Optimal Design of Multisite Batch-Storage Network Under Scenario-Based Demand Uncertainty

Yi, Gyeongbeom; Kim, Yong-Ha; Lee, Euy Soo

doi:10.1080/00986440701399871

Cited by 4 publications

(7 citation statements)

References 10 publications

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“…It can easily treat the costs that are dependent on operation frequency, such as setup costs and inventory holding costs, which also require many binary variables under ordinary planning models. Finally, through application of a method described previously, 8,9 the model can significantly reduce the computational burden required to analyze a variety of uncertainties. For the sake of brevity, discussion of some important issues, such as multitasking batch/semi-continuous processes and uncertainty, shall be included in a future study.…”

Section: V C 2011 American Institute Of Chemical Engineersmentioning

confidence: 99%

“…BSN can account for most supply chain components, for example the purchase of raw materials, production, transportation, and demand for the finished product. The periodic square wave (PSW) model has been used successfully to find analytical solutions for a variety of supply chain systems, including a parallel system with batch processes and storage units, 1 a sequential multistage BSN, 2 a nonsequential BSN including recycling streams, 3 a BSN supported by financial transactions and cash flows, 4,5 multitasking semi-continuous processes in a BSN, 6 a BSN that considers uncertainty and waste streams, 7,8 a multisite BSN, 9 and a BSN that includes the effects of exchange rates and taxes on a multinational corporation (MNC). 5,10 Additional Supporting Information may be found in the online version of this article.…”

Section: Introductionmentioning

confidence: 99%

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Optimal design of multiperiod batch‐storage network including transportation processes

Yi¹,

Reklaitis²

2011

AIChE Journal

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An optimally designed of batch-storage network that uses a periodic square wave model provides analytical lot-sizing equations for a complex supply chain network characterized as a multisupplier, multiproduct, multi-stage, nonserial, multicustomer, cyclic system, with recycling or remanufacturing. The network structure includes multiple currency flows and material flows. The processes involve multiple feedstock and product materials with fixed compositions that are highly suitable for production processes. Transportation processes that carry multiple materials of unknown composition are included in this study, and the time frame is varied from a single infinite period to multiple finite periods to accommodate nonperiodic parameter variations. The objective function in the optimization is chosen to minimize the difference between the opportunity costs of currency/material inventories and stockholder benefits given in the numeraire currency. Expressions for the Kuhn-Tucker conditions for the optimization problem are reduced to a multiperiod subproblem describing the average flow rates and the analytical lot-sizing equations. The multiperiod lot-sizing equations are shown to differ from their single-period counterparts. The multiperiod subproblem yields a multiperiod planning model that has many advantages over existing planning models. For example, it contains terms that represent operation frequency dependent costs. Realistically sized numerical examples that deal with multinational corporations are formulated and tested. The effects of corporate income taxes, interest rates, and exchange rates are presented.

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Section: V C 2011 American Institute Of Chemical Engineersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal design of multiperiod batch‐storage network including transportation processes

Yi¹,

Reklaitis²

2011

AIChE Journal

Self Cite

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“…Transportation processes are considered a subset of batch processes without loss of generality. 7 Each storage unit is involved in six types of material movement: purchasing from suppliers (k [ K(j)), shipping in response to consumer demand (m [ M(j)), discharging to waste disposal sinks (n [ N(j)), feeding to production processes, producing from production processes, and producing waste materials from production processes. Figure 5 shows a typical configuration of a process and Figure 6 shows the flows of that process.…”

Section: Optimization Modelmentioning

confidence: 99%

“…4,5 The range of physical process structures for which the PSW model can be used has expanded from batch processes to multitasking batch or semicontinuous processes 6 and a multisite batch production/transportation network (i.e., a largescale supply chain system). 7 A BSN basically resembles a state task network (STN) in which batch corresponds to task and storage corresponds to state; thus, BSNs provide an effective representation of supply chains such as purchasing, production, transportation, and demand processes.…”

Section: Introductionmentioning

confidence: 99%

“…A review paper for optimization under uncertainty has been published. 8 Recent research in this area can be divided into various categories: simulation-based optimization, 9 scenariobased optimization, 7 chance-constrained programming, 10 multistage stochastic programming with recourse, 11 approximate dynamic programming, 12 multiparametric programming, 13 genetic algorithm with mathematical programming, 14 robust optimization with bounded uncertainty, 15 a fuzzy optimization model, 16 and a branch and cut algorithm that uses Lagrangean decomposition. 17 However, the models developed to date are unsuitable for use in real applications due to severe computational complexity arising from the treatment of uncertainty as well as unknown probabilistic parameters.…”

Section: Introductionmentioning

confidence: 99%

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Optimal design of batch‐storage network under joint uncertainties

Yi¹

2008

AIChE Journal

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This study sought to find analytic solutions to the problem of determining the optimal capacity of a batch-storage network to meet demand for finished products in a system undergoing joint random variations of operating time and batch quantity. The raw material purchasing flow and final product demand flow are susceptible to joint random variations in the order cycle time and batch size. The production processes also have joint random variations in production cycle time and product quantity. Waste regeneration or disposal processes are included into the network to treat the spoiled materials from failed batches. The objective function of the optimization is minimizing the expected total cost, which is composed of setup and inventory holding costs as well as the capital costs of constructing processes and storage units. A production and inventory method, the PSW (periodic square wave) model, provides a unique graphical method to find the upper/lower bounds and average of random flows, which are used to construct terms of the objective function and constraints of the optimization model. The advantage of this model is that it provides a set of simple analytic solutions while also maintaining a realistic description of the random material flows between processes and storage units; as a consequence of these analytic solutions, the computation burden is significantly reduced. The proposed method has the potential to rapidly provide very useful data on base investment decisions during the early plant design stage. It should be particularly useful when these decisions must be made in a highly uncertain business environment.

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Optimal Design of Batch-Storage Network under Sporadic Operating Time Loss

Lee

2013

Ind. Eng. Chem. Res.

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The purpose of this study was to find an analytic solution for the optimal capacity of a batch-storage network that meets the demand for finished products under sporadic operating time losses. Batch processes are prone to random but infrequent operating time losses. Two common remedies for such failures are the duplication of the process and increases in the process and storage capacity, which are both very costly. A model that minimizes the total cost, which consists of setup and inventory holding costs as well as the capital costs of constructing processes and storage units, has been developed by using the framework of a batch-storage network with the flows that are susceptible to sporadic operating time losses. A graphical analysis for the estimation of the upper and lower bounds of the flows under sporadic shutdowns is used in this approach. The advantage of our model is that it provides a set of simple analytic solutions in spite of its realistic description of the material flows between processes and storage units.

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Optimal Design of Multisite Batch-Storage Network Under Scenario-Based Demand Uncertainty

Cited by 4 publications

References 10 publications

Optimal design of multiperiod batch‐storage network including transportation processes

Optimal design of multiperiod batch‐storage network including transportation processes

Optimal design of batch‐storage network under joint uncertainties

Optimal Design of Batch-Storage Network under Sporadic Operating Time Loss

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