problem of determining the ship speed, bunkering ports, and bunkering amount at the ports over a given ship route to minimize the bunker fuel and ship time costs as well as the carbon tax which is a regulatory measure aiming at reducing CO2 emissions. The ship time cost is included in the problem because slow steaming increases transit times, which implies increased in-transit inventory costs in terms of shippers. We formulate the problem as a nonlinear lot-sizing model and suggest a Lagrangian heuristic to solve the problem. The performance of the heuristic algorithm is evaluated using the data obtained from reliable sources. Although the problem is an operational problem, the heuristic algorithm is used to address various strategic issues facing shipping companies, including the effects of bunker prices, carbon taxes, and ship time costs on the ship speed, bunkering amount and number of bunkering ports. For this, we conduct sensitivity analyses of these factors and finally discuss study findings.

In this study, we propose an efficient two-phase heuristic policy, called an acceptance tolerance control policy, for Infrastructure as a Service (IaaS) cloud services that considers both the service provider and customer in terms of profit and satisfaction, respectively. Each time an IaaS cloud service is requested, this policy determines whether the service is accepted or rejected by calculating the potential for realizing the two performance objectives. Moreover, it uses acceptance tolerance to identify the possibility for error with the chosen decision while compensating for both future fluctuations in customer demand and error possibilities based on past decisions. We conducted a numerical experiment to verify the performance of the proposed policy using several actual IaaS cloud service specifications and comparing it with other heuristics.

An efficient vehicle routing heuristic for different vehicle moving times for forward and backward between two points is studied in this research. Symmetric distance or moving times are assumed to move back and forth between two points in general, but it is not true in reality. Also, various moving speeds along time zones are considered such as the moving time differences between rush hours or not busy daytimes. To solve this type of extremely complicated combinatorial optimization problems, delivery zones are specified and delivery orders are determined for promising results on the first stage. Then delivery orders in each zone are determined to be connected with other zones for a tentative complete delivery route. Improvement steps are followed to get an effective delivery route for unsymmetric-time-varing vehicle moving speed problems. Performance evaluations are done to show the effectiveness of the suggested heuristic using computer programs specially designed and developed using C++.

To ensure that the production system of a factory is efficient, the factory layout design should consider the location and material flow plans of facilities, workshops, and storage areas. Highly productive factories need to have an optimized layout planning process, and a customized design methodology of the production system is a necessity for feasible layout planning. This paper presents a method for designing a layout module's size and shape and provides a heuristic location-allocation algorithm for the modules. The method is implemented and validated using a rich internet application-based platform. The layout design method is based on the leisure ship production process; this method can be used for designing the layout of a new factory or remodeling an existing factory and its production system. In contrast to existing layout methods, the inputs required for the proposed method, such as target products, production processes, and human-resource plans, are simple. This layout design method provides a useful solution for the initial stage of factory design.

효율적인 최적화 문제 해법을 GIS와 연계하면 입지 선정 문제에서 공간적 재현의 중요성에 관한 통찰력을 제공할 수 있다. 특히 발견적 해법 중 라그란지안 완화법에 기반한 해법은 메타휴리스틱에 비해 이론적으로 정교하며 결과해의 품질이 보장된다. 본 연구는 최소의 시설물로 전체 수요를 커버할 수 있도록 시설물 입지를 최적화하는 LSCP 문제에 초점을 두었다. 구체적으로 LSCP 문제 구조 및 특성 분석을 통해 문제의 효율적 해결을 위한 라그란지안 기법을 개발하고 이를 GIS 환경에 구현, 실세계 자료에 대해 입지선정 분석을 수행하였다. 실험을 통해 개발된 알고리즘은 고품질의 결과를 효율적으로 제공한다는 점을 확인하였다.

The purpose of this study is to derive factors affecting online game developers’ knowledge adoption in third-party online game user communities(OGUCs) and suggest a research model for the future empirical study. To accomplish this goal, we conduct an exploratory case study grounded on one of famous OGUCs operated by the third party company. Based on the heuristic systematic model(HSM) and interviews with three experienced online game developers, we identified three poster-related heuristic factors(reputation, experience, and participation level), three posting-related heuristic factors(the number of views, the number of comments, and the number of recommendations), and three posting-related systematic factors(knowledge quality, degree of disconfirmation, and degree of referencing) that influence developers’ knowledge adoption. We further developed measures for these influencing factors and suggested a research model with five propositions for the future empirical study. We conclude with discussing theoretical and practical implications, along with future research of the study.

선거구 구획문제는 선거구를 인구등분, 인접성, 공간적 조밀도 등의 일정한 기준에 따라서 나누는 작업을 말한다. 미국의 경우 의회 선거구는 필수불가결한 요소로 완벽한 인구 등분과 인접성을 헌법으로 요구하고 있다. 그러나 기존의 휴리스틱은 여러 가지 선거구 선정요인들을 동시에 만족시키는데 주력하고 있어 의회선거구에 맞는 완벽한 인구등분을 가진 선거구 플랜을 찾는데 한계를 보이고 있다. 그리하여, 이 논문에서는 의회선거구 구성 요건에 맞는 선거구 구획 연구를 하고자 최적화 연구방법 중 시뮬레이티드 어닐링(모의 담금질 기법) 휴리스틱 기법을 기본 바탕으로 선거구를 구획하였다. 또한 새로운 인접성 방법을 제시하여, 구획연구를 하는데 있어 하나의 접근법으로 제시하고자 한다. 연구 결과 이 논문에서 이용된 기법은 실제 이용 중인 선거구 플랜보다 훨씬 더 인구등분에 가까운 선거구 플랜을 찾아내며, 효과적으로 인접성 문제를 해결하고 있음을 알 수 있었다. 또한, 새로 개발한 휴리스틱은 다양한 선거구 플랜을 제시하고 있어, 의사결정자들로 하여금 선택의 용이성을 제공하고 있음을 알 수 있었다. 이러한 시도는 의회 선거구를 구성하는데 있어서 새로운 대안으로 제시될 수 있을 것이다. 또한, 이러한 선거구 선정 연구는 한국의 국회의원 선거구 선정문제에서 인접성과 인구등분의 선정기준을 이용할 시 적용 할 수 있다는데 그 의의가 있다.

A possible heuristic to solve metropolitan area vehicle routing problems with variable vehicle speeds is suggested in this research. Delivery hours are classified into 4 different time zones to make variable vehicle speeds no change within the same time zone to make TDVRP simple to solve. The suggested heuristic consists of 2 stages such as initial solution development step and initial solution improvement step. A computer program using C++ is constructed to evaluate the suggested heuristic. Randomly generated vehicle routing problems are used for the experiments. This heuristic could be helpful to logistics companies by increasing delivery efficiencies since the 4 zone classification is taken from the observed traffic information offered by a local government.

Recently, the optimisation of end-of-life (EOL) product remanufacturing processes has been highlighted. In particular, computer remanufacturing becomes important as the amount of disposed of computers is rapidly increasing. At the computer remanufacturing, depending on the selections of used computer parts, the value of remanufactured computers will be different. Hence, it is important to select appropriate computer parts at the reassembly. To this end, this study deals with a decision making problem to select the best combination of computer parts for minimising the total remanufacturing computer cost. This problem is formulated with an integer nonlinear programming model and heuristic search algorithms are proposed to resolve it.

This study aimed to investigate the characteristics of pre-service elementary teachers' understanding about scientific inquiry in terms of designing exploration and reasoning that is used to formulate explanations based on evidence. The research context was an open inquiry with using the Science Writing Heuristic (SWH) template in which participant students were not provided with inquiry questions. As data, lab. 39 pre-service elementary teachers participated in this study while taking their science methods course. Analyses of the reports were framed by the cognitive processes of inquiry (Chinn and Malhotra, 2002) and each report was coded and analyzed by the framework of inquiry (Tytler and Peterson, 2004). Results showed that groups' works that utilized the SWH template encouraged the participants to interact each other about scientific inquiry. They came up with more relevant and testable questions for their scientific inquiry. It implicates that children will be able to have chances of testing their own questions more properly by using the SWH template in science classes just as the participants did in this study. The use of the SWH template would help pre-service teachers to teach appropriately how to test inquiry questions to their students in the future. Discussion was made to figure out the characteristics or Korean pre-service elementary teachers' understanding about scientific inquiry.