Depth First:Optimal Path Discovery Between Designated Nodes in Random Ring-Based Graphs

This paper focuses on optimally determining the existence of connected paths between some given nodes in random ring-based graphs.Serving as a fundamental underlying structure in network modeling,ring topology appears as commonplace in many realistic scenarios.Regarding this,we consider graphs composed of rings,with some possible connected paths between them.Without prior knowledge of the exact node permutations on rings,the existence of each edge can be unraveled through edge testing at a unit cost in one step.The problem examined is that of determining whether the given nodes are connected by a path or separated by a cut,with the minimum expected costs involved.Dividing the problem into different cases based on different topologies of the ring-based networks,we propose the corresponding policies that aim to quickly seek the paths between nodes.A common feature shared by all those policies is that we stick to going in the same direction during edge searching,with edge testing in each step only involving the test between the source and the node that has been tested most.The simple searching rule,interestingly,can be interpreted as a delightful property stemming from the neat structure of ring-based networks,which makes the searching process not rely on any sophisticated behaviors.We prove the optimality of the proposed policies by calculating the expected cost incurred and making a comparison with the other class of strategies.The effectiveness of the proposed policies is also verified through extensive simulations,from which we even disclose three extra intriguing findings:i)in a onering network,the cost will grow drastically with the number of designated nodes when the number is small and will grow slightly when that number is large;ii)in ring-based network,Depth First is optimal in detecting the connectivity between designated nodes;iii)the problem of multi-ring networks shares large similarity with that of two-ring networks,and a larger number of ties between rings will not influence the expected cost.

Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.

Modeling of Combined Economic and Emission Dispatch Using Improved Sand Cat Optimization Algorithm

Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs.The disadvantage of the conventional method is its incapability to avoid falling in local optimal,particularly when handling nonlinear and complex systems.Metaheuristics have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black box.Therefore,this paper focuses on the design of an improved sand cat optimization algorithm based CEED(ISCOA-CEED)technique.The ISCOA-CEED technique majorly concen-trates on reducing fuel costs and the emission of generation units.Moreover,the presented ISCOA-CEED technique transforms the equality constraints of the CEED issue into inequality constraints.Besides,the improved sand cat optimization algorithm(ISCOA)is derived from the integration of tra-ditional SCOA with the Levy Flight(LF)concept.At last,the ISCOA-CEED technique is applied to solve a series of 6 and 11 generators in the CEED issue.The experimental validation of the ISCOA-CEED technique ensured the enhanced performance of the presented ISCOA-CEED technique over other recent approaches.

Distributed Periodic Event-Triggered Optimal Control of DC Microgrids Based on Virtual Incremental Cost

This article presents a distributed periodic eventtriggered(PET)optimal control scheme to achieve generation cost minimization and average bus voltage regulation in DC microgrids.In order to accommodate the generation constraints of the distributed generators(DGs),a virtual incremental cost is firstly designed,based on which an optimality condition is derived to facilitate the control design.To meet the discrete-time(DT)nature of modern control systems,the optimal controller is directly developed in the DT domain.Afterward,to reduce the communication requirement among the controllers,a distributed event-triggered mechanism is introduced for the DT optimal controller.The event-triggered condition is detected periodically and therefore naturally avoids the Zeno phenomenon.The closed-loop system stability is proved by the Lyapunov synthesis for switched systems.The generation cost minimization and average bus voltage regulation are obtained at the equilibrium point.Finally,switch-level microgrid simulations validate the performance of the proposed optimal controller.

Mining truck scheduling with stochastic maintenance cost

Open pit mining operations utilize large scale and expensive equipment. For the mines implementing shovel and truck operation system, trucks constitute a large portion of these equipment and are used for hauling the mined materials. In order to have sustainable and viable operation, these equipment need to be utilized efficiently with minimum operating cost. Maintenance cost is a significant proportion of the overall operating costs. The maintenance cost of a truck changes non-linearly depending on the type, age and truck types. A new approach based on stochastic integer programming （SIP） techniques is used for annually scheduling a fixed fleet of mining trucks in a given operation, over the life of mine （multi-year time horizon） to minimize maintenance cost. The maintenance cost data in mining usually has uncertainty caused from the variability of the operational conditions at mines. To estimate the cost, usually historic data from different operations for new mines, and/or the historic data at the operating mines are used. However, maintenance cost varies depending on road conditions, age of equipment and many other local conditions at an operation. Traditional models aim to estimate the maintenance cost as a deterministic single value and financial evaluations are based on the estimated value. However, it does not provide a confidence on the estimate. The proposed model in this study assumes the truck maintenance cost is a stochastic parameter due to the significant level of uncertainty in the data and schedules the available fleet to meet the annual production targets. The scheduling has been performed by applying both the proposed stochastic and deterministic approaches. The approach provides a distribution for the maintenance cost of the optimized equipment schedule minimizing the cost.

On Cost Minimization for Cache-Enabled D2D Networks with Recommendation

To accommodate the tremendous increase of mobile data traffic,cache-enabled device-to-device(D2D)communication has been taken as a promising technique to release the heavy burden of cellular networks since popular contents can be pre-fetched at user devices and shared among subscribers.As a result,cellular traffic can be offloaded and an enhanced system performance can be attainable.However,due to the limited cache capacity of mobile devices and the heterogeneous preferences among different users,the requested contents are most likely not be proactively cached,inducing lower cache hit ratio.Recommendation system,on the other hand,is able to reshape users’request schema,mitigating the heterogeneity to some extent,and hence it can boost the gain of edge caching.In this paper,the cost minimization problem for the social-aware cache-enabled D2D networks with recommendation consideration is investigated,taking into account the constraints on the cache capacity budget and the total number of recommended files per user,in which the contents are sharing between the users that trust each other.The minimization problem is an integer non-convex and non-linear programming,which is in general NP-hard.Therewith,we propose a timeefficient joint recommendation and caching decision scheme.Extensive simulation results show that the proposed scheme converges quickly and significantly reduces the average cost when compared with various benchmark strategies.

A TRUST REGION ALGORITHM VIA BILEVEL LINEAR PROGRAMMING FOR SOLVING THE GENERAL MULTICOMMODITY MINIMAL COST FLOW PROBLEMS

This paper proposes a nonmonotonic backtracking trust region algorithm via bilevel linear programming for solving the general multicommodity minimal cost flow problems.Using the duality theory of the linear programming and convex theory,the generalized directional derivative of the general multicommodity minimal cost flow problems is derived.The global convergence and superlinear convergence rate of the proposed algorithm are established under some mild conditions.

Time and Quantity Based Hybrid Consolidation Algorithms for Reduced Cost Products Delivery

In today’s competitive business environment,the cost of a product is one of the most important considerations for its sale.Businesses are heavily involved in research strategies to minimize the cost of elements that can impact on the final price of the product.Logistics is one such factor.Numerous products arrive from diverse locations to consumers in today’s digital era of online businesses.Clearly,the logistics sector faces several dilemmas from order attributes to environmental changes in this regard.This has specially been noted during the ongoing Covid-19 pandemic where the demands on online businesses have increased several fold.Consequently,the methodology to optimise delivery cost and its impact on environmental focus by reducing CO_(2) emissions has gained relevance.The resultant strategy of Shipment Consolidation that has evolved is an approach that combines one or more transport orders in the same vehicle for delivery.Shipment Consolidation has been categorized in three order scheduling approaches:Time based consolidation,Quantity based consolidation,and a Hybrid(Time-Quantity)based consolidation.In this paper,a new Hybrid Consolidation approach is presented.Using the Hybrid approach,it has been shown that order delivery can be facilitated by taking into account not only the order pick up time,but also the total order quantity.These results have shown that if a time window is available in respect of the order delivery time,then the order can be delayed from pickup to consolidate it with other orders for cost optimization.This hybrid approach is based on four consolidation principles,two of which work on fixed departure and two,on demand departure.Three of these rules have been implemented and tested here with an application case study.Statistical analysis of the results is illustrated with different planning evaluation indicators.The Result analyses indicate that consolidation of orders is increased with each implemented rule hence motivating us towards the implementation of the fourth rule.Testing with bigger data sets is required.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

A Set of Three Technologies Will Save Mankind from Climate Change in the Most Inexpensive Way Possible

This paper formulates a set of three technologies that should deal with the greatest threat to mankind—climate change at the lowest cost.The main technology will be“Sunny Rain”.It considers technology to prevent eruptions of submarine volcanoes at shallow depths and technologies that provide scalable and impactful solutions to reduce carbon emissions across diverse industries as complementary technologies used to reduce cost.A list of submarine volcanoes at shallow depths that are likely to spew waterborne dust into the atmosphere has begun to be created.If the governments of Japan,Italy,and Greece,which have submarine volcanoes at shallow depths(Kiki,Marsili,Columbo),prevent eruptions of these volcanoes,it will provide electricity to these countries,save many of their citizens from death,and save humanity from the greatest threat—climate change—in the most inexpensive way possible!

Grid Integration of Electric Vehicles for Economic Benefits:A Review

Emissions from the internal combustion engine(ICE) vehicles are one of the primary cause of air pollution and climate change. In recent years, electric vehicles(EVs) are becoming a more sensible alternative to these ICE vehicles. With the recent breakthroughs in battery technology and large-scale production, EVs are becoming cheaper. In the near future,mass deployment of EVs will put severe stress on the existing electrical power system(EPS). Optimal scheduling of EVs can reduce the stress on the existing network while accommodating large-scale integration of EVs. The integration of these EVs can provide several economic benefits to different players in the energy market. In this paper, recent works related to the integration of EV with EPS are classified based on their relevance to different players in the electricity market. This classification refers to four players: generation company(GENCO), distribution system operator(DSO), EV aggregator, and end user. Further classification is done based on scheduling or charging strategies used for the grid integration of EVs. This paper provides a comprehensive review of technical challenges in the grid integration of EVs along with their solution based on optimal scheduling and controlled charging strategies.

ALLOCATING REPAIRABLE SYSTEM'S RELIABILITY SUBJECT TO MINIMAL TOTAL COST-AN INTEGER PROGRAMMING APPROACH

Reliability allocation problem is commonly treated using a closed-form expression relating the cost to reliability. A recent approach has introduced the use of discrete integer technique for un-repairable systems. This research addresses the allocation problem for repairable systems. It presents an integer formulation for finding the optimum selection of components based on the integer values of their Mean Time to Failure （MTTF） and Mean Time to Repair （MTTR）. The objective is to minimize the total cost under a system reliability constraint, in addition to other physical constraints. Although, a closed-form expression relating the cost to reliability may not be a linear; however, in this research, the objective function will always be linear regardless of the shape of the equivalent continuous closed-form function. An example is solved using the proposed method and compared with the solution of the continuous closed-form version. The formulation for all possible system configurations, components and subsystems are also considered.

Optimal Control of Microgrids with Multi-stage Mixed-integer Nonlinear Programming Guided Q-learning Algorithm

This paper proposes an energy management system(EMS)for the real-time operation of a pilot stochastic and dynamic microgrid on a university campus in Malta consisting of a diesel generator,photovoltaic panels,and batteries.The objective is to minimize the total daily operation costs,which include the degradation cost of batteries,the cost of energy bought from the main grid,the fuel cost of the diesel generator,and the emission cost.The optimization problem is modeled as a finite Markov decision process(MDP)by combining network and technical constraints,and Q-learning algorithm is adopted to solve the sequential decision subproblems.The proposed algorithm decomposes a multi-stage mixed-integer nonlinear programming(MINLP)problem into a series of single-stage problems so that each subproblem can be solved by using Bellman’s equation.To prove the effectiveness of the proposed algorithm,three case studies are taken into consideration:(1)minimizing the daily energy cost;(2)minimizing the emission cost;(3)minimizing the daily energy cost and emission cost simultaneously.Moreover,each case is operated under different battery operation conditions to investigate the battery lifetime.Finally,performance comparisons are carried out with a conventional Qlearning algorithm.

Remote monitoring cost minimization for an unreliable sensor network with guaranteed network throughput

In this paper we consider a link-unreliable remote monitoring scenario where the monitoring center is geographically located far away from the region of the deployed sensor network,and sensing data by the sensors in the network will be transferred to the remote monitoring center through a third party telecommunication service.A cost associated with this service will be incurred,which will be determined by the number of gateways employed and the cumulative volume of data successfully received within a specified monitoring period.For this scenario,we first formulate a novel constrained optimization problem with an objective to minimize the service cost while a pre-defined network throughput is guaranteed.We refer to this problem as the throughput guaranteed service cost minimization problem and prove that it is NP-complete.We then propose a heuristic for it.The key ingredients of the heuristic include identifying gateways and finding an energy-efficient forest of routing trees rooted at the gateways.We also perform theoretical analysis on the solution obtained.Finally,we conduct experiments by simulations to evaluate the performance of the proposed algorithm.Experimental results demonstrate the proposed algorithm outperforms other algorithms in terms of both the service cost and the network lifetime.

IoT-Based Cost Saving Offloading System for Cellular Networks

Nowadays, with the new techniques available in hardware and software, data requests generated by applications of mobile devices have grown explosively. The large amount of data requests and their responses lead to heavy traffic in cellular networks. To alleviate the transmission workload, offloading techniques have been proposed, where a cellular network distributes some popular data items to other wireless networks, so that users can directly download these data items from the wireless network around them instead of the cellular network.In this paper, we design a Cost Saving Offloading System（CoSOS）, where the Internet of Things（IoT） is used to undertake partial data traffic and save more bandwidth for the cellular network. Two types of algorithms are proposed to handle the popular data items distribution among users. The experimental results show that CoSOS is useful in saving bandwidth and decreasing the cost for cellular networks.

MULTI-OBJECTION OPTIMIZATION MODEL BASED ON RELIABILITY FOR WATER DISTRIBUTION SYSTEMS

A new methodology is developed in this paper for determining the optimization (minimum cost) of water distribution systems based on reliability. This modelcan overcome the defect of consuming long computer time in general optimization based onre1iability. In this model the optimal design based on reliability of water distribution systems is conducted by multi-objective technique.

Infra-Marginal Analysis Model for Provision Mode Selection for E-commerce Services

E-commerce has grown extraordinarily since the emergence of the internet, and many types of services are employed to accelerate this process. Service quality and productivity are two critical indicators to evaluate the competitiveness of e-commerce companies. Deciding which provision mode of e-commerce services （buy, sell, or self-provide） to adopt is a key operational strategy issue. This paper investigates the conditions and limitations of e-commerce services＇ optimal supply modes, and proposes a cost oriented infra-marginal model where service demand is considered an exogenous variable due to its non-elastic and unprofitable characteristics. By analyzing the main impact factors of this model, this paper infers provision mode selection strategies, which are determined by four factors： transaction cost, service price, service demand, and competitive advantages. Decision trees are derived from these strategies to help e-commerce companies make appropriate decisions. Finally, the proposed model＇s feasibility is verified by two case studies.

Reliability Analysis of Multi-State Repairable System with Dynamic Transition Probabilities

Inadequate maintenance decisions lead to incremental overall costs. In order to minimize costs in maintenance of the multi-state repairable system, we model a preventive maintenance(PM) scheme of the multistate repairable system using non-Markov process. The periodically decreasing reliability model of the non-Markov dynamic system with dynamic transition probabilities is established to satisfy the probability change. The diesel engine system is taken as an example to illustrate the model. The reliability of the diesel engine is analyzed and its PM scheme is worked out. RENO software is used to simulate the diesel engine system. The maintenance cost of components and the optimal PM interval data of the system are obtained by using the minimal average cost as the objective function. The adaptability of PM is judged, and the optimal PM scheme is presented.

Timing and hamming weight attacks on minimal cost encryption scheme

The timing and Hamming weight attacks on the data encryption standard （DES） cryptosystem for minimal cost encryption scheme is presented in this article. In the attack, timing information on encryption processing is used to select and collect effective plaintexts for attack. Then the collected plaintexts are utilized to infer the expanded key differences of the secret key, from which most bits of the expanded secret key are recovered. The remaining bits of the expanded secret key are deduced by the correlations between Hamming weight values of the input of the S-boxes in the first-round. Finally, from the linear relation of the encryption time and the secret key＇s Hamming weight, the entire 56 bits of the secret key are thoroughly recovered. Using the attack, the minimal cost encryption scheme can be broken with 2^23 known plaintexts and about 2^21 calculations at a success rate a 〉 99%. The attack has lower computing complexity, and the method is more effective than other previous methods.