Capacity sharing in a network of independent factories: A cooperative game theory approach

Renna, Paolo; Argoneto, Pierluigi

doi:10.1016/j.rcim.2010.08.009

Cited by 39 publications

(14 citation statements)

References 17 publications

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“…The capacity-sharing problem in a network of independent plants is analyzed under a cooperative game approach [14] and collaborative demand protocols [15]; however, the composition of the network in the aforementioned works is considered static.…”

Section: Literature Reviewmentioning

confidence: 99%

Order allocation in a multiple suppliers-manufacturers environment within a dynamic cluster

Renna

Perrone

2015

Int J Adv Manuf Technol

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This study faces the problem of how to evaluate order allocation strategies in a multiple suppliersmanufacturers environment within a dynamic industry cluster that is an environment in which suppliers adopt a policy based on their performance to select the clusters in which to operate. Therefore, the allocation strategy performed by the manufacturers is crucial to keep a long-term supply chain partnership with a specific industry cluster. We use simulation to evaluate order allocation strategies under different market conditions. We show how an allocation strategy that takes into account the whole supply chain's perspective leads to a sustainable development of the clusters of suppliers.

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Section: Literature Reviewmentioning

confidence: 99%

Order allocation in a multiple suppliers-manufacturers environment within a dynamic cluster

Renna

Perrone

2015

Int J Adv Manuf Technol

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“…Example studies on the route assignment decisions include Toba et al (2005) and Wu et al (2009aWu et al ( , 2009b. Example studies on the capacity-trading decisions include Wu and Chang (2007), Chen et al (2008), Chiang et al (2010) and Renna and Argoneto (2011).…”

Section: Relevant Literaturementioning

confidence: 99%

A combined dispatching criteria approach to scheduling dual flow-shops

Chiou

Chen

Wu³

2013

International Journal of Production Research

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This study investigates a dual flow-shops scheduling problem. In the scheduling context, there are two flow shops and each shop involves three processing stages. The two shops are functionally identical but their stage processing times for a job are different. While sequentially going through the three processing stages, each job is allowed to travel between the two shops. That is, for a job, each of its three stages could be processed in any of the two shops. Such a context is called dual flow-shops in the sense that the two flow shops' capacities are completely shared. The scheduling problem involves two decisions: (1) route assignment (i.e. assigning the processing stages of a job to a shop), and (2) job sequencing (i.e. sequencing the jobs waiting before each stage). The scheduling objective is to minimise the coefficient of variation of slack time (CV S ), in which the slack time (also called lateness) denotes the difference between the due date and total completion time of a job. We propose five genetic-algorithm-based (GA-based) solution methods to solve the scheduling problem, which are called GA-EDD, GA-FIFO, GA-SPT, GA-LFO, and GA-COMBO respectively. Numerical experiments indicate that GA-COMBO outperforms the other four methods.

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“…In terms of the effects, some scholars believe that the effects are positive, helping alleviate shortages of capacity, realize multiparty win-win situations and improve the efficiency of resource allocation in the whole economy. Renna and Argoneto (2011) propose a capacity sharing coordination mechanism in a production network composed of independent factories based on cooperative game theory and verify the effectiveness of this mechanism in improving plant benefits through simulation. Li and Zhang (2015) study capacity sharing behaviour between two freight forwarders and argue that capacity reservation between agents could achieve a win-win situation for both carriers and agents.…”

Section: Introductionmentioning

confidence: 99%

Capacity sharing, product differentiation and welfare

Chen

Wang

Chu

2020

Economic Research-Ekonomska Istraživanja

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This article constructs a duopoly market with product differentiation and analyses profits, consumer surplus and social welfare under three conditions: (a) two enterprises have sufficient capacity; (b) one enterprise has insufficient capacity, and another enterprise has excess capacity that is not shared; and (c) one enterprise has insufficient capacity, and another enterprise has excess capacity and engages in capacity sharing. Through comparison, the implementation conditions for and effects of capacity sharing and the role of product differentiation are revealed. The results show that capacity sharing helps increase producer surplus and social welfare. Capacity constraints reduce social welfare but can be solved by capacity sharing. Capacity sharing can only be realised when both enterprises are profitable, and the charge for capacity sharing should not be too high or too low. Product differentiation has impacts on output, profit, consumer surplus and social welfare, and these impacts are restricted by the existence of capacity constraints and capacity sharing. ARTICLE HISTORY

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Capacity sharing in a network of independent factories: A cooperative game theory approach

Cited by 39 publications

References 17 publications

Order allocation in a multiple suppliers-manufacturers environment within a dynamic cluster

Order allocation in a multiple suppliers-manufacturers environment within a dynamic cluster

A combined dispatching criteria approach to scheduling dual flow-shops

Capacity sharing, product differentiation and welfare

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