Search results

This paper endeavors to critically examine the trade‐offs among project objectives and their underlying assumptions.

Effect‐cause‐effect (ECE) methodology of theory of constraints (TOC) has been applied to examine the assumptions behind successfully managing business projects.

The essence of discussion in this paper leads towards the realization that a possibility exists for time, cost and quality objectives to be pursued collectively in a project management environment.

This paper evaluates to what extent trade‐offs among project objectives actually exist and explores the possibility of their co‐existence in a project management environment. This realization can significantly impact the project trade‐off models in existing literature.

Time, cost and quality have been recognized to be important objectives to successfully complete a project and several studies have acknowledged the necessity to address their trade‐offs. However, most of these studies have taken the trade‐offs for granted without critically examining the assumptions behind such trade‐offs. The present paper fills that gap by applying ECE approach of TOC to examine project management trade‐off assumptions. There‐in lies the value of the current paper.

Time-cost trade-off is normal conduct in construction projects when projects are expectedly late for delivery. Existing research on time-cost trade-off strategic management mostly focused on the technical calculation towards the optimal combination of activities to be accelerated, while the managerial aspects are mostly neglected. This paper aims to understand the managerial efforts necessary to prepare construction projects ready for an upcoming trade-off implementation.

A preliminary list of critical factors was first identified from the literature and verified by a Delphi survey. Quantitative data was then collected by a questionnaire survey to first shortlist the preliminary factors and quantify the predictive model with different machine learning algorithms, i.e. k-nearest neighbours (kNN), radial basis function (RBF), multiplayer perceptron (MLP), multinomial logistic regression (MLR), naïve Bayes classifier (NBC) and Bayesian belief networks (BBNs).

The model's independent variable importance ranking revealed that the top challenges faced were the realism of contractual obligation, contractor planning and control and client management and monitoring. Among the tested machine learning algorithms, multilayer perceptron was demonstrated to be the most suitable in this case. This model accuracy reached 96.5% with the training dataset and 95.6% with an independent test dataset and could be used as the contingency approach for time-cost trade-offs.

The identified factor list contributed to the theoretical explanation of the failed implementation in general and practical managerial improvement to better avoid such failure. In addition, the established predictive model provided an ad-hoc early warning and diagnostic tool to better ensure time-cost implementation success.

Facility design standardization strategy has considerable advantages, highlighted by its widespread and consistent use in the shipbuilding and manufacturing industries. However, capital projects have failed to realize these benefits. The primary rationale behind this problem is the lack of proper understanding of design standardization, more specifically the benefits and equally importantly, the trade-offs of design standardization in capital projects. Therefore, this study highlights 13 benefits and six trade-offs of standardization in connection to design standardization, along with specific examples.

To achieve the study objectives, the researchers identified the most impactful benefits and trade-offs in terms of economic impact by surveying prominent players in the industry. Furthermore, the researchers examined 43 actual case projects (a case study) executed with the standardization strategy to evaluate the industry's status in terms of the levels of advantage achievement and disadvantage incurrence.

The results of this survey show that design once, reuse multiple times and design and procurement in advance are the most impactful benefits. Similarly, susceptible to changes in the market conditions is one of the top trade-offs that can be incurred in capital projects when implementing standardization. The results also highlight that design once, reuse multiple times is one of the most achieved benefits in standardized capital projects today, while cost of establishing the design standard is the most incurred trade-off.

This study provides important insight into how standardization strategy can be advantageous while also enriching the literature about pitfalls expected from standardization. Moreover, this study's results will help the industrial sector achieve higher levels of design standardization by providing a better understanding of the benefits and trade-offs of design standardization.

The purpose of this paper is to propose a new framework for time–cost trade-off. The new framework provides the optimum time–cost value taking into account the float loss impact.

The stochastic framework uses Monte Carlo Simulation to calculate the effect of float loss on risk. This is later translated into an added cost to the trade-off problem. Five examples, from literature, are solved using the proposed framework to test the applicability of the developed framework.

The results confirmed the research hypothesis that the new optimum solution will be at a higher duration and cost but at a lower risk compared to traditional methods. The probabilities of finishing the project on time using the developed framework in all five cases were better than those using the classical deterministic optimization technique.

The objective of time–cost trade-off is to determine the optimum project duration corresponding to the minimum total cost. Time–cost trade-off techniques result in reducing the available float for noncritical activities and thus increasing the schedule risks. Existing deterministic optimization technique does not consider the impact of the float loss within the noncritical activities when the project duration is being crashed. The new framework allows project managers to exercise new trade-offs between time, cost and risk which will ultimately improve the chances of achieving project objectives.

Achieving project objectives in constructionprojects such as time, cost and quality is a challenging task. Minimizing project cost often results in additional project duration and might jeopardize quality, and minimizing project duration often results in additional cost and might jeopardize quality. Also, increasing construction quality often results in additional cost and time. The purpose of this paper is to identify and analyze trade-offs among the project objectives of time, cost and quality.

The optimization model adopted a quantitative research method and is developed in two main steps formulation step that focuses on identifying model decision variables and formulating objective functions, and implementation step that executes the model computations using multi-objective optimization of Non-Dominated Sorting Genetic Algorithms to identify the aforementioned trade-offs, and codes the model using python. The model performance is verified and tested using a case study of construction project consisting of 20 activities.

The model was able to show practical and needed value for construction managers by identifying various trade-off solutions between the project objectives of time, cost and quality. For example, the model was able to identify the shortest project duration at 84 days while keeping cost under $440,000 and quality higher than 85 percent. However, with an additional budget of $20,000 (4.5 percent increase), the quality can be increased to 0.935 (8.5 percent improvement).

The present research work is limited to project objectives of time, cost and quality. Future expansion of the model will focus on additional project objectives such as safety and sustainability. Furthermore, new optimization models can be developed for construction projects with repetitive nature such as roads, tunnels and high rise buildings.

The present model advances existing research in planning construction projects efficiently and achieving important project objectives. On the practical side, the optimization model will support the construction industry by allowing construction managers to identify the highest quality to deliver a construction project within specified budget and duration, lowest cost for specified duration and quality or shortest duration for specified budget and quality.

The present model introduces new and innovative method of increasing working hours per day and number of working days per shift while analyzing labor working efficiency and overtime rate to identify optimal trade-offs among important project objectives of time, cost and quality.

The purpose of this paper is to attempt to achieve customization while maintaining quick delivery to customers by use of a hybrid of make-to-order (MTO) and make-to-stock (MTS) strategies.

The paper reports on various studies based on simulation, optimization, and Markov analysis to respond to some of the unique challenges of the hybrid strategy.

Implementation of the make-to-forecast (MTF) hybrid strategy provides considerable competitive advantage in certain manufacturing situations by achieving as much as 50 percent shorter delivery times for highly customized products than would be possible with a pure MTO strategy. It also has potential to offer competitive advantages to pure MTS and MTO operations.

The manufacturing situations, appropriate for MTF paradigm, are fairly complex. The paper reviews only several of the many managerial challenges. For instance, it assumes a given product variety but does not dwell on what the optimum product variety offering should be. Additional research to this and other aspects of the situation is warranted.

The paper reports on a real managerial problem. The suggested approaches can be easily implemented by certain manufacturing companies striving to achieve competitive advantage by offering shorter delivery time and substantial customization to their customers.

Managing the trade-off between delivery time and degree of customization is not new. Assemble-to-order, mass customization, and postponement strategies have been suggested to soften the trade-off. This paper provides a new paradigm for the same challenge based on pre-release of units into the manufacturing system in anticipation of customer orders.

The purpose of this paper is to test the integrated model of operations strategy as proposed by Schmenner and Swink to explain whether firms trade‐off or accumulate capabilities, taking into account their positions relative to their asset and operating frontiers.

The four major airlines based in Australia were studied. The paper is based on longitudinal data obtained from secondary sources. The four operations capabilities cost, quality, delivery and flexibility, and asset and operating frontiers, were all measured with proxy variables.

The study provides some support for the integrated model. Firms do appear to trade‐off capabilities when their asset and operating frontiers are close to each other. Firms show signs of accumulation when the asset frontiers are expanding significantly over time. There is indirect evidence that firms could be accumulating capabilities when the gap between the two frontiers is large.

The study provides insights into when firms trade‐off or accumulate capabilities. A good understanding of asset and operating frontiers is important in this regard. Managers need to better identify, establish and combine their firms' capabilities in response to varying internal and external contingencies.

The paper provides an original and detailed empirical validation of Schmenner and Swink's integrated model. In doing so, this study contributes to informing and clarifying the debate in the operations strategy area relating to the circumstances in which firms trade‐off and/or accumulate capabilities.

The concept of sustainability is about the relationships among people, the planet, and profit. Nowadays, it has been spread to many areas, including construction projects. Sustainability in construction projects has been so important in the present time. It is commonly pointed to the environment and construction materials. Nevertheless, it is also important in construction management which addresses sustainable project scheduling. Sustainable project scheduling will help to deliver the project under the deadlines, within the available budget, and also ensuring about the quality of the works. This study presents the trade-off among time, cost, and quality in the construction project to provide the decision-maker some alternative solutions in the planning phase to conduct a sustainable construction project. Multi-objective optimisation using Symbiotic Organism Search (SOS) algorithm is done to find the best solutions to the problem under investigation. Then, fuzzy logic approach is utilised to model the diverse environment and uncertainty in the construction project. A case study in repetitive construction project is analysed to see the capability of the model to work in time cost quality trade-off to reach into a sustainable construction project.

A transformative service aims to improve wellbeing; however, current approaches have an implicit assumption that all wellbeing dimensions are equal and more dimensions led to higher wellbeing. The purpose of this paper is to present evidence for a new framework that identifies the paradox of competing wellbeing dimensions for both the individual and others in society – the transformative service paradox (TSP).

Data is drawn from a mixed-method approach using qualitative (interviews) and quantitative data (lab experiment) in an electricity service context. The first study involves 45 household interviews (n = 118) and deals with the nature of trade-offs at the individual level to establish the concept of the TSP. The second study uses a behavioral economics laboratory experiment (n = 110) to test the self vs. other nature of the trade-off in day-to-day use of electricity.

The interviews and experiment identified that temporal (now vs. future) and beneficiary-level factors explain why individuals make wellbeing trade-offs for the transformative service of electricity. The laboratory experiment showed that when the future implication of the trade-off is made salient, consumers are more willing to forego physical wellbeing for environmental wellbeing, whereas when the “now” implication is more salient consumers forego financial wellbeing for physical wellbeing.

This research introduces the term “Transformative Service Paradox” and identifies two factors that explain why consumers make wellbeing trade-offs at the individual level and at the societal level; temporal (now vs. future) and wellbeing beneficiary.

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.