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Optimization of Labor Flow Efficiency in Steel Fabrication Project Planning
This research investigates resource inter-activity utilization efficiency arising from resource shared use and transfer observed in planning projects at a structural steel fabrication shop. Aided with discrete event simulation-based resource-constrained scheduling analysis, the necessity of defining the resource inter-activity utilization efficiency factor is justified in project scheduling and budgeting in practice. The Simplified Scheduling Simulation (S3) platform based on the simplified discrete-event simulation approach (SDESA) is utilized to create a resource loaded schedule for a simple project case in order to facilitate analyzing resource inter-activity utilization efficiency. Further, the problem of how to optimize labor inter-activity utilization efficiency is formulated conceptually, which is vitally important to project cost budgeting and earned value management in applications resembling operating a major steel fabrication facility.
The construction industry is considered among the largest consumers of natural resources (non-renewable materials, fossil fuels, water...). It is also an important source of generation of solid waste and greenhouse gas emissions. In addition to its negative impacts on the environment, most construction projects are characterized by the non-respect of the triptych (Cost, Time, Quality) and a high accident rate compared to other sectors. Lean construction (LC) is a new production philosophy which has the potential of bringing innovative improvements in the construction sector. It is a systemic approach to meeting customer expectations by maximizing added value and reducing all forms of waste. Based on international standards (AFNOR, GRI, UNEP, ISO 26000...) and recent researches published in the most reliable databases, this study aims at exploring the concept of sustainable development in the context of the construction industry and examines how the LC tools (Prefabrication, Value Stream Mapping, Poka-Yoke, visual Management, and 5S) can have an impact on the three dimensions of sustainable development (environment, economy, society). This work brings a new reflection by constructing an interaction matrix between the Lean Construction tools and sustainable development.
Resource allocation and leveling are among the top challenges in project management. Due to the complexity of projects, resource allocation and leveling have been dealt with as two distinct subproblems solved mainly using heuristic procedures that cannot guarantee optimum solutions. In this paper, improvements are proposed to resource allocation and leveling heuristics, and the Genetic Algorithms (GAs) technique is used to search for near-optimum solution, considering both aspects simultaneously. In the improved heuristics, random priorities are introduced into selected tasks and their impact on the schedule is monitored. The GA procedure then searches for an optimum set of tasks' priorities that produces shorter project duration and better-leveled resource profiles. One major advantage of the procedure is its simple applicability within commercial project management software systems to improve their performance. With a widely used system as an example, a macro program is written to automate the GA procedure. A case study is presented and several experiments conducted to demonstrate the multiobjective benefit of the procedure and outline future extensions.
Effective production control systems are structured around the assignment as the unit of analysis. The quality of work assignments to production units such as construction crews and engineering squads is the key to production control and a key factor determining production unit productivity. Research has revealed that the quality of assignments can be substantially improved by forming and selecting them to meet soundness, sequence, and size criteria. Making quality assignments shields production units from work flow uncertainty, enabling those units to improve their own productivity, and also to improve the productivity of the production units downstream. The associated reduction in task duration can shorten projects. Further reduction of project duration comes from reducing the buffers previously needed to accommodate flow uncertainty.
Traditional time-cost trade-off analysis assumes that the time and cost of an option within an activity are deterministic. However, in reality the time and cost are uncertain. Therefore, in analyzing the time-cost trade-off problem, uncertainties should be considered when minimizing project duration or cost. Simulation techniques are useful for analyzing stochastic effects, but a general strategy/algorithm is needed to guide the analysis to obtain optimal solutions. This paper presents a hybrid approach that combines simulation techniques and genetic algorithms to solve the time-cost trade-off problem under uncertainty. The results show that genetic algorithms can be integrated with simulation techniques to provide an efficient and practical means of obtaining optimal project schedules while assessing the associated risks in terms of time and cost of a construction project. This new approach provides construction engineers with a new way of analyzing construction time/cost decisions in a more realistic manner. Historical time/cost data and available options to complete a project can be modeled, so that construction engineers can identify the best strategies to take to complete the project at minimum time and cost. Also, what-if scenarios can be explored to decide the desired/optimal time and/or cost in planning and executing project activities.
Looks at a broad range of topics related to the processes of design and construction. Its overall aim is to look at ways that clients can improve the value for money outcomes of their decisions to construct buildings.
The arguments in favour of introducing shift work clearly depend on productivity and safety being maintained at an acceptable level. However, the evidence reviewed in this paper clearly indicates that both productivity and safety may be compromised at night. More specifically, safety declines over successive night shifts, with increasing hours on duty and between successive rest breaks. The only known way to minimize these problems is to improve shift systems with respect to these factors. However, these factors need to be considered in combination with one another since, for example, a long night shift that includes frequent rest breaks might well prove safer than a shorter night shift with less frequent breaks.
The complexity and uncertain nature of bridge construction projects require simulation for analyzing and planning these projects. On the other hand, optimization can be used to address the inverse relationship between the cost and time of a project and to find a proper trade-off between these two key elements. In addition, the large number of resources required in large-scale bridge construction projects results in a very large search space. Therefore, there is a need for using parallel computing to reduce the computational time of the simulation-based optimization problem. Another problem in this area is that most of the construction simulation tools need an integration platform to be combined with optimization techniques. To alleviate these limitations, an integrated simulation-based optimization framework is developed within one High Performance Computing (HPC) platform, and its performance is analyzed by carrying out a case study. A master-slave (or global) parallel Genetic Algorithm (GA) is used to decrease the computation time and to efficiently use the full capacity of the computer. In addition, sensitivity analysis is applied to identify the promising configuration for GA and the best number of cores used in parallel and to analyze the impact of GA parameters on the overall performance of the simulation-based optimization model. Using NSGA-ΙΙ as the optimization algorithm resulted in better near-optimal solutions compared to those of fast-messy GA. Moreover, performing the proposed framework on multiple nodes using the cluster system led to 31% saving in the computation time on average. Furthermore, the GA was tuned using sensitivity analyses, which resulted in the selection of the best parameters of the GA.
Measuring and improving productivity in construction is a complex issue. This paper introduces the development of an internal Benchmarking and Metrics (BM&M) model for industrial construction enterprises to help them understand and implement mechanisms for continuously improving construction productivity. Processes are developed in the model for: (1) measuring and reporting craft labor productivity performance; (2) examining productivity influencing factors with respect to construction environment factors and construction practices implementation; (3) establishing a productivity performance evaluation model to understand mechanisms by which the environment factors and construction practices impact productivity, and (4) conducting strategic gaps analysis of construction practices implementation. The model is validated on data collected by the designed BM&M process from an industrial construction company, which implemented the model. In conclusion, the model can be effectively used to understand the impact of practices implementation levels on productivity. This contributes knowledge towards implementation of productivity improvement methods in a company.
Purpose
The main purpose of this study is to evaluate the waste occurrence level in the construction industry. It includes: (1) identifying the mean value of frequency of waste occurrence according to respondents’ characteristics; (2) identifying the main predictive factors for waste occurrence based on latent relationships between initial waste factors; and (3) identifying the waste occurrence level indicator (WOLI) for the construction industry based on the main waste measurement factors.
Design/methodology/approach
A total of nineteen waste factors were sorted from the literature review. A structured questionnaire was adopted to carry out the survey. The respondents are been professionals who have much experience in construction and management of project. Shapiro-Wilk test of normality, Levene's test, ANOVA test, and factor analysis technique were used to analyze the collected data.
Findings
• Frequency of waste occurrence in construction projects is quite high. • There was no statistically and practically significant difference in means for waste occurrence between selected population categories. • Based on factor analysis technique, there were five principal components extracted with 56.7% of total variance. • The waste occurrence level indicator (WOLI) in the construction industry was found as 61.55 per the scale of 100.
Research limitations/implications
• The non-probability sampling was applied to collect data because of several certain limitations and difficulties.
• The number of data sets is relatively small.
• This study has only examined the frequency of waste occurrence without quantitative information.
Practical implications
• This is another study of waste factors in the construction industry, which is different from traditional waste studies.
Originality/value
The contribution of this study to the practical project management is that a proposed evaluation sheet for WOLI could be applied for any construction firm.
Given the characteristics of linear construction projects, considerable research has been devoted to work productivity and continuity. However, most of these studies only consider single-skilled crews working on linear projects, neglecting the flexibility of multi-skilling in construction. Recently, researchers have revealed that multi-skilling in construction can lead to increased productivity, flexibility, and work continuity. Therefore, this study proposes a duration optimization model while introducing the concept of multi-skilling to integrate single/multiple-skilled crews to improve work performance. Moreover, to enhance the efficiency of problem solving, constraint programming (CP) is used to handle complicated combinatorial scheduling problems, and several heuristic rules involving schedules are engaged. The CP-based optimization minimizes project duration while two crew types, including single-skilled and multi-skilled crews, are integrated in the proposed model. A bridge example is used for several scenarios to illustrate the proposed model's handling of linear scheduling problems, demonstrating how multi-skilling can be used to optimize project duration and improve work continuity/efficiency in a construction context.
This paper presents a new approach for resource optimization by combining a flow-chart based simulation tool with a powerful genetic optimization procedure. The proposed approach determines the least costly, and most productive, amount of resources that achieve the highest benefit/cost ratio in individual construction operations. To further incorporate resource optimization into construction planning, various genetic algorithms (GA)-optimized simulation models are integrated with commonly used project management software. Accordingly, these models are activated from within the scheduling software to optimize the plan. The result is a hierarchical work-breakdown-structure tied to GA-optimized simulation models. Various optimization experiments with a prototype system on two case studies revealed its ability to optimize resources within the real-life constraints set in the simulation models. The prototype is easy to use and can be used on large size projects. Based on this research, computer simulation and genetic algorithms can be an effective combination with great potential for improving productivity and saving construction time and cost.
Variability is common on construction projects and must be managed effectively. New management thinking, like that of lean production, has suggested that better labor and cost performance can be achieved by reducing output variability. Efforts to utilize lean thinking in construction, so far, have generated limited evidence to support this claim. This paper investigates the relationship between variability and project performance to test the notion that reducing output variability will result in improved labor performance. Using productivity data from concrete formwork activities on multiple projects, various measures of output variability are tested against construction performance. It is shown that variability in output is inevitable and that there is little correlation between output variability and project performance, but that variability in labor productivity is closely correlated to project performance. It is concluded that lean improvement initiatives should be redirected to adaptable workforce management capabilities to reduce variability in labor productivity instead of output in order to improve project performance.
Construction industry craft divisions in the United States are currently based on narrowly defined skill groupings. The steady demand for improved productivity and the shortage of skilled craft workers call into question this traditionally accepted single-skilled strategy. While these craft patterns are prevalent throughout the union and nonunionized sectors of the industry, they are not necessarily responsive to construction sequence or the optimal use of worker skills. Alternative labor utilization strategies may provide increased project performance and may reduce craft shortages through better utilization of the existing workforce. An analysis model is developed in this study to measure the project impact of alternative labor utilization strategies. The model is used to assess four multiskilling strategies on the construction of a $70,000,000 project. Benefits of multiskilled labor utilization strategies were demonstrated including potentially a 5-20% labor cost savings, a 35% reduction in required workforce, a 47% increase in average employment duration, and an increase in earning potential for multiskilled construction workers.
A new approach for resource scheduling using genetic algorithms (GAs) is presented here. The methodology does not depend on any set of heuristic rules. Instead, its strength lies in the selection and recombination tasks of the GA to learn the domain of the specific project network. By this it is able to evolve improved schedules with respect to the objective function. Further, the model is general enough to encompass both resource leveling and limited resource allocation problems unlike existing methods, which are class-dependent. In this paper, the design and mechanisms of the model are described. Case studies with standard test problems are presented to demonstrate the performance of the GA-scheduler when compared against heuristic methods under various resource availability profiles. Results obtained with the proposed model do not indicate an exponential growth in the computational time required for larger problems.
New management thinking, like that of lean production, has suggested that better labor and cost performance can be achieved by improving the reliability of flows. In this context, lean thinking portrays reliable flows as the timely availability of resources, i.e., materials, information, and equipment. Little attention has been given to labor as a flow. Further, little discussion can be found related to flexible capacity management strategies. Efforts to utilize lean thinking in construction, so far, have generated limited evidence to support the need for more reliable labor flows. This paper investigates the lean principle that more reliable flows lead to better labor performance. Actual data from three bridge construction projects are examined to document the instances of poor flow (resource) reliability and its effect on labor performance. Inefficient labor hours are calculated. The results show that there is strong support that more reliable material, information, and equipment availability contributes to better performance. However, the projects showed considerable deficiencies in the utilization of the labor resource. It is concluded that lean improvement initiatives should focus more on workforce management strategies to improve labor utilization that will lead to better labor performance.
This paper presents a mathematical model for resource scheduling considering project scheduling characteristics generally ignored in prior research, including precedence relationships, multiple crew-strategies, and time cost trade-off. Previous resource scheduling formulations have traditionally focused on project duration minimization. The proposed model considers the total project cost minimization. Furthermore, resource leveling and resource-constrained scheduling have traditionally been solved independently. In the new formulation, resource leveling and resource-constrained scheduling are performed simultaneously. The proposed model is solved using the patented neural dynamics model of Adeli and Park. A case study is presented to demonstrate the performance of the method under various resource availability profiles.
Optimizing resource utilization can lead to significant reduction in the duration and cost of re- petitive construction projects such as highways, high-rise buildings, and housing projects. This can be achieved by identifying an optimum crew size and interruption strategy for each activity in the project. Available dynamic programming formulations can be applied to provide solutions for this optimization problem; however, their application is limited, as they require planners to specify an arbitrary and an unbounded set of interruption options prior to scheduling. Such a requirement is not practical and may render the optimization problem infeasible. To circumvent the limitations of available formulations, this paper presents an automated and practical optimization model. The model utilizes dynamic programming formulation and incorporates a scheduling al- gorithm and an interruption algorithm so as to automate the generation of interruptions during scheduling. This transforms the consideration of interruption options, in optimizing resource utilization, from an unbounded and impractical problem to a bounded and feasible one. A numerical example from the literature is analyzed to illustrate the use and capabilities of the model.
Economists have been saying it so have constructors, organized labour - everybody: to remain competitive, we have to procedure more for each dollar spent on construction. And "we" is everybody - every worker at a job site can contribute to improved productivity. Productivity issues can be divided into macro- and micro-level. At the macro-level, one deals with contracting methods, labour legislation, and labour organization; at the micro-level, with the management and operation of a project, mainly at the job site. Les économistes l'ont constaté, et il en va de même pour les entrepreneurs en construction, les mouvements syndicaux, etc. : pour demeurer compétitifs, il nous faut produire davantage pour chaque dollar dépensé à construire. Et ce « nous » inclut tous les intervenants; chaque travailleur sur le chantier peut en effet contribuer à améliorer la productivité. On peut répartir les questions liées à la productivité selon qu'elles relèvent du macro-niveau, soit ce qui vise les méthodes de passation de marché, la législation du travail et l'organisation syndicale, ou du micro-niveau, soit ce qui concerne la gestion et l'exploitation d'un projet, principalement sur le chantier. RES
The impact of frequent short rest breaks on the productivity and well being of a group of 30 workers in a meat-processing plant was studied. Two rest break schedules were tested, both of which provided 36 min of extra break time over the regular break schedule (30-min lunch and two 15-min breaks). In the first experimental rest break schedule, workers were given 12 3-min breaks evenly distributed over the workday (3-min break for every 27 min of work). In the second schedule, workers were given four 9-min breaks evenly distributed over the workday (9-min break every 51 min of work). Outcome measures included production rate and discomfort and stress ratings. Results showed that neither of the two experimental rest break schedules had a negative effect on production, and the 9-min break schedule improved discomfort ratings for the lower extremities. The workers in the study mostly preferred the 9-min rest break schedule, indicating that workers in general might not as readily accept fragmentation of break time into short, frequent breaks.
“Assignment and allocation optimization of partially multiskilled workforce
- J E Gomar
- C T Haas
- D P Morton
- Gomar J. E.
Using Excel® to Do Statistical Analysis
- Y A W Shardt
- Shardt Y. A. W.
Construction Labor Productivity Management and Methods Improvement
- A S Hanna
Construction scheduling based on resource constraints
- Y Lee
- T M Gatton
- Lee Y.