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Nested vehicle routing problem: Optimizing drone-truck surveillance operations

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2022年第0期139卷

作者： Zeng, Fanruiqi Chen, Zaiwei Clarke, John-Paul Goldsman, David Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

Unmanned aerial vehicles or drones are becoming increasingly popular due to their low cost and high mobility. In this paper we address the routing and coordination of a drone-truck pairing where the drone travels to multiple locations to perform specified observation tasks and rendezvous periodically with the truck to swap its batteries. We refer to this as the Nested-Vehicle Routing Problem (Nested-VRP) and develop a mixed integer Quadratically Constrained programming (MIQCP) formulation with critical operational constraints, including drone battery capacity and synchronization of both vehicles during scheduled rendezvous. An enhancement of the MIQCP model for the Nested-VRP is achieved by deriving the equivalent mixed integer Linear programming (MILP) formulation as well as leveraging lifting and Reformulation-Linearization techniques to strengthen the subtour elimination constraints of the drone. Given the NP-hard nature of the Nested-VRP, we further propose an efficient neighborhood search (NS) heuristic where we generate and improve on a good initial solution by iteratively solving the Nested-VRP on a local scale. We provide comparisons of both the exact approaches based on MIQCP or its enhanced formulations and NS heuristic methods with a relaxation lower bound in the cases of small and large problem sizes, and present the results of a computational study to show the effectiveness of the MIQCP model and its variants as well as the efficiency of the NS heuristic, including for a real-life instance with 631 locations. We envision that this framework will facilitate the planning and operations of combined drone-truck missions.

关键词： Aerial surveillance Cooperative vehicle routing mixed integer programming Persistent operation Unmanned aerial vehicle

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An integrated multi-objective model for disaster waste clean-up systems optimization

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2022年第0期165卷

作者： Cheng, Cheng Lu, Jia-Wei Zhu, Rui Xiao, Zuopeng Costa, Alysson M. Thompson, Russell G. Southeast Univ Sch Transportat Nanjing 211189 Jiangsu Peoples R China Minist Ecol & Environm South China Inst Environm Sci Guangzhou 510655 Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Land & Space Hong Kong Peoples R China Harbin Inst Technol Sch Architecture Shenzhen Peoples R China Univ Melbourne Sch Math & Stat Melbourne Vic 3010 Australia Univ Melbourne Dept Infrastruct Engn Melbourne Vic 3010 Australia

Post-disaster waste clean-up systems are complex and expensive operations that need to consider multiple stakeholders with different objectives. We propose a mixed-integer programming model that models the waste clean-up operations as a two-echelon system. The model decides on the location of waste processing facilities, the use of demolition resources, and the number and type of vehicles to be assigned to each echelon at each time slot of the planning horizon. The objectives considered in the model include minimizing environmental impacts, economic costs, and total time spent on the operations. Numerical results obtained on a case study based on the '2009 Victoria Black Saturday Bush-fires' case and on synthetically generated instances are used to obtain Pareto frontiers. The research concludes that the three objectives considered are indeed conflictive, and the explicit consideration of each goal can help decision-makers find the best trade-off solutions.

关键词： Disaster waste management Heavy-duty vehicles Environmental emission mixed integer programming Optimization Pareto frontier

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A Heuristic Algorithm for Solving Capacitated Facility Location Problems Using a Greedy-Based Iterative LP Relaxation Procedure

A Heuristic Algorithm for Solving Capacitated Facility Locat...

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作者： Yamamoto, Hiromichi Texas A&M University

A new methodology to solve the capacitated facility location problem (CFLP) is presented. This optimization problem can be explicitly formulated and solved as a mixed integer program (MIP); however, because binary variables are used, obtaining exact solutions can be computationally intensive. This issue is apparent for solving large-scale problems, where the problem complexity is known to increase exponentially in the number of location variables. The proposed approach will instead solve the problem in a heuristic manner, returning an approximate solution rather than an exact one. A linear program (LP) relaxation to the problem is solved, while iteratively fixing select binary location variables to 0 or 1 until a feasible solution is obtained. Experimental results show that the proposed methodology can be effective in obtaining solutions in a fraction of CPU (central processing unit) time compared to exact methods. The quality of the solution is also shown to be extremely close to optimal for problems with relatively high fixed cost parameters. An application to a real-life problem is also explored to validate the practicality of the proposed methodology. Not only does the algorithm offer a new approach to solving the CFLP, but it also presents a fast approximation method which can be applied to solve MIP models in general. Additional ideas for improving the algorithm are also presented.

关键词： Capacitated Facility Location Problem Heuristic Linear programming mixed integer programming Thesis

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Three Essays on Bio-security

Three Essays on Bio-security

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作者： Gao, Qi Texas A&M University

In this dissertation, several essays in the field of bio-security are presented. The estimation of the probability of an FMD outbreak by type and location of premises is important for decision making. In Essay I, we estimate and predict the probability/risk of an FMD outbreak spreading to the various premises in the study area. We first used a Poisson regression model with adjustment dispersion associated with random simulation results from the AusSpead model to estimate the parameters of the model. Our estimation and prediction show that large cattle loss could be concentrated in three counties-Deaf Smith, Parmer, and Castro. These results are based on approximately 70% of the feedlots with over 10,000 cattle located in the three counties previously mentioned. In Essay II, our objective is to determine the best mitigation strategies in minimizing animal loss based on AusSpead simulation model. We tested 15 mitigation strategies by using multiple comparison. The results show that the best mitigation strategies for all four scenarios are regular surveillance, slaughter of the infected animals, and early detection. We then used the mixed integer programming to estimate costs of disposing of animal carcasses and transportation. Results show that the unit disposal cost will vary with carcass scale and the unit transportation cost also varies with the distribution of the infected premises and disposal locations. FMD seems to have varying impacts on equity markets. In Essay III, we studied returns at three different levels of the stock market. We determined results in a structural break, and then estimated the impact of the announcement of confirmed cases of FMD disease on the volatility of stock market returns by using a GARCH-Mean model. Our results show that the structure break occurs on the day with the largest number of confirmed cases for meat product firms rather than the day of the first confirmed case. We found that the conditional volatilities over the FMD perio

关键词： bio-security FMD vulnerable Poisson regression multiple comparison mixed integer programming carcass disposal conditional volatility GARCH-Mean model Book Thesis

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OPTIMUM MULTI-PERIOD, MULTI-PLANT, AND MULTI-SUPPLIER PRODUCTION PLANNING FOR MULTI-GRADE PETROCHEMICALS

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2016年第6期23卷 399-411页

作者： Mrad, Mehdi Alfares, Hesham K. King Saud Univ Dept Ind Engn Riyadh Saudi Arabia King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran Saudi Arabia

A multi-period production and inventory control problem for a multi-grade, multi-supplier petrochemical product is formulated as a mixed integer Linear Program (MILP) and then optimally solved. Raw materials are available from several suppliers, and several plants (chemical reactors) are used for making the petrochemical product. Several grades of the petrochemical product can be produced by changing the conditions inside each reactor. During transitions from one grade to another, certain amounts of off-spec material are produced. The quantity of off-spec production is sequence dependent, i.e. it depends on the two grades between which the transition takes place. The objective is to maximize the total profit, which is equal to the sale revenue of all regular grades and off-spec materials, minus the raw material costs and inventory holding costs.

关键词： petrochemical production optimization multi-grade petrochemicals production and inventory control graphical model mixed integer programming

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Reducing the elastic generalized assignment problem to the standard generalized assignment problem

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2010年第11期61卷 1582-1595页

作者： Buether, M. Univ Kiel Inst Betriebswirtschaftslehre D-24098 Kiel Germany

The elastic generalized assignment problem (eGAP) is a natural extension of the generalized assignment problem (GAP) where the capacities are not fixed but can be adjusted;this adjustment can be expressed by continuous variables. These variables might be unbounded or restricted by a lower or upper bound, respectively. This paper concerns techniques aiming at reducing several variants of eGAP to GAP, which enables us to employ standard approaches for the GAP. This results in a heuristic, which can be customized in order to provide solutions having an objective value arbitrarily close to the optimal. Journal of the Operational Research Society (2010) 61, 1582-1595. doi: 10.1057/jors.2009.108 Published online 14 October 2009

关键词： assignment binary representation capacity planning combinatorial optimization mixed integer programming

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A new mathematical programming model for production schedule optimization in underground mining operations

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JOURNAL OF THE SOUTHERN AFRICAN INSTITUTE OF MINING AND METALLURGY 2010年第8期110卷 437-446页

作者： Nehring, M. Topal, E. Little, J. Univ Queensland Sch Mech & Min Engn CRC Min Brisbane Qld Australia Curtin Univ Technol Western Australia Sch Mines Kalgoorlie WA Australia Univ Queensland Sch Mech & Min Engn Anglo Coal Australia Pty Ltd Drayton Mine Qld Australia

mixed integer programming (MIP) has been used for optimizing production schedules of mines since the 1960s and is-recognized as having significant potential for optimizing production scheduling problems for both surface and underground mining. The major problem in long-term production scheduling for underground orebodies generally relate to the large number of variables needed to formulate a MIP model, which makes it too complex to solve. As the number of variables in the model increase, solution times are known to increase at an exponential rate. In many instances-the more extensive use of MIP models has been limited due to excessive solution times. This paper reviews production schedule optimization studies for underground mining operations. It also presents a classical MIP model for optimized production scheduling of a sublevel stoping operation and proposes a new model formulation to significantly reduce solution times without altering results while maintaining all constraints. A case study is summarized investigating solution times as five stopes are added incrementally to an initial ten stope operation, working up to a fifty stope operation. It shows substantial improvement in the solution time required when. using the new formulation technique. This increased efficiency in the solution time of the MIP model allows it to solve much larger underground mine scheduling problems within a reasonable time frame with the potential to substantially increase the net present value (NPV) of these projects. Finally, results from the two Models are also compared to that of a manually generated schedule which show the clear advantages of mathematical programming in obtaining optimal solutions.

关键词： Underground mine optimization mixed integer programming long-term scheduling mathematical programming application

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Optimální rozvrhovací systém pro outdoorové aktivity

Optimální rozvrhovací systém pro outdoorové aktivity

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作者： Rykala, Kryštof Brno University of Technology

Práce se zabývá technologiemi a přístupy pro vytvoření informačního systému, jehož součástí a motivací je automatické plánování rozvrhu aktivit. Za... 详细信息

Práce se zabývá technologiemi a přístupy pro vytvoření informačního systému, jehož součástí a motivací je automatické plánování rozvrhu aktivit. Za pomocí smíšeného celočíselného lineárního programování je definován model optimalizačního problému plánování se zdroji a omezeními. Součástí práce jsou programy klientské, serverové a plánovací části, které dohromady tvoří systém pro správu outdoorového centra s podporou automatického plánování.

关键词： JavaScript Node.js TypeScript Python plánování s omezeními a zdroji smíšené celočíselné programování SPA Gurobi JavaScript Node.js TypeScript Python resource-constraint project scheduling mixed integer programming SPA Gurobi Text

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Optimization of a Simulated Well Cluster using Surrogate Models

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IFAC Proceedings Volumes 2012年第8期45卷 275-280页

作者： Bjarne Grimstad Håvard Ausen Victoria Lervik Vidar Gunnerud Dag Ljungquist Department for Engineering Cybernetics NTNU Department of Industrial Economics and Technology Management NTNU FMC Technologies Asker Norway

In this paper we present an implementation of a partly Derivative-Free Optimization (DFO) algorithm for production optimization of a simulated multi-phase flow network. The network consists of well and pipeline simulators, considered to be black-box models without available gradients. The algorithm utilizes local approximations as surrogate models for the complex simulators. A mixed integer Nonlinear programming (MINLP) problem is built from the surrogate models and the known structure of the flow network. The core of the algorithm is IBM's MINLP solver Bonmin, which is run iteratively to solve optimization problems cast in terms of surrogate models. At each iteration the surrogate models are updated to fit local data points from the simulators. The algorithm is tested on an artificial subsea network modeled in FlowManager ™ , a multi-phase flow simulator from FMC Technologies. The results for this special case show that the algorithm converges to a point where the surrogate models fit the simulator, and they both share the optimum.

关键词： Derivative-Free Optimization Non-linear Optimization Non-convex Optimization mixed integer programming Process Simulators Interpolation Algorithm Numerical Algorithm

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A decision framework for decomposed stowage planning for containers

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 183卷

作者： Gao, Yinping Zhen, Lu Shanghai Univ Sch Management Shanghai Peoples R China

Stowage planning is crucial to the efficiency of loading containers onto vessels, which can affect the competitiveness of ports. In this paper, we study the stowage problem and consider the storage locations of containers in the yard. A decision framework is proposed to optimize the stowage, which is decomposed into three phases: allocating storage locations to container blocks, the stacking slots of vessel bays, and the stowing sequences through which containers are stowed. We formulate mixed integer programming models to minimize container relocations, the moving distances from blocks to bays, and operation times of containers in the proposed decision framework. An adaptive large neighborhood search (ALNS) algorithm based on heuristic rules is then designed to solve the optimization problem. Numerical experiments with different scales are conducted to verify the models and algorithm. Comparisons of various methods such as CPLEX and particle swarm optimization, also demonstrate the effectiveness of the ALNS algorithm in terms of its solution performance. A sensitivity analysis of the relocation and bay utilization rates is also conducted, which can provide port operators with managerial insights. Robustness is tested by comparing the objective value gaps when encountering deviations in container weight and size between the actual and expected information. There is a small gap of less than 2% in the solutions, which are solved from the models with parameter deviations. Port operators can develop stowage plans according to the classification of container attributes, and the stowing can achieve fewer relocations within the optimal operation time.

关键词： Port operations Container stowage Decomposed decisions mixed integer programming Heuristic-based optimization

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