Multi-objective multi-mode resource constrained project scheduling problem using Pareto-based algorithms

Tirkolaee, Erfan Babaee; Goli, Alireza; Hematian, Milad; Sangaiah, Arun Kumar; Han, Tao

doi:10.1007/s00607-018-00693-1

Cited by 92 publications

(40 citation statements)

References 42 publications

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“…• As the number of objectives increase, more information has to be given by the user. Assume that it is decided to minimize Objective Function (53) subject to Constraints (54) and (55).…”

Section: Solution Techniquementioning

confidence: 99%

A robust time-cost-quality-energy-environment trade-off with resource-constrained in project management: A case study for a bridge construction project

Lotfi¹,

Yadegari²,

Hosseini³

et al. 2022

JIMO

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<p style='text-indent:20px;'>Sustainable development requires scheduling and implementation of projects by considering cost, environment, energy, and quality factors. Using a robust approach, this study investigates the time-cost-quality-energy-environment problem in executing projects and practically indicates its implementation capability in the form of a case study of a bridge construction project in Tehran, Iran. This study aims to take into account the sustainability pillars in scheduling projects and uncertainties in modeling them. To model the study problem, robust nonlinear programming (NLP) involving the objectives of cost, quality, energy, and pollution level is applied with resource-constrained. According to the results, as time diminished, the cost, energy, and pollution initially decreased and then increased, witha reduction in quality. To make the model close to the real world by considering uncertainties, the cost and quality tangibly improved, and pollution and energy consumption declined. We applied the augmented <inline-formula><tex-math id="M1">\begin{document}$ \varepsilon $\end{document}</tex-math></inline-formula>-constraint method to solve the proposed model. According to the result of the research, with regard to the time-cost, time-quality, time-energy, and time-pollution charts, as uncertainty increases, the cost and quality will improve, and pollution and energy will decrease.</p><p style='text-indent:20px;'>The proposed model can be employed for all industrial projects, including roads, construction, and manufacturing.</p>

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“…• As the number of objectives increase, more information has to be given by the user. Assume that it is decided to minimize Objective Function (53) subject to Constraints (54) and (55).…”

Section: Solution Techniquementioning

confidence: 99%

A robust time-cost-quality-energy-environment trade-off with resource-constrained in project management: A case study for a bridge construction project

Lotfi¹,

Yadegari²,

Hosseini³

et al. 2022

JIMO

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“…The VRP problem is a NP-hard problem. Therefore, it is natural to use a heuristic to solve the problem [32], such as the adaptive Harmony Search Algorithm (HSA) [33], adaptive Large Neighborhood Search (LNS) algorithm [34], Iterated Greedy Algorithm (IGA) [35], Genetic Algorithm (GA) [3] and Particle Swarm Optimization (PSO) algorithm [36], Simulated Annealing Algorithm (SAA) [28,37,38], Multiple Neighborhood Search (MNS) algorithm [24], etc.…”

Section: Research About Some Algorithms For Waste Collection and Tranmentioning

confidence: 99%

A Chance-Constrained Vehicle Routing Problem for Wet Waste Collection and Transportation Considering Carbon Emissions

Tao

Qiao

et al. 2020

IJERPH

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In order to solve the optimization problem of wet waste collection and transportation in Chinese cities, this paper constructs a chance-constrained low-carbon vehicle routing problem (CCLCVRP) model in waste management system and applies certain algorithms to solve the model. Considering the environmental protection point of view, the CCLCVRP model combines carbon emission costs with traditional waste management costs under the scenario of application of smart bins. Taking into the uncertainty of the waste generation rate, chance-constrained programming is applied to transform the uncertain model to a certain one. The initial optimal solution of this model is obtained by a proposed hybrid algorithm, that is, particle swarm optimization (PSO); and then the further optimized solution is obtained by simulated annealing (SA) algorithm, due to its global optimization capability. The effectiveness of PSOSA algorithm is verified by the classic database in a capacitated vehicle routing problem (CVRP). What’s more, a case of waste collection and transportation is applied in the model for acquiring reliable conclusions, and the application of the model is tested by setting different waste fill levels (WFLs) and credibility levels. The results show that total costs rise with the increase of credibility level reflecting dispatcher’s risk preference; the WFL value range between 0.65 and 0.75 can obtain the optimal solution under different credibility levels. Finally, according to these results, some constructive proposals are propounded for the government and the logistics organization dealing with waste collection and transportation.

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“…Equation (41) calculates total transportation costs among different echelons of the supply chain. Equation (42) states that the total supply chain costs are equal to the configuration, transportation, and operation costs.…”

Section: Physical and Financial Flow Integration Constraintsmentioning

confidence: 99%

Multiobjective fuzzy mathematical model for a financially constrained closed‐loop supply chain with labor employment

Goli

Zare

Tavakkoli‐Moghaddam

et al. 2019

Computational Intelligence

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This paper addresses the multiobjective, multiproducts and multiperiod closed‐loop supply chain network design with uncertain parameters, whose aim is to incorporate the financial flow as the cash flow and debts' constraints and labor employment under fuzzy uncertainty. The objectives of the proposed mathematical model are to maximize the increase in cash flow, maximize the total created jobs in the supply chain, and maximize the reliability of consumed raw materials. To encounter the fuzzy uncertainty in this model, a possibilistic programming approach is used. To solve large‐sized problems, the multiobjective simulated annealing algorithm, multiobjective gray wolf optimization, and multiobjective invasive weed optimization are proposed and developed. The numerical results demonstrate that these algorithms solve the problems within about 1% of the required solving time for the augmented ε‐constraint and have similar performance and even better in some cases. The multiobjective simulated annealing algorithm with a weak performance takes less time than the other two algorithms. The multiobjective gray wolf optimization and multiobjective invasive weed optimization algorithms are superior based on the multiobjective performance indices.

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Multi-objective multi-mode resource constrained project scheduling problem using Pareto-based algorithms

Cited by 92 publications

References 42 publications

A robust time-cost-quality-energy-environment trade-off with resource-constrained in project management: A case study for a bridge construction project

A robust time-cost-quality-energy-environment trade-off with resource-constrained in project management: A case study for a bridge construction project

A Chance-Constrained Vehicle Routing Problem for Wet Waste Collection and Transportation Considering Carbon Emissions

Multiobjective fuzzy mathematical model for a financially constrained closed‐loop supply chain with labor employment

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