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The purpose of this study is to determine whether there is a correlation between mobilization costs and project schedule performance of highway projects. In addition to this, the study will also determine if the mobilization costs are helping small or large highway projects in terms of improving the schedule performance.

The data of 206 highway projects were collected from the Department of Transportation of two states with the help of questionnaire survey. The cost, schedule and mobilization costs data were collected. The performance metrics related to construction schedule growth and construction intensity were developed in order to test the research hypotheses: mobilization costs will increase the schedule performance of highway projects. The data were also divided into two groups based on project cost and analyzed to check whether the mobilization costs impact the schedule performance of these highway projects. Spearman's correlation test was conducted to determine the correlation between dependent and independent variables. In addition, a Mann–Whitney test was conducted to determine the difference in medians of construction schedule growth and the construction intensity of these two groups of projects.

One major study finding was that there was no strong linear correlation between the mobilization cost percentage and the construction schedule growth and construction intensity of highway projects. However, the study found the projects that have 9% or more mobilization costs had significantly better schedule growth compared to the projects that have less than 9% mobilization costs. When data were analyzed based on the project size, it was found that this pattern was seen only in large projects costing equal to or more than $5 million.

This study's findings have very crucial practical implications to state DOTs contract engineers. This study shows that the highway contract engineers need to provide the right amount of mobilization costs to complete their projects on and before schedule. If the correct amount of mobilization costs is not provided to the contractors, the impact of these mobilization costs on reducing the schedule growth will be negligible. The findings of this study will assist public agency decision makers to complete their projects on or before time by including the mobilization costs provision in the contract. The state DOTs can improve their schedule performance by providing enough financial help to the contractors so that they can improve their cash flows and complete projects successfully within the given timeframe.

This paper contributes to the existing body of knowledge by validating the impact of mobilization costs on the schedule performance of highway projects. There has been no empirical study conducted prior to this to identify the role of mobilization costs on reducing the schedule growth of highway projects.

The purpose of this paper is to identify optimum crew formations at unit execution level of repetitive projects that minimize project duration, project cost, crew work interruptions and interruption costs, simultaneously.

The model consists of four modules. The first module quantifies uncertainties associated with the crew productivity rate and quantity of work using the fuzzy set theory. The second module identifies feasible boundaries for activity relaxation. The third module computes direct cost, indirect cost and interruption costs, including idle crew cost as well as mobilization and demobilization costs. The fourth module identifies near-optimum crew formation using a newly developed multi-objective optimization model.

The developed model was able to provide improvements of 0.2, 16.86 and 12.98% for minimization of project cost, crew work interruptions and interruption costs from US$1,505,960, 8.3 days and US$8,300, as recently reported in the literature, to US$1,502,979, 6.9 days and US$7,222, respectively, without impacting the optimized project duration.

The novelty of this paper lies in its activity-relaxation free float that considers the effect of postponing early finish dates of repetitive activities on crew work interruptions. The introduced new float allows for calculating the required crew productivity rate that minimizes crew work interruptions without delaying successor activities and without impacting the optimized project duration. It safeguards against assignment of unnecessary costly resources.

Planning for increased contractor profits should start at the time the contract is signed because low profits and lack of profitability are the primary causes of contractor failure. The purpose of this paper is to propose an integrated profit maximization model (IPMM) that aims for maximum expected profit by using time-cost tradeoff analysis, adjusted start times of activities, minimized financing cost and minimized extension of work schedule beyond the contract duration. This kind of integrated approach was never researched in the past.

IPMM is programmed into an automated system using MATLAB 2016a. It generates an optimal work schedule that leads to maximum profit by means of time-cost tradeoff analysis considering different activity acceleration/deceleration methods and adjusting the start/finish times of activities. While doing so, IPMM minimizes the contractor’s financing cost by considering combinations of different financing alternatives such as short-term loans, long-term loans and lines of credit. IPMM also considers the impact of extending the project duration on project profit.

IPMM is tested for different project durations, for the optimality of the solutions, differing activity start/finish times and project financing alternatives. In all cases, contractors can achieve maximum profit by using IPMM.

IPMM considers a deterministic project schedule, whereas stochastic time-cost tradeoff analysis can improve its performance. Resource allocation and resource leveling are not considered in IPMM, but can be incorporated into the model in future research. Finally, the long computational time is a challenge that needs to be overcome in future research.

IPMM is likely to increase profits and improve the chances of contractors to survive and grow compared to their competitors. The practical value of IPMM is that any contractor can and should use IPMM since all the data required to run IPMM is available to the contractor at the time the contract is signed. The contractor who provides information about network logic, schedule data, cost data, contractual terms, and available financing alternatives and their APRs can use an automated IPMM that adjusts activity start times and durations, minimizes financing cost, eliminates or minimizes time extensions, minimizes total cost and maximizes expected profit.

Unlike any prior study that looks into contractors’ profits by considering the impact of only one or two factors at a time, this study presents an IPMM that considers all major factors that affect profits, namely, time-cost tradeoff analysis, adjusted start times of activities, minimized financing cost and minimized extension of work schedule beyond the contract duration.

Using a case study for electrical power equipment, the purpose of this paper is to investigate the importance of dependence between series-connected system components in maintenance decisions.

A continuous-time Markov decision model is formulated to find a minimum cost maintenance policy for a circuit breaker as an independent component while considering a downstream transformer as a dependent component. Maintenance of the dependent component is included implicitly in terms of the costs associated with certain state-action pairs. For policy and cost comparisons, a separate model is also formulated that considers only the circuit breaker as the independent component. After uniformizing the continuous-time models to discrete time, standard methods are used to solve for the average-cost-optimal policies of each model.

The optimal maintenance policy and its cost differ significantly depending on whether or not the dependent component is considered.

Data used are from manufacturer databases; additional model validation could be conducted if applied to an electric utility asset fleet within their generation, transmission, and/or distribution system. This model and methodology are already being applied in other contexts such as industrial machinery and equipment, jet engines, amusement park rides, etc.

The outcome of this model can be utilized by asset and operations managers to make maintenance decisions based on prediction rather than more traditional time- or condition-based maintenance methodologies. This model is being developed for use as a module in a larger maintenance information system, specifically linking condition monitor data from the field to a predictive maintenance model. Similar methods are being applied to other applications outside the electrical equipment case detailed herein.

This model provides a structured approach for managers to decide how to best allocate their resources across a network of inter-connected equipment. Work in this area has not fully considered the importance of dependency on systems maintenance, particularly in applications with highly variable repair and replacement costs.

This research is inspired by a real case study from a pump rental business company across the US. The company was looking to increase the utilization of its rental assets while, at the same time, keeping the cost of fleet mobilization as efficient as possible. However, decisions for asset movement between branches were largely arranged between individual branch managers on an as-needed basis.

The authors propose an improvement for the company's asset management practice by modeling an integrated decision tool which involves evaluation of several machine learning algorithms for demand prediction and mathematical optimization for a centrally-planned asset allocation.

The authors found that a feed-forward neural network (FNN) model with single hidden layer is the best performing predictor for the company's intermittent product demand and the optimization model is proven to prescribe the most efficient asset allocation given the demand prediction from FNN model.

The implementation of this new tool will close the gap between the company's current and desired future level of operational performance and consequently increase its competitiveness

The results show a superior prediction performance by a feed-forward neural network model and an efficient allocation decision prescribed by the optimization model.

The purpose of this paper is to investigate design alternatives for pump‐included water distribution networks considering sustainability and reliability aspects. The aim is to demonstrate that CO₂ emissions could be reduced at a reasonable cost. The paper also investigates the trade‐offs between cost and reliability of water distribution networks.

An existing genetic algorithm optimization tool is customized in this research to perform multi‐objective optimization with various objectives and constraints. The developed model is demonstrated using a benchmark water distribution network.

The results from this research suggest that CO₂ emissions from water distribution networks could be reduced at a reasonable cost by choosing better objectives during the design stage. High system reliability could also be ensured for the lifetime by paying reasonable additional cost. This research presents various design alternatives for an engineer to choose from.

The design of water distribution networks is a computationally complex process and often requires significant CPU time to arrive at an optimal solution. This problem is significant in the case of larger networks, especially when all the failed states need to be simulated. Simpler measures of reliability could be adopted in the future.

Although a significant amount of research had been undertaken in the area of optimal water distribution network design, only limited research includes environmental impacts as a design objective. This paper not only includes environmental aspects but also considers reliability. The model proposed in this research is a useful tool for engineers for considering various alternatives before choosing the best design.

Properly planned module installation on an industrial site is a critical factor in delivering a project safely, on time and within budget. Different sizes of heavy-duty mobile cranes are used to pick, swing and place the modules. Crane selection depends on module size and weight, as well as crane availability, location and configuration. Weeks can be spent in trial and error to prepare and improve module installation plans due to the large number of ways to install the modules on site, high crane operating costs and other crane-module constraints. A tool to automatically generate module installation plans is essential.

This paper presents a novel heuristic-based methodology for planning and sequencing module installation on industrial construction sites that takes into account proposed technological constraints.

Case studies are presented to demonstrate the ease and effectiveness of the developed methodology in planning module installations.

On a complex project, the tool can save time in preparing the installation plan, while also reducing the amount of crane supporting tasks (foundation preparation and crane movement).

As often in project scheduling, when the project duration is shortened to reduce total cost, the total float is lost resulting in more critical or nearly critical activities. This, in turn, results in reducing the probability of completing the project on time and increases the risk of schedule delays. The objective of project management is to complete the scope of work on time, within budget in a safe fashion of risk to maximize overall project success. The purpose of this paper is to present an effective algorithm, named as adaptive multiple objective differential evolution (DE) for project scheduling with time, cost and risk trade-off (AMODE-TCR).

In this paper, a multi-objective optimization model for project scheduling is developed using DE algorithm. The AMODE modifies a population-based search procedure by using adaptive mutation strategy to prevent the optimization process from becoming a purely random or a purely greedy search. An elite archiving scheme is adopted to store elite solutions and by aptly using members of the archive to direct further search.

A numerical construction project case study demonstrates the ability of AMODE in generating non-dominated solutions to assist project managers to select an appropriate plan to optimize TCR problem, which is an operation that is typically difficult and time-consuming. Comparisons between the AMODE and currently widely used multiple objective algorithms verify the efficiency and effectiveness of the developed algorithm. The proposed model is expected to help project managers and decision makers in successfully completing the project on time and reduced risk by utilizing the available information and resources.

The paper presented a novel model that has three main contributions: First, this paper presents an effective and efficient adaptive multiple objective algorithms named as AMODE for producing optimized schedules considering time, cost and risk simultaneously. Second, the study introduces the effect of total float loss and resource control in order to enhance the schedule flexibility and reduce the risk of project delays. Third, the proposed model is capable of operating automatically without any human intervention.

This paper provides an overview of a recently developed system for selecting and locating mobile cranes on construction sites. The proposed system provides direct help on two fronts: cost and time savings, and improved safety arrangements. The system has a number of interesting features: a relational database designed to store the cranes' geometry‐related variables and to present them using powerful graphics; a selection module supported by an algorithm designed to satisfy geometrical requirements and necessary clearances, accounting for site constraints and lift configurations; and 3D animation to facilitate the planning of crane operations. The system provides a near‐optimum selection of crane lift configurations, considering available cranes. This paper focuses mainly on case examples to demonstrate and to illustrate the use and capabilities of the developed system. Two actual cases, featuring different site constraints and lift configurations, are presented. In these cases, cranes were selected and their operations planned using the developed system. The findings of the two cases are discussed and the benefits of the proposed methodology are highlighted.