start-ver=1.4
cd-journal=joma
no-vol=56
cd-vols=
no-issue=4
article-no=
start-page=2581
end-page=2593
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2022
dt-pub=20220818
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Collaborative bargaining solution in tandem supply chain through cooperative game theoretical approach
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=There are many studies about negotiation procedures for contract problems in supply chains. Several recent papers have considered a new negotiation procedure for a repurchase contract problem in a supply chain consisting of a manufacturer and a retailer. There, usually, are some wholesalers between a manufacturer and a retailer. Therefore, a supply chain including some wholesalers in addition to a manufacturer and a retailer should be considered. In this study, we call the supply chain in which three or more members are arranged in series the tandem supply chain. We, firstly, address a negotiation problem for a contract about wholesale and repurchase prices in the tandem supply chain in which three members, that is, a manufacturer, a wholesaler and a retailer are arranged in series. The whole contract in the tandem supply chain is composed of two contracts dependent mutually, i.e., the contract between the manufacturer and wholesaler and the contract between the wholesaler and retailer. The collaborative bargaining solution in the tandem supply chain consisting of three members is discussed. This paper, finally, formulates the tandem supply chain including several wholesalers.
en-copyright=
kn-copyright=

en-aut-name=ArizonoIkuo
en-aut-sei=Arizono
en-aut-mei=Ikuo
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=TakemotoYasuhiko
en-aut-sei=Takemoto
en-aut-mei=Yasuhiko
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

affil-num=1
en-affil=Okayama University,
kn-affil=

affil-num=2
en-affil=Kindai University
kn-affil=
en-keyword=supply chain contract
kn-keyword=supply chain contract
en-keyword=collaborative coordination approach
kn-keyword=collaborative coordination approach
en-keyword=Nash bargaining solution
kn-keyword=Nash bargaining solution
en-keyword=Hessian matrix
kn-keyword=Hessian matrix
END

start-ver=1.4
cd-journal=joma
no-vol=2021
cd-vols=
no-issue=
article-no=
start-page=6610670 
end-page=
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2021
dt-pub=20210108
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Ant Colony Optimization Using Common Social Information and Self-Memory
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Ant colony optimization (ACO), which is one of the metaheuristics imitating real ant foraging behavior, is an effective method to ?nd a solution for the traveling salesman problem (TSP). The rank-based ant system (AS(rank)) has been proposed as a developed version of the fundamental model AS of ACO. In the AS(rank), since only ant agents that have found one of some excellent solutions are let to regulate the pheromone, the pheromone concentrates on a specific route. As a result, although the AS(rank) can find a relatively good solution in a short time, it has the disadvantage of being prone falling into a local solution because the pheromone concentrates on a specific route. This problem seems to come from the loss of diversity in route selection according to the rapid accumulation of pheromones to the specific routes. Some ACO models, not just the AS(rank), also suffer from this problem of loss of diversity in route selection. It can be considered that the diversity of solutions as well as the selection of solutions is an important factor in the solution system by swarm intelligence such as ACO. In this paper, to solve this problem, we introduce the ant system using individual memories (ASIM) aiming to improve the ability to solve TSP while maintaining the diversity of the behavior of each ant. We apply the existing ACO algorithms and ASIM to some TSP benchmarks and compare the ability to solve TSP.
en-copyright=
kn-copyright=

en-aut-name=TamuraYoshiki
en-aut-sei=Tamura
en-aut-mei=Yoshiki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=SakiyamaTomoko
en-aut-sei=Sakiyama
en-aut-mei=Tomoko
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=ArizonoIkuo
en-aut-sei=Arizono
en-aut-mei=Ikuo
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

affil-num=1
en-affil=Graduate School of Natural Science and Technology, Okayama University
kn-affil=

affil-num=2
en-affil=Department of Information Systems Science, Faculty of Science and Engineering, Soka University
kn-affil=

affil-num=3
en-affil=Graduate School of Natural Science and Technology, Okayama University
kn-affil=
END