A study on the denitrification of reverse osmosis(RO) concentrated wastewater from sewage reuse treatment plant in P city was conducted using waste desulfurization agent obtained from desulfurization process. Sulfur-based autotrophic denitrifying carrier comprises the predetermined amount of waste iron sulfide (FeS, Fe2S3), mine drainage sludge and elemental sulfur showing mesoporisity with 9.9 nm (99 Å) of average pore size. Sulfur denitrifying bacteria and sulfur reducing bacteria were implanted into the pores of sulfur denitrifying carrier. Nitrate was not affected by empty bed contact time (EBCT). It is probably due to larger reducing capacity of the carrier than the concentration of nitrate in RO concentrated wastewater. Total nitrogen (T-N) removal efficiency exhibited about 90% after 4 days. Sulfate ion was surprisingly decreased with sulfur autotropic process due to the reduction of sulfate ion to HS- and S2- by sulfur reducing bacteria. Sulfide and hydrogen sulfide ions were then taken by Fe(OH)3, main component of mine drainage sludge, releasing OH-. Alkalinity was therefore maintained between 7.5 and 8.5 in pH by the released OH-. Also, it had the effect of suppressing the production of H2S, which causes bad odor.

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.

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.

It is highly challenging to measure the efficiency of electric vehicle charging stations (EVCSs) because factors affecting operational characteristics of EVCSs are time-varying in practice. For the efficiency measurement, environmental factors around the EVCSs can be considered because such factors affect charging behaviors of electric vehicle drivers, resulting in variations of accessibility and attractiveness for the EVCSs. Considering dynamics of the factors, this paper examines the technical efficiency of 622 electric vehicle charging stations in Seoul using data envelopment analysis (DEA). The DEA is formulated as a multi-period output-oriented constant return to scale model. Five inputs including floating population, number of nearby EVCSs, average distance of nearby EVCSs, traffic volume and traffic congestion are considered and the charging frequency of EVCSs is used as the output. The result of efficiency measurement shows that not many EVCSs has most of charging demand at certain periods of time, while the others are facing with anemic charging demand. Tobit regression analyses show that the traffic congestion negatively affects the efficiency of EVCSs, while the traffic volume and the number of nearby EVCSs are positive factors improving the efficiency around EVCSs. We draw some notable characteristics of efficient EVCSs by comparing means of the inputs related to the groups classified by K-means clustering algorithm. This analysis presents that efficient EVCSs can be generally characterized with the high number of nearby EVCSs and low level of the traffic congestion.

Evaluating the operational efficiency of electric vehicle charging stations (EVCSs) is important to understand charging network evolution and the charging behavior of electric vehicle users. However, aggregation of efficiency performance metrics poses a significant challenge to practitioners and researchers. In general, the operational efficiency of EVCSs can be measured as a complicated function of various factors with multiple criteria. Such a complex aspect of managing EVCSs becomes one of the challenging issues to measure their operational efficiency. Considering the difficulty in the efficiency measurement, this paper suggests a way to measure the operational efficiency of EVCSs based on data envelopment analysis (DEA). The DEA model is formulated as constant returns of output-oriented model with five types of inputs, four of them are the numbers of floating population and nearby charging stations, distance of nearby charging stations and traffic volume as desirable inputs and the other is the traffic speed in congestion as undesirable one. Meanwhile, the output is given by the charging frequency of EVCSs in a day. Using real-world data obtained from reliable sources, we suggest operational efficiencies of EVCSs in Seoul and discuss implications on the development of electric vehicle charging network. The result of efficiency measurement shows that most of EVCSs in Seoul are inefficient, while some districts (Nowon-gu, Dongdaemun-gu, Dongjak-gu, Songpa-gu, Guro-gu) have relatively more efficient EVCSs than the others.

The vehicle routing problem is one of the vibrant research problems for half a century. Many studies have extensively studied the vehicle routing problem in order to deal with practical decision-making issues in logistics. However, developments of new logistics strategies have inevitably required investigations on solution methods for solving the problem because of computational complexity and inherent constraints in the problem. For this reason, this paper suggests a simulated annealing (SA) algorithm for a variant of vehicle routing problem introduced by a previous study. The vehicle routing problem is a multi-depot and multi-trip vehicle routing problem with multiple heterogeneous vehicles restricted by the maximum permitted weight and the number of compartments. The SA algorithm generates an initial solution through a greedy-type algorithm and improves it using an enhanced SA procedure with three local search methods. A series of computational experiments are performed to evaluate the performance of the heuristic and several managerial findings are further discussed through scenario analyses. Experiment results show that the proposed SA algorithm can obtain good solutions within a reasonable computation time and scenario analyses show that a transportation system visiting non-dedicated factories shows better performance in truck management in terms of the numbers of vehicles used and trips for serving customer orders than another system visiting only dedicated factories.

This paper considers the problem of scheduling loading and unloading operations of a crane in a railway terminal motivated from rail-road container transshipment operations at Uiwang Inland Container Depot (ICD). Unlike previous studies only considering the total handling time of containers, this paper considers a bi-criteria objective of minimizing the weighted sum of the total handling time and tenant service time. The tenant service time is an important criterion in terms of terminal tenants who are private logistics companies in charge of moving containers from/to the terminal using their trucks. In the rail-road container shipment yard, the tenant service time of a tenant can be defined by a time difference between beginning and finishing loading and unloading operations of a crane. Thus, finding a set of sequences and time of the crane operations becomes a crucial decision issue in the problem. The problem is formulated as a nonlinear program which is improved by linearizing a nonlinear constraint in the model. This paper develops a genetic algorithm to solve the problem and performs a case study on the Uiwang ICD terminal. Computational experiment results show that the genetic algorithm shows better performance than commercial optimization solvers. Operational implications in terms of tenants are drawn through sensitivity analyses.

In South Korea, Jeju Island has a role as a test bed for electric vehicles (EVs). All conventional cars on the island are supposed to be replaced with EVs by 2030. Accordingly, how to effectively set up EV charging stations (EVCSs) that can charge EVs is an urgent research issue. In this paper, we present a case study on planning the locations of EVCS for Jeju Island, South Korea. The objective is to determine where EVCSs to be installed so as to balance the load of EVCSs while satisfying demands. For a public service with EVCSs by some government or non-profit organization, load balancing between EVCS locations may be one of major measures to evaluate or publicize the associated service network. Nevertheless, this measure has not been receiving much attention in the related literature. Thus, we consider the measure as a constraint and an objective in a mixed integer programming model. The model also considers the maximum allowed distance that drivers would detour to recharge their EV instead of using the shortest path to their destination. To solve the problem effectively, we develop a heuristic algorithm. With the proposed heuristic algorithm, a variety of numerical analysis is conducted to identify effects of the maximum allowed detour distance and the tightness of budget for installing EVCSs. From the analysis, we discuss the effects and draw practical implications.

For air express service providers offering various express delivery services such as overnight delivery and next-business day delivery services, establishing quickly cargo loading plans is one of important issues owing to the characteristics of air express business, i.e., a short amount of time is available to complete all cargo loading operations before flight departure after receiving air express containers, pallets and bulks. On the other hand, one of major concerns in the air cargo loading planning is to make a plan that insures the stability of an aircraft to avoid take-off, flight, and landing accidents. To this end, this paper considers an air cargo loading planning problem, which is the problem of determining locations in the aircraft cargo space where air containers, pallets and bulks to be loaded while insuring the aircraft stability, motivated from DHL and Air Hong Kong. The objective of the problem is to maximize the total revenue gained from loading air express containers, pallets and bulks. To solve the problem, this paper suggests a simulated annealing algorithm to overcome impracticality of the integer programming model developed by a previous study requiring excessive computation time. The results of computational experiments show that the heuristic algorithm is a viable tool for establishing express cargo loading plans as giving robust and good solutions in a short amount of computation time. Scenario analyses are performed to investigate the effect of the current activities of air express carriers on the revenue change and to draw practical implications for air express service providers.

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.

지속적으로 강화되고 있는 국제 환경규제로, 해운사들은 선박의 온실가스 배출량을 줄이기 위한 노력을 해야 하는 상황에 직면해 있다. 본 연구에서는 총 온실가스 배출량에 대한 제약이 있는 상황에서 복수개의 항로를 운영하는 해운사에서 일 평균 선박 운영비용의 총합을 최소화하는 항로별 최적 선박대수와 운항속도를 결정하는 문제를 다룬다. 이 문제를 풀기위해 라그랑지안 휴리스틱 알고리즘을 개발하고 라그랑지안 쌍대문제를 풀어 최적해에 대한 하한값을 구한다. 제시한

철근 콘크리트 구조물에서 발생하는 균열에 의한 손상은 과도한 하중이나 사용성에 의해 발생한다. 이러한 손상을 검사하는 방법으로 육안으로 확인하거나 비파괴 시험법을 주로 이용하고 있다. 후자의 경우, 콘크리트 내부 철근의 배근 방향성에 의해 균열에 의한 손상인지 판별하기 어려운 문제가 발생하게 되며, 비파괴시험(Non-destructive Test)에 사용되는 대부분의 센서(Sensor)는 1축 가속도 센서이기 때문에 중첩된 전달파를 분석하기에는 어렵다. 따라서, 이를 해결하기 위해 중공 유리관을 이용하였고 콘크리트 보 내부에 매입하여 철근이 매입되어 있는 경우에 대하여 3축 가속도계를 이용하여 탄성파로 가진 하였을 경우에 발생하는 파(Wave)를 비교 분석하였다.

구조물의 모니터링과 손상 및 진동예측에 많은 센서들이 사용되고 있으며, 압전소자 및 변형게이지는 재료 및 구조물의 손상에 사용되고 있다. 그러나 진동에 대한 실험은 미진한 실정이다. 압전소자는 구조물의 변형되었을 때 로드셀의 경우에서처럼 작용되는 외력을 전기적인 신호로 바꾸어주는 센서이다. 이를 이용하여, 철근 콘크리트 판에서 진동예측을 압전소자의 전압변화로 사용하였다. 본 연구는 판에서 압전소자를 사용하여 진동을 예측하기 위한 기초적 연구이다.

본 논문은 기하학적 비선형과 재료적 비선형을 고려하여 강구조 부재 및 골조의 비선형 해석을 위한 프로그램을 개발하고 강재의 응력도 변형도 관계를 정식화하였다. 본 프로그램의 효율성을 검증하기 위하여 단조 하중을 받는 H형강 보와 각형강관 기둥, 그리고 반복 수평력을 받는 브레이스 강재 라멘에 대한 수치해석을 행하였다. 본 그로그램에 의해 얻어진 결과는 대체적으로 기존의 실험 및 해석결과와 일치하였다.

Converter slag contains free-CaO and free-MgO that has a problem causing a breakdown in the water and reaction. To solve this problem, it was treated the aging process that is inserting a converter slag aggregate in water before placing concrete. As a result, aging process was very effective in the compressive strength of the concrete.

The evolution of the electro-mechanical impedance (EMI) of piezoelectricity (PZT) sensor was investigated to determine the setting times of fiber reinforced cementitious materials in this study. Penetration resistance test was also conducted to validate the EMI sensing technique. As a result, the setting times of fiber reinforced cementitious materials can be effectively monitored through the EMI sensing technique using an embedded PZT sensor.

In recent years, a rapid growth in the population and urbanization led to an increasing industrial growth. The inadequate treated-wasted water from industry and various non-point sources causes significant negative effects on the stream water. For past few decades, extensive researches have been performed on water purification process. The purpose of this study is to investigate mechanical performance and water purification properties of porous concrete by using effective microorganisms through the site assessment test. The mechanical performance evaluation results showed that the increase void ration caused an decrease in the strength. The optimal mix rate was found to be 15% void rate From the site assessment, it was evaluated that the porous concrete improved the quality of the water and the purification ratios are 34.1 for SS, 14.6% for BOD, 34.9% for COD, 11.4% for T-N, and 12.6% for T-P. The porous concrete and the related purification technique can reduce the non-point pollution sources flowing into the river.