Two-echelon supply chain coordination with advertising-driven demand under Stackelberg game policy

Noh, Jiseong; Kim, Jong Soo; Sarkar, Biswajit

doi:10.1504/ejie.2019.098516

Cited by 30 publications

(14 citation statements)

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“…is competition model fully reflects the decision-making position between the manufacturer and the remanufacturer. Following recent work in the general supply chain management literature [23][24][25][26][27][28], as well as works in take-back regulation literature [10,29,30], we model Stackelberg competition.…”

Section: Literature Reviewmentioning

confidence: 99%

The Elimination Effect of Take-Back Regulation on Backward Production Capacity Remanufacturing Supply Chains

Xintong

Wei

et al. 2021

Mathematical Problems in Engineering

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With more and more end-of-life products in daily life, many companies are engaging in remanufacturing, including backward production capacity (BPC) enterprises. Meanwhile, take-back regulation always asks the manufacturer to take back end-of-life products to reduce pollution. However, the effect of take-back regulation on remanufacturers remains unclear. In this paper, we first analyzed the take-back regulation threshold with the elimination effect. We then discussed the impact on stakeholders, such as the manufacturer, the remanufacturer, consumers, and the government. A two-stage dynamic market model is proposed, which considers the market with/without BPC remanufacturer. Take-back regulation’s elimination effect is studied, and the results show that when the collection target reaches the elimination threshold, the manufacturer’s profit declines, the BPC remanufacturer is eliminated, consumer surplus decreases, and social welfare is improved. Besides, to cope with a high take-back regulation target, the manufacturer will reduce new product output, which leads to BPC remanufacturer’s benefits decline. A numerical study is given with a different collecting strategy of the BPC remanufacturer, the incentive interval, the inhibition interval, and the elimination interval of the take-back regulation for stakeholders which are described. At last, some managerial insights are given to help the regulator implement take-back regulation.

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Section: Literature Reviewmentioning

confidence: 99%

The Elimination Effect of Take-Back Regulation on Backward Production Capacity Remanufacturing Supply Chains

Xintong

Wei

et al. 2021

Mathematical Problems in Engineering

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“…Step 2: Check the vector x in constraints (2)(3). If the solution meets these constraints, then go to step 3; otherwise, go to step 4.…”

Section: Problem Statementmentioning

confidence: 99%

“…Other initiatives, moreover, have been taken to provide insights into interdiction location-routing networks, including adversarial risk analysis, system survivability, and interdiction networks [2]. Network interdiction addresses distributor-interdictor models, known as Stackelberg game, in which decisions made by two intelligent players, called distributor and interdictor, affect network performance [3]. The distributor tries to optimize his objective function, for example, detecting the shortest path [4], maximizing shipping flow [5,6], and minimizing the maximum profit [7].…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of a Bilevel Logistics Network and an Arc Interdiction Location-routing Problem

Babaeinesami

Tohidi

Seyedaliakbar

2020

IJE

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There is a high interest in optimization of transportation and logistics networks due to its high impact on the economic performance of supply chain networks. This paper presents a bilevel mixed-integer programming model as well as a solution method to manage distribution process in a logistic network, where two decision makers, called distributor and interdictor, make efforts to achieve their contradictory targets. This problem, known as arc interdiction location-routing problem (AI-LRP), is in fact a new, extended version of the classical LRP. The distributor strives to deliver goods to customers with minimal risk and cost, while the interdictor, by contrast, endeavors to disrupt products flow through a few critical arcs. AI-LRP has wide applications in reality, including distribution of particular goods like money, precious metals, hazardous materials, and prisoners that may need security measures. The interplay between two decision makers is formulated as a bilevel model. To solve the model, a novel genetic algorithm (NGA) is devised in which the density ordered heuristic of the knapsack problem is applied to generate an initial population of solutions. Computational results illustrate that NGA outperforms a commercial solver in terms of computational time and quality of solutions.

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“…In this model, they studied co-operative issues in advertising. Recently, Noh et al [49] developed a two-echelon supply chain model, where the demand depends on the advertisement. They used the Stackelberge game policy to solve this model.…”

Section: Introductionmentioning

confidence: 99%

Multi-Product Production System with the Reduced Failure Rate and the Optimum Energy Consumption under Variable Demand

2019

Self Cite

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The advertising of any smart product is crucial in generating customer demand, along with reducing sale prices. Naturally, a decrease in price always increases the demand for any smart product. This study introduces a multi-product production process, taking into consideration the advertising- and price-dependent demands of products, where the failure rate of the production system is reduced under the optimum energy consumption. For long-run production systems, unusual energy consumption and machine failures occur frequently, which are reduced in this study. All costs related with the production system are included in the optimum energy costs. The unit production cost is dependent on the production rate of the machine and its failure rate. The aim of this study is to obtain the optimum profit with a reduced failure rate, under the optimum advertising costs and the optimum sale price. The total profit of the model becomes a complex, non-linear function, with respect to the decision variables. For this reason, the model is solved numerically by an iterative method. However, the global optimality is proved numerically, by using the Hessian matrix. The numerical results obtained show that for smart production, the maximum profit always occurs at the optimum values of the decision variables.

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Two-echelon supply chain coordination with advertising-driven demand under Stackelberg game policy

Cited by 30 publications

References 0 publications

The Elimination Effect of Take-Back Regulation on Backward Production Capacity Remanufacturing Supply Chains

The Elimination Effect of Take-Back Regulation on Backward Production Capacity Remanufacturing Supply Chains

Joint Optimization of a Bilevel Logistics Network and an Arc Interdiction Location-routing Problem

Multi-Product Production System with the Reduced Failure Rate and the Optimum Energy Consumption under Variable Demand

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