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The aim of this research is to propose a buffer sizing and buffer controlling algorithm (BSCA) as a heuristic algorithm for calculating project buffer and feeding buffers as well as dynamic controlling of buffer consumption in different phases of a wind power plant project in order to achieve a more realistic project duration.

The BSCA algorithm has two main phases of planning and buffer sizing and construction and buffer consumption. Project buffer and feeding buffers are determined in the planning and buffer sizing phase, and their consumption is controlled in the construction and buffer consumption phase. The heuristic algorithm was coded and run in MATLAB software. The sensitivity analysis was conducted to show the BSCA influence on project implementation. Then, to evaluate the BSCA algorithm, inputs from this project were run through several algorithms recently presented by researchers. Finally, the data of 20 projects previously accomplished by the company were applied to compare the proposed algorithm.

The results show that BSCA heuristic algorithm outperformed the other algorithms as it shortened the projects' durations. The average project completion time using the BSCA algorithm was reduced by about 15% compared to the previous average project completion time.

The proposed BSCA algorithm determines both the project buffer and feeding buffers and simultaneously controls their consumption in a dynamic way.

The purpose of this paper is to present an efficient model for project buffer sizing by taking failure mode and effects analysis (FMEA) into account to reach a more realistic schedule.

In the first phase of the project, several turbines were installed according to the primary schedule with an average duration of 142 days. Then, some of critical chain project management algorithms were separately applied in the implementation and installation of the other wind turbines. The adaptive procedure with resource tightness (APRT) method turned out to be the best method in terms of obtaining a more realistic schedule in this case study. Finally, FMEA was simultaneously applied with APRT.

Applying the hybrid method to the scheduling of the wind turbines, yielded the more realistic schedule than traditional.

The proposed hybrid APRT-FMEA algorithm was implemented on a real wind farm construction project which was completed with 37 percent shorter duration than the initial estimation; in spite of the initial estimation of 142 days, the project completed in 103 days.

Introducing and implementing a new algorithm which is a combination of buffer sizing algorithms and one of the well-known and mostly used risk assessment methods in order to provide the more realistic project schedule in the construction of wind turbines.

Introducing and implementing a novel algorithm which is a combination of conventional buffer sizing method and one of the efficient risk assessment methods in order to make the schedule more realistic.

The assorted piece-wise retail orders in a cosmetics warehouse are fulfilled through a separate fast-picking area called Forward Buffer (FB). This study determines “just-right” size of FB to ensure desired Customer Service Level (CSL) at least storage wastages. It also investigates the impact of FB capacity and demand variations on FB leanness.

A Value Stream Mapping (VSM) tool is applied to analyse the warehouse activities and mathematical model is implemented in MATLAB to quantify the leanness at desired CSL. A comprehensive framework is developed to determine lean FB buffer size for a Retail Distribution Centre (RDC) of a cosmetics industry.

The CSL increases monotonically; however, the results concerning spent efforts towards CSL improvement gets diminished with raised demand variances. The desired CSL can be achieved at least FB capacity and fewer Storage Waste (SW) as it shifts towards more lean system regime. It is not possible to improve Value Added (VA) time beyond certain constraints and therefore, it is recommended to reduce Non-Value Added (NVA) order processing activities to improve leanness.

This study determines “just-right” capacity and investigates the impact of buffer and demand variations on leanness. It helps managers to analyse warehouse processes and design customized distribution policies in food, beverage and retail grocery warehouse.

Proposed buffering model offers customized strategies beyond pre-set CSL by varying it dynamically to reduce wastages. The mathematical model deriving lean sizing and mitigation guidelines are constructive development for managers.

This research provides an inventive approach of VSM model and Mathematical algorithm endorsing lean thinking to design effective buffering policies in a forward warehouse.

This study aims to introduce an efficient project buffer and resource management (PBRM) model for project resource leveling and project buffer sizing and controlling of project buffer consumption of a wind power plant project to achieve a more realistic project duration.

The methodology of this research consists of three main phases. In the first phase of the research methodology, resource leveling is done in the project and resource conflicts of activities are identified. In the second phase, the project critical chain is determined, and the appropriate size of the project buffer is specified. In the third phase of the methodology, buffer consumption is controlled and monitored during the project implementation. After using the PBRM method, the results of this project were compared with those of the previous projects.

According to the obtained results, it can be concluded that using PBRM model in this wind turbine project construction, the project duration became 25 per cent shorter than the scheduled duration and also 29 per cent shorter than average duration of previous similar projects.

One of the major problems with projects is that they are not completed according to schedule, and this creates time delays and losses in the implementation of projects. Today, as projects in the energy sector, especially renewable projects, are on the increase and also we are facing resource constraint in the implementation of projects, using scheduling techniques to minimize delays and obtain more realistic project duration is necessary.

This research was carried out in a wind farm project. In spite of the initial plan duration of 142 days and average duration of previous similar projects of 146 days, the project was completed in 113 days.

This paper introduces a practical project buffer and resource management model for project resource leveling, project buffer sizing and buffer consumption monitoring to reach a more realistic schedule in energy sector. This study adds to the literature by proposing the PBRM model in renewable energy sector.

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.

The effectiveness of alternative buffering strategies in complex multilevel assembly manufacturing systems using the Material Requirement Planning (MRP) methodology is explored and assessed. The safety stock, safety lead time, “hard” safety capacity, and forecast inflation buffering strategies are tested under uncertainties of end‐item demand, resource supply, and task control. The MRP methodology is applied for scheduling along with a complex, realistic simulation model for execution of operations. Average inventory levels for end and component items, capacity utilisation, end‐item backorders and customer undersupport are used as performance measures. Experimental results establish safety stock and “hard” safety capacity as dominant buffering strategies under all uncertainty conditions, and task control is shown as the most disruptive uncertainty source.

This paper aims to obtain the optimal wire sizing of buffered global interconnects and to investigate the impact of weight factor on the optimized system performance for various technology nodes.

The width and spacing of interconnects are optimized under two scenarios, and corresponding optimum line width is determined by minimizing the value of power‐delay product which is defined as a figure of merit (FOM). Based on the results, the impact of weight factor on the optimized system performance, such as delay and power dissipation per unit length, is analyzed for various technology nodes.

The analytical expressions of the optimum width are derived under two scenarios. Better FOMs can be achieved for the S=W scenario, but the wireability of the chip degrades considerably. The optimized delay increases with the increasing of weight factor, while the optimized power dissipation decreases with it. For a given weight factor, smaller latency and less power dissipation can be achieved for the S=W case.

The analytical expressions of the optimum width of interconnects are given, and a comprehensive study of the impact of weight factor on the optimized results under two scenarios is presented.

The purpose of this study is to develop a joint production, maintenance and quality control strategy involving a periodic preventive maintenance policy.

The proposed integrated policy is defined and modeled mathematically.

The paper focuses on finding simultaneously the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval and the control chart limits, such that the expected total cost per time unit is minimized.

The paper attempts to integrate in a single model the three main aspects of any manufacturing system: production, maintenance and quality. The considered system consists of one machine subject to a degradation process that directly affects the quality of products. The process and product quality control is carried out using an “x-bar” control chart. In the proposed model, a preventive maintenance action is performed every α inspections of product quality in order to reduce the shift rate to the “out-of-control” state. A corrective maintenance action is undertaken once the control limits are exceeded. In order to palliate perturbations caused by the stopping of the machine to undergo maintenance actions, a buffer stock is built up to ensure the continuous supply of the subsequent machine. The main goal of this work is to develop a model that captures the underlying link between the preventive maintenance policy, the buffer stock size and the parameters of an “x-bar” control chart used to control the quality of the product. Numerical experiments and a study of the effects of the input parameters variation on the obtained results are performed.

The existing models that simultaneously consider maintenance, inventory and control charts consist of a condition-based maintenance (CBM) policy. Periodic preventive maintenance (PM) has not been considered in such models. The proposed integrated model is original, in that it links production through buffer stocks, quality through a control chart and maintenance through periodic preventive maintenance (different practical settings and modeling approach than when CBM is used). Hence, this paper addresses practical situations where, for economic or technical reasons, only systematic periodic preventive maintenance is possible.

A variety of buffer allocation methods exist to distribute an aggregated time buffer among project activities. However, these methods do not pay simultaneous attention to two key attributes of disruptive events that may occur during the construction phase: probability and impact. This paper fills this research gap by developing a buffer allocation method that takes into account the synergistic impact of these two attributes on project activities.

This paper develops a three-step method, calculating the probability that project activities are disrupted in the first step, followed by measuring the potential impact of disruption on project activities, and then proposing a risk-informed buffer allocation index by simultaneously integrating probability and impact outputs from the first two steps.

The proposed method provides more accurate results by sidestepping the shortcomings of conventional fuzzy-based and simulation-based methods that are purely based on expert judgments or historical precedence. Further, the paper provides decision-makers with a buffer allocation method that helps in developing cost-effective buffering and backup strategies by prioritizing project activities and their required resources.

This paper develops a risk-informed buffer allocation method that differs from those already available. The simultaneous pursuit of the probability and impact of disruptions distinguishes our method from conventional buffer allocation methods. Further, this paper intertwines the research domains of complexity science and construction management by performing centrality analysis and incorporating a key attribute of project complexity (i.e. the interconnectedness between project activities) into the process for buffer allocation.

This simulation study explores and compares the potential benefits of three work‐in‐process (WIP) inventory drive systems and their associated inventory buffer characteristics. The three inventory drives are a push, a pull and a hybrid push/pull system. While these systems have some aspects in common, their buffer management systems vary. The statistical analysis associated with the study was based on data gathered from three computer simulated flow‐shop assembly line environments. Hypotheses concerning the financial performance measurements were established. The independent variables were controlled and manipulated for each of the models. From the statistical analysis, a conclusion was drawn as to which system would afford the operation optimum results. While inventory has traditionally been considered and is currently being shown as an asset from an accounting point of view, it is obvious from the findings of this study, that excess WIP inventory, above the minimal requirements for production, will have a negative effect on the financial measurements evaluated in this study.