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Facility location problems on graphs with non-convex neighborhoods

COMPUTERS & OPERATIONS RESEARCH 2023年第1期159卷

作者： Espejo, I. Paez, R. Puerto, J. Rodriguez-Chia, A. M. Univ Cadiz Dept Estadist & Invest Operat Cadiz Spain Univ Seville Dept Estadist & Invest Operat Seville Spain Univ Seville IMUS Inst Matemat Seville Spain

In this paper, we deal with some variants of classical facility location problems on graphs in which both the customers and the facilities belong to a non-necessarily convex neighborhood. Therefore, a point in each neighborhood representing the customer/facility has to be determined, and the customers have to be assigned to the facilities depending on a criterion. In particular, the p-median, the p-center, and the p-maximal covering versions of this problem on graphs are analyzed. An important difference with respect to their classical versions is that the lengths of the arcs depend on the location of the points chosen in the neighborhoods. Therefore, the lengths are not part of the input but part of the decision process. Assuming that the neighborhoods are Mixed-Integer second order cone representable, different mixed-integer non-linear programming formulations are proposed for each one of the considered problems. Moreover, solution procedures providing bounds and a preprocessing phase are developed to reduce the number of variables and constraints of the proposed formulations. The results of an extensive computational experience are reported.

关键词： Facility location problems second order cone programming Neighborhoods

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Finding an energy efficient path for plug-in electric vehicles with speed optimization and travel time restrictions

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期176卷

作者： Erdogan, Bilgenur Tural, Mustafa Kemal Khoei, Arsham Atashi Middle East Tech Univ Dept Ind Engn Ankara Turkiye Univ Bath Sch Management Bath England

Transportation is one of the main factors when global total energy consumption is considered and is a significant contributor to emissions of harmful gases including carbon dioxide (CO2). Due to their lower tailpipe CO2 emissions compared to the vehicles with internal combustion engines, electric vehicles provide an opportunity to reduce environmental impacts of transportation. In this direction, a problem for plug-in electric vehicles (PEVs) is studied where the aim is to find an energy efficient path. Given an origin-destination pair over a directed network, this problem involves determining a path joining origin and destination, the speed of the PEV on each road segment, i.e., arc, along the path, the charging stations the PEV will stop by, and how much to recharge at each stop so as to minimize the total amount energy consumption. There are speed limits on each road segment, and PEV has to arrive at the destination on or before a given total time limit. For this problem, firstly, a mixed-integer second order cone programming formulation (MISOCP) is proposed. secondly, to be able to solve larger size instances, a matheuristic is developed. Lastly, an iterated local search (ILS) algorithm is designed for this problem. Solution quality and computation times of the heuristics and the exact algorithm are compared on different instances. Differently from the literature, the speed values of the PEV on the arcs are considered as continuous decision variables in all proposed solution approaches. Moreover, consideration of the speed limits which can be legal limits or limits imposed by congestion makes our problem more realistic. The analysis of the results of the computational experiments gives the user an insight to select the proper solution approach based on the instance settings. MISOCP formulation becomes inadequate for larger instances. On the other hand, the heuristic solution approaches can solve such instances within reasonable computational times and therefore

关键词： Plug-in electric vehicle Minimum cost path problem second order cone programming Matheuristic Iterated local search Speed optimization

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Hub Network Design Problem with Capacity, Congestion, and Stochastic Demand Considerations

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TRANSPORTATION SCIENCE 2023年第5期57卷 1276页

作者： Bayram, Vedat Yildiz, Baris Farham, M. Saleh TED Univ Ind Engn Dept TR-06420 Ankara Turkiye Univ Kent Ctr Logist & Heurist Optimisat Kent Business Sch Dept Analyt Canterbury CT2 7NZ England Koc Univ Ind Engn Dept TR-34450 Istanbul Turkiye Univ Alberta Alberta Sch Business Edmonton AB T6G 2R3 Canada

Our study introduces the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decisions and congestion cost effect into the problem and allowing dynamic routing for origin-destination (OD) pairs. Connecting strategic and operational level decisions, HNDC jointly decides hub locations and capacity acquisitions by considering the expected routing and congestion costs. A path-based mixed-integer second-order cone programming (SOCP) formulation of the HNDC is proposed. We exploit SOCP duality results and propose an exact algorithm based on Benders decomposition and column generation to solve this challenging problem. We use a specific characterization of the capacity-feasible solutions to speed up the solution procedure and develop an efficient branch-and-cut algorithm to solve the master problem. We conduct extensive computational experiments to test the proposed approach's performance and derive managerial insights based on realistic problem instances adapted from the literature. In particular, we found that including hub congestion costs, accounting for the uncertainty in demand, and whether the underlying network is complete or incomplete have a significant impact on hub network design and the resulting performance of the system.

关键词： hub location problem hub congestion capacity building multiple allocation second order cone programming Benders decomposition column generation branch-and-cut

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Framework Planning of Distribution Network with High Proportion of Distributed Renewable Energy Sources 11

Framework Planning of Distribution Network with High Proport...

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11th International Conference on Power and Energy Systems (ICPES)

作者： Cheng, Zhuo Zeng, Weidong Ci, Hai Shang, Longlong Shenzhen Power Supply Bur Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781665413251

In order to complete the requirements of distribution network planning and overcome the randomness and volatility of distributed renewable energy after high proportion of distributed renewable energy are connected, a bi-layer programming model considering the uncertainty of distributed new energy is established in this paper. Firstly, active management measures are considered on the basis of framework planning, the upper-level planning take the least overall annual cost as goal, and the lower-level model take the least amount of distributed generation output removal as goal. secondly, considering the operation of the distribution network, the reliability constraint is added to the upper-level model. Then, Monte Carlo sampling method is used to obtain random wind speed and light intensity. The upper model is solved by a partheno-genetic algorithm encoded by tree structure, and the lower model is solved by a mixed-integer second order cone programming method. Finally, the validity of the model is verified by the 29-bus system.

关键词： active management framework planning second order cone programming Monte Carlo sampling bi-layer planning partheno-genetic algorithm

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Planning of Soft Open Point Integrated with ESS in Active Distribution Network 3

Planning of Soft Open Point Integrated with ESS in Active Di...

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3rd Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Ding, Zijia Wang, Chunyi Wang, Fei Feng, Liang Li, Guangxiao Xu, Zhendong Jining Power Supply Co State Grid Shandong Elect Jining 272100 Shandong Peoples R China State Grid Shandong Elect Power Co Jinan 250001 Shandong Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Shandong Peoples R China

ISBN: (纸本)9781665425513

With the increasing penetration of renewable energy in the distribution network, a large number of distributed generation access network, making the traditional passive network into active network, and changing the power flow distribution of radial network. The Soft Open Point Integrated with ESS (ESOP) can regulate the network power flow from two dimensions of time and space, and improve the economy of system operation. Due to the high investment cost and complex model of Soft Open Point Integrated with ESS, considering the time sequence characteristics of load and the fluctuation of distributed generation, the capacity of converter and energy storage part should be reasonably planned to achieve the ideal optimization effect with minimum economic loss. First of all, the soft open point integrated with ESS is modeled. Taking the minimum annual comprehensive cost as the objective function, the second-order cone programming model is established, which is solved by commercial solver and verified by the modified PG&E 69 node example. The results show that the economic efficiency of distribution system can be effectively improved by selecting reasonable planning scheme.

关键词： soft open point integrated with ESS temporal characteristic second order cone programming economy

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Wireless Sensor Network Node Localization via second order cone programming

Wireless Sensor Network Node Localization via Second Order C...

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10th International Symposium on Electronics and Telecommunications (ISETC)

作者： Rusnac, Ruxandra Ioana Jivulescu, Maria Anastasia Politehn Univ Timisoara Fac Elect & Telecommun Timisoara Romania Politehn Univ Timisoara Dept Math Timisoara Romania

the localization of nodes in wireless sensor networks is crucial information for using and processing the data provided by the network. There are many approaches to localization, among which second order cone programm... 详细信息

ISBN: (纸本)9781467311762

关键词： second order cone programming anchors sensors positioning error

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Resilient airline scheduling to minimize delay risks

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2022年第0期141卷

作者： Simsek, Deniz Akturk, M. Selim Northwestern Univ Kellogg Sch Management Evanston IL 60201 USA Bilkent Univ Dept Ind Engn TR-06800 Ankara Turkey

Airlines tend to design their flights schedules with the primary concern of the minimization of operational costs. However, the recently emerging idea of resilient scheduling defined as staying operational in case of unexpected disruptions and adaptability should be of great importance for airlines as well due to the high opportunity costs caused by the flight cancellations and passenger inconvenience caused by delays in the schedule. In this study, we integrate resilient airline schedule design, aircraft routing and fleet assignment problems with uncertain non cruise times and controllable cruise times. We follow a data-driven method to estimate flight delay probabilities to calculate the airport congestion coefficients required for the probability distributions of non-cruise time random variables. We formulate the problem as a bi-criteria nonlinear mixed integer mathematical model with chance constraints. The nonlinearity caused by the fuel consumption and CO2 emission function associated with the controllable cruise times in our first objective is handled by second order conic inequalities. We minimize the total absolute deviation of the aircraft path variability's from the average in our second objective to generate balanced schedules in terms of resilience. We compare the recovery performances of our proposed schedules to the minimum cost schedules by a scenario-based posterior analysis.

关键词： Resilient airline scheduling Aircraft routing and fleeting Cruise time controllability Chance constraints second order cone programming

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Robust design of service systems with immobile servers under demand uncertainty

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COMPUTERS & OPERATIONS RESEARCH 2022年 148卷

作者： Saif, Ahmed Madani, Nazanin Dalhousie Univ Dept Ind Engn 5269 Morris St Halifax NS B3H 4R2 Canada

The service system design problem aims to select the location and capacity of service facilities, and the assignment of customers to them in order to minimize the setup, access, and waiting time costs. We address the case when there is uncertainty about the demand for service, considering a service system that can be modeled as an independent network of M/M/1 queues. Robust optimization, with both budget and ball uncertainty sets, is used when the demand rate is unknown, but the arrival pattern can still be reasonably approximated as a Poisson process. Furthermore, we use distributionally robust optimization with a Wasserstein ambiguity set to address the case when the demand distribution is estimated from a limited sample. We are able to reformulate both the robust and distributionally robust problems as mixed-integer second-order cone programs that can be solved directly on commercial solvers. Extensive numerical experiments on benchmark test instances are conducted to compare the proposed approaches and to investigate the effect of problem size and parameters.

关键词： Service systems Queuing second order cone programming Robust optimization Distributionally robust optimization

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Improved Algorithms for Online Load Balancing 47th

Improved Algorithms for Online Load Balancing

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47th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)

作者： Liu, Yaxiong Hatano, Kohei Takimoto, Eiji Kyushu Univ Dept Informat Fukuoka Japan Kyushu Univ Fac Arts & Sci Fukuoka Japan RIKEN AIP Tokyo Japan

ISBN: (纸本)9783030677305;9783030677312

We consider an online load balancing problem and its extensions in the framework of repeated games. On each round, the player chooses a distribution (task allocation) over K servers, and then the environment reveals the load of each server, which determines the computation time of each server for processing the task assigned. After all rounds, the cost of the player is measured by some norm of the cumulative computation-time vector. The cost is the makespan if the norm is L-infinity-norm. The goal is to minimize the regret, i.e., minimizing the player's cost relative to the cost of the best fixed distribution in hindsight. We propose algorithms for general norms and prove their regret bounds. In particular, for L-infinity-norm, our regret bound matches the best known bound and the proposed algorithm runs in polynomial time per trial involving linear programming and second order programming, whereas no polynomial time algorithm was previously known to achieve the bound.

关键词： Online learning Blackwell approachability Online load balancing Makespan second order cone programming

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A new neural network framework for solving convex second-order cone constrained variational inequality problems with an application in multi-finger robot hands

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2020年第2期32卷 181-203页

作者： Nazemi, Alireza Sabeghi, Atiye Shahrood Univ Technol Fac Math Sci Shahrood Iran

In this paper, we consider a new neural network model to simply solve the convex second-order cone constrained variational inequality problem. Based on a smoothing method, the variational inequality (VI) problem is first converted to a convex second-order cone programming (CSOCP). Using a high-performance model, the obtained convex programming problem is solved. According to Karush-Kuhn-Tucker conditions of convex optimisation, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the CSOCP problem. By employing Lyapunov function approach, it is also shown that the presented neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original optimisation problem. The capability of the method is demonstrated by several numerical results.

关键词： Neural network variational inequalities second order cone programming stability convergent

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