Optimizing inventory policies in process networks under uncertainty

García-Herreros, Pablo; Agarwal, Anshul; Wassick, John M.; Grossmann, Ignacio E.

doi:10.1016/j.compchemeng.2016.05.014

Cited by 17 publications

(13 citation statements)

References 54 publications

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“…To our knowledge, the main inventory policies have not been incorporated in supply chain planning models despite being used in practice. Garcia‐Herreros et al incorporated a basestock inventory policy and optimized its parameters using stochastic programming.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…In this article, we resolve the discrepancy between the inventory curves obtained from a planning model and the implementation of inventory policies by proposing mixed‐integer programming models capable of simultaneously determining the optimal flows and the inventory policy parameters. García‐Herreros et al propose logic formulations to implement inventory policies in production systems with arrangements of inventories in series and in parallel. They optimize the inventory policy parameters using stochastic programming.…”

Section: Introductionmentioning

confidence: 99%

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Inventory policies and safety stock optimization for supply chain planning

Brunaud

Laínez-Aguirre²,

Pinto³

et al. 2018

AIChE Journal

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In this article, traditional supply chain planning models are extended to simultaneously optimize inventory policies. The inventory policies considered are the (r,Q) and (s,S) policies. In the (r,Q) inventory policy an order for Q units is placed every time the inventory level reaches level r, while in the s,S policy the inventory is reviewed in predefined intervals. If the inventory is found to be below level s, an order is placed to bring the level back to level S. Additionally, to address demand uncertainty four safety stock formulations are presented: (1) proportional to throughput, (2) proportional to throughput with risk-pooling effect, (3) explicit risk-pooling, and (4) guaranteed service time. The models proposed allow simultaneous optimization of safety stock, reserve, and base stock levels in tandem with material flows in supply chain planning. The formulations are evaluated using simulation.

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Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inventory policies and safety stock optimization for supply chain planning

Brunaud

Laínez-Aguirre²,

Pinto³

et al. 2018

AIChE Journal

Self Cite

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“…Modern industrial chemical plants consist of many process units arranged in a complex structure which produces a process network [1]. Such a process network will contain many subsystems interacting with each other through mass and energy interconnections [2].…”

Section: Introductionmentioning

confidence: 99%

Robust distributed model predictive control for systems of parallel structure within process networks

Zhang

Zhao

Spurgeon

2019

Journal of Process Control

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The parallel structure is one of the basic system architectures found in process networks. In order to achieve robust control of complex process networks, it is necessary to formulate control strategies that specifically accommodate the characteristics of such parallel systems. In this paper, the competitive coupling and competitive constraints in parallel systems are initially defined. A novel robust distributed model predictive control algorithm is then developed for such parallel systems which deals explicitly with competitive couplings, competitive constraints and uncertainties. The Lyapunov Method is used for the theoretical analysis which produces tractable linear matrix inequalities (LMI). Two simulation studies and an experimental trial are provided to validate the effectiveness of the proposed approach. These consider control of 40 user and 100 user gas boiler heating systems as well as control of two continuous stirred tank reactors (CSTRs) which are connected in parallel.

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“…Configurations of the distribution systems are getting increasingly complex and sophisticated structurally. In order to face this challenge a variety of resource management policies [17,18] and heuristics [19,20] are being developed. The main difficulty of their implementation is related to the issue of computational complexity and, thus, hardware requirements.…”

Section: Introductionmentioning

confidence: 99%

Continuous Genetic Algorithms as Intelligent Assistance for Resource Distribution in Logistic Systems

Wieczorek

Ignaciuk

2018

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This paper addresses the problem of resource distribution control in logistic systems influenced by uncertain demand. The considered class of logistic topologies comprises two types of actors—controlled nodes and external sources—interconnected without any structural restrictions. In this paper, the application of continuous-domain genetic algorithms (GAs) is proposed in order to support the optimization process of resource reflow in the network channels. GAs allow one to perform simulation-based optimization and provide desirable operating conditions in the face of a priori unknown, time-varying demand. The effectiveness of inventory management process governed under an order-up-to policy involves two different objectives—holding costs and service level. Using the network analytical model with the inventory management policy implemented in a centralized way, GAs search a space of candidate solutions to find optimal policy parameters for a given topology. Numerical experiments confirm the analytical assumptions.

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Optimizing inventory policies in process networks under uncertainty

Cited by 17 publications

References 54 publications

Inventory policies and safety stock optimization for supply chain planning

Inventory policies and safety stock optimization for supply chain planning

Robust distributed model predictive control for systems of parallel structure within process networks

Continuous Genetic Algorithms as Intelligent Assistance for Resource Distribution in Logistic Systems

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