Due to the complexity of urban area, the city vehicle routing problem has been a difficult problem. The problem has involved factors such as parking availability, road conditions, and traffic congestion, all of which increase transportation costs and delivery times. To resolve this problem, one effective solution can be the use of parcel lockers located near customer sites, where products are stored for customers to pick up. When a vehicle delivers products to a designated parcel locker, customers in the vicinity must pick up their products from that locker. Recently, identifying optimal locations for these parcel lockers has become an important research issue. This paper addresses the parcel locker location problem within the context of urban traffic congestion. By considering dynamic environmental factors, we propose a Markov decision process model to tackle the city vehicle routing problem. To ensure more real situations, we have used optimal paths for distances between two nodes. Numerical results demonstrate the viability of our model and solution strategy.

This paper presents a path planning optimization model for the engineering units to install obstacles in the shortest time during wartime. In a rapidly changing battlefield environment, engineering units operate various engineering obstacles to fix, bypass, and delay enemy maneuvers, and the success of the operation lies in efficiently planning the obstacle installation path in the shortest time. Existing studies have not reflected the existence of obstacle material storage that should be visited precedence before installing obstacles, and there is a problem that does not fit the reality of the operation in which the installation is continuously carried out on a regional basis. By presenting a Mixed Integrer Programming optimization model reflecting various constraints suitable for the battlefield environment, this study attempted to promote the efficient mission performance of the engineering unit during wartime.

As technologies have been more quickly developed in this 4th Industry Revolution era, their application to defense industry has been also growing. With these much advanced technologies, we attempt to use Manned-Unmanned Teaming systems in various military operations. In this study, we consider the Location-Routing Problem for reconnaissance surveillance missions of the maritime manned-unmanned surface vehicles. As a solution technique, the two-phase method is presented. In the first location phase, the p-median problem is solved to determine which nodes are used as the seeds for the manned vehicles using Lagrangian relaxation with the subgradient method. In the second routing phase, using the results obtained from the location phase, the Vehicle Routing Problems are solved to determine the search routes of the unmanned vehicles by applying the Location Based Heuristic. For three network data sets, computational experiments are conducted to show the performance of the proposed two-phase method.

도서관은 문화 공간으로서 누구나 이용할 수 있고 다양한 분야의 지식과 정보를 제공하여 삶의 질을 높이는 중요한 사회기반 시설 중 하나이다. 현재 한국의 도서관 수는 수요에 비해 공급이 부족한 상황이며, 이를 해결하기 위해 일부 지자체는 차량을 수단으로 이동형 도서 서비스를 제공하는 분관 형태의 이동 도서관을 운영하고 있다. 주로 도서관을 이용하기 어려운 사람들을 대상으로 순회하며 도서관 서비스를 제공하지만, 비효율적인 운행 노선과 균일하지 않은 서비스로 실질적인 효과를 발휘하지 못하고 있다. 따라서 본 연구에서는 성남시 새마을 이동 도서관을 대상으로 순회 경로 현황을 파악하고, 최소 이동 거리로 개선된 이동 도서관 노선을 제시하고자 한다. 더욱 효율적인 운영을 위해 서비스 권역을 나누고, 시간 제약을 결합한 차량경로설정 문제를 사용하여 도서 서비스의 이용 격차를 줄인 새로운 운행 노선을 구축하였다. 본 연구는 이동 도서관의 효과적인 노선 운영에 대한 기초적인 자료로 활용될 수 있다는 점에서 의의가 있다. 향후 이동 도서관 뿐만 아니라 이동형 공공 서비스를 위한 유용하고 현실적인 가이드라인으로 활용될 수 있을 것이다.

Due to the issue of the sustainability in transportation area, the number of electric vehicles has significantly increased. Most automakers have decided or planned to manufacture the electric vehicles rather than carbon fueled vehicles. However, there are still some problems to figure out for the electric vehicles such as long charging time, driving ranges, supply of charging stations. Since the speed of growing the number of electric vehicles is faster than that of the number of charging stations, there are lack of supplies of charging stations for electric vehicles and imbalances of the location of the charging stations. Thus, the location problem of charging stations is one of important issues for the electric vehicles. Studies have conducted to find the optimal locations for the charging stations. Most studies have formulated the problem with deterministic or hierarchical models. In this paper, we have investigated the fluctuations of locations and the capacity of charging stations. We proposed a mathematical model for the location problem of charging stations with the vehicle routing problem. Numerical examples provide the strategy for the location routing problems of the electric vehicles.

Transportation in urban area has been getting hard to fulfill the demand on time. There are various uncertainties and obstacles related with road conditions, traffic congestions, and accidents to interrupt the on-time deliveries. With this situation, the last mile logistics has been a keen issue for researchers and practitioners to find the best strategy of the problem. A way to resolve the problem is to use parcel lockers. Parcel locker is a storage that customers can pick up their products. Transportation vehicles deliver the products to parcel lockers instead of all customer sites. Using the parcel lockers, the total delivery costs can be reduced. However, the inconvenience of customer has to increase. Thus, we have to optimal solution to balance between the total delivery costs and customers' inconvenience. This paper formulates a mathematical model to find the optimal solution for the vehicle routing problem and the location problem of parcel lockers. Experimental results provide the viability to find optimal strategy for the routing problem as well as the location problem.

An automated material handling system (AMHS) has been emerging as an important factor in the semiconductor wafer manufacturing industry. In general, an automated guided vehicle (AGV) in the Fab’s AMHS travels hundreds of miles on guided paths to transport a lot through hundreds of operations. The AMHS aims to transfer wafers while ensuring a short delivery time and high operational reliability. Many linear and analytic approaches have evaluated and improved the performance of the AMHS under a deterministic environment. However, the analytic approaches cannot consider a non-linear, non-convex, and black-box performance measurement of the AMHS owing to the AMHS’s complexity and uncertainty. Unexpected vehicle congestion increases the delivery time and deteriorates the Fab’s production efficiency. In this study, we propose a Q-Learning based dynamic routing algorithm considering vehicle congestion to reduce the delivery time. The proposed algorithm captures time-variant vehicle traffic and decreases vehicle congestion. Through simulation experiments, we confirm that the proposed algorithm finds an efficient path for the vehicles compared to benchmark algorithms with a reduced mean and decreased standard deviation of the delivery time in the Fab’s AMHS.

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Network(SCN). One of keys issues in the current SCN research area involves minimizing both production and distribution costs. This study deals with finding an optimal solution for minimizing the total cost of production and distribution problems in supply chain network. First, we presented an integrated mathematical model that satisfies the minimum cost in the supply chain. To solve the presented mathematical model, we used a genetic algorithm with an excellent searching ability for complicated solution space. To represent the given model effectively, the matrix based real-number coding schema is used. The difference rate of the objective function value for the termination condition is applied. Computational experimental results show that the real size problems we encountered can be solved within a reasonable time.

The Dynamic Vehicle Routing Problem (DVRP) involves a combinatorial optimization problem where new customer demands become known over time, and old routes must be reconfigured to generate new routes while executing the current solution. We consider the high level of dynamism problem. An application of highly dynamic DVRP is the ambulance service where a patient contacts the service center, followed by an evaluation of case severity, and a visit by a practitioner/ ambulance is scheduled accordingly. This paper considers a variant of the DVRP and proposes a decentralized algorithm in which collaborators (Depot and Vehicle), both have only partial information about the entire system. The DVRP is modeled as a periodic re optimization of VRP using the proposed decentralized algorithm where collaborators exchange local information to achieve the best global objective for the current state of the system. We assume the existence of a dispatcher e.g., headquarter of the company who can communicate to vehicles in order to gather information and assigns the new visits to them. The effectiveness of the proposed decentralized coordination algorithm is further evaluated using benchmark data given in literature. The results show that the proposed method performed better than the compared algorithms which utilize the centralized coordination in 12 out of 21 benchmark problems.

After Dantzig and Rasmer introduced Vehicle Routing Problem in 1959, this field has been studied with numerous approaches so far. Classical Vehicle Routing Problem can be described as a problem of multiple number of homogeneous vehicles sharing a same starting node and having their own routes to meet the needs of demand nodes. After satisfying all the needs, they go back to the starting node. In order to apply the real world problem, this problem had been developed with additional constraints and pick up & delivery model is one of them. To enhance the effectiveness of pick up & delivery, hub became a popular concept, which often helps reducing the overall cost and improving the quality of service. Lots of studies have suggested heuristic methods to realize this problem because it often becomes a NP-hard problem. However, because of this characteristic, there are not many studies solving this problem optimally. If the problem can be solved in polynomial time, optimal solution is the best option. Therefore, this study proposes a new mathematical model to solve this problem optimally, verified by a real world problem. The main improvements of this study compared to real world case are firstly, make drivers visit every nodes once except hub, secondly, make drivers visit every nodes at the right time, and thirdly, make drivers start and end their journey at their own homes.

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.

Recently domestic manufacturing companies have been experiencing worsening profitability and stunted growth due to the long-term economic recession and the rapid rise of developing countries such as China and Southeast Asia. These difficulties force many companies to concentrate their core competencies on new value creation and innovation in order to gain momentum for new growth. Enterprise Resource Planning (ERP) has been considered as one of viable solutions. Among the various modules in ERP, shop floor control function in the production management module is rather limited. In order to overcome this problem, Manufacturing Execution System (MES) has been used as a subsystem which has a strong information gathering power and flexibility. Both systems interact closely with each other. In particular, ERP requires fast, accurate shop floor information at MES. This paper describes how to synchronize relevant information between ERP and MES with theory of constraints (TOC). The processing time information transmitted from the MES workplace is received at the ERP workplace. In the process, the received processing time is causing information distortion in ERP, when the information gathering standard of MES is different from the ERP information interpretation standard. The Drum-Buffer-Rope theory of TOC was applied to resolve this problem, therefore, information synchronization between both systems was made. As a precondition, the standard time of the upper ERP system was rearranged according to the capacity constraints resource. As a result, standard time restructuring has affected changes in labor costs. Standard labor costs have come close to actual ones, and information synchronization of MES transmission data has improved the reliability of standard product costs, such that it enabled various company-wide restructuring actions to be much more effective.

Recently in Construction field, It has been the big issues to produce an Eco-friendly Construction material and to solve problems about the First grade–Aggregates’ supply&demand. While the Eco-friendly Construction materials which are refurbished and reproduced from construction wastes and industrial by-products have a great deal of effectiveness such as cost or CO2 emission reduction, there is an additional logistical cost due to go through with some processes for recycle such as Intermediary treatments or management and collection of materials. Furthermore, Demand of the First grade-Aggregates is rising and spreading all over the nation for the improvement of Road driving performance, But there is also an additional logistical cost for supply&demand due to the cost of transport growth by sites of construction. In this study, the process and methodology of the new material supply and demand route routings using the Arc Gis Program and the calculation of the available distance through economic analysis are presented. After examining the cost status of construction materials and logistics costs by examining the literature review and related industry, economic feasibility was obtained by comparing the price of general construction materials with the total cost of comparable materials and logistics costs. After an economic analysis, ArcGis3.0 was used to visualize the materials’ supply&demand route and As a result, We can observe the economically secured route from the construction materials’ production plant to where the domestic transportable route and nodes mapped. Throughout the study, the pre-groundwork for an efficient use of the construction materials is able to be prepared and It will be helpful to invigorate supply&demand. In addition to the economic analysis in the future, If the real-time traffic information (traffic volume, speed, environment, etc.) and the performance (structure, functionality, etc.) of each construction materials are reflected, It will be possible to build a decision system for selecting construction materials which meet consumers’ various needs.

Efficient and sustainable sea transport is a key aspect to ensure cost competitive ship operation. The constant need to increase economic feasibility, energy efficiency and safety while complying with emission regulations motivates further developments and improvements in voyage optimization and weather routing systems. These systems optimize a voyage based on meteorological and oceanographic information taking into account ship characteristics and routing information. The quality of the provided route not only depends on the quality of this data, but also on the modeling of the optimization problem and the algorithm chosen to solve it. Due to the wide range of mathematical approaches and consequently challenges in decision making, this paper aims to give a comprehensive and comparative overview of the existing state-of-the-art methods by a thorough literature review and elaboration of different modeling approaches, optimization algorithms, and their application in weather routing systems. The research shows that approaches range from modeling the weather routing problem as a constrained graph problem, a constrained nonlinear optimization problem or as combination of both. Based on the formulation of the ship weather routing optimization problem different methods are used to solve it ranging from Dijkstra’s algorithm, dynamic programing and optimal control methods to isochrone methods or iterative approaches for solving nonlinear optimization problems. However, it can be concluded that the determination whether an approach is suitable, produces sufficient results and may be recommended, strongly depends on the specific requirements concerning optimization objectives, control variables and constraints as well as the implementation.

UAV (Unmanned Aerial Vehicle), the pilotless plane or drone, draws researchers’ attention at these days for its extended use to various area. The research was initiated for military use of the UAV, but the area of applicable field is extended to surveillance, communication, and even delivery for commercial use. As increasing the interest in UAV, the needs of research for operating the flying object which is not directly visible when it conducts a certain mission to remote place is obviously grown as much as developing high performance pilotless plane is required. One of the project supported by government is related to the use of UAV for logistics fields and controlling UAV to deliver the certain items to isolated or not-easy-to-access place is one of the important issues. At the initial stage of the project, the previous researches for controlling UAV need to be organized to understand current state of art in local researches. Thus, this study is one of the steps to develop the unmanned system for using in military or commercial. Specifically, we focused on reviewing the approaches of controlling UAV from origination to destination in previous in-country researches because the delivery involves the routing planning and the efficient and effective routing plan is critical to success to delivery mission using UAV. This routing plan includes the method to avoid the obstacles and reach the final destination without a crash. This research also present the classification and categorization of the papers and it could guide the researchers, who conduct researches and explore in comparable fields, to catch the current address of the research.

This study is examining the potential benefits of routing in hot mix asphalt pavement prior to installing crack sealant. (1) Definition. Crack sealing and crack filling are two separate activities. While both crack sealing and crack filling involve placing sealants in pavement cracks, they differ in process. Generally, crack sealing is defined as using a router or saw to create a reservoir in a crack which is then filled with a sealant material. Crack filling is defined as minor crack preparation, such as using an air gun to blow debris out of cracks, prior to installation of the sealant. There is no pavement removed with crack filling. Additionally, crack sealing is performed on working cracks, whereas crack filling is generally the term used to refer to the treatment of nonworking cracks. (2) Implementation. Crack sealing should be carried out on structurally sound pavement which has low pavement distress. The pavement selection consideration should be based on pavement age, pavement and geometric design, pavement selection boundaries, traffic, type and extent of previous maintenance treatments and condition rating. The best candidates for crack sealing are newer pavements which are in the range of 1 to 3 years, and the majority of pavement distress can be found in terms of longitudinal or transverse having slight to moderate crack density. (3) Evaluation. The performance life of a treatment mostly depends on the preparation of crack and the type of the material used. One inspection should be made each year to chart the rate of failure and plan for subsequent maintenance. A mid winter evaluation is highly recommended as it will indicate treatment effectiveness when there is maximum pavement contraction and the crack is near the maximum opening. A small representative sample of the pavement, minimum of 150 m length should be selected for the evaluation. The first step in determining a treatment’s effectiveness is establishing how much of the treatment has failed in relation to the total length of treatment applied: Percent failure = (failed length after treatment / total length of treatment) × 100 After that the treatment’s effectiveness can be determined by subtracting the percentage of treatment failure from 100 percent: Effectiveness = 100 - Percent failure After a number of inspections a graph of effectiveness versus time can be developed. (4) Cost. Crack treatments can be considered as effective if it delays pavement deterioration and extends the pavement service life. Generally, the effective treatment extends the pavement life by two to five years. The effectiveness of rout and seal maintenance depends upon three points: (a) Performance of the sealant materials and appropriate rout width and depth; (b) restraining of crack development and delaying the existing pavement distress; and (c) crack treatment implication period. Chip seal treatment cost 3-14 times more than crack sealing and an overlay cost 8-26 times as much as crack sealing. The cost of crack sealing varies depending on state, materials, whether or not routing is required, and unit being priced.