Cost Minimization Objective Research Articles

Cost-aware workflow offloading in edge-cloud computing using a genetic algorithm

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs.

A bi-level optimization framework for the power-side virtual power plant participating in day-ahead wholesale market as a price-maker considering uncertainty

This paper develops a novel bilevel decision-making framework for a power-side virtual power plant (VPP) comprising refined power to gas (P2G), wherein the upper-level is the cost minimization objective of VPP, while the lower-level is social cost minimization objective of wholesale electricity market. Meanwhile, wind power uncertainty is handled by distributionally robust optimization method. By utilizing Karush-Kuhn Tucker optimality condition and strong duality theory, the bilevel model is interpreted as a tractable single-level mixed integer programming model. Simulation results show that: relative to the models with removing uncertainty and refined P2G device, 1) the proposed approach realizes outstanding economic benefit by obtaining an increase with a rate by 7.47 % and 105.09 % in total actual profit, respectively. Regarding environmental benefit, CO2 emission volume experiences a 0.89 % decline and 0.90 % increase, and the CO2 intensity of the proposed strategy is the lowest with the value of 193. 25kg/MWh. 2) The proposed model can achieve the economic and low-carbon dispatching of the VPP. Wind power curtailment in the proposed model witnesses a trivial increase and reduced by 53.19 %, respectively. 3) The model exhibits merits due to its trade-off between robustness and computational complexity.

Project acceleration using temporary workers with heterogeneous efficiency: optimal and best recruitment policies

ABSTRACT This paper investigates recruitment decisions of temporary work with several levels of experience, from beginners to experts (heterogeneous efficiency) to accelerate a manufacturing project with the objective of labour cost minimisation. A Mixed Integer Linear Program formulation of the problem is developed to find the optimal pool of temporary work of each experience level so as to minimise the cost subject to a desired deadline. The deadline is decreased decrementally in order to identify any potential patterns of recruitment along the reduction in the project duration. A variety of projects, efficiency levels with respect to years of experience and cost scenarios are considered in an experimental framework. Simulation results show a high sensitivity of the optimal pool of temporary work to the project type, efficiency levels and costs. A worst-case analysis suggests that using solely expert temporary workers might be the best recruiting policy to accelerate a project, whatever the desired deadline.

Facilitating large-scale group decision-making in social networks: A bi-level consensus model with social influence

Nowadays, more and more decision-makers (DMs) are engaging in group decision-making (GDM) within certain social relationship networks. Therefore, understanding how to leverage differences in DMs’ opinions and social relationships to promote consensus in large-scale group decision-making (LSGDM) is an important issue. This study proposes a bi-level consensus model for LSGDM in social networks, well considering social influence to achieve the objective of minimum cost of the upper-level mediator and maximum satisfaction of lower-level subgroups. Firstly, the Louvain algorithm is employed to reduce the dimensions of LSGDM, segmenting DMs in social networks into distinct subgroups in a directed graph. Then, a dynamic opinion experiment based on the Friedkin–Johnsen model is utilized to assess the confidence levels of subgroup members and enhance opinion coherence within subgroups. Operating at the subgroup perspective and adopting a dual-layer framework, this study establishes the minimum cost maximum satisfaction consensus model (MCMSCM) to better balance the objectives between the upper and lower levels. Furthermore, a bi-level nested algorithm, based on genetic algorithm, is employed to determine corresponding unit costs and adjusted opinions, thereby achieving consensus rapidly and effectively. The proposed methodology provides a robust tool for LSGDM in social networks. Finally, through an illustrative example accompanied by corresponding analysis, the rationality and effectiveness of this pattern are demonstrated.

The effect of electric vehicle charging demand variability on optimal hybrid power systems with second-life lithium-ion or fresh Na–S batteries considering power quality

Second-life batteries (SLB) in energy storage systems (ESS) can provide an economical solution to hybrid power systems (HPS) by reducing environmental concerns in battery production. Could Na–S batteries be a suitable alternative for ESS applications if the toxicity problem can be solved? This article performs the optimal sizing and feasibility analysis of an electric vehicle charging station with a minimum cost objective under daily, hourly, and hybrid demand variability with multi-year sensitivity analyses for both ESS technologies. Moreover, the scope is deepened with sensitivity analyses based on solar horizontal irradiation, wind speed, and electricity selling prices. Due to the HPS connection to the distribution busbar, power quality issues are included in the cost-based optimization framework, and the effects of power quality issues on the sizing and feasibility outputs are compared at higher demand variability. The results show that using SLB at wind speeds lower than 4.7 m/s and irradiances lower than 3 kWh/m2/day or above 0.035 $/kWh electric sale price is not economical. Grid-connected HPSs can increase current and voltage harmonics at the distribution busbar by up to 55.3 % and 4.3 %, reduce the power factor by up to 46.8 %, and extend the payback period by up to 1 year.

A new mathematical model integrating the carbon tax and horizon planning to optimize the textile and clothing industry supply chain

Energy costs have surged significantly in recent years across various regions of the world. This inflation has a direct impact on households and businesses, by increasing production costs. It risks slowing the global economic recovery by reducing purchasing power and consumption. This study presents a new mixed-integer non-linear programming (MINLP) model with the objective of cost minimization within the direct supply chain of the textile and clothing industry. To consider different aspects in our study, the cost function is decomposed to the supply, production, energy, transportation, storage, and carbon tax emissions costs. It deals also with the management of multiple energy sources (specifically, electricity for production and fuel for transportation). This model simultaneously incorporates several critical factors, including life cycle assessment, carbon tax emissions, and planning horizon. To do this, a product’s carbon emissions are quantified from raw material extraction through to the finished product, and three different planning horizons as long term t=∞, medium term t=3 month, and short term t=1 month are considered to evaluate their impacts on the costs and emissions values. A shorter planning horizon leads to increased costs and reduced emissions reductions compared to a medium or long-term planning horizon. Additionally, the study introduces a new benchmark based on real-world data available in the literature, which serves as a valuable tool for evaluating strategic and tactical planning issues within textile supply chains. To evaluate the performance of our proposed model, we defined three different size of the problem by changing the number of factories, suppliers, production sites, warehouses and customers.The study’s findings reveal that, over a medium and long-term planning horizon, there is an average cost increase of about 2% for different problem sizes, when considering a carbon tax ranging from 44.6€ to 86.2€ per tonne of emissions. When a higher carbon tax is applied, the total cost increases, while the carbone emissions and subcontracting activities will be decrease.

Evaluation of energy saving and emission reduction effect of distributed energy system

The conventional evaluation methods of energy conservation and emission reduction lack the discussion of quantification index of energy conservation and emission reduction potential, and the evaluation effect is poor. Therefore, an evaluation method for energy conservation and emission reduction of distributed energy systems is proposed, which considers system dynamics and demand response. According to the requirements of system dynamics analysis, the relational variables in the system dynamics model are determined. The input-output relationship of energy saving, and emission reduction effect evaluation is described by system dynamics equation, and the input-output relationship of energy saving, and emission reduction effect evaluation is determined. Based on the maximum profit objective function and the minimum cost objective function of the user, the continuum equation of the medium transmission process in the distributed energy system is obtained. The evaluation indexes of energy saving, and emission reduction effect are selected from the three aspects of power consumption side, power generation side and the system itself to realize the evaluation of energy saving and emission reduction effect. The experimental results show that the evaluation curve obtained by this method is basically consistent with the actual curve, and the evaluation accuracy is high.

Optimal output‐constrained control of medium‐voltage DC shipboard power systems for pulsed power load accommodation

AbstractFor pulsed power load (PPL) accommodation in a medium‐voltage DC (MVDC) shipboard power system (SPS), the charging control of energy storage systems (ESSs) and the generation control of distributed generators (DGs) need to be properly coordinated. Targeting the important but not well‐studied problem, an optimal output‐constrained control algorithm for the offline PPL accommodation strategy is presented. Three control objectives including realising the generation and charging control references, maintaining the DC bus and supercapacitor voltages within the safe operating ranges, and minimising the total generation cost of DGs, are fulfilled concurrently. First, an SPS model with multiple DGs, a supercapacitor ESS, and regular loads is developed. By restricting the DC bus and supercapacitor voltages within pre‐defined constraints, both the transient‐ and steady‐state performances of the SPS are guaranteed. Furthermore, by incorporating the cost minimisation objective into designed virtual control signals, the third control objective on energy efficiency is realised. The stability of the presented algorithm is rigorously proven based on the Lyapunov method. Finally, detailed case studies are conducted to validate the performance of the designed algorithm.

OPTIMAL SIZING AND OPERATION OF HYBRID POWER SYSTEMS CONSIDERING THE BATTERY CAPACITY DEGRADATION LIMITATIONS

Evaluating clean energy alternatives in hybrid power systems (HPS) is critical within sustainable development and zero-carbon policies. Considering the synchronization issues between energy generation and consumption, determining the optimal operating performance of battery energy storage systems (BESS) will likely increase support and interest in HPS investments. In this study, HPSs using shared BESSs for prosumers in a common bus distribution network are optimally sized with a minimum cost objective in a multi-year sensitivity analysis. Most importantly, the optimal C-rate and maximum depth of discharge (DODmax) operation are determined to match the supply-demand balance and maximize the HPS benefit at lower end-of-life (EOL) limits. The impact of increases in EOL limits on the technical, economic, and environmental feasibility of HPS and BESS aging is also evaluated. At the same time, all operations are performed considering four different sub-degradation models using the Arrhenius strategy and Rainflow Counting algorithm. The results show that increasing the C-rate reduces CO2 by up to 19% while increasing BESS equivalent cycles and cycling degradation by 28.26% and 10%, respectively. HPS performance is maximized based on optimum BESS operating at 80% DODmax. Based on the obtained results, it is also emphasized that the impact of BESS operating performance on HPS feasibility and aging analysis will be valuable for many stakeholders.

Optimal Power Source Configuration of Independent Microgrid Based on Natural Resource Evaluation

Power source configuration is an important stage of independent microgrid planning, which guarantees the economic and reliable operation of the microgrid system. The type and capacity of power source for the independent microgrid are affected by the factors such as load level, geographical location, wind, solar, water, and other natural resources. This paper analyzed the characteristics of natural resources in different regions. The index evaluation system of wind, solar, and hydropower resource was constructed, and the fuzzy evaluation method was proposed to evaluate the wind-solar-hydropower resource. Considering the power supply reliability and environmental friendliness, the optimization model of the microgrid power source was established with the objective of minimum comprehensive generation cost and the power and energy balance as constraints. Generative Adversarial Networks (GANs) were applied to simulate the uncertainty of wind, solar, and hydropower output and generate multiple operation scenarios of independent microgrids. The improved method for the K-Medoids clustering algorithm was studied to reduce the number of operational scenarios and enhance computational efficiency. Finally, an example was presented to illustrate the analysis processes and verify the feasibility and rationality of the proposed method. The wind-solar-hydropower resource levels of 30 provinces in China were obtained, and five representative regions were selected. The power source configuration scheme of an independent microgrid in these regions was proposed, which provided a reference for independent microgrid planning.

Fixed charge 4-dimensional transportation problem for breakable incompatible items with type-2 fuzzy random parameters under volume constraint

Real-life problems are full of uncertainties combined with impreciseness and randomness. Transportation problems become difficult when its parameters are type-2 fuzzy random type. This paper presents a 4-dimensional fixed charge transportation problem for incompatible and compatible breakable items with type-2 fuzzy random parameters under the availability, demand, conveyance’s weight and volume capacity constraint. The model is developed for usual transportation cost minimization objective, where transportation costs, fixed charges, availabilities, demands, conveyances’ weights and volume capacities are type-2 fuzzy random. A type-2 fuzzy random parameter is converted to type-1 fuzzy random one by critical value-based reduction method and then converted to a random parameter using two different methods-generalized credibility measure and centroid method. The random parameter is converted to crisp parameter using probability chance constraint method for constraints and expectation for objective. A detailed conversion procedure from type-2 fuzzy random to crisp is also presented. The reduced deterministic problem is solved by the Generalized Reduced Gradient method using Lingo 11.0 and numerically illustrated. A real-life example is introduced. Particular models are deduced for non-breakable items or different pairwise incompatible items. The sensitivity analyses against different generalized credibility levels of different parameters are presented for total transportation cost. The numerical results show the efficiency of the proposed critical value reduction-generalized credibility-chance constraint, and expectation method. The importance of uncertainty, multi-path and vehicles’ volume capacity in transportation problems is demonstrated.

Pipeline sharing: A supplementary strategy to enhance 3E performance of multi-product pipelines in China

Single-rate pipeline prices have resulted in low competitive flexibility and have led to high costs and consumption in China's refined oil logistics system. This study aims to propose a framework for multi-product pipelines supplementary pricing strategy that creates a win-win situation for both shippers and pipeline carriers while quantifying economic, energy and environmental performance. Based on the objective of cost minimization, a lower limit constraint on pipeline transportation quantity is added, and a discount coefficient is introduced for pipeline prices, thus improving the transportation optimization model. Moreover, this paper develops an implementation approach for a supplementary pricing strategy that can ensure that the discounted price is not lower than the sum of cost and minimum profit. This framework usability is confirmed using a Chinese oil company as an example. The results show that the optimal scheme can increase pipeline revenue by ¥79.8 million while reduce shipper’s logistics costs by ¥1.68 million during the practice cycle. Meanwhile, this work reduces energy consumption by 1.37 tce, CO2 emission by 444 tons, SO2 emission by 5.65 tons, and NOX emission by 7.07 million tons for the logistics system. Compared with past studies, this study is more economical or more in line with market-oriented behavior.

Distributionally Robust Collaborative Dispatch of Integrated Energy Systems with DNE Limits Considering Renewable and Contingency Uncertainties

Collaborative optimisation of system reserves and utilisation of renewable energy is an efficient approach to achieving robust optimal dispatch of integrated energy systems (IES). However, conventional robust dispatch methods are often too conservative and lack the ability to consider uncertainties such as renewable energy and contingency probabilities. To address these limitations, this paper proposes a distributionally robust dispatch model that co-optimises reserves and do-not-exceed (DNE) limits while considering these uncertainties. First, a deterministic optimisation model of IES is established with a minimum operational cost objective and security constraints. Next, a two-stage robust collaborative optimisation framework of IES is built, based on the Wasserstein measure, with random equipment faults represented by an adjustable ambiguity set. Finally, to overcome the computational challenges associated with robust approaches, duality theory and Karush-Kuhn-Tucker (KKT) conditions are used to convert the formulation into a mixed integer linear programming (MILP) model. The Simulation results on the modified IEEE 33-bus system demonstrate the effectiveness of the proposed model and solution methodology.

A Hybridization Approach to Solve the Capacitated Asymmetric Allocation Hub Location Problem

This paper deals with a capacitated asymmetric allocation hub location problem (CAAHLP). We determine the number of hubs, the locations of hubs, and asymmetric allocation of non-hub nodes to hub with the objective of minimum total transportation costs satisfying the required service level. To solve the problem optimally, we present a 0-1 integer programming model and find an optimal solution using CPLEX. As the CAAHLP has impractically demanding for the large-sized problem, a solution method based on combined ant colony optimization algorithm and genetic algorithm is developed which solve hub location problem and node allocation problem respectively. We investigate performance of the proposed solution method through the comparative study. Key Words: Hub Location, Asymmetric Allocation, Ant Colony Optimization, Genetic Algorithm

An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey

Drawing upon economic and environmental sustainability, this study explores how developing the operational resilience of the medical supply chain (MSC) contributes to maintaining healthcare in the face of disruption risks, such as the COVID-19 pandemic. To this end, an optimization-based roadmap is proposed by employing lean tools to achieve and realize MSC resilience. A novel two-stage stochastic optimization model and robust counterpart are developed with the objective of overall cost minimization to cope with the unknowable demand uncertainty represented by scenarios. The reason behind proposing a scenario-based stochastic model is to implement preparedness strategies during the (re)design phase by making strategic and operational level decisions. That being the case, seven cases are generated based on the demand uncertainty intervals along with seven different reliability levels for sensitivity analysis. Computational experiments are conducted through a real case study to compare the centralized and decentralized distribution models in terms of efficiency and responsiveness. The results obtained by the stochastic model and robust counterpart are compared to demonstrate how strong the proposed model is. On top of that, lean tools are used to visualize and analyze the improvement opportunities to contribute to the methodology. By doing so, this paper presents novel theoretical and empirical insights regarding MSC resilience. The computational results emphasize the importance of employing a pre-disruption strategy via the proposed methodology to design a resilient MSC to be prepared for pandemic-related risk. The findings from the sensitivity analysis also verify that regardless of the disruption degree, the developed roadmap with the centralized distribution model leads to up to 40% improvements in terms of the overall cost, order lead time, emission amount, and inventory shortage metrics.

Trade-offs between economic and environmental goals of production-inventory-routing problem for multiple perishable products

In this paper, we study an integrated production-inventory-routing problem for multiple perishable products (MPPIRP). We introduce two models to evaluate the trade-off between total emissions and total costs. The first model concentrates on minimizing carbon emissions from production, inventory, and routing activities. Notably, carbon emission in transportation depends on several factors, including vehicle load (empty load and carried load), velocity, and traveled distance. The other model considers the value loss of products due to deterioration during their lifetime in the total cost minimization objective function. We impose constraints that perishable products cannot be held, delivered, or used after their lifetimes have expired in both models. Then, we devise a PSO-based method to solve them. Computational experiments verify the effectiveness of the PSO-based method and the rationality of models. Furthermore, we compare costs, emissions, and key performance indicators under both objective functions. The impact of shelf-life on both objectives also has been analyzed. Computational results help us gain significant insights on generating strategy of production, inventory, and routing to minimize total costs or achieve a fully environmental-friendly goal.

Exploring the value of electric vehicles to domestic end-users

Owing to the recent ban on the sales of new petrol and diesel cars in the United Kingdom (UK) by 2030, combined with the UK’s commitment to net-zero emission of greenhouse gases by 2050, a projected increase in the growth rate of electric vehicles (EVs) is inevitable. In recent years, there has been an increase in the adoption of EVs, but not at a rate sufficient to meet net-zero targets. Although benefits do exist for current EV owners, barriers such as the availability of charging infrastructure, total cost of ownership, battery costs, amongst others still present a challenge for the required adoption rate. In this work, we therefore aim to address some of these barriers, specifically the total cost of ownership and battery costs, by exploring the value a range of EVs on the market give to domestic end-users with different usage classes. Using a techno-economic-environmental mixed integer linear optimisation model which considers local energy demands, retail electricity tariffs, local renewable energy generation and battery degradation, potential benefits for EVs adopters are analysed from a cost or Carbon dioxide (CO2) minimisation objective. This model adopted considered a range of vehicle types – EVs and non-EVs – and properties, installed PV sizes, and user travel behaviour classes, and results showed that although EVs have a relatively higher purchase costs, total cost values are comparable, in some cases cheaper, when compared with conventional non-EVs. EV users further gain from environmental benefits through a reduction in the CO2 emitted irrespective of the user’s desired goal. A dominance analysis was also carried out to determine the order of importance of key input variables to the optimisation model in predicting costs and CO2 emission quantities. The results obtained are helpful to end-users in prioritising EV features during purchase based on personal goals of cost or carbon emissions reduction.

Multi-Objective Energy Management of a Smart Home in Real Time Environment

In home energy management, the occupants schedule the operating appliances to achieve lowest optimal energy cost with minimum discomfort. Smart home energy management turns out to be challenging due to the penetration of rooftop intermittent solar power generations. This article develops a multi-objective and real time smart residential load management and hence energy management topology considering variation in rooftop solar power generation with variation in price of distribution grid energy. Primarily, the optimization problem is established as a time average stochastic optimization formulation and then simplified to mixed integer linear programming (MILP) using Lyapunov optimization. The energy cost minimization objective is added with ocular and thermal discomfort minimization which also lowers the discomfort level for occupants due to actions caused by real time load management. The home loads are managed by an Internet-of-Things (IoT) based controller which also alerts the occupants for any incipient malfunction in the household loads. The proposed control technique is authenticated on a real-life smart residential data with simulations and experiments. The resultant controller can thus optimally control the smart residential loads in real time.

Dynamic Sensor Scheduling for Target Tracking in Wireless Sensor Networks With Cost Minimization Objective

Wireless sensor networks (WSNs) are demonstrated to be the increasingly essential systems for various Internet of Things (IoT)-based sensing applications. This article proposes a cost-aware dynamic sensor scheduling (CADSS) framework for WSNs with multiple tasks. At its core, a system cost function is designed to quantify the expenses of the WSNs due to task executions, and a task quality function is modeled to indicate the performance of the corresponding tasks. The proposed CADSS is further formulated as an optimization problem to minimize the system cost while maintaining the desired task qualities. In this way, a comprehensive task utility evaluation methodology for self-organized WSNs is constituted. Furthermore, by modeling the posterior Cramér–Rao lower bound (PCRLB) as the task quality function and a weighted sum of the communication and sensor scheduling cost as the system cost, the CADSS is instantiated into a multitarget tracking (MTT) application. It is shown that the formulated CADSS is a nonconvex optimization problem involving two coupled binary variables that, respectively, correspond to the scheduling of sensor and cluster head. We then propose a parallel convex relation approach to solve it effectively. Numerical results verify the effectiveness of the proposed CADSS by comparing it with state-of-the-art strategies.

Artificial intelligence-enabled probabilistic load demand scheduling with dynamic pricing involving renewable resource

Residential demand response is one of the key enabling technologies which plays an important role in managing the load demand of prosumers. However, the load scheduling problem becomes quite challenging due to the involvement of dynamic parameters and renewable energy resources. This work has proposed a bi-level load scheduling mechanism with dynamic electricity pricing integrated with renewable energy and storage system to overcome this problem. The first level involves the formulation of load scheduling and optimization problems as optimal stopping problems with the objective of energy consumption and delay cost minimization. This problem involved the real-time electricity pricing signal, customers load scheduling priority, machine learning (ML) based forecasted load demand, and renewable & storage unit profiles, which is solved using mathematical programming with branch-and-cut & branch-and-bound algorithms. Since the first-level optimization problem is formulated as a stopping problem, the optimal time slots are obtained using a one-step lookahead rule to schedule the load with the ability to handle the uncertainties. The second level is used to further model the load scheduling problem through the dynamic electricity pricing signal. The cost minimization objective function is then solved using the genetic algorithm (GA), where the input parameters are obtained from the first-level optimization solution. Furthermore, the impact of load prioritization in terms of time factor and electricity price is also modeled to allow the end-users to control their load. Analytical and simulation results are conducted using solar-home electricity data, Ausgrid, AUS to validate the proposed model. Results show that the proposed model can handle uncertainties involved in the load scheduling process along with a cost-effective solution in terms of cost and discomfort reduction. Furthermore, the bi-level process ensures cost minimization with end-user satisfaction regarding the dynamic electricity price signal.