Production scheduling of off-site prefabricated construction components considering sequence dependent due dates

Yazdani, Maziar; Kabirifar, Kamyar; Fathollahi-Fard, Amir M.; Mojtahedi, Mohammad

doi:10.1007/s11356-021-16285-0

Cited by 56 publications

(35 citation statements)

References 101 publications

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“…All calculations are performed on the computer with Intel (R) Core (Tm) i5-323M CPU @ 2.60 GHz 6.0 GB specifications. Potts et al [26] showed that two-stage assembly scheduling problem is NP-Hard and Single machine scheduling problem with considering the sequence dependent set-up times are classified in NP-Hard problems [41][42][43], then given that the our problem is three-stage and multi-factory mode, so, is more complex than [26]. Therefore, according to our problem complexity, it proves to be NP-Hard.…”

Section: Comparisons and Calculation Resultsmentioning

confidence: 86%

Distributed Production Assembly Scheduling with Hybrid Flowshop in Assembly Stage

2022

IJE

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A new three stage production-assembly problem is considered in this paper. To the best of our knowledge, considering parallel machines in the third stage, identical parallel factories including the three stage production-assembly system and identical parallel factories with parallel machines in the third stage of the production-assembly system, has been specifically investigated in this paper. To minimize the maximum completion time (Makespan) of all jobs in the all factories, jobs assignment to factories and their processing sequence should be done properly. A Mixed Integer Linear Programming (MILP) model is presented to solve small size problem by using cplex solver. According to the problem computational complexity, large size of problem is not possible to solve using the cplex, so to solve it and to control the computational complexity, a new improved genetic algorithm (GA) is proposed by combining GA and Longest Proseccing Time (LPT) method that is called Hybrid Genetic Algorithm Longest Proseccing Time (HGALPT). The problem parameters values are determined using one-way analysis of variance (ANOVA). Finally, in order to evaluate the efficiency and effectiveness of the proposed algorithm, and to specify each parameter impact on the objective function, sensitivity analysis is performed on the problem parameters.

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Section: Comparisons and Calculation Resultsmentioning

confidence: 86%

Distributed Production Assembly Scheduling with Hybrid Flowshop in Assembly Stage

2022

IJE

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“…Operation Research is a branch of study that use sophisticated analytical techniques to comprehend complicated systems and make appropriate decisions [ 29 , 30 , 31 ]. OR assists firms and organisations in a variety of methods, including simulation approaches [ 32 , 33 , 34 ], mathematical optimisation [ 35 , 36 ], and decision analysis [ 37 , 38 ]. OR, with its focus on boosting efficiency [ 39 , 40 ], cost-effectiveness [ 41 , 42 ], and decision making [ 43 , 44 ], is especially useful for analysing complex health sectors challenges.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

et al. 2021

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The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method’s strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented.

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“…Therefore, it is more worthy to test and compare such recent SI metaheuristics than to introduce another metaheuristic that reuses existing concepts from previous metaheuristics [41]. Therefore, many researchers have conducted comparison studies among multiple metaheuristics in different research fields in order to evaluate their performances in different optimization problems, such as deterministic construction TCTP [88], bridge deck repairs [89], the design of water distribution networks [90], concrete foundation design [91], the domain decomposition of finite element models [92], supply chain management [93], the production scheduling of precast components [61], aircraft routing problems [94], and the design of steel-frame structures [95,96]. Moreover, the SO of OSC represents a new test field to validate and compare the potential of these new SI metaheuristics to solve a new set of real-life optimization problems characterized by uncertainty and complexity [26].…”

Section: Literature Review Of Simulation-optimization (So) In the Con...mentioning

confidence: 99%

“…This reduction in the half-width can reduce the number of replications [114]. The fourth (Equation ( 60)) states that the fitness values of the two candidate solutions (X 1 , X 2 ) should be positively correlated which results into a positive covariance (Cov(X 1 , X 2 ) > 0); thus, the variance of Z(N) could be reduced according to Equation (61).…”

Section: Common Random Numbers (Crn)mentioning

confidence: 99%

Simulation-Optimization for the Planning of Off-Site Construction Projects: A Comparative Study of Recent Swarm Intelligence Metaheuristics

et al. 2021

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Off-site construction is a modern construction method that brings many sustainability merits to the built environment. However, the sub-optimal planning decisions (e.g., resource allocation, logistics and overtime planning decisions) of off-site construction projects can easily wipe away their sustainability merits. Therefore, simulation modelling—an efficient tool to consider the complexity and uncertainty of these projects—is integrated with metaheuristics, developing a simulation-optimization model to find the best possible planning decisions. Recent swarm intelligence metaheuristics have been used to solve various complex optimization problems. However, their potential for solving the simulation-optimization problems of construction projects has not been investigated. This research contributes by investigating the status-quo of simulation-optimization models in the construction field and comparing the performance of five recent swarm intelligence metaheuristics to solve the stochastic time–cost trade-off problem with the aid of parallel computing and a variance reduction technique to reduce the computation time. These five metaheuristics include the firefly algorithm, grey wolf optimization, the whale optimization algorithm, the salp swarm algorithm, and one improved version of the well-known bat algorithm. The literature analysis of the simulation-optimization models in the construction field shows that: (1) discrete-event simulation is the most-used simulation method in these models, (2) most studies applied genetic algorithms, and (3) very few studies used computation time reduction techniques, although the simulation-optimization models are computationally expensive. The five selected swarm intelligence metaheuristics were applied to a case study of a bridge deck construction project using the off-site construction method. The results further show that grey wolf optimization and the improved bat algorithm are superior to the firefly, whale optimization, and salp swarm algorithms in terms of the obtained solutions’ quality and convergence behaviour. Finally, the use of parallel computing and a variance reduction technique reduces the average computation time of the simulation-optimization models by about 87.0%. This study is a step towards the optimum planning of off-site construction projects in order to maintain their sustainability advantages.

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Production scheduling of off-site prefabricated construction components considering sequence dependent due dates

Cited by 56 publications

References 101 publications

Distributed Production Assembly Scheduling with Hybrid Flowshop in Assembly Stage

Distributed Production Assembly Scheduling with Hybrid Flowshop in Assembly Stage

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

Simulation-Optimization for the Planning of Off-Site Construction Projects: A Comparative Study of Recent Swarm Intelligence Metaheuristics

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