Joint Optimization Model Research Articles

Multi-objective joint optimization for concurrent execution of design-construction tasks in design-build mode

PERT (Program Evaluation and Review Technique) is suitable for evaluating schedule optimization of DBB (Design-Bid-Build) projects based on serial execution of design and construction. However, it is not suitable for evaluating the multi-objective joint optimization of DB (Design-Build) projects based on concurrent execution of design and construction. Therefore, focusing on the unique construction method and construction objective under DB mode, this study adopts the multi-parameter grey GERT (Graphical Evaluation and Review Technique) based on z-tag to construct a multi-objective joint optimization model of concurrent execution of design-construction tasks. The research methods adopted are as follows: Firstly, based on the modeling idea of PERT, a multi-parameter grey GERT was constructed, which was composed of sub-GERTs representing the execution flow of design-construction tasks. Second, based on discussing the functional relationship between time, cost and quality in normal execution and rework execution, a method of constructing a moment generating function for concurrent execution time, cost and quality to characterize the uncertainty of input and output parameters is proposed. Thirdly, on the basis of analyzing the solving methods of each parameter of the multi-parameter GERT, the solving methods for starting execution time, information transmission duration, and total execution duration of the critical time nodes are given. Finally, according to the requirements of DB projects, the multi-objective joint optimization models based on progress oriented and quality oriented are constructed respectively, and the gamultiobj algorithm is used to solve the models. The proposed approach provides a scientific quantitative analysis method for evaluating the progress, cost and quality objectives of the concurrent execution of design-construction tasks, and also lays theoretical guidance for further efficiency popularization of DB mode.

Joint Optimization of Maintenance and Spare Parts Inventory Strategies for Emergency Engineering Equipment Considering Demand Priorities

To respond to emergencies in a timely manner, emergency engineering equipment has been an important tool to implement emergency strategies. However, random failures of the equipment may occur during operation. Therefore, appropriate maintenance and spare parts inventory strategies are crucial to ensure the smooth operation of the equipment. Furthermore, the urgency degree of emergencies varies in practice. Nevertheless, existing studies rarely consider the impact of urgency degree and demand priorities on the service order of the equipment. To bridge the research gaps, this paper establishes a joint optimization model of maintenance and spare parts inventory strategies for emergency engineering equipment considering demand priorities. The proposed model includes two types of emergency engineering equipment with different service rates. The more urgent demand can be fulfilled by the equipment with a higher priority. Corrective maintenance and spare parts inventory policies are simultaneously performed for the equipment. The Markov process imbedding method is utilized to derive the probabilistic indexes of the system. To maximize the system availability, the number of maintenance engineers and the spare parts inventory strategy is optimized via the construction of the joint optimization model. The optimal solution for the optimization problem is obtained using the branch-and-bound method. Finally, this study presents practical examples to verify the effectiveness of the model and methods.

Joint case pack size and unpacking location optimization in a retail supply chain including product returns

This paper investigates a case pack size (CPs) selection and unpacking location (store versus distribution center (DC)) problem considering simultaneous product returns and DC picking capacity constraints (CSULP-PRPC). Product returns and DC picking capacity are neglected in existing package literature. This paper presents an joint optimization model of CPs and unpacking location to identify the trade-offs among the relevant costs, such as DC picking, store handling, store inventory, store shortage and product returns. We minimize the total costs under the constraint of the picking capacity at the DC. To solve the model, an efficient two-stage hybrid algorithm is designed. The proposed methodology is illustrated on an application from a leading retailer in southwest China. Numerical analysis indicates that the total costs significantly reduce 17.28% (compared to the current solution). The stock-keeping units (SKUs) with low values and large CPs contribute 38% of the cost savings. Products with low demand and low value change to larger CPs and unpacked at DC, and products with high demand and low value change to smaller CPs and unpacked at DC. This paper also conducts sensitivity analysis on the various parameters and presents some managerial implications.

Joint optimization of fleet-level sequential selective maintenance and repairpersons assignment for multi-state manufacturing systems

Due to limited maintenance resources, the joint optimization of fleet-level selective maintenance and repairpersons assignment aims to determine the optimal subset of maintenance actions for each repairperson to ensure that manufacturing systems in the fleet complete subsequent missions. However, related studies ignore the uncertain maintenance duration and flow dependency (a stochastic dependency caused by dynamic material flow) resulting in overestimation of system reliability and total profit. Therefore, this paper proposes a novel joint optimization model of fleet-level sequential selective maintenance and repairpersons assignment under flow dependency and uncertain maintenance duration. The model requires identifying a subset of maintenance actions, assigning the selected maintenance actions to repairpersons, and planning sequence of maintenance actions executed by each repairperson. Moreover, the degradation model of multi-state machines under flow dependency is established by analyzing dynamic characteristics of production processes. Further, the reliability of multi-state manufacturing systems considering the interaction among repairpersons, flow dependency, and uncertain maintenance duration is incorporated into the optimization model. The objective is to maximize the total profit under predetermined system reliability thresholds. Then, the optimization problem is solved by a tailored genetic algorithm with segment-based evolution strategy. Finally, numerical examples are given to verify the effectiveness of the proposed method.

Multi-category Fresh Agricultural Products Are Matched Joint Optimization of Load and Distribution

The core of the VRP problem is the process of distribution from the distribution center through multi-demand points under the condition of considering a variety of factors, and the VFP problem, as the precursor to the VRP problem, is that the distribution center carries out reasonable loading and loading according to the needs of multiple demand points. Both are important research topics in the logistics industry. The two influence and restrict each other in the distribution link, and are an indispensable part of the distribution link. This paper combines the two to study the joint optimization problem of VRP problem and VFP problem of urban logistics distribution, which is conducive to the effective integration of logistics and transportation resources. In this study, multi-category fresh agricultural products are taken as the research object, and the joint optimization of three-dimensional loading and distribution routes of multi-category fresh agricultural products at multiple task demand points in a distribution center is studied. In the process of joint optimization, the fragile characteristics of some products and the different sizes of each product feature are considered, and a three-dimensional loading-distribution route joint optimization model is established. In the process of joint optimization of stowage and distribution, the fragile characteristics of some products and the different sizes of each product feature are considered, and a three-dimensional joint optimization model of stowage and distribution route is established. The multi-objective problem with the largest load rate and the smallest mileage is converted into a single-objective function with the lowest cost; Secondly, the application of GA-TS hybrid algorithm to joint optimization is proposed. Finally, a study simulation is performed. The distribution scheme and loading scheme affect the size of the loading rate and the number of miles delivered. Distribution centers have found that proper stacking can effectively ensure vehicle load rates while reducing operating costs.

Joint optimization of low-carbon product family configuration and smart production line selection

To meet the requirements of small batch orders and rapid production, many enterprises began to use robots, artificial intelligence, cloud computing, and other new technologies to construct smart production lines, which are high-flexible and capable of autonomously diagnosing their health and autonomously designing continuous improvement projects. In this study, a joint optimization model is established for concurrently determining the optimal product family configuration and the optimal construction plan for a smart production line. Greenhouse gas emissions are considered in the joint optimization model to realize the low-carbon design concept. A quantum evolutionary algorithm and a genetic algorithm are developed to solve the established optimization model. A case study of the electronic dictionary product family is provided to illustrate the proposed method. The numeric experiments show that the proposed model can reap more profit from a systematic perspective compared with the traditional two-stage approach, and the proposed quantum evolutionary algorithm has a good performance in solving the joint optimization problem.

Joint Optimization of Platoon Control and Resource Scheduling in Cooperative Vehicle-Infrastructure System

Vehicle platooning technology is essential in achieving group consensus, on-road safety, and fuel-saving. Meanwhile, Vehicle-to-Infrastructure (V2I) communication significantly facilitates the development of connected vehicles. However, the coupled effects of the longitudinal vehicle's mobility, platoon control and V2I communication may result in a low reliable communication network between the platoon vehicle and the roadside unit, there is a tradeoff between the platoon control and communication reliability. In this paper, we investigate a bi-objective joint optimization problem where the first objective is to maximize the success probability of data transmission (communication reliability) and the second objective function is to minimize the traffic oscillation flow. The vehicle's mobility state of the platoon vehicle affects the channel capacity and transmission performance. In this context, we deeply explore the relationship between control signals and resource scheduling and theoretically deduce a closed-form expression of the optimal communication reliability objective. Through this closed expression, we transform the bi-objective model into a single objective MPC model by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -constraint method. We design an efficient algorithm for solving the joint optimization model and prove the convergence. To verify the effectiveness of the proposed method, we finally evaluate the spacing error, speed error, and resource scheduling of platooning vehicles through simulation experiments in two experimental scenarios. The results show that the proposed control-communication co-design can improve the platoon control performance while satisfying the high reliability of V2I communications.

Joint optimization of bi‐level imperfect maintenance and spare parts inventory considering order quantity

AbstractThe joint optimization of various maintenance actions and the supply of spare parts has received extensive attention in recent years owing to its advantage in reducing the overall operational costs of industrial systems. In this study, a joint optimization model is proposed considering bi‐level imperfect preventive maintenance and spare part ordering. A virtual age reduction model that considers bi‐level imperfect preventive maintenance (PM) is established. Upon each system shutdown, the PM level is determined based on the cost‐effectiveness ratio. Furthermore, an improved (s, S) inventory policy is proposed utilizing the information of the remaining number of PM and accumulated consumption of spare parts. Finally, the joint optimization models consisting of different PM policies and spare parts inventory policies are solved. Case studies show that compared with the traditional (s, S) inventory policy, the proposed joint optimization model can coordinate the ordering quantity of spare parts with the PM policy, and reduce the expected maintenance cost. A sensitivity analysis of the cost parameters is performed to explore their impact on the maintenance economy.

Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection

Hyperspectral band selection plays an important role in overcoming the curse of dimensionality. Recently, clustering-based band selection methods have shown promise in the selection of informative and representative bands from hyperspectral images (HSIs). However, most existing clustering-based band selection methods involve the clustering of original HSIs, limiting their performance because of the high dimensionality of hyperspectral bands. To tackle this problem, a novel hyperspectral band selection method termed joint learning of correlation-constrained fuzzy clustering and discriminative non-negative representation for hyperspectral band selection (CFNR) is presented. In CFNR, graph regularized non-negative matrix factorization (GNMF) and constrained fuzzy C-means (FCM) are integrated into a unified model to perform clustering on the learned feature representation of bands rather than on the original high-dimensional data. Specifically, the proposed CFNR aims to learn the discriminative non-negative representation of each band for clustering by introducing GNMF into the model of the constrained FCM and making full use of the intrinsic manifold structure of HSIs. Moreover, based on the band correlation property of HSIs, a correlation constraint, which enforces the similarity of clustering results between neighboring bands, is imposed on the membership matrix of FCM in the CFNR model to obtain clustering results that meet the needs of band selection. The alternating direction multiplier method is adopted to solve the joint optimization model. Compared with existing methods, CFNR can obtain a more informative and representative band subset, thus can improve the reliability of hyperspectral image classifications. Experimental results on five real hyperspectral datasets demonstrate that CFNR can achieve superior performance compared with several state-of-the-art methods.

Economic Dispatch Optimization of a Microgrid with Wind–Photovoltaic-Load-Storage in Multiple Scenarios

The optimal economic power dispatching of a microgrid is an important part of the new power system optimization, which is of great significance to reduce energy consumption and environmental pollution. The microgrid should not only meet the basic demand of power supply but also improve the economic benefit. Considering the generation cost, the discharge cost, the power purchase cost, the electricity sales revenue, the battery charging and discharging power constraints, and the charging and discharging time constraints, a joint optimization model for a multi-scenario microgrid with wind–photovoltaic-load storage is proposed in our study. Additionally, the corresponding model solving algorithm based on particle swarm optimization is also given. In addition, taking the Wangjiazhai project in Baiyangdian region as a case study, the effectiveness of the proposed model and algorithm is verified. The joint optimization model for a microgrid with wind–photovoltaic-load storage in multiple scenarios is discussed and investigated, and the optimal economic power dispatching schemes in multiple scenarios are also provided. Our research shows that: (1) the battery can play a role in peak shaving and valley filling, which can make microgrids more economical; (2) when the power purchase price is lower than the cost of renewable energy power generation, if the wind turbine and the photovoltaics are allowed to be discarded the microgrid will produce higher economic benefits; and (3) restricting the exchange power between the microgrid and the main power network will lead to a negative impact on the economy for the microgrid.

Measurement Matrix Optimization Based on Target Prior Information for Radar Imaging

Radar target imaging can provide important information for target monitoring and recognition. In order to reduce the data amount and save radar resources, the target imaging methods based on compressed sensing have received extensive attention. In these methods, the measurement matrix will directly affect the target imaging quality and data down-sampling rate. However, most of the existing researches on measurement matrix optimization did not take into account the full use of target characteristic information and the internal consistency between the measurement matrix structure and the data sampling process, which makes it difficult to obtain the optimal imaging quality and down-sampling rate. To solve the above problems, this paper makes full use of the target imaging prior information, and takes the minimum dimension of the measurement matrix and the maximum similarity between the obtained image and expected image as the goal of measurement matrix optimization. On this basis, aims at the kind of signal which is composed of a group of sub-pulses with stepped frequency, a joint optimization model of measurement matrix and imaging performance based on the target imaging prior is established with the consideration of the 0-1 constraint of the measurement matrix structure caused by the physical observation process of the sparse stepped frequency signals. And then the corresponding sparse reconstruction algorithm is proposed. Thus, the optimal measurement matrix and high-resolution imaging results can be obtained, and the radar data down-sampling rate can be improved significantly. Simulation results indicate the effectiveness of the proposed method.

Extended belief rule-based system using bi-level joint optimization for environmental investment forecasting

Scientific investment forecasting can effectively avoid the blind investments of environmental management. Among existing studies in developing investment forecasting models, the extended belief rule-based system (EBRBS) showed its potential to accurately predict environment investments but also exposed two challenges to be further addressed: (1) how to select antecedent attributes from various environmental indicators for the EBRBS; (2) how to optimize basic parameters of the EBRBS based on the selected antecedent attributes. Since these two challenges are connected, a bi-level joint optimization model is proposed to improve the EBRBS for better environmental investment forecasting, in which the selection of antecedent attributes is described as an upper-level optimization model using Akaike information criterion (AIC) and the optimization of basic parameters is as a lower-level optimization model using mean absolute error (MAE). Moreover, a corresponding bi-level joint optimization algorithm is proposed to solve the bi-level joint optimization model, where ensemble feature selection and swarm intelligence optimization are regarded as the engine of upper-level and lower-level optimizations, respectively. The real environmental data collected from 2005 to 2020 of 30 Chinese provinces are studied to verify the effectiveness of the proposed approach. Experimental results show that the EBRBS with bi-level joint optimization not only can effectively predict environmental investments, but also is able to have desired accuracy better than previous investment forecasting models.

Joint optimization for autonomous intersection management and trajectory smoothing design with connected automated vehicles

Trajectory smoothing design (TSD) may significantly reduce fuel consumption and improve driving comfort at intersections. In this paper, a mixed integer linear programming (MILP) model with discrete time is formulated to jointly optimize autonomous intersection management and TSD, aiming to improve traffic efficiency, fuel economy and driving comfort simultaneously. Driving safety of car-following and collision avoidance at conflict points, diverge points and converge points, as well as constraints of acceleration and jerk are considered. To reasonably describe vehicle movement within intersection areas, the vehicle trajectory within the intersection is treated as a channel considering the vehicle width. A rolling horizon framework is used to solve the model. We have compared the traffic efficiency, fuel economy, monetary cost and driving comfort of the joint optimization model with that of the state-of-the-art two-stage strategy. Finally, sensitivity analysis with respect to left-turn ratio, weighted coefficient of TSD and control zone length is conducted.

Joint optimization of parcel allocation and crowd routing for crowdsourced last-mile delivery

Urban last-mile delivery providers are facing more and more challenges with the explosive development of e-commerce. The advancement of smart mobile and communication technology in recent years has stimulated the development of a new business model of city logistics, referred to as crowdsourced delivery or crowd-shipping. In this paper, we investigate a form of crowdsourced last-mile delivery that utilizes the journeys of commuters/travelers (crowd-couriers) to deliver parcels from intermediate stations to customers. We consider a logistics service provider that jointly optimizes parcel allocation to intermediate stations and the delivery routing of the crowd-couriers. The joint optimization model gives rise to a new variant of the last-mile delivery problem. We propose a data-driven column generation algorithm to solve the problem based on a set-partitioning formulation. Additionally, a rolling-horizon approach is proposed to address large-scale instances. Extensive numerical experiments are conducted to verify the efficiency of our model and solution approach, as well as the significance of the joint optimization of parcel allocation and the delivery route of the crowdsourced last-mile delivery. The results show that our data-driven column generation algorithm can obtain (near-)optimal solutions for up to 200 parcels in significantly less time than the exact algorithm. For larger instances, the combination of the data-driven column generation algorithm and the rolling-horizon approach can obtain good-quality solutions for up to 1000 parcels in 15 min. Moreover, compared with crowd-courier route optimization only, the joint optimization of parcel allocation and crowd-routing reduces the total cost by 32%.

Joint Voyage Planning and Onboard Energy Management of Hybrid Propulsion Ships

Maritime transportation decarbonization has become a crucial factor in reducing carbon emissions and mitigating climate change. As an industry that historically relies on fossil fuels, in particular, heavy fuel oil, the reinvention of the maritime transportation system is occurring at an unprecedented speed to integrate renewable and green energy, low-/zero- carbon fuels, and green infrastructure to support emission-free shipping. In this paper, a two-stage joint optimization model is proposed to optimize the voyage planning of a ship among multiple ports as well as its onboard energy management during each section of the voyage. More specifically, in the first stage, the arrival time of ships is optimized according to the mission of the ship and the electricity prices offered at each port. In the second stage, the speed of the ship, the dispatch of the onboard diesel engine, and the usage of energy storage systems (ESSs) are optimized based on emission control areas and maritime meteorological conditions. Simulation results have shown that the proposed approach would help ship operators minimize the operating cost over the whole voyage while significantly contributing to carbon emissions reduction.

Joint Optimization of Time-Dependent Line Planning and Differential Pricing with Passenger Train Choice in High-Speed Railway Networks

Line planning problems and differential pricing problems are complementary processes in the railway system, but these two problems were generally considered separate processes in most of the existing literature. This paper studies a time-dependent line planning problem and differential pricing problem with passenger train choice in high-speed railway networks under elastic origin-destination-period demand. After clearly and flexibly describing the organization cost of operators, the price cost, and the time cost of passengers in a physical infrastructure-based directed graph, a non-linear joint optimization model is designed with a diversity of optimization goals of maximizing the total revenue of railway operators minus the total travel cost of passengers. An algorithm based on a simulated annealing framework is designed to solve the joint optimization model, and six neighborhood search strategies are designed by combining the features of the studied problem and designed model closely to improve the efficiency of the solution search. The results based on both a toy railway network and a real-world railway network show that the optimized time-dependent line plan and differential price plan are beneficial to increasing the total revenue of railway operators and improving the travel service of railway passengers.

Research on the Planning of Electric Vehicle Fast Charging Stations Considering User Selection Preferences

The global energy and environmental crisis promotes the development of electric vehicles (EVs), and the rational planning of EV fast charging stations is an important influencing factor for their development. In this paper, for the EV fast charging station capacity planning problem, a joint-optimization model for optimal planning of EV fast charging stations and the economic operation of a distribution network is constructed, considering the impact of user preference selection and EV access on the regional distribution network. To address the problems of low efficiency and local convergence found in traditional heuristic optimization algorithms, an improved krill swarm optimization algorithm (CKHA) that introduces chaotic optimization parameters to make the initial population as uniformly distributed as possible is proposed to find the optimal planning scheme for EV fast charging stations. The case results show that the optimal planning model and its solution method are effective.

Research on the joint optimal allocation of hydropower, wind energy and photovoltaic power generation in the basin

To improve the constancy of hydropower, wind energy and photovoltaic hybrid power generation systems for power furnish to the grid, the joint optimal allocation of hydropower, wind, and photovoltaic is studied. Firstly, the goal of the highest load-matching degree of hydropower, wind energy, and photovoltaic power is put forward. Considering the constraints such as the maximum total power generation ability of hydropower, wind energy, and photovoltaic power generation and the water balance of the reservoir, a joint optimal allocation model of hydropower, wind energy, and photovoltaic power generation in the basin is created, and solved by a progressive optimization algorithm. An example is given to illustrate the influence of source load matching degree on optimal allocation, and the validity of this approach is verified. The simulation results indicate that during grid-connected operation, hydropower, wind energy and photovoltaic complementary power generation can ensure high electricity furnish reliability and deliver more stable power to the grid.

Vendor managed inventory system considering deteriorating items and probabilistic demand for a three-layer supply chain

This paper proposes a Vendor Managed Inventory model for a three-layer supply chain comprised of multiple suppliers, manufacturers, and retailers. During the production and storage of products, rates of defective and deteriorated products are generated, respectively. The imperfect quality and deteriorating items are removed from the lot size. The decision variables include the replenishment cycles and production rates of finished products, order times, and raw materials production rates. It is assumed that deteriorating rates are constant, and the demand rates of retailers are probabilistic. A joint optimization model is developed to maximize the benefit function of the supply chain. Cost of order, deterioration, holding, screening, production, disposal, fixed, labor, purchasing, and others. A collaborative approach is considered among the chain members where the manufacturers lead the inventory policies to avoid risks associated with deterioration items. Moreover, the Taylor series expansion approximates the exponential terms, and a solution procedure is proposed to find the optimal solutions. A case study of the Dairy Industry for highly perishable products is provided to apply the VMI model and perform a sensitivity analysis. This collaboration scheme is developed according to confidentiality policies to share information related to sales forecast, operational cost, storage strategies, quality conditions, and increased product rotation to reduce deterioration. The results show greater participation of the retailers and manufacturers in the benefits of the chain, which is consistent with the VMI policy. Finally, the conclusion, managerial implications, and future research are presented.

Intrinsic and Complete Structure Learning Based Incomplete Multiview Clustering

In the real-world, some views of samples are often missing for the collected multiview data. Faced with the incomplete multiview data, most of the existing clustering methods tended to learn a common graph from the available views, where the hidden information of the absent views was ignored. Furthermore, some methods filled the absent instances with the average vector of the available samples for each view, which could not reflect a real distribution of the data. To solve these problems, in this paper an intrinsic and complete structure learning based incomplete multiview clustering method (ICSL_IMC) is proposed. Firstly, we calculate the initial complete graphs for all views by exploring the available incomplete graphs, which are further taken as the constraints for the reconstruction of the absent data integrating the self-representation method. Afterwards, encouraged by the complete multiview data, a complete structure inferring strategy is proposed to learn the intrinsic and complete structures for all views, such that the real distribution of the absent instances can be reflected in the completed structure of each view. We integrate these three learning phases into a joint optimization model, which can promote each other in the iterative learning procedure, simultaneously. Comparing with the other state-of-the-art methods, the proposed ICSL_IMC can achieve the best performances on different databases.