Incorporating Quality Considerations Into Project Time/Cost Tradeoff Analysis and Decision Making

Pollack-Johnson, Bruce; Liberatore, Matthew J.

doi:10.1109/tem.2006.883705

Cited by 82 publications

(37 citation statements)

References 19 publications

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“…Studies that combine goal programming with the critical path method for project planning include Pollack-Johnson and Liberatore (2006) where authors combined project time/cost information in an engineering application on quality management tradeoffs and Kull and Talluri (2008) which examined risk assessment in supply chain supplier selection projects. More recently Gagnon et al (2012) demonstrated how GP could be combined with a critical path method to include manager's preferential input into the decision process.…”

Section: Value Analysis and Gpmentioning

confidence: 99%

Value analysis planning with goal programming

Schniederjans

Cao

2015

Ann Oper Res

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This paper presents a model to support planning decisions in the engineering methodology referred to as value analysis. A goal programming model incorporating elements of critical path method and concurrent engineering is proposed as a means to augment the planning of value analysis projects. The modeling approach builds and extends prior goal programming research by uniquely incorporating both timing and cost information to ease modeling complexity and reveal scheduling resource tradeoffs. An illustrative example is presented to demonstrate the modeling approach informational efficacy.

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Section: Value Analysis and Gpmentioning

confidence: 99%

Value analysis planning with goal programming

Schniederjans

Cao

2015

Ann Oper Res

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“…De Reyck et al [9] introduced the discrete time/resource trade off problem (DTRTP), which exemplifies the case that activities can be performed with different amounts of resources, resulting in different possible durations. Pollack-Johnson and Liberatore [10] studied discrepancies in quality between activity modes of DTRTP. Ranjbar et al [11] extended a generalization of DTRTP with multiple resource types (MDTRTP).…”

Section: Multi-mode Resource Constrained Project Scheduling Problemmentioning

confidence: 99%

An Entropy-Based Upper Bound Methodology for Robust Predictive Multi-Mode RCPSP Schedules

Chen¹,

Liang

Padilla

2014

Entropy

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Abstract:Projects are an important part of our activities and regardless of their magnitude, scheduling is at the very core of every project. In an ideal world makespan minimization, which is the most commonly sought objective, would give us an advantage. However, every time we execute a project we have to deal with uncertainty; part of it coming from known sources and part remaining unknown until it affects us. For this reason, it is much more practical to focus on making our schedules robust, capable of handling uncertainty, and even to determine a range in which the project could be completed. In this paper we focus on an approach to determine such a range for the Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP), a widely researched, NP-complete problem, but without adding any subjective considerations to its estimation. We do this by using a concept well known in the domain of thermodynamics, entropy and a three-stage approach. First we use Artificial Bee Colony (ABC)-an effective and powerful meta-heuristic-to determine a schedule with minimized makespan which serves as a lower bound. The second stage defines buffer times and creates an upper bound makespan using an entropy function, with the advantage over other methods that it only considers elements which are inherent to the schedule itself and does not introduce any subjectivity to the buffer time generation. In the last stage, we use the ABC algorithm with an objective function that seeks to maximize robustness while staying within the makespan boundaries defined OPEN ACCESSEntropy 2014, 16 5033 previously and in some cases even below the lower boundary. We evaluate our approach with two different benchmarks sets: when using the PSPLIB for the MRCPSP benchmark set, the computational results indicate that it is possible to generate robust schedules which generally result in an increase of less than 10% of the best known solutions while increasing the robustness in at least 20% for practically every benchmark set. And, in an attempt to solve larger instances with 50 or 100 activities, we also used the MRCPSP/max benchmark sets, where the increase of the makespan is approximately 35% with respect to the best known solutions at the same time as with a 20% increase in robustness.

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“…Following the general approach of [12], Pollack-Johnson and Liberatore [14] illustrate how the quality of a task option can be determined using the AHP. This example shows how quality of different approaches for laying a foundation in construction could be related to two quality attributes: compressive strength of the concrete used, and the depth of the excavation.…”

Section: A Project Qualitymentioning

confidence: 99%

Quality, time, and cost tradeoffs in project management decision making

Liberatore

Pollack-Johnson

2009

PICMET '09 - 2009 Portland International Conference on Management of Engineering &Amp; Technology

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This paper presents a mathematical programming model that allows quality to be explicitly considered in project planning and scheduling, while addressing the tradeoffs between quality, time, and cost. A quality function is used to represent the relationships between time, cost, and quality for individual tasks. The initial problem formulation maximizes the minimum quality (weakest link) over all project tasks, subject to bounds on cost and completion time. Using a construction example we show how this model can be adapted to generate quality level curves to illustrate the trade-offs among time, cost, and quality. These level curves can then be used by project managers to make project scheduling decisions that explicitly model and consider quality as well as time and cost, so that better and more appropriate decisions can be made for a particular situation. We also offer some managerial insights for project planning and scheduling that are derived from the analysis through improved understanding of these choices and tradeoffs.

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Incorporating Quality Considerations Into Project Time/Cost Tradeoff Analysis and Decision Making

Cited by 82 publications

References 19 publications

Value analysis planning with goal programming

Value analysis planning with goal programming

An Entropy-Based Upper Bound Methodology for Robust Predictive Multi-Mode RCPSP Schedules

Quality, time, and cost tradeoffs in project management decision making

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