Rationing mechanisms and inventory control-policy parameters for a divergent supply chain operating with lost sales and costs of review

Paul, Brijesh; Rajendran, Chandrasekharan

doi:10.1016/j.cor.2010.11.002

Cited by 24 publications

(8 citation statements)

References 31 publications

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“…In this line of research, Koulouriotis et al [43] applied GA-based optimization to obtain suitable control parameters in serial distribution systems with backordering. Then, Paul and Rajendran [44] showed how to adjust RSLs and review periods in divergent two-stage supply chains with a single distributor and multiple retailers. Ambekar and Kapoor [45], in turn, investigated food grain distribution systems with arborescent topologies.…”

Section: Related Workmentioning

confidence: 99%

Continuous Genetic Algorithms in the Optimization of Logistic Networks: Applicability Assessment and Tuning

Ignaciuk

Wieczorek

2020

Applied Sciences

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Globalization opens up new perspectives for handling goods distribution in logistic networks. However, establishing an efficient inventory policy is challenging by virtue of the analytical and computational complexity. In this study, the goods distribution process that was governed by the order-up-to policy, implemented in either a distributed or centralized way, was investigated in the logistic systems with complex interconnection topologies. Uncertain demand may be imposed at any node, not just at conveniently chosen contact points, with a lost-sales assumption that introduces a non-linearity into the node dynamics. In order to adjust the policy parameters, the continuous genetic algorithm (CGA) was applied, with the fitness function incorporating both the operational costs and customer satisfaction level. This study investigated how to select the parameters of the popular inventory management policy when operating in the non-trivial networked structures. Moreover, precise guidelines for the CGA tuning in the considered class of problems were provided and evaluated in extensive numerical experiments.

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Section: Related Workmentioning

confidence: 99%

Continuous Genetic Algorithms in the Optimization of Logistic Networks: Applicability Assessment and Tuning

Ignaciuk

Wieczorek

2020

Applied Sciences

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“…stochastic approximation, response surface methodology, and sample path optimization (April et al, 2003)). Metaheuristic algorithms are highly relevant for complex optimization problems as they provide high quality solutions in short computing time (Juan et al, 2015, Figueira andAlmada-Lobo, 2014) and they are effective in solving multi-echelon inventory optimization problems (Paul and Rajendran, 2011). Metaheuristic approaches can guide other procedures, such as heuristics, to overcome local optimality for complex problems (Fu et al, 2005).…”

Section: Determining (Ss) Values: Scatter Search Metaheuristic (Ss)mentioning

confidence: 99%

A simulation–optimization approach for a service-constrained multi-echelon distribution network

Noordhoek

Dullaert

Lai

et al. 2018

Transportation Research Part E: Logistics and Transportation Re

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Academic research on (s,S) inventory policies for multi-echelon distribution networks with deterministic lead times, backordering, and fill rate constraints is limited. Inspired by a real-life Dutch food retail case we develop a simulation-optimization approach to optimize (s,S) inventory policies in such a setting. We compare the performance of a Nested Bisection Search (NBS) and a novel Scatter Search (SS) metaheuristic using 1280 instances from literature and we derive managerial implications from a real-life case. Results show that the SS outperforms the NBS on solution quality. Additionally, supply chain costs can be saved by allowing lower fill rates at upstream echelons.

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“…They extended the concept of basic Petri nets to discover supply chain conflict before it occurs and has detrimental effects on system performance. Brijesh and Chandrasekharan [15] determined the best installation inventory control policy or order policy parameters in a static divergent two-stage supply chain with one distributor and many retailers. They did this by using a genetic algorithm-based heuristic methodology to optimally solve problems over a large finite time horizon.…”

Section: Supply Chain Managementmentioning

confidence: 99%

A Petri Net Pareto ISO 31000 Workflow Process Decision Making Approach for Supply Chain Risk Trigger Inventory Decisions in Government Organizations

Rodger

Pankaj²,

Nahouraii³

2014

IIM

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The Petri Net Pareto method proposed in this study has the advantages of acting directly on computing results by assessment of workflow process information. Our study contributes to the literature because it has investigated an ISO 31000 workflow process approach to group decision making for reducing backorders in the supply chain, from an integrated perspective utilizing Pareto charts and Petri nets. The contribution to the literature is also enhanced by the L-Project illustrative example that presents an evaluation hierarchy of supply chain risk. The proposed method is appropriate for use in situations in which assessment information may be qualitative or precise quantitative information is either unavailable or too costly to compute.

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Rationing mechanisms and inventory control-policy parameters for a divergent supply chain operating with lost sales and costs of review

Cited by 24 publications

References 31 publications

Continuous Genetic Algorithms in the Optimization of Logistic Networks: Applicability Assessment and Tuning

Continuous Genetic Algorithms in the Optimization of Logistic Networks: Applicability Assessment and Tuning

A simulation–optimization approach for a service-constrained multi-echelon distribution network

A Petri Net Pareto ISO 31000 Workflow Process Decision Making Approach for Supply Chain Risk Trigger Inventory Decisions in Government Organizations

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