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This paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been formulated to minimize the number of EVs and the maximum handling time by specifying the EVs and sequence of all the delivery tasks.

First, a mathematical programming model of point-to-point part feeding scheduling problem (PTPPFSP) with EVs is presented. Because the PTPPFSP is NP-hard, an improved multi-objective cuckoo search (IMCS) algorithm is developed with novel search strategies, possessing the self-adaptive Levy flights, the Gaussian mutation and elite selection strategy to strengthen the algorithm’s optimization performance. In addition, two local search operators are designed for deep optimization. The effectiveness of the IMCS algorithm is verified by dealing with the PTPPFSP in different problem scales.

Numerical experiments are used to demonstrate how the IMCS algorithm serves as an efficient method to solve the PTPPFSP with EVs. The effectiveness and feasibility of the IMCS algorithm are validated by approximate Pareto fronts obtained from the instances of different problem scales. The computational results show that the IMCS algorithm can achieve better performance than the other high-performing algorithms in terms of solution quality, convergence and diversity.

This study is applicable without regard to the breakdown of EVs. The current research contributes to the scheduling of in-plant logistics for automotive assembly lines, and it could be modified to cope with similar part feeding scheduling problems characterized by just-in-time (JIT) delivery.

Both limited electricity capacity and no earliness and tardiness constraints are considered, and the scheduling problem is solved satisfactorily and innovatively for an efficient JIT part feeding with EVs applied to in-plant logistics.

The purpose of this paper is to cut down energy consumption and eliminate production waste on mixed-model assembly lines. Therefore, a supermarket integrated dynamic cyclic kitting system with the application of electric vehicles (EVs) is introduced. The system resorts to just-in-time (JIT) and segmented sub-line assignment strategies, with the objectives of minimizing line-side inventory and energy consumption.

Hybrid opposition-based learning and variable neighborhood search (HOVMQPSO), a multi-objective meta-heuristics algorithm based on quantum particle swarm optimization is proposed, which hybridizes opposition-based learning methodology as well as a variable neighborhood search mechanism. Such algorithm extends the search space and is capable of obtaining more high-quality solutions.

Computational experiments demonstrated the outstanding performance of HOVQMPSO in solving the proposed part-feeding problem over the two benchmark algorithms non-dominated sorting genetic algorithm-II and quantum-behaved multi-objective particle swarm optimization. Additionally, using modified real-life assembly data, case studies are carried out, which imply HOVQMPSO of having good stability and great competitiveness in scheduling problems.

The feeding problem is based on static settings in a stable manufacturing system with determined material requirements, without considering the occurrence of uncertain incidents. Current study contributes to assembly line feeding with EV assignment and could be modified to allow cooperation between EVs.

The dynamic cyclic kitting problem with sub-line assignment applying EVs and supermarkets is solved by an innovative HOVMQPSO, providing both novel part-feeding strategy and effective intelligent algorithm for industrial engineering.

This paper aims to investigate a multi-objective electric vehicle’s (EV’s) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism to coordinate multiple EVs is proposed to fulfill part feeding tasks.

A chaotic reference-guided multi-objective evolutionary algorithm based on self-adaptive local search (CRMSL) is constructed to deal with the problem. The proposed CRMSL benefits from the combination of reference vectors guided evolutionary algorithm (RVEA) and chaotic search. A novel directional rank sorting procedure and a self-adaptive energy-efficient local search strategy are then incorporated into the framework of the CRMSL to obtain satisfactory computational performance.

The involvement of the chaotic search and self-adaptive energy-efficient local search strategy contributes to obtaining a stronger global and local search capability. The computational results demonstrate that the CRMSL achieves better performance than the other two well-known benchmark algorithms in terms of four performance metrics, which is inspiring for future researches on energy-efficient co-scheduling topics in manufacturing industries.

This research fully considers the cooperation and coordination of handling devices to reduce energy consumption, and an improved multi-objective evolutionary algorithm is creatively applied to solve the proposed engineering problem.

Construction projects are complex projects taking place in dynamic environments, which necessitates accounting for different uncertainties during the planning stage. There is a significant lack of management tools for repetitive projects accounting for uncertainties in the construction environment. The purpose of this paper is to present an algorithm for the optimized scheduling of repetitive construction projects under uncertainty.

Fuzzy set theory is utilized to model uncertainties associated with various input parameters. The developed algorithm has two main components: optimization component and buffering component. The optimization component presents a dynamic programming approach that processes fuzzy numbers. The buffering component converts the optimized fuzzy schedule into a deterministic schedule and inserts time buffers to protect the schedule against anticipated delays. Agreement Index (AI) is used to capture the user’s desired level of confidence in the produced schedule while sizing buffers. The algorithm is capable of optimizing for cost or time objectives. An example project drawn from literature is analysed to demonstrate the capabilities of the developed algorithm and to allow comparison of results to those previously generated.

Testing the algorithm revealed several findings. Fuzzy numbers can be utilized to capture uncertainty in various inputs without the need for historical data. The modified algorithm is capable of optimizing schedules, for different objectives, under uncertainty. Finally AI can be used to capture users’ desired confidence in the final schedule.

Project planners can utilize this algorithm to optimize repetitive projects schedules, while modelling uncertainty in different input parameters, without the need for relevant historical data.

Earned Value Management (EVM) is widely used as a project performance measurement and forecasting technique. Nonetheless, it has not been fully explored in Pakistani construction industry; where conventional progress reporting methodology (CPRM) is being followed having certain confines. It reports only the financial progress of a project, expresses feeble association between the duration and cost of activities, and forecasts flawed schedule and completion cost. This research implements EVM on under-construction building projects in Pakistan, and compares its upshots with the projects' actual records and with the outcomes of CPRM.

To assess the implementation of EVM on building projects, a set of specific criteria was established. Work Breakdown Structure, Organization Breakdown Structure and Control Points were established. The study has compared the EVM metrics with CPRM outcomes on three under-study building projects, and has deliberated on their mutual differences as well as their relationship with actual cost and schedule performance. Monthly figures of actual spending and completed activities were periodically recorded and compared with planned values for status indication. The graphs were generated to observe the correlation between the results of EVM and CPRM. The data was then extrapolated to forecast the schedule and cost values at completion.

The study discovered that trends of EVM in quantifying the project's cost and schedule performance were strongly correlated and were closer to the actual progress. It has also verified the EVM's soundness in forecasting the cost and schedule, required for project's completion. Contrarily, CPRM metrics could not precisely visualize the current and future, cost and schedule performance.

The case study concludes that EVM's incorporation in progress reporting regime can revolutionize the assessment procedures in Pakistan by rightly indicating the project's current status as well as visualizing the future performance. The study's methodology can also be extrapolated in other countries having similar work environment and economic conditions.

There are many potential measures of performance for evaluating the success of a construction project. All address performance in three key areas: scope, schedule and budget. In a performance measurement framework where senior management wishes to minimise the numberof performance measures it employs, while ensuring maximal coverage or visibility into the programme, having a tool that captures each of the three areas would be ideal. Project managers have used Earned Value Management (EVM) for over 40 years to track actual schedule progress and actual costs against project plans. Earned Value Management has traditionally been applied to individual projects on which the manager is accountable for both schedule and cost variances. This paper proposes methods to apply EVM principles to allow: (1) analysis of portfolios of construction projects; (2) incorporation of the analysis into an innovative pay‐for‐performance human resources practice; and (3) use of regression analysis to develop baseline earned value curves. These extensions fit the needs of many government managers, who oversee a range of projects by multiple contractors, and whose cost risk is primarily due to schedule slips and change orders. Earned Value Management is described in the context of project oversight, and a dashboard system of performance measures is proposed for quickly assessing individual projects and portfolios. A method of generating standardised planned value curves is then specified, based on data from previous and ongoing projects. The paper concludes by showing how the US General Services Administration’s Public Buildings Service is using these methods to analyse and oversee its portfolio of new construction and major repair and alteration projects.

The purpose of this paper is to propose an integrated earned value management (EVM) approach to control quality, cost, schedule and risk of projects.

This study represents a new EVM framework by considering a quality control index. Particularly, some control indices and cumulative buffers are defined by two proposed, methods, namely the linear- and Taguchi-based methods. These methods are implemented in three different projects in different industries.

According to the results, integration of the quality index creates a better control situation by providing more accurate information. Hence, project managers could comprehensively monitor the status of important factors to make more precise decisions while maintaining the simplicity of their analysis.

From the methodological and theoretical features, this paper offers new visions because, to the best of authors’ knowledge, no comparable study has been conducted before.

The purpose of this paper is to assess different labor hiring policies for construction projects using system dynamics (SD) which have a considerable impact on project performance. Time intervals and work crew composition are two such policies. Through the implementation of a variety of policies, a managerial opportunity presents itself for the effective allocation of human resources and improvement in project performance.

The study developed a dynamic model to assess different labor hiring policies using SD based on literature. To further distinguish between findings, the effects of the applied policies on performance were considered using earned value management. Based on a real case for validating the model, the paper discusses the potential benefits of the model, including: having a systematic and holistic view, considering dynamic the labor need and allocation, identifying alternative strategies for performance improvement and simulating the reality of the projects in a virtual model.

The achieved simulation results show how different hiring policies affect project performance. This research model can aid decision makers to assess labor hiring policies in various time intervals with different compositions and assist them in selecting the best policies for effective implementation of project.

The proposed model would be a major attempt using SD to model labor hiring policies more accurate in construction projects performance. In fact, an accurate estimate of labor needed, along with the proper planning and implementing of various labor hiring policies, presents a managerial opportunity whereby the effective allocation of workforces can be optimized leading to drastic improvement in project performance.

The fluctuations that occurred between the power requirements have shown a higher range of voltage regulations and frequency. The fluctuations are caused because of substantial changes in the energy dissipation. The operational efficiency has been reduced when the power grid is enabled with the help of electric vehicles (EVs) that were created by the power resources. The model showed an active load matching for regulating the power and there occurred a harmonic motion in energy. The main purpose of the proposed research is to handle the energy sources for stabilization which has increased the reliability and improved the power efficiency. This study or paper aims to elaborate the security and privacy challenges present in the vehicle 2 grid (V2G) network and their impact with grid resilience.

The smart framework is proposed which works based on Internet of Things and edge computations that managed to perform an effective V2G operation. Thus, an optimum model for scheduling the charge is designed on each EV to maximize the number of users and selecting the best EV using the proposed ant colony optimization (ACO). At the first, the constructive phase of ACO where the ants in the colony generate the feasible solutions. The constructive phase with local search generates an ACO algorithm that uses the heterogeneous colony of ants and finds effectively the best-known solutions widely to overcome the problem.

The results obtained by the existing in-circuit serial programming-plug-in electric vehicles model in terms of power usage ranged from 0.94 to 0.96 kWh which was lower when compared to the proposed ACO that showed power usage of 0.995 to 0.939 kWh, respectively, with time. The results showed that the energy aware routed with ACO provided feasible routing solutions for the source node that provided the sensor network at its lifetime and security at the time of authentication.

The proposed ACO is aware of energy routing protocol that has been analyzed and compared with the energy utilization with respect to the sensor area network which uses power resources effectively.

The objective of this paper is to develop a tracking and control system that automates the process of data collection from construction sites for fast and accurate measurement of work progress.

The proposed system integrates different data acquisition hardware and software technologies including barcoding, radio frequency identification (RFID), laser distance and ranging (LADAR), digital images, and a tablet PC as integrating media.

The paper briefly highlights the advantages and limitations associated with each technology, and presents a methodology that best utilizes these technologies in an integrated system. At the core of the developed system is its database, which is designed to organize and store data collected from construction sites in a way that supports the developed methodology in progress reporting.

The accuracy and timeliness of these reports are crucial for management teams to take corrective actions, if needed, so as to assist in delivering projects on time and within budget.

The paper presents the layout of an IT platform designed to facilitate automated data acquisition from construction sites to support efficient time and cost tracking and control of construction projects. The system presented is capable of capturing text, numerical and graphical data to report efficiently on the progress of a project.