A decentralized supply chain planning model: a case study of hardboard industry

Ghasemi, Peiman; Khalili‐Damghani, Kaveh; Hafezolkotob, Ashkan; Raissi, Sadigh

doi:10.1007/s00170-017-0802-3

Cited by 21 publications

(12 citation statements)

References 39 publications

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“…Ali et al [49], while modeling a reverse supply chain, examined its application to air conditioning products. The uncertainty in the considered sustainable supply chain was fuzzy; in fact supply chaimn modeling (SCM) is extensively discussed in literature [50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65]. Locating collection and recycling centers was one of the most important objectives of this study.…”

Section: Modeling Uncertainty In Supply Chainmentioning

confidence: 99%

Designing a Sustainable Reverse Logistics Network Considering the Conditional Value at Risk and Uncertainty of Demand under Different Quality and Market Scenarios

Sajedi

Sarfaraz

Bamdad

et al. 2020

IJE

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Section: Modeling Uncertainty In Supply Chainmentioning

confidence: 99%

Designing a Sustainable Reverse Logistics Network Considering the Conditional Value at Risk and Uncertainty of Demand under Different Quality and Market Scenarios

Sajedi

Sarfaraz

Bamdad

et al. 2020

IJE

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“…Constraints (3) make sure that the interdictor can only attack those arcs through which a route is passed, meaning that if there is no planned route which runs across the arc ( , ) ij, the relevant interdiction variable (i.e., ij w ) will inevitably take zero. Constraints (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) are related to the formulation of the constrained location-routing problem (CLRP). Indeed, Constraints (5) and (6) guarantee that all customer nodes are served within vehicle capacity and are assigned within depot capacity, respectively.…”

Section: Problem Statementmentioning

confidence: 99%

“…Constraints (10) state that each vehicle must be employed once. Constraints (11) warrant that a customer has to be assigned to a depot only if there is a route between them. Finally, Constraints (12)- (16) specify the variables used in the formulation.…”

Section: Problem Statementmentioning

confidence: 99%

“…It consists of two primary problems, each of which is known as a hard combinatorial optimization problem; facility location problem (FLP) and vehicle routing problem (VRP). Simultaneous consideration of these two concepts will complicate the problem [10][11][12]. Although these two problems have traditionally been taken into account at different levels of planning, it has been shown that the integrated approach of LRP can reduce the overall cost of the long-term planning horizon significantly.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The study proposed in using game theory to investigate the interdependence between firms (Xu and Beamon, 2006). Building upon the work of Xu and Beamon (2006), Ghasemi et al (2017) proposed another coordination mechanism for production and distribution problems using a decentralised approach. The works of Lozano et al (2013) and Min et al (2008) provide useful contributions in cooperative game theory, where the former looked into horizontal cooperation and the latter studied cluster supply chains.…”

Section: Cooperative and Non-cooperative Gamesmentioning

confidence: 99%

Game theory in supply chain management: current trends and applications

Vasnani

Chua

Ocampo³

et al. 2019

IJADS

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As game theory continues to be a relevant approach in evaluating supply chain coordination and competition, this paper explores the various applications and current trends of game theory to supply chain management through an exhaustive review on the literature. This work reviews more than 200 papers which cover several supply chain formations, structures, environments, and decisions together with different game theoretic concepts and applications. First, the paper elaborates on the basic concepts of Nash and Stackelberg games to allow further understanding of their applications in supply chain analysis. The review is then presented and divided into three segments: 1) the development of game theory; 2) the use of Nash and Stackelberg solution concepts in analysing supply chain management; 3) the integration of game theory to different supply chain structures, decisions, and present conditions. Moreover, descriptive analytics are also provided to highlight the distribution of trending topics in the domain field. Finally, the paper provides a summary of the review and recommendation for future works in line with using game theory for supply chain analysis.

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A decentralized supply chain planning model: a case study of hardboard industry

Cited by 21 publications

References 39 publications

Designing a Sustainable Reverse Logistics Network Considering the Conditional Value at Risk and Uncertainty of Demand under Different Quality and Market Scenarios

Designing a Sustainable Reverse Logistics Network Considering the Conditional Value at Risk and Uncertainty of Demand under Different Quality and Market Scenarios

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

Game theory in supply chain management: current trends and applications

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