Time-Cost Tradeoff Analysis in Project Management: An Ant System Approach

Mokhtari, Hadi; Kazemzadeh, Reza Baradaran; Salmasnia, Ali

doi:10.1109/tem.2010.2058859

Cited by 39 publications

(22 citation statements)

References 27 publications

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“…Many scholars designed heuristic algorithms for the problem. Skutella [26] first designed an approximation algorithm for the DTCTP, and some recent heuristic algorithms for the problem have been reported [27][28][29][30][31][32][33][34]. Akkan et al [27], in particular, proposed a simplification for the problem, and their main approach was to compute path lengths, especially the shorter paths in the network.…”

Section: Introductionmentioning

confidence: 99%

A Float-Path Theory and Its Application to the Time-Cost Tradeoff Problem

Wei

2015

Journal of Applied Mathematics

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Activity floats are vital for project scheduling, such as total floats which determine the maximum permissible delays of activities. Moreover, activity paths in activity networks present essences of many project scheduling problems; for example, the time-cost tradeoff is to shorten long paths at lower costs. We discovered relationships between activity floats and paths and established a float-path theory. The theory helps to compute path lengths using activity floats and analyze activity floats using paths, which helps to transmute a problem into the other simpler one. We discussed applications of the float-path theory and applied it to solve the time-cost tradeoff problem (TCTP), especially the nonlinear and discrete versions. We proposed a simplification from an angle of path as a preprocessing technique for the TCTP. The simplification is a difficult path problem, but we transformed it into a simple float problem using the float-path theory. We designed a polynomial algorithm for the simplification, and then the TCTP may be solved more efficiently.

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Section: Introductionmentioning

confidence: 99%

A Float-Path Theory and Its Application to the Time-Cost Tradeoff Problem

Wei

2015

Journal of Applied Mathematics

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“…Xiong and Kuang [59] proposed an ACO to solve the time-cost trade-off problems in which the modified adaptive weight approach developed in [60] was adopted to guide the solution to the Pareto-front. Mokhtari et al [61] developed an ACO for stochastic DTCTP which was aimed to improve the project completion probability by a predefined deadline on program evaluation and review technique networks. In [61], the activities were subjected to a discrete cost function and assumed to be normally distributed.…”

Section: Population-based Meta-heuristicsmentioning

confidence: 99%

“…Mokhtari et al [61] developed an ACO for stochastic DTCTP which was aimed to improve the project completion probability by a predefined deadline on program evaluation and review technique networks. In [61], the activities were subjected to a discrete cost function and assumed to be normally distributed. Then, the model was formulated as a nonlinear mathematical 0-1 programming problem, where the mean and variance of the activity durations were decision variables and the objective function was to maximize the project completion probability.…”

Section: Population-based Meta-heuristicsmentioning

confidence: 99%

Optimizations in Project Scheduling: A State-of-Art Survey

Wang

Jiang

2014

Intelligent Systems, Control and Automation: Science and Engineering

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Project scheduling is concerned with an optimal allocation of resources to activities realized over time. To survive in today's competitive environment, efficient scheduling for project development becomes more and more important. The classical project scheduling is based on the critical path method (CPM) in which resources required are assumed unlimited. This is however impractical. To overcome CPM's drawback, several techniques and optimizations have been proposed in project scheduling literature. In this chapter, we will present a state-of-art survey on project scheduling from the optimization point of view. In particularly, we will focus on the advancements of optimization formulations and solutions on project scheduling in the recent years.

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“…Table 1 provides a general overview of the timecost-quality trade-off problem. In addition to time, cost and quality, another factor that affects on project performance and is important in project completion is [10][11][12][13][14][15] Non-nonlinear TCTP Simulated annealing and MOGA [16] TCQTP Pareto solution genetic algorithm ,GA [17,18] DTCQTP Particle swarm optimization algorithm [19] DTCTP NSGA II, Ant colony optimization [20,21] Quality loss cost in the time-cost trade-off problem -constraint method and a dynamic self-adaptive multi-objective evolutionary algorithm [22] PERT environment Hybrid scatter search [23] Renewable and nonrenewable resource Ant colony optimization [24] Setup times after preemption Genetic algorithm [25] Tardiness and earliness Genetic algorithm [26] Time-cost-environment trade-off Adaptive-hybrid genetic algorithm [27] Multi-mode resource constrained project scheduling problem (MM-RCPSP) Branch-and-bound algorithm [28] an uncertainty condition, which is resulted from three factors of an external environment, changes in business objectives and unsuitable methods designed for execution. Uncertainty is not just the result of insufficient knowledge and low experience of the project team, but can mostly be related to the complexity and newness of the project.…”

Section: Introductionmentioning

confidence: 99%

A New Multi-objective Model for Multi-mode Project Planning with Risk

Mollaei

Tavakkoli–Moghaddam

Eshlaghy

2018

IJE

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The aim of this problem is to choose a set of project activities for crashing in such a way that the expected project time, cost and risk are minimized and the expected quality is maximized. In this problem, each project activity can be performed with a specific executive mode. Each executive mode is characterized with four measures, namely the expected time, cost, quality and risk. In this paper, linear relationships between time and cost, and between time and quality are omitted and the problem of the expected time-cost-quality trade-off is considered in a probabilistic and discrete state (DTCQRTP). Then, to make the problem more real, the combination of four measures are considered as uncertain for each executive mode. It means that the time, cost, quality or risk (or all of them) of each activity in each executive mode is considered as the expected numbers (probabilistic means). After modeling four-objective problems, a test problem with nine activities is presented and solved by the non-dominated sorting genetic algorithm (NSGA-II). In order to improve the results and speed of the proposed algorithm in accessing Pareto solutions, a new hybrid algorithm, called MEM-NSGA, is presented that gives better solutions than the original NSGA-II at the same conditions.

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Time-Cost Tradeoff Analysis in Project Management: An Ant System Approach

Cited by 39 publications

References 27 publications

A Float-Path Theory and Its Application to the Time-Cost Tradeoff Problem

A Float-Path Theory and Its Application to the Time-Cost Tradeoff Problem

Optimizations in Project Scheduling: A State-of-Art Survey

A New Multi-objective Model for Multi-mode Project Planning with Risk

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