An efficient global optimization approach for solving mixed-integer nonlinear programming problems

Wang, Pei-Chun; Tsai, Jung‐Fa; Ma, Wei-Nung; Lee, Chia-Chien

doi:10.1109/iccie.2010.5668338

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…MINLP problem is an important category of optimization problems, which can be applied in various real-world problems. For example, there are a lot of applications of MINLP applied in financial, engineering design problems, chemical engineering, process operations research and management [47].…”

Section: Mixed Integer Non-linear Programmentioning

confidence: 99%

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Joint admission control and power allocation in hospital networks based on cognitive radios.

Liu¹

2021

Preprint

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In this thesis, we present an approach to solve the joint call admission control and power allo- cation problem in a hospital environment based on cognitive radio. Specifically, a multi-objective non-convex mixed integer non-linear programming (MINLP) problem with weighted-sum method for wireless access in an indoor hospital environment has been formulated in order to maximize the number of admitted secondary users and minimize transmit power while guaranteeing the through- put of all secondary users and satisfying the interference constraints for the protected and primary users. To solve this MINLP problem with different weights given to different objectives, we pro- pose to use the standard branch and bound algorithm as appropriately modified to find the optimal solution. We also coded a specific program using OPTI Toolbox to find the minimum objective function value, number of admitted secondary users and all related values such as total system power and throughput. To analyze the numerical results, we considered three cases with equal and non-equal weights. We also changed the values of interference and maximum source power to obtain and analyze different results comparing with the normal one. Our results indicate that more power is allocated and better throughput is guaranteed while the number of admitted users is increasing. However, as they increase, the objective function value increases steadily as well, which means that it is more difficult to reach our minimizing objective.

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Section: Mixed Integer Non-linear Programmentioning

confidence: 99%

“…However, many approaches have been proposed and used to solve MINLP problems. For convex MINLP problems, there are many methods including Branch and Bound (BB), Outer-Approximation (OA), Extended Cutting Plane Method (ECP), and Generalized Disjunctive Programming (GDP) to solve convex MINLP problems [47].…”

Section: Mixed Integer Non-linear Programmentioning

confidence: 99%

Joint admission control and power allocation in hospital networks based on cognitive radios.

Liu¹

2021

Preprint

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“…MINLP problems involve both continuous and discrete variables that arise in many applications of engineering design, chemical engineering, and process operations research and management. These applications are extensively surveyed in [58][59][60]. Although many optimization approaches have been developed for the MINLP problems, these methods still have drawbacks including only finding local or approximate solutions or using too many extra binary variables and constraints to reformulate the problem.…”

Section: Day-ahead Optimal Power Flow Formulationmentioning

confidence: 99%

Evaluating the Economic Benefits of a Smart-Community Microgrid with Centralized Electrical Storage and Photovoltaic Systems

et al. 2020

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In this paper, an innovative method for managing a smart-community microgrid (SCM) with a centralized electrical storage system (CESS) is proposed. The method consists of day-ahead optimal power flow (DA-OPF) for day-ahead SCM managing and its subsequent evaluation, considering forecast uncertainties. The DA-OPF is based on a data forecast system that uses a deep learning (DL) long short-term memory (LSTM) network. The OPF problem is formulated as a mathematical mixed-integer nonlinear programming (MINLP) model. Following this, the developed DA-OPF strategy was evaluated under possible operations, using a Monte Carlo simulation (MCS). The MCS allowed us to obtain potential deviations of forecasted data during possible day-ahead operations and to evaluate the impact of the data forecast errors on the SCM, and that of unit limitation and the emergence of critical situations. Simulation results on a real existing rural conventional community endowed with a centralized community renewable generation (CCRG) and CESS, confirmed the effectiveness of the proposed operation method. The economic analysis showed significant benefits and an electricity price reduction for the considered community if compared to a conventional distribution system, as well as the easy applicability of the proposed method due to the CESS and the developed operating systems.Energies 2020, 13, 1764 2 of 21 Centralized and Decentralised Energy Storage SystemFor an SCM, there are two main common options to place storage units-a centralised energy storage system (CESS) and a decentralised energy storage system (DESS). Existing research deals with CESS and DESS, describing their useful properties, limitations, and applications.Generally, energy storage systems (ESSs) are used in microgrids to compensate the intermittent nature of RE, to improve the RE penetration level and to grow the self-consumption rate, reducing dependence on the grid [6]. The CESS represents a central ESS that is directly operated by the energy management system (EMS) of a microgrid. A DESS represents numerous small ESSs allocated in a microgrid. They can be operated by local users, by aggregators, or by the central EMS. The CESS increases the overall efficiency and the resiliency of the microgrid [7], whereas, on the other side, the DESS maximises the local impact of ESSs to adjust to local load profiles [8].According to the study presented in [9], the deployment of a DESS reduces the power losses in the distribution network and can support the load demand with a rapid response [10]. Nonetheless, the deployment of a DESS needs an appropriate mature infrastructure and a control mechanism, both of which are more expensive and complex than those required in the case of a CESS.According to [1], for the same operating conditions of the photovoltaics (PV) generation and ESS capacity, a CESS exhibits a better (of about 17%) reduction of the grid power use, compared to a DESS. In [11], the authors revealed that by using a CESS for the operation of a microgrid, lower operational costs c...

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“…We use Optimization Interface Toolbox (OPTI toolbox) for solving this mixed integer non-linear programming (MINLP) problem. Lot of applications of MINLP are in engineering design problems, operations research and management [111,112]. OPTI toolbox is based on mesh adaptive direct search (MADS) [113][114][115][116][117] and we are using it for solving our optimization problem, which has not been used in M2M research for optimization of channel access and resource allocation.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation in clustered M2M networks: a q-learning approach

Hussain¹

2021

Preprint

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Machine to machine (M2M) communication has received increasing attention in recent years. A M2M network exhibits salient features such as large number of machines/devices, low data rates, delay tolerant/sensitive, small sized packets, energy-constrained and low or no mobility. A large number of M2M terminals may exist in a small area with many trying to simultaneously and randomly access for channel resources - which will result in overload and access problem. This increased signaling overhead and diverse requirements of machine type communication devices (MTCDs) call for the development of flexible and efficient scheduling and random access techniques. In this thesis, we first review and compare various scheduling and random access techniques in LTE-based cellular networks for M2M communication. We also discuss how successful they are to fulfill the unique requirements of M2M communication and networking. Resource management in M2M networks with a large number devices is also reviewed from the access point of view. We propose a multi-objective optimization based solution to the problem of resource allocation in interference-limited M2M communication. We consider MTCDs in a clustered network structure, where they are divided into clusters and the devices belonging to a cluster communicate to cluster head (or controller). We maximize the number of admitted MTCD controllers and throughput with least interference caused to conventional primary users. We formulate the problem as a mixed-integer non-linear problem with multiple objectives and solve it using meshed adaptive direct search (MADS) algorithm. Simulation results show the effects of varying different parameters on cumulative throughput and the number of admitted iii MTCD controllers. We then formulate the slot selection problem in M2M networks with admitted MTCDs as an optimization problem. We present a solution using the Q-learning algorithm to select conflict-free slot assignment in a random access network with MTCD controllers. The performance of the solution is dependent on parameters such as learning rate and reward. We thoroughly analyze the performance of the proposed algorithm considering different parameters related to its operation. We also compare it with simple ALOHA and channel-based scheduled allocation and show that the proposed Q-learning based technique has a higher probability of assigning slots compared to these techniques. We then present a block based Q-learning algorithm for the scheduling of MTCDs in clustered M2M communication networks. At first centralized slot assignment is done and an algorithm is proposed for minimizing the inter-cluster interference. Then we propose to use an Q-learning algorithm to assign slots in a distributed manner and comparison is made between the two schemes. Afterwards, we show the effects of distributed slot-assignment with respect to varying signal-to-interference ratio on convergence rate and convergence probability. Cumulative distribution function is used to study the effect of various SIR threshold levels on the convergence probability. With the increase in SIR threshold levels, increase in convergence time and decrease in convergence probability are observed, as less block configuration fulfills the required threshold in the M2M network.

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An efficient global optimization approach for solving mixed-integer nonlinear programming problems

Cited by 6 publications

References 25 publications

Joint admission control and power allocation in hospital networks based on cognitive radios.

Joint admission control and power allocation in hospital networks based on cognitive radios.

Evaluating the Economic Benefits of a Smart-Community Microgrid with Centralized Electrical Storage and Photovoltaic Systems

Resource allocation in clustered M2M networks: a q-learning approach

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