Solving multi-mode time–cost–quality trade-off problems under generalized precedence relations

Khalili‐Damghani, Kaveh; Tavana, Madjid; Abtahi, Amir-Reza; Santos‐Arteaga, Francisco J.

doi:10.1080/10556788.2015.1005838

Cited by 45 publications

(21 citation statements)

References 49 publications

(85 reference statements)

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“…However, MOP-SO's elapsed time is less than that of epsilon-constraint according to this Example. e metrics definition is summarized in Table 10, where ‖x i -y i ‖ is the Euclidean distance between two nondominated solutions x i and y i [69]. Comparison metrics were calculated for Test problems (Table 1) over 10 runs of the MOPSO algorithm.…”

Section: Mopso Resultsmentioning

confidence: 99%

Schedule-Allocate and Robust Sequencing in Three-Machine Robotic Cell under Breakdowns

Vaisi

Farughi

Raissi

2020

Mathematical Problems in Engineering

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The purpose of this paper is to model two problems comprising schedule-allocate (in case of producing identical parts) and sequencing of parts (in case of producing different parts). The first model is used for minimizing the cycle time and operational cost, and the second one for minimizing both the mean and standard deviation of the total production cost as well the cycle time, in an unreliable three-machine robotic cell which confronted with many uncertainty factors. In the current article, mathematical modelling and simulation-based optimization method have been presented to schedule-allocate similar parts and trace the optimal sequence of different parts. Several solution procedures, including epsilon-constraint method and multiobjective particle swarm optimization algorithm, for identical parts case and response surface methodology for different parts case are applied. The results derived from solving numerical examples revealed some advantages in terms of time to attain the optimal solution.

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Section: Mopso Resultsmentioning

confidence: 99%

Schedule-Allocate and Robust Sequencing in Three-Machine Robotic Cell under Breakdowns

Vaisi

Farughi

Raissi

2020

Mathematical Problems in Engineering

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“…MIP mathematical models have been developed for RCPSPs, for example, Chiu and Tsai (2002), Rostami et al (2017) and Tavana et al (2014). Khalili-Damghani et al (2015) solved multi-objective trade-off problems under generalized precedence relations using MIP. Geiger (2017) proposed a mathematical model for optimizing the two objectives, total makespan (TMS) and total project delay (TPD).…”

Section: Studies On Resource-constrained Project Scheduling Problems In a Multi-project Environmentmentioning

confidence: 99%

“…minimizing average project delay (Adhau et al , 2012, 2013; Wang et al , 2017). Research works related to multi-objective variants: optimizing time, cost and quality (Kannimuthu et al , 2019; Khalili-Damghani et al , 2015; Luong et al , 2018; Monghasemi et al , 2015; Mungle et al , 2013; Tavana et al , 2014). optimizing time, cost, resource moments and cash flow (El-Abbasy et al , 2017; Elbeltagi et al , 2016; Farshchian and Heravi, 2018).…”

Section: Studies On Resource-constrained Project Scheduling Problems In a Multi-project Environmentmentioning

confidence: 99%

Comparing optimization modeling approaches for the multi-mode resource-constrained multi-project scheduling problem

Kannimuthu

Raphael

Palaneeswaran

et al. 2019

ECAM

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Purpose Construction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an organization. Optimal allocation of resources is a key challenge in such situations. Several approaches and heuristics have been proposed for this task. The purpose of this paper is to compare two approaches for multi-mode resource-constrained project scheduling in a multi-project environment. These are the single-project approach (portfolio optimization) and the multi-project approach (each project is optimized individually, and then heuristic rules are used to satisfy the portfolio constraint). Design/methodology/approach A direct search algorithm called Probabilistic Global Search Lausanne is used for schedule optimization. Multiple solutions are generated that achieve different trade-offs among the three criteria, namely, time, cost and quality. Good compromise solutions among these are identified using a multi-criteria decision making method, Relaxed Restricted Pareto Version 4. The solutions obtained using the single-project and multi-project approaches are compared in order to evaluate their advantages and disadvantages. Data from two sources are used for the evaluation: modified multi-mode resource-constrained project scheduling problem data sets from the project scheduling problem library (PSPLIB) and three real case study projects in India. Findings Computational results prove the superiority of the single-project approach over heuristic priority rules (multi-project approach). The single-project approach identifies better solutions compared to the multi-project approach. However, the multi-project approach involves fewer optimization variables and is faster in execution. Research limitations/implications It is feasible to adopt the single-project approach in practice; realistic resource constraints can be incorporated in a multi-objective optimization formulation; and good compromise solutions that achieve acceptable trade-offs among the conflicting objectives can be identified. Originality/value An integer programming model was developed in this research to optimize the multiple objectives in a multi-project environment considering explicit resource constraints and maximum daily costs constraints. This model was used to compare the performance of the two multi-project environment approaches. Unlike existing work in this area, the model used to predict the quality of activity execution modes is based on data collected from real construction projects.

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“…The essence of this method is to turn a multi-objective problem into a single-objective problem, while setting boundaries for others. In addition, some scholars have considered multi-objective optimization from the perspective of overall project performance and evaluated the comprehensive effect of resource allocation and completion time (Khalili-Damghani et al , 2015; Mungle et al , 2013). The essence of this method is to create a Pareto front that includes a set of non-dominated solutions and use Pareto filtering to obtain all created solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

A Big Data and FMEA-based construction quality risk evaluation model considering project schedule for Shanghai apartment projects

Guo

2019

IJQRM

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Purpose The purpose of this paper is to mine information on the construction process of previous projects to develop a construction plan that meets both quality requirements and schedule constraints. Design/methodology/approach This paper uses a failure mode and effect analysis to evaluate the construction quality of 311 apartments in Shanghai. The authors also evaluate construction-scheduling control using the earned value management technique and implement an artificial neural network to correlate the results. The authors then develop a quality risk and schedule correlation model based on Big Data. The model can predict the relationship between the planned schedule and the project quality risk using multiple variables such as the number of layers, the schedule performance index and budget costs. Findings The methodology offers an innovative approach for assessment on the relationship between quality risk and project schedule. The authors have also built a multiple regression analysis model for comparative purposes with the model. The results show that the proposed model can better describe the relationship. The model can provide a quantitative quality risk value that changes with the planned schedule, as well as help project managers to understand the relationship between quality risk and project scheduling more accurately. Research limitations/implications The research approach only focuses on quality risk under the impact of scheduling. Future efforts might focus on developing a model that connects failure models with project schedules and costs in order to improve the effort of quality management. Practical implications The model based on Big Data in this paper is developed using real projects and reflects the relationship between project quality risk and scheduling in real environments. The created application provides support for project managers to develop and adjust quality plans and schedules, thereby reducing deviations in quality and scheduling objectives. Originality/value The authors make full use of historical project data from the perspective of both quality and schedule management, and provide a novel method to intelligently and objectively analyze the relationship between quality risk and scheduling.

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Solving multi-mode time–cost–quality trade-off problems under generalized precedence relations

Cited by 45 publications

References 49 publications

Schedule-Allocate and Robust Sequencing in Three-Machine Robotic Cell under Breakdowns

Schedule-Allocate and Robust Sequencing in Three-Machine Robotic Cell under Breakdowns

Comparing optimization modeling approaches for the multi-mode resource-constrained multi-project scheduling problem

A Big Data and FMEA-based construction quality risk evaluation model considering project schedule for Shanghai apartment projects

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