A rolling horizon approach for production planning and condition-based maintenance under uncertain demand

Lin, Wang; Liu, Zhiqiang; Ren, Yifei

doi:10.1177/1748006x19853671

Cited by 6 publications

(9 citation statements)

References 29 publications

(40 reference statements)

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“…The effect of timedependent demand variability is modeled using a linear model to predict future demand. 22 In this work, it is assumed that the parameters for the linear model are available. The demand increase was parameterized to study the effect of increasing demand on the interaction between the risk tolerance and the expected cost.…”

Section: Supply Chain Optimization Modelmentioning

confidence: 99%

Modular supply chain optimization considering demand uncertainty to manage risk

2021

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Supply chain under demand uncertainty has been a challenging problem due to increased competition and market volatility in modern markets. Flexibility in planning decisions makes modular manufacturing a promising way to address this problem. In this work, the problem of multiperiod process and supply chain network design is considered under demand uncertainty. A mixed integer two-stage stochastic programming problem is formulated with integer variables indicating the process design and continuous variables to represent the material flow in the supply chain. The problem is solved using a rolling horizon approach. Benders decomposition is used to reduce the computational complexity of the optimization problem. To promote riskaverse decisions, a downside risk measure is incorporated in the model. The results demonstrate the several advantages of modular designs in meeting product demands.A pareto-optimal curve for minimizing the objectives of expected cost and downside risk is obtained.

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Section: Supply Chain Optimization Modelmentioning

confidence: 99%

Modular supply chain optimization considering demand uncertainty to manage risk

2021

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“…This scenario is considered by many researchers to simplify the modelling process. 3,4 Under this scenario, the failure time of single-component system should be the combination of each component's failure time in scenario 1. The optimal job sequence, end time of each job, and delay cost in scenario 3 are the same as that in scenario 1, the only difference is that total profit reduces to 1,902,550 due to the increasing system replacement cost (113,210) when any component fails on M2, M3, M4, and M5.…”

Section: Scenariomentioning

confidence: 99%

“…1,2 Although some scholars considered maintenance and production scheduling jointly, they only considered the system-level maintenance policy irrespective of single-component or multi-component system. 3,4 However, for some valued multi-component repairable systems, maintenance may be conducted at componentlevel and system-level simultaneously for cost saving. Therefore, there is a genuine need to develop an integrated model for maintenance and production scheduling and the maintenance is conducted at both component-level and system-level.…”

Section: Introductionmentioning

confidence: 99%

An integrated model for maintenance policies and production scheduling based on immune–culture algorithm

Chen

Zhang

2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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The development of integrated modelling for maintenance policies of multi-component repairable system and production scheduling is challenging for two reasons. First, capturing dependency of this multi-component repairable system is difficult because different failure types associated with different components are under competing risks and their complicated relationships may lead to overall system dependency. Second, the integrated model is difficult to optimize because it is an NP-hard problem that exact optimization methods are intractable. For coping with these two difficulties, we propose a parametric statistical model using copula function to capture the overall system dependency. Under partially perfect maintenance policy at component-level, the likelihood functions for observed failures are derived and maximum likelihood method is used to estimate unknown parameters. Then relying on this parametric statistical model, the system hazard function is derived to depict the reliability-based imperfect preventive maintenance policy at system-level. Finally, to obtain the optimal solution(s) of the integrated model, we design an adaptive immune clone selection–culture algorithm, which is inspired from immune clone selection algorithm and culture algorithm. Results of the case study validate that our proposed maintenance policies and methodology have great advantages over the component-level or system-level maintenance policy and immune clone selection algorithm.

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“…en, these PPPs usually contain indeterminate and incomplete information of their objective functions and/or constraints in real production environments. erefore, many uncertain optimization/planning methods [7][8][9][10][11][12][13][14] have been proposed and widely used in engineering management and decisionmaking to solve uncertain optimization/planning problems in actual problems. However, most uncertain optimization/ planning methods used interval numbers or fuzzy numbers to express uncertain parameters in objective functions and constraints and then transformed the objective functions and constraints into deterministic planning problems in order to yield the optimal exact/crisp solutions in the solution process of uncertain problems [7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…erefore, many uncertain optimization/planning methods [7][8][9][10][11][12][13][14] have been proposed and widely used in engineering management and decisionmaking to solve uncertain optimization/planning problems in actual problems. However, most uncertain optimization/ planning methods used interval numbers or fuzzy numbers to express uncertain parameters in objective functions and constraints and then transformed the objective functions and constraints into deterministic planning problems in order to yield the optimal exact/crisp solutions in the solution process of uncertain problems [7][8][9][10][11][12][13][14]. Furthermore, some researchers [15][16][17] also proposed trapezoidal or triangular neutrosophic number programming methods to solve single-objective or multiobjective linear programming problems in indeterminate environments.…”

Section: Introductionmentioning

confidence: 99%

Confidence Neutrosophic Number Linear Programming Methods Based on Probability Distributions and Their Applications in Production Planning Problems

2022

Mathematical Problems in Engineering

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A neutrosophic number linear programming (NN-LP) method reveals a useful tool for solving optimal production planning problems in an indeterminate environment. The optimal feasible solutions of the decision variables and the objective function in the NN-LP method are obtained only depending on the subjectively specified uncertain range of neutrosophic numbers without considering some distribution and confidence level of product sample data. Due to the lack of some probability distribution and confidence level/interval in indeterminate situations, existing indeterminate optimization methods are difficult to guarantee the reliability and credibility of the optimal interval feasible solutions. To solve these problems, this study proposes confidence neutrosophic number linear programming methods based on some probability distributions to objectively determine the confidence level/interval of a sample dataset/multivalued set in the NN-LP problems and strengthen the reliability and credibility of the optimal interval feasible solutions. Therefore, this article first introduces a transformation method from product sample datasets (multivalued sets) with normal and log-normal distributions to confidence neutrosophic numbers (CNNs) (confidence intervals) from a probabilistic point of view. Then, CNN linear programming (CNN-LP) models and solution methods are proposed according to the normal and log-normal distributions and confidence levels of product sample datasets to obtain the optimal interval feasible solutions. Furthermore, the proposed CNN-LP methods are applied to two real cases in a manufacturing company of Shaoxing City in China to perform the production planning problems under normal and log-normal distributions and 95% CNN/confidence interval of the product sample datasets. Compared with existing related linear programming methods, the proposed CNN-LP methods can make the optimal interval feasible solutions more reasonable and credible/reliable than the existing linear programming methods and overcome the defects of the existing linear programming methods in indeterminate situations.

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A rolling horizon approach for production planning and condition-based maintenance under uncertain demand

Cited by 6 publications

References 29 publications

Modular supply chain optimization considering demand uncertainty to manage risk

Modular supply chain optimization considering demand uncertainty to manage risk

An integrated model for maintenance policies and production scheduling based on immune–culture algorithm

Confidence Neutrosophic Number Linear Programming Methods Based on Probability Distributions and Their Applications in Production Planning Problems

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