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In April 2016, Amazon won a patent for a flying warehouse that deploys unmanned aerial vehicles (UAVs) to deliver parcels to customers. In this system, the airborne fulfillment center (AFC) remains or moves at a high altitude, and UAVs are deployed from the AFC to deliver orders to customers. Smaller airships, also known as shuttles, may be used to replenish the AFC with inventory, UAVs, fuel, supplies, and staff. Amazon's AFC delivery system is a novel and challenging system that can convert same-day deliveries into 10 min deliveries and provide a wide range of UAV delivery services for Amazon Prime members. However, to operate the AFC delivery system efficiently, a quantitative operation methodology needs to be developed, which considers the related regulations and consumable replenishment. In this study, mixed-integer linear programming is developed to derive optimal operation schedules for AFC, shuttles, and UAVs simultaneously. The proposed model can be used in real time to address system changes and derive new operation schedules. The effects of system components are analyzed via a numerical experiment to provide managerial guidelines for the implementation of the AFC delivery system. Furthermore, cooperation between the new AFC delivery system and the existing stationary UAV delivery service was investigated. The results demonstrate the possibility of complementary cooperation between the two systems.

PurposeIn the tourism industry, unmanned aerial vehicles (UAVs) can perform monitoring and patrol missions to protect assets and tourists at attractions such as coastal areas, canyons, national parks, etc. Such use of UAVs can improve safety and security of tourism attraction and facilitate tourism industry. However, there is a key issue regarding economic investment and efficient operation for actual system implementation. The purpose of this paper is to provide a guideline for supporting economic investment and the efficient operation of UAV system in the tourism industry.Design/methodology/approachIdeas and methodologies have been proposed to overcome the fundamental limitations of commercial UAVs. A mathematical optimization model is developed to determine the optimal number of UAVs to be purchased, and its operation schedules simultaneously.FindingsUsing proposed concept and methodology, UAVs can support long duration of missions. Economic system design as well as the operation schedule is derived with the hypothesis data in Kkot-Ji beach in Korea. The proposed methodology and approach is expected to have huge potential at many tourism attractions to achieve the safety and security of tourists.Practical implicationsThe result of this study can facilitate practical use of UAVs in the tourism industry. Furthermore, it is applicable in many industries that need UAV systems such as national defence, agriculture, disaster management, etc.Originality/valueThe proposed study suggests a solution to handle fundamental weakness of UAVs and support long duration of missions. In addition, this study incorporates economic system design issue and operation issue simultaneously.

PurposeThis study investigates the pricing and inventory control decisions of a company that owns a coffeehouse chain providing drip coffee and a coffee manufacturing factory producing ready-to-drink (RTD) coffee. To determine a way to maximize profit, optimal pricing and inventory control strategies are studied for both the RTD coffee as well as the coffeehouse chain network.Design/methodology/approachIt is assumed that the company sells only drip coffee through its coffeehouse chain and would like to launch RTD coffee via other channels such as convenience stores, supermarkets and so on to maximize its total profit. A mathematical model–based optimization is adopted to address the decision-making for the given problem situation, where the demand for both drip and RTD coffee are dependent on the values of decision variables. To solve the proposed mathematical model, particle swarm optimization (PSO) is applied due to nonlinearity of the developed model.FindingsIt is confirmed that a company can earn more profit by launching RTD coffee, even though the profit from drip coffee would reduce. In addition, the scenario analysis shows that by launching RTD coffee under various conditions, the total profits would also improve.Originality/valueThe value of this study is the proposed basic framework for the industry. In addition to the modeling framework and cost structure, realistic cost figures and technical details can be considered when applying the model to a practical setting.

In reverse logistic networks, end-of-use products are collected and transported to the factory for remanufacturing. The economic and environmental values of a reverse network depend significantly o...

The meaning of sustainability is very broad and has many pillars such as the economy, environment and society. In the tourism industry, another important pillar is security. Tourism security affects the satisfaction and pleasure of tourists on a trivial level, and life and injuries on a significant level. Thus, unless security is guaranteed, tourists will not be able to fully enjoy the attractions and will not even consider the travel itself. Such tourist behavior has a significant impact on short-term and long-term tourism sustainability. Therefore, to enhance tourism security, many policies and frameworks have been suggested and announced in recent years. However, without efficient guidelines for the installation and operation of security devices, it may be hard to obtain actual effectiveness. To support real implementation of security systems in the tourism industry, this study quantitatively addresses the installation and operation issue of security devices in an optimal manner. A complementary cooperation of Closed-Circuit Television (CCTV) and Unmanned Aerial Vehicles (UAV) is suggested to efficiently monitor the key locations of tourism destinations and improve the security. Two mathematical models are developed to derive the optimal location of CCTVs, and the optimal operation schedule of UAVs over multiple time periods. Security requirements, service range, and budget are considered as realistic constraints. The validity of the models is demonstrated through a realistic case study of Nice, France.

This paper proposes a new service design using variable opaque products to generate additional revenue for airlines. Variable opaque products involve the products, which have a level of uncertainty...

The truck-drone hybrid delivery system uses a truck as a station for drones in addition to its delivery function and is getting attention because the strengths of these individual vehicles can be selectively and synergistically exploited. In this study, we extended the previous vehicle routing models to the hybrid delivery systems by taking into account two important practical issues: the effect of parcel weight on drone energy consumption and restricted flying areas. The flight range of the drones is heavily susceptible to the loaded weight due to the limited battery life. Drones are also not allowed to fly over sensitive facilities regulated by the Federal Aviation Administration (FAA) or temporarily in certain areas due to weather-related conditions. We developed a mathematical model that incorporates these issues and propose a two-phase constructive and search heuristic algorithm to provide computational efficiency of the real-world cases problems. The result of the numerical study demonstrates the effectiveness and efficiency of the proposed algorithm.

Sustainable tourism has true sustainability when it is designed economically and in an environmentally friendly fashion. A variety of methods has been explored to achieve sustainable tourism, and t...

Rare earth elements (REEs) such as neodymium (Nd) and dysprosium (Dy) are indispensable in modern society because they are vital for creating powerful neodymium-iron-boron (NdFeB) magnets used in many technological applications. These elements have been subject to a significant supply uncertainty. A promising strategy to alleviate supply risk is value recovery from end-of-life (EOL) products. This paper proposes a reverse supply network to optimize: 1) the locations and capacities of dismantling and recycling facilities, and 2) the transportation flows between collection centers, dismantling facilities, recycling facilities, and sales points. Two objectives are considered: maximizing profit and environmental benefits. A Pareto front is created that shows non-dominated optimal solutions. When the model was applied to the United States, California was found to be the most important state for EOL product collection, representing up to 22% of the total collection volume. Nevada, Tennessee, and Delaware were the key states for EOL product dismantling, and Nevada, Texas, and Delaware were the key states for NdFeB magnet recycling. A sensitivity analysis was performed to measure the impact of different NdFeB magnet price on the overall network design. While REE recovery is still at an early stage of the development, this research serves as a blueprint for constructing a profitable and environmentally friendly NdFeB magnet reverse supply chain.

One of the strengths of drone is the mobility. Drone can fly without avoiding obstacles and provide services in a timely manner. However ironically, the mobility of drone cannot last for long duration due to the low battery capacity and limited loadable products. As a consequence, the use of drones may be extremely limited in a wide field of operation. One of the solutions for complementing such weaknesses of drone is the use of movable stations. Stations move across a wide field of operation and replenish consumables for drones, such as battery and service product. By visiting movable stations and replenishing consumables, drones can provide persistent service in a wide range of operation area. In this study, design and operation issues of movable unmanned aerial system (MUAS) are address for long term and persistent drone service. A two-phase optimization approach is developed to efficiently handle the system complexity. In the phase I, locations of movable stations and allocation of tasks are identified for multi-period of service duration. In the phase II, number of drones for each station and their routings are optimized based on the result of phase I. Mathematical models are proposed and verified with a case study.

The high efficiency, flexibility and low cost of Unmanned Aerial Vehicles (UAVs) present huge application opportunities in various industries. Among those various applications, we focus herein on the use of UAVs in delivery logistics. The UAV logistics system has some fundamental characteristics that distinguish it from the usual ground logistics such as limited flight time, loadable capacity, effect of cargo weight on flight ability, and others. To handle the above issues, we propose a Mixed Integer Linear Programming (MILP) formulation for derivation of persistent UAV delivery schedules. To address the computational issues, a Receding Horizon Task Assignment (RHTA) heuristic is developed and tested with numerical examples for island-area delivery.

Neodymium–iron–boron (NdFeB) magnets are widely used in clean energy applications such as wind turbines and electric vehicles whose demand is escalating. However, rare earth elements (REEs) for manufacturing NdFeB magnets are subject to significant supply uncertainty due to Chinese near-monopolistic supply. To mitigate the risk, companies are actively pursuing value recovery from end-of-life magnets. However, the questions of how to collect used magnets and smoothly transfer them through the reverse supply chain require further investigation. To address this challenge, this paper designs an efficient NdFeB magnet recovery infrastructure by identifying the optimal processing facility locations and defining the capacities and transportation flows that maximize the economic and environmental benefits and social support for the new business. Mathematical model and a multi-objective network design genetic algorithm (MONDGA) were designed to calculate solutions. When compared with the exact solutions implemente...

Neodymium-iron-boron (NdFeB) magnets play a critical role in clean power products, e.g., electric vehicles and wind turbines. Since China has near monopolistic control of the supply of these magnets, many parties are interested in recovering end-of-life magnets for additional use cycles. Such a strategy requires a cost-effective approach to collect and process used magnets while maximizing the economic and environmental benefits. This paper employs fuzzy logic and non-dominated sorting genetic algorithm (NSGA-II) to solve a location-allocation problem for NdFeB magnet recovery under supply and demand uncertainties. A Pareto front is constructed to evaluate the performance of the proposed design.

Accommodation revenue and demand are affected by hotel location, leading a lot of research to perform theoretical, empirical and operational approaches to analyze and determine the ideal location of hotels. However, operational approaches such as mathematical modeling based optimization have not received sufficient interest in hotel location research while it has been widely used for various kinds of facility locations. Therefore, two mathematical models for opening of new hotels and the closing of existing hotels to a manage hotel chain network are developed using the demand estimation and existing hotel information. In this research, hotel demand is estimated based on the accessibilities to sightseeing, transportation, business points, and market share. Gaining and/or losing of demand and the agglomeration effect are introduced as the objective of two models. Proposed mathematical models are tested to design a hotel chain network with the real data in Seoul, Korea.

As technological innovations develop, new categories of services are emerging. In the tourism industry, one of the limelight technologies is an unmanned aerial vehicle (UAV) for virtual tourism systems, monitoring, and patrol activities for safety and security. However, for the successful application of a UAVs system, it is important to construct the system in an economic and efficient manner. In this study, quantitative approaches for a UAV system are introduced to support the economic establishment and its efficient operation. Two mathematical models are developed to construct and operate UAVs systems for recreation and safety purposes in the tourism industry. The validity of the proposed models is demonstrated via numerical experiments for both cases. To our knowledge, this is the first approach to address the issues of a UAV system design for tourism purposes. This study is expected to be the theoretical basis for future use of UAVs in the tourism industry.

Unmanned aerial vehicles (UAVs) are expected to make groundbreaking changes in the logistics industry. Leading logistics companies have been developing and testing their usage of UAVs recently as an environmentally friendly and cost-effective option. In this paper, we investigate how much the UAV delivery service is environmentally friendly compared to the traditional ground vehicle (GV) delivery service. Since there are fuel (battery) and loadable weight restrictions in the UAV delivery, multi-hopping of UAV is necessary, which may cause a large consumption of electrical energy. We present a two-phase approach. In Phase I, a new vehicle routing model to obtain optimal delivery schedules for both UAV-alone and GV-alone delivery systems is proposed, which considers each system&rsquo, s restrictions, such as the max loadable weight and fuel replenishment. In Phase II, CO2 emissions are computed as a sustainability measure based on the travelling distance of the optimal route obtained from Phase I, along with various GV travel-speeds. A case study finds that the UAV-alone delivery system is much more CO2 efficient in all ranges of the GV speeds investigated.

The platooning of autonomous smart vehicles in a high-speed driving environment is a technology that can be achieved in the near future, and some similar driving-assistive technologies have already been put in practice. There are a variety of advantages to platooning, and one of them is the environmental aspect. This study endeavored to perform a mathematical analysis of the environmental aspect of forming a platoon of smart vehicles. For that, a mathematical experiment was designed with four kinds of scenarios, and each scenario had a different operation profile in terms of the velocity and acceleration of each smart vehicle. The power consumption and both forming distance and time were derived variously according to each scenario through mathematical experiments, and it was observed that the length of the acceleration period and the average operation velocity of the smart vehicles had a significant impact on the environmental effect of platoon formation. It is hoped that this study will be a touchstone for various kinds of platooning algorithm research considering the environment.

Mathematical model was developed to support design of emergency medical system.Location, capability and capacity of emergency medical center were designed.To ensure the survival of patients, minimum survival rate was applied.As an alternative solution procedure, hybrid genetic algorithm was developed.Deterministic solution was derived and validated via simulation studies. This paper studies the location, allocation and capacity design of emergency medical centers (EMC) in a given region under the closest assignment rule. It is assumed that for each EMC candidate center the capability and initial capacity on each category of treatable medical diseases are provided. Selected medical center receives a prescribed amount of subsidies from the government in return for the offering of medical services at a competitive cost. It is further assumed that with additional subsidies EMC candidate center can not only enlarge its capacities for treatable medical diseases but also newly begin medical treatment for the diseases not included in the original capability. The number of patients of each patient group node during a unit time is assumed to be known along with the categories of their diseases. The problem is formulated as an integer program (IP) with the objective of minimizing the total amount of subsidies paid by the government. We select from among a set of candidate EMCs satisfying minimum desired survival rate constraints and determine both the capability and capacity of each EMC. The CPLEX version 12.4 is used to derive an optimal solution. Hybrid genetic algorithm was developed as a solution procedure for generating near-optimal solution. In addition, simulation studies are conducted to evaluate the performance of the proposed deterministic models in a stochastic context.

This study examines a vehicle routing problem that encompasses both refrigerated- and general-type of vehicles for multi-commodity perishable food products delivery. It is assumed that both the location and the volume of the ordered food products for each customer are known. Furthermore, the capacity, maximum delivery time, and available number of both refrigerated- and general-type of vehicles are predetermined. By reflecting these characteristics, we develop a nonlinear mathematical model and a heuristic algorithm to generate efficient vehicle routings with the objective of maximizing the total level of the customer satisfaction which is dependent on the freshness of delivered food products. In addition, numerical examples and sensitivity analysis are provided to show the validity of the model. The aim of this study is to confirm the performance and the availability of refrigerated-type vehicle for perishable food products delivery compared with general-type one.

Current research results are extended by allowing more than one type of facilities.Also, the capacity of facilities is considered with closest assignment requirement.A branch and bound algorithm is developed for exact solution with pruning rules, lower bound and upper bound.A genetic algorithm is presented for solving large sized problem. Life expectancy is going up and the demand of long term care facilities is increasing in most countries. This study deals with designing problem of facility networks for long-term care services in a city consisting of a number of regions. Assuming that in each region a candidate site for long-term care facility exists, we seek to identify regions where opening of a long-term care facility is desirable and also determine the type of new facility. For the problem, an integer programming model is formulated with the objective of minimizing the total construction cost. The closest assignment rule is adopted to reflect the preference of patient in choosing long term care facility by assigning patient to an open facility closest from his home. To solve the model, we develop a branch and bound algorithm for exact solution and a genetic algorithm to solve large sized problem. The validity of the mathematical model and the proposed algorithms are illustrated through a number of problem instances.

A networked system consisting of unmanned aerial vehicles (UAVs), automated logistic service stations (LSSs), customer interface software, system orchestration algorithms and UAV control software can be exploited to provide persistent service to its customers. With efficient algorithms for UAV task planning, the UAVs can autonomously serve the customers in real time. Nearly uninterrupted customer service may be accomplished via the cooperative hand-off of customer tasks from weary UAVs to ones that have recently been replenished at an LSS. With the goal of enabling the autonomy of the task planning tasks, we develop a mixed integer linear programming (MILP) formulation for the problem of providing simultaneous. UAV escort service to multiple customers across a field of operations with multiple sharable LSSs. This MILP model provides a formal representation of our problem and enables use in a rolling horizon planner via allowance of arbitrary UAV initial locations and consumable reservoir status (e.g., battery level). As such, it enables automation of the orchestration of system activities. To address computational complexity, we develop efficient heuristics to rapidly derive near optimal solutions. A receding horizon task assignment (RHTA) heuristic and sequential task assignment heuristic (STAH) are developed. STAH exploits properties observed in optimal solutions obtained for small problems via CPLEX. Numerical studies suggest that RHTA and STAH are 45 and 2100 times faster than solving the MILP via CPLEX, respectively. Both heuristics perform well relative to the optimal solution obtained via CPLEX. An example demonstrating the use of the approach for rolling horizon planning is provided.

People are living longer than ever before. As a result, life expectancy is going up and the demand of long-term care facilities is increasing in most countries. The facilities provide rehabilitative, restorative, and skilled nursing care to patients or residents in need of assistance with activities of daily living. This study deals with the capacitated location and allocation problem of long-term care facilities in a city that consists of a finite number of regions. Assuming that in each region candidate locations for three types of facilities are already given, two integer programming models are developed under the closest assignment rule reflecting the demand characteristics of the facilities. Both the location and type of the facilities to be built become decision variables. To show the validity of the models, numerical problems are solved with commercial software, CPLEX. Also, sensitivity studies were conducted to identify relationships between the system parameters.

The phrase ''not in my backyard'' (NIMBY) refers to the well-known social phenomena in which residents oppose the construction or location of undesirable facilities near their homes. Examples of such facilities include electric transmission lines, recycling centers and crematoria. Due to the opposition typically encountered in constructing an undesirable facility, the facility planner should understand the nature of the NIMBY phenomena and consider it as a key factor in determining facility location. We examine the characteristics of NIMBY phenomena and suggest two alternative mathematical optimization models with the objective of minimizing the total degree of NIMBY sentiments. Genetic algorithms are proposed to solve our linear and nonlinear integer programs. The results obtained via genetic algorithms for our linear integer programs are compared with those of CPLEX to evaluate their performance. The nonlinear programs are tested with various allocation policies. Sensitivity analysis is conducted about several system parameters.

According to the environment change of market, supply chain network needs to be redesigned for efficient provision of product within the budget constraint. Also, it is desired that the customer satisfaction such as on time delivery should be considered as an important element at redesigning of supply chain network in addition to the cost reduction. In this paper redesign of supply chain network for its expansion is treated as a problem situation and a related mathematical model is suggested. Moreover, the numerical examples about the total weighted distance of the redesigned supply chain network are presented with various budget constraints by using genetic algorithm to help the managerial decision.

The duration of missions that can be accomplished by a system of unmanned aerial vehicles (UAVs) is limited by the battery or fuel capacity of its constituent UAVs. However, a system of UAVs that is supported by automated refueling stations may support long term or even indefinite duration missions. We develop a mixed integer linear program (MILP) model to formalize the problem of scheduling a system of UAVs and multiple shared bases in disparate geographic locations. There are mission trajectories that must be followed by at least one UAV. A UAV may hand off the mission to another in order to return to base for fuel. To address the computational complexity of the MILP formulation, we develop a genetic algorithm to find feasible solutions when a state-of-the-art solver such as CPLEX cannot. In practice, the approach allows for a long-term mission to receive uninterrupted UAV service by successively handing off the task to replacement UAVs served by geographically distributed shared bases.

In the green manufacturing system that pursues the reuse of used products, the residual value of collected used products (CUP) hugely affects a variety of managerial decisions to construct profitable and environmental remanufacturing plans. This paper deals with a closed-loop green manufacturing system for companies which perform both manufacturing with raw materials and remanufacturing with collected used products (CUP). The amount of CUP is assumed as a function of buy-back cost while the quality level of CUP, which means the residual value, follows a known distribution. In addition, the remanufacturing cost can differ according to the quality of the CUP. Moreover, nowadays companies are subject to existing environment-related laws such as Extended Producer Responsibility (EPR). Therefore, a company should collect more used products than its obligatory take-back quota or face fines from the government for not meeting its quota. Through the development of mathematical models, two kinds of inspection policies are examined to validate the efficiency of two different operation processes. To find a managerial solution, a genetic algorithm is proposed and tested with numerical examples.

The automation of logistics tasks for fleets of UAVs is a key element of persistent operation. Such automation includes the provision of robotic service stations to replace consumables and orchestration algorithms enabling the UAVs to simultaneously pursue their objectives and manage the logistics process. Here we consider a system of UAVs and service stations distributed across a field of operations whose purpose is to provide continuous escort/surveillance to customers traversing known time-space trajectories. Our goal is to develop centralized real-time large scale-system orchestration methods for such a service. This goal is pursued in three directions. 1)We extend an existing mixed integer linear program (MILP) formulation to allow for arbitrary UAV initial locations and fuel levels. The MILP uses a more general service station recharge model. The new MILP is incorporated into a rolling horizon optimization for real time use. 2) We extend an RHTA heuristic to allow for arbitrary fuel levels and UAV locations. 3) Based on insight from the problem formulation, the STAH heuristic is developed. Numerical studies assess the effectiveness and numerical character of the proposed approaches. STAH was at least 30 times faster than RHTA with similar values. Both are much faster than the MILP solved via CPLEX. A real time scheduling example is considered.

The flight duration of unmanned aerial vehicles (UAVs) is limited by their battery or fuel capacity. As a consequence, the duration of missions that can be pursued by UAVs without supporting logistics is restricted. However, a system of UAVs that is supported by automated logistics structures, such as fuel service stations and orchestration algorithms, may pursue missions of conceivably indefinite duration. This may be accomplished by handing off the mission tasks to fully fueled replacement UAVs when the current fleet grows weary. The drained UAVs then seek replenishment from nearby logistics support facilities. To support the vision of a persistent fleet of UAVs pursuing missions across a field of operations, we develop an improved mixed integer linear programming (MILP) model that can serve to support the system's efforts to orchestrate the operations of numerous UAVs, missions and logistics facilities. Further, we look toward the future implementation of such a persistent fleet out-doors and develop prototype components required for such a system. In particular, we develop and demonstrate the concerted operation of a scheduling model, UAV onboard vision-based guidance system and replenishment stations.