Developing Multiobjective Equilibrium Optimization Method for Sustainable Uncertain Supply Chain Planning Problems

Liu, Yankui; Chen, Yanju; Yang, Guang‐Fu

doi:10.1109/tfuzz.2018.2851508

Cited by 24 publications

(6 citation statements)

References 42 publications

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“…Since managers are always risk averse (Chen et al., 2018), risk management becomes one of the most important objectives in corporate management. On the one hand, the literature on supply chain management mainly focuses on the risks related to total costs (Yang and Liu, 2015), operational risk (Sadghiani et al., 2015), environmental risk (Liu et al., 2019b), and service risk (Mousazadeh et al., 2015). However, few studies have considered the risk related to pharmaceutical safety and incorporated it into the objective function in their problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Two‐stage distributionally robust optimization model for a pharmaceutical cold supply chain network design problem

Liu

Yang

2023

Int Trans Operational Res

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Pharmaceutical safety has received increasing attention from governments and corporations, and building a safe and effective pharmaceutical cold supply chain network has become an important issue. This paper proposes a two‐stage pharmaceutical cold supply chain network design problem considering drug safety, in which the drug demand, transportation costs, and drug safety risk costs are assumed to be random variables. Due to the presence of uncertainty and the fact that information about the distribution of uncertain parameters is often only partially known, a distributionally robust optimization method is used to handle the uncertainty. A two‐stage distributionally robust optimization model is constructed, in which the reliability of the estimation of the demand necessary to satisfy the entire cold chain network is ensured to be greater than a certain predetermined level by introducing an ambiguous joint chance constraint. The decision process for the problem can be divided into strategic and operational decisions with the goal of minimizing the total costs related to facility construction, the purchase of raw materials, drug production, transportation, and safety risks. By introducing an ambiguity set with mean and covariance information to describe the uncertain parameters, the two‐stage model is eventually reformulated as a standard second‐order cone program, thus making it computationally tractable. Finally, numerical experiments are presented to demonstrate the effectiveness of the proposed models and optimization methods.

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Section: Literature Reviewmentioning

confidence: 99%

Two‐stage distributionally robust optimization model for a pharmaceutical cold supply chain network design problem

Liu

Yang

2023

Int Trans Operational Res

Self Cite

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“…Therefore, it is often unrealistic for a single enterprise to obtain information on all of the products in the market, and how to deal with uncertain market demand in the production of products has gradually become a key link in manufacturing research. In the existing research, uncertain market demand is mainly divided into stochastic uncertain demand and fuzzy uncertain demand [8,11]. For the production problem under stochastic uncertain demand, after considering the dependence of equipment conditions and production rate on output, Zhang et al [12] proposed an integrated decision-making strategy for single-machine production and maintenance under uncertain demand and solved it through a two-stage stochastic programming model.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiobjective Lot Sizing and Scheduling of Multiproduct Switching Production in the Process Industry Considering Uncertain Market Information Under Mass Customization

Tang

Wang

2022

IEEE Access

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With the gradual diversification of customer demand, to improve the rapid response ability of enterprises, this paper fully considers uncertain market information under the background of mass customization and establishes a new process industry multi-product switching production lot sizing and scheduling model with the goal of minimizing the maximum completion time and total switching cost. Fuzzy chance-constrained programming is used to explicitly incorporate market demand with uncertain quantities into the model. Starting from reality, this paper considers the switching cost of equipment when processing multiple varieties of products and skillfully integrates the conversion rate of materials during processing into the novel model, making the entire production system closer to the real state. It provides a new concept to consider cost reduction for actual workshop scheduling management. In addition, this paper proposes an improved multi-objective genetic particle swarm optimization (SMOPSO-IIs) algorithm to simulate and solve the model. The operation results show that the Pareto solution obtained by the SMOPSO-IIs algorithm is better overall. Finally, the model is solved by example simulation, and the operation results are analyzed along with a scheduling Gantt chart to verify its applicability and effectiveness. The model presented in this paper can be used to further shorten the gap between production theory and practical application and improve the current workshop scheduling management system of the process industry.INDEX TERMS Mass customization, process industry lot sizing and scheduling; uncertain market information; product switching

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“…However, their research does not consider more complex cost types, such as trans-shipment costs, and focuses only on design decisions. Liu et al [26] introduced a twostage mathematical optimization model to the supply-chain problem under uncertain demand. In the first stage, the production quantity of each facility of the manufacturing network and the production quantity to be transported between the network facilities are calculated.…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Stage Production Planning Under Stochastic Demand: Case Study of Fertilizer Manufacturing

Wang¹,

Syu²,

Chou³

et al. 2022

Computers, Materials &Amp; Continua

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Agriculture is a key facilitator of economic prosperity and nourishes the huge global population. To achieve sustainable agriculture, several factors should be considered, such as increasing nutrient and water efficiency and/or improving soil health and quality. Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effectiveness of crop yields. Fertilizer supplies most of the necessary nutrients for plants, and it is estimated that at least 30%-50% of crop yields is attributable to commercial fertilizer nutrient inputs. Fertilizer is always a major concern in achieving sustainable and efficient agriculture. Applying reasonable and customized fertilizers will require a significant increase in the number of formulae, involving increasing costs and the accurate forecasting of the right time to apply the suitable formulae. An alternative solution is given by two-stage production planning under stochastic demand, which divides a planning schedule into two stages. The primary stage has non-existing demand information, the inputs of which are the proportion of raw materials needed for producing fertilizer products, the cost for purchasing materials, and the production cost. The total quantity of purchased material and produced products to be used in the blending process must be defined to meet as small as possible a paid cost. At the second stage, demand appears under multiple scenarios and their respective possibilities. This stage will provide a solution for each occurring scenario to achieve the best profit. The two-stage approach is presented in this paper, the mathematical model of which is based on linear integer programming. Considering the diversity of fertilizer types, the mathematical model can advise manufacturers about which products will generate as much as profit as possible. Specifically, two objectives are taken into account. First, the paper's thesis focuses on minimizing overall system costs, e.g., including inventory cost, purchasing cost, unit cost, and ordering cost at Stage 1. Second, the thesis

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Developing Multiobjective Equilibrium Optimization Method for Sustainable Uncertain Supply Chain Planning Problems

Cited by 24 publications

References 42 publications

Two‐stage distributionally robust optimization model for a pharmaceutical cold supply chain network design problem

Two‐stage distributionally robust optimization model for a pharmaceutical cold supply chain network design problem

Multiobjective Lot Sizing and Scheduling of Multiproduct Switching Production in the Process Industry Considering Uncertain Market Information Under Mass Customization

Two-Stage Production Planning Under Stochastic Demand: Case Study of Fertilizer Manufacturing

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