Joint replenishment of multi retailer with variable replenishment cycle under VMI

Verma, Nishant Kumar; Chakraborty, Abhishek; Chatterjee, Ashis Kumar

doi:10.1016/j.ejor.2013.10.001

Cited by 30 publications

(12 citation statements)

References 6 publications

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“…Verma et al. () extend the model of Darwish and Odah () to a replenishment scheme allowing for different replenishment cycles for each retailer and show that more relevant costs can be saved. Donohue () studies a supply chain with two production modes (the second production is similar to a replenishment) and uses a return contract to coordinate the supply chain.…”

Section: Introductionmentioning

confidence: 99%

Supply chain coordination with an option contract under vendor‐managed inventory

Cai

Han

et al. 2015

Int Trans Operational Res

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In this paper, we propose an option contract for a two‐echelon supply chain operating under vendor‐managed inventory (VMI). We find that the supply chain coordination and Pareto improvement can be achieved synchronously. As a comparison, a subsidy contract is introduced to coordinate the supply chain. The paper further considers the influence of a replenishment tactic on the performance of the supply chain. After introducing a replenishment tactic, the supply chain can replenish the products to satisfy unmet demand during the selling season. We further use an option contract to coordinate the supply chain, and a Pareto improvement can be gained. All our findings are numerically illustrated.

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Section: Introductionmentioning

confidence: 99%

Supply chain coordination with an option contract under vendor‐managed inventory

Cai

Han

et al. 2015

Int Trans Operational Res

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“…At present, the research on joint ordering problem and algorithms has made great achievement [21][22][23][24][25][26]; most of them adopt heuristic algorithms to solve it, such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution algorithm (DE), and improved or hybrid algorithms. Furthermore, heuristic algorithms are combined with simulation methods.…”

Section: Jointmentioning

confidence: 99%

An Improved Swarm Intelligence Algorithm for Multi-Item Joint Ordering Strategy of Cruise Ship Supply

Huang

Yang

2020

Mathematical Problems in Engineering

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From the perspective of the global value chain of the cruise industry, the output value of cruise operation accounts for 50%, with the highest added value, and cruise ship supply is the crucial link in cruise operation management, with substantial economic benefits. erefore, optimizing the purchasing process can not only save costs for cruise companies but also improve cruise level service. Aiming at the purchasing characteristics and modes of cruise ship supply, an optimization model of multi-item joint ordering is constructed under global cruise ship supply chain, considering different order cycles, integrated operation of purchase, delivery, and inventory based on a cruise distribution center. And an improved swarm intelligence algorithm, called fireworks algorithm with inertia weight (WFWA), is proposed for global optimization of the objective function. By comparing the optimization results with fireworks algorithm (FWA), genetic algorithm (GA), and particle swarm optimization (PSO) through experimental tests, it demonstrates that WFWA has higher optimization accuracy and better global convergence.

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“…Results show that greater reduction in the total cost of the supply chain can be obtained using VMI. Verma et al (2014) proposed an alternative replenishment scheme allowing for different replenishment cycles for each retailer in the single-vendor multi-retailer supply chain under VMI partnership. Taleizadeh and Noori-daryan (2014) considered a decentralized three-layer supply chain including a supplier, a producer and arbitrary number of retailers.…”

Section: Literature Reviewmentioning

confidence: 99%

A discrete particle swarm optimization algorithm with local search for a production-based two-echelon single-vendor multiple-buyer supply chain

2015

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This paper formulates a two-echelon singleproducer multi-buyer supply chain model, while a single product is produced and transported to the buyers by the producer. The producer and the buyers apply vendormanaged inventory mode of operation. It is assumed that the producer applies economic production quantity policy, which implies a constant production rate at the producer. The operational parameters of each buyer are sales quantity, sales price and production rate. Channel profit of the supply chain and contract price between the producer and each buyer is determined based on the values of the operational parameters. Since the model belongs to nonlinear integer programs, we use a discrete particle swarm optimization algorithm (DPSO) to solve the addressed problem; however, the performance of the DPSO is compared utilizing two well-known heuristics, namely genetic algorithm and simulated annealing. A number of examples are provided to verify the model and assess the performance of the proposed heuristics. Experimental results indicate that DPSO outperforms the rival heuristics, with respect to some comparison metrics.

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Joint replenishment of multi retailer with variable replenishment cycle under VMI

Cited by 30 publications

References 6 publications

Supply chain coordination with an option contract under vendor‐managed inventory

Supply chain coordination with an option contract under vendor‐managed inventory

An Improved Swarm Intelligence Algorithm for Multi-Item Joint Ordering Strategy of Cruise Ship Supply

A discrete particle swarm optimization algorithm with local search for a production-based two-echelon single-vendor multiple-buyer supply chain

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