Rational bidding using reinforcement learning: an application in automated resource allocation

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized

A theoretical and computational basis for CATNETS

The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing

Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges

In recent years, blockchain has gained widespread attention as an emerging technology for decentralization, transparency, and immutability in advancing online activities over public networks. As an essential market process, auctions have been well studied and applied in many business fields due to their efficiency and contributions to fair trade. Complementary features between blockchain and auction models trigger a great potential for research and innovation. On the one hand, the decentralized nature of blockchain can provide a trustworthy, secure, and cost-effective mechanism to manage the auction process; on the other hand, auction models can be utilized to design incentive and consensus protocols in blockchain architectures. These opportunities have attracted enormous research and innovation activities in both academia and industry; however, there is a lack of an in-depth review of existing solutions and achievements. In this paper, we conduct a comprehensive state-of-the-art survey of these two research topics. We review the existing solutions for integrating blockchain and auction models, with some application-oriented taxonomies generated. Additionally, we highlight some open research challenges and future directions towards integrated blockchain-auction models

Theoretical and Computational Basis for Economical Ressource Allocation in Application Layer Networks - Annual Report Year 1

This paper identifies and defines suitable market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. --Grid Computing

A market based approach for resolving resource constrained task allocation problems in a software development process

We consider software development as an economic activity, where goods and services can be modeled as a resource constrained task allocation problem. This paper introduces a market based mechanism to overcome task allocation issues in a software development process. It proposes a mechanism with a prescribed set of rules, where valuation is based on the behaviors of stakeholders such as biding for a task. A bid process ensures that a stakeholder, who values the resource most, will have it allocated for a limited number of times. To observe the bidders behaviors, we initiate an approach incorporated with a process simulation model. Our preliminary results support the idea that our model is useful for optimizing the value based task allocations, creating a market value for the project assets, and for achieving proper allocation of project resources specifically on large scale software projects

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

Coordination mechanisms with mathematical programming models for decentralized decision-making, a literature review

[EN] The increase in the complexity of supply chains requires greater efforts to align the activities of all its members in order to improve the creation of value of their products or services offered to customers. In general, the information is asymmetric; each member has its own objective and limitations that may be in conflict with other members. Operations managements face the challenge of coordinating activities in such a way that the supply chain as a whole remains competitive, while each member improves by cooperating. This document aims to offer a systematic review of the collaborative planning in the last decade on the mechanisms of coordination in mathematical programming models that allow us to position existing concepts and identify areas where more research is needed.Rius-Sorolla, G.; Maheut, J.; Estelles Miguel, S.; García Sabater, JP. (2020). Coordination mechanisms with mathematical programming models for decentralized decision-making, a literature review. Central European Journal of Operations Research. 28(1):61-104. https://doi.org/10.1007/s10100-018-0594-zS61104281Acar Y, Atadeniz SN (2015) Comparison of integrated and local planning approaches for the supply network of a globally-dispersed enterprise. Int J Prod Econ 167:204–219. https://doi.org/10.1016/j.ijpe.2015.05.028Agnetis A, Hall NG, Pacciarelli D (2006) Supply chain scheduling: sequence coordination. Discrete Appl Math 154(15):2044–2063. https://doi.org/10.1016/j.dam.2005.04.019Agnetis A, Aloulou MA, Fu LL (2016) Production and interplant batch delivery scheduling: Dominance and cooperation. Int J Prod Econ 182:38–49. https://doi.org/10.1016/j.ijpe.2016.08.007Albrecht M (2010) Supply chain coordination mechanisms Lecture notes in economics and mathematical systems, vol 628. Springer, Berlin. https://doi.org/10.1007/978-3-642-02833-5Albrecht M, Stadtler H (2015) Coordinating decentralized linear programs by exchange of primal information. Eur J Oper Res 247(3):788–796. https://doi.org/10.1016/j.ejor.2015.06.045Arkan A, Hejazi SR (2012) Coordinating orders in a two echelon supply chain with controllable lead time and ordering cost using the credit period. Comput Ind Eng 62(1):56–69. https://doi.org/10.1016/j.cie.2011.08.016Arshinder, Kanda A, Deshmukh SG (2008) Supply chain coordination: perspectives, empirical studies and research directions. Int J Prod Econ 115(2):316–335. https://doi.org/10.1016/j.ijpe.2008.05.011Attanasio A, Ghiani G, Grandinetti L, Guerriero F (2006) Auction algorithms for decentralized parallel machine scheduling. Parallel Comput 32(9):701–709. https://doi.org/10.1016/j.parco.2006.03.002Badole CM, Jain R, Rathore APS, Nepal B (2012) Research and opportunities in supply chain modeling: a review. Int J Supply Chain Manag 1(3):63–86Bajgiran OS, Zanjani MK, Nourelfath M (2016) The value of integrated tactical planning optimization in the lumber supply chain. Int J Prod Econ 171:22–33. https://doi.org/10.1016/j.ijpe.2015.10.021Behnamian J (2014) Multi-cut Benders decomposition approach to collaborative scheduling. Int J Comput Integr Manuf 28(11):1–11. https://doi.org/10.1080/0951192X.2014.961963Ben-Daya M, Darwish M, Ertogral K (2008) The joint economic lot sizing problem: review and extensions. Eur J Oper Res 185(2):726–742. https://doi.org/10.1016/j.ejor.2006.12.026Benders JF (1962) Partitioning procedures for solving mixed-variables programming problems. Numer Math 4(1):238–252. https://doi.org/10.1007/BF01386316Bhatnagar R, Chandra P, Goyal SK (1993) Models for multi-plant coordination. Eur J Oper Res 67(2):141–160. https://doi.org/10.1016/0377-2217(93)90058-UBuer T, Homberger JJ, Gehring H (2013) A collaborative ant colony metaheuristic for distributed multi-level uncapacitated lot-sizing. Int J Prod Res 51(17):5253–5270. https://doi.org/10.1080/00207543.2013.802822Buer T, Ziebuhr M, Kopfer H (2015) A coordination mechanism for a collaborative lot-sizing problem with rivaling agents. In: Mattfeld D, Spengler T, Brinkmann J, Grunewald M (eds) Logistics management. Springer, Cham. https://doi.org/10.1007/978-3-319-13177-1_26Buxmann P, Ahsen A Von, Díaz LM (2008) Economic evaluation of cooperation scenarios in supply chains. J Enterp Inf Manag 21(3):247–262. https://doi.org/10.1108/17410390810866628Chakraborty A, Chatterjee AK (2015) A surcharge pricing scheme for supply chain coordination under JIT environment. Eur J Oper Res 253(1):14–24. https://doi.org/10.1016/j.ejor.2016.02.001Chen IJ, Paulraj A, Lado AA (2004) Strategic purchasing, supply management, and firm performance. J Oper Manag 22(5):505–523. https://doi.org/10.1016/j.jom.2004.06.002Cheng JH (2011) Inter-organizational relationships and information sharing in supply chains. Int J Inf Manag 31(4):374–384. https://doi.org/10.1016/j.ijinfomgt.2010.09.004Cheng R, Forbes JF, San Yip W, Fraser Forbes J, Yip WS (2008) Dantzig–Wolfe decomposition and plant-wide MPC coordination. Comput Chem Eng 32(7):1507–1522. https://doi.org/10.1016/j.compchemeng.2007.07.003Cooper MC, Lambert DM, Pagh JD (1997) Supply chain management: more than a new name for logistics. Int J Logist Manag 8(1):1–14. https://doi.org/10.1108/09574099710805556Dantzig GB, Wolfe P (1960) Decomposition principle for linear programs. Oper Res 8(1):101–111. https://doi.org/10.1287/opre.8.1.101Dash RK, Vytelingum P, Rogers A, David E, Jennings NR (2007) Market-based task allocation mechanisms for limited-capacity suppliers. IEEE Trans Syst Man Cybern Part A Syst Hum 37(3):391–405. https://doi.org/10.1109/TSMCA.2007.893474Dudek G, Stadtler H (2005) Negotiation-based collaborative planning between supply chains partners. Eur J Operat Res 163(3):668–687. https://doi.org/10.1016/j.ejor.2004.01.014Dudek G, Stadtler H (2007) Negotiation-based collaborative planning in divergent two-tier supply chains. Int J Prod Res 45(2):465–484Ertogral K, David Wu S (2000) Auction-theoretic coordination of production planning in the supply chain. IIE Trans 32:931–940. https://doi.org/10.1080/07408170008967451Eslikizi S, Ziebuhr M, Kopfer H, Buer T (2015) Shapley-based side payments and simulated annealing for distributed lot-sizing. IFAC-PapersOnLine 48(3):1592–1597. https://doi.org/10.1016/j.ifacol.2015.06.313Fan M, Stallaert J, Whinston AB (2003) Decentralized mechanism design for supply chain organizations using an auction market. Inf Syst Res 14(1):1–22. https://doi.org/10.1287/isre.14.1.1.14763Feng Y, D’Amours S, Beauregard R (2008) The value of sales and operations planning in oriented strand board industry with make-to-order manufacturing system: cross functional integration under deterministic demand and spot market recourse. Int J Prod Econ 115(1):189–209. https://doi.org/10.1016/j.ijpe.2008.06.002Fisher ML (1985) An applications oriented guide to Lagrangian relaxation. Interfaces 15(2):10–21. https://doi.org/10.1287/inte.15.2.10Fisher ML (2004) The Lagrangian relaxation method for solving integer programming problems. Manag Sci 50(12 Supplement):1861–1871. https://doi.org/10.1287/mnsc.1040.0263Frazzon E, Makuschewits T, Scholz-Reiter B, Novaes AGN (2010) Assessing the integrated scheduling of manufacturing and transportation systems along global supply chains. In: World conference on transport research, LisbonGaudreault J, Forget P, Frayret JMJ, Rousseau A, Lemieux S, D’Amours S (2010) Distributed operations planning in the softwood lumber supply chain: models and coordination. Int J Ind Eng Theory Appl Pract 17(3):168–189Gunnerud V, Foss B (2010) Oil production optimization—a piecewise linear model, solved with two decomposition strategies. Comput Chem Eng 34(11):1803–1812. https://doi.org/10.1016/j.compchemeng.2009.10.019Harb H, Paprott JN, Matthes P, Schütz T, Streblow R, Mueller D (2015) Decentralized scheduling strategy of heating systems for balancing the residual load. Build Environ 86:132–140. https://doi.org/10.1016/j.buildenv.2014.12.015Held M, Karp RM (1970) The traveling-salesman problem and minimum spanning trees. Oper Res 18(6):1138–1162. https://doi.org/10.1287/opre.18.6.1138Held M, Karp RM (1971) The traveling-salesman problem and minimum spanning trees: part II. Math Program 1(1):6–25. https://doi.org/10.1007/BF01584070Homberger J (2010) Decentralized multi-level uncapacitated lot-sizing by automated negotiation. 4OR 8(2):155–180. https://doi.org/10.1007/s10288-009-0104-1Homberger J (2011) A generic coordination mechanism for lot-sizing in supply chains. Electron Commer Res 11(2):123–149. https://doi.org/10.1007/s10660-010-9053-1Homberger J, Gehring H (2010) A pheromone-based negotiation mechanism for lot-sizing in supply chains. In: 2010 43rd Hawaii international conference on system sciences. IEEE, pp 1–10. https://doi.org/10.1109/hicss.2010.26Homberger J, Gehring H (2011) An ant colony optimization-based negotiation approach for lot-sizing in supply chains. Int J Inf Process Manag 2(3):86–99. https://doi.org/10.4156/ijipm.vol2.issue3.10Homberger J, Gehring H, Buer T (2015) Integrating side payments into collaborative planning for the distributed multi-level unconstrained lot sizing problem. In: Bui TX, Sprague RH (eds) 2015 48th Hawaii international conference on system sciences, vol 2015. IEEE, pp 1068–1077. https://doi.org/10.1109/hicss.2015.131Huang GQ, Lau JSK, Mak KL (2003) The impacts of sharing production information on supply chain dynamics: a review of the literature. Int J Prod Res 41(7):1483–1517. https://doi.org/10.1080/0020754031000069625Jeong I-J (2012) A centralized/decentralized design of a full return contract for a risk-free manufacturer and a risk-neutral retailer under partial information sharing. Int J Prod Econ 136(1):110–115. https://doi.org/10.1016/j.ijpe.2011.09.019Jeong IJ, Leon VJ (2002) Decision-making and cooperative interaction via coupling agents in organizationally distributed systems. IIE Trans (Inst Ind Eng) 34(9):789–802. https://doi.org/10.1023/A:1015548705266Jeong IJ, Yim SB (2009) A job shop distributed scheduling based on Lagrangian relaxation to minimise total completion time. Int J Prod Res 47(24):6783–6805. https://doi.org/10.1080/00207540701824217Jia ZZ, Deschamps JC, Dupas R (2016) A negotiation protocol to improve planning coordination in transport-driven supply chains. J Manuf Syst 38:13–26. https://doi.org/10.1016/j.jmsy.2015.10.003Jung H, Chen FF, Jeong B (2008) Decentralized supply chain planning framework for third party logistics partnership. Comput Ind Eng 55(2):348–364. https://doi.org/10.1016/j.cie.2007.12.017Katok E, Pavlov V (2013) Fairness in supply chain contracts: a laboratory study. J Oper Manag 31(3):129–137. https://doi.org/10.1016/j.jom.2013.01.001Kelly JD, Zyngier D (2008) Hierarchical decomposition heuristic for scheduling: coordinated reasoning for decentralized and distributed decision-making problems. Comput Chem Eng 32(11):2684–2705. https://doi.org/10.1016/j.compchemeng.2007.08.007Kong J, Rönnqvist M (2014) Coordination between strategic forest management and tactical logistic and production planning in the forestry supply chain. Int Trans Oper Res 21(5):703–735. https://doi.org/10.1111/itor.12089Kovács A, Egri P, Kis T, Váncza J (2013) Inventory control in supply chains: alternative approaches to a two-stage lot-sizing problem. Int J Prod Econ 143(2):385–394. https://doi.org/10.1016/j.ijpe.2012.01.001Kumar BK, Nagaraju D, Narayanan S (2016) Supply chain coordination models: a literature review. Indian J Sci Technol. https://doi.org/10.17485/ijst/2016/v9i38/86938Kutanoglu E, David Wu S (1999) On combinatorial auction and Lagrangean relaxation for distributed resource scheduling. IIE Trans 31(9):813–826. https://doi.org/10.1080/07408179908969883Lau HC, Zhao ZJ, Ge SS, Lee TH (2011) Allocating resources in multiagent flowshops with adaptive auctions. IEEE Trans Autom Sci Eng 8(4):732–743. https://doi.org/10.1109/TASE.2011.2160536Lee DJ, Jeong IJ (2010) A distributed coordination for a single warehouse-multiple retailer problem under private information. Int J Prod Econ 125(1):190–199. https://doi.org/10.1016/j.ijpe.2010.02.001Lehoux N, D’Amours S, Frein Y, Langevin A, Penz B (2010a) Collaboration for a two-echelon supply chain in the pulp and paper industry: the use of incentives to increase profit. J Oper Res Soc 62(4):581–592. https://doi.org/10.1057/jors.2009.167Lehoux N, D’Amours S, Langevin A (2010b) A win–win collaboration approach for a two-echelon supply chain: a case study in the pulp and paper industry. Eur J Ind Eng 4(4):493. https://doi.org/10.1504/EJIE.2010.035656Lehoux N, D’Amours S, Langevin A (2014) Inter-firm collaborations and supply chain coordination: review of key elements and case study. Prod Plan Control 25(10):858–872. https://doi.org/10.1080/09537287.2013.771413Li X, Wang Q (2007) Coordination mechanisms of supply chain systems. Eur J Oper Res 179(1):1–16. https://doi.org/10.1016/j.ejor.2006.06.023Lu SYP, Lau HYK, Yiu CKF (2012) A hybrid solution to collaborative decision-making in a decentralized supply-chain. J Eng Technol Manag 29(1):95–111. https://doi.org/10.1016/j.jengtecman.2011.09.008Mahdiraji HA, Zavadskas EK, Hajiagha SHR (2015) Game theoretic approach for coordinating unlimited multi echelon supply chains. Transform Bus Econ 14(2):133–151Maheut J, Besga JM, Uribetxebarria J, Garcia-Sabater JP (2014a) A decision support system for modelling and implementing the supply network configuration and operations scheduling problem in the machine tool industry. Prod Plan Control 25(8):679–697. https://doi.org/10.1080/09537287.2013.798087Maheut J, Garcia-Sabater JP, Garcia-Sabater JJ, Marin-Garcia J (2014b) Coordination mechanism for MILP models to plan operations within an advanced planning and scheduling system in a motor company: a case study. In: Prado-Prado JC, García-Arca J (eds) Annals of industrial engineering 2012. Springer, London, pp 245–253. https://doi.org/10.1007/978-1-4471-5349-8_29Manrodt KB, Vitasek K (2004) Global process standardization: a case study. J Bus Logist 25(1):1–23. https://doi.org/10.1002/j.2158-1592.2004.tb00168.xMarin-Garcia JA, Ramirez Bayarri L, Atares Huerta L (2015) Protocol: comparing advantages and disadvantages of rating scales, behavior observation scales and paired comparison scales for behavior assessment of competencies in workers. A systematic literature review. Work Pap Oper Manag 6(2):49. https://doi.org/10.4995/wpom.v6i2.4032Mason AN, Villalobos JR (2015) Coordination of perishable crop production using auction mechanisms. Agric Syst 138:18–30. https://doi.org/10.1016/j.agsy.2015.04.008McAfee RP, McMillan J (1987) Auctions and bidding. J Econ Lit 25(2):699–738Medina-Lopez C, Marin-Garcia JA, Alfalla-Luque R (2010) Una propuesta metodológica para la realización de búsquedas sistemáticas de bibliografía (A methodological proposal for the systematic literature review). Work Pap Oper Manag. https://doi.org/10.4995/wpom.v1i2.786Mouret S, Grossmann IE, Pestiaux P (2011) A new Lagrangian decomposition approach applied to the integration of refinery planning and crude-oil scheduling. Comput Chem Eng 35(12):2750–2766. https://doi.org/10.1016/j.compchemeng.2011.03.026Mula J, Peidro D, Díaz-Madroñero M, Vicens E (2010) Mathematical programming models for supply chain production and transport planning. Eur J Oper Res 204(3):377–390. https://doi.org/10.1016/j.ejor.2009.09.008Nie L, Xu X, Zhan D (2008) Collaborative planning in supply chains by lagrangian relaxation and genetic algorithms. Int J Inf Technol Decis Mak 7(1):183–197. https://doi.org/10.1142/s0219622008002879Nishi T, Shinozaki R, Konishi M (2008) An augmented Lagrangian approach for distributed supply chain planning for multiple companies. IEEE Trans Autom Sci Eng 5(2):259–274. https://doi.org/10.1109/TASE.2007.894727Ouelhadj D, Petrovic S (2009) A survey of dynamic scheduling in manufacturing systems. J Sched 12(4):417–431. https://doi.org/10.1007/s10951-008-0090-8Pibernik R, Sucky E (2007) An approach to inter-domain master planning in supply chains. Int J Prod Econ 108(1–2):200–212. https://doi.org/10.1016/j.ijpe.2006.12.010Pittman SD, Bare BB, Briggs DG (2007) Hierarchical production planning in forestry using price-directed decomposition. Can J For 37(10):2010–2021. https://doi.org/10.1139/X07-026Polyak BT (1969) Minimization of unsmooth functionals. USSR Comput Math Math Phys 9(3):14–29. https://doi.org/10.1016/0041-5553(69)90061-5Pukkala T, Heinonen T, Kurttila M (2009) An application of a reduced cost approach to spatial forest planning. For Sci 55(1):13–22Qu T, Nie DX, Chen X, Chen XD, Dai QY, Huang GQ (2015) Optimal configuration of cluster supply chains with augmented Lagrange coordination. Comput Ind Eng 84(SI):43–55. https://doi.org/10.1016/j.cie.2014.12.026Reiss F, Buer T (2014) A coordination mechanism for capacitated lot-sizing in non-hierarchical n-tier supply chains. In: 2014 IEEE symposium on computational intelligence in production and logistics systems (Cipls), pp 9–15. https://doi.org/10.1109/cipls.2014.7007155Rius-Sorolla G, Maheut J, Estelles-Miguel S, Garcia-Sabater JP (2017) Protocol: systematic literature review on coordination mechanisms for the mathematical programming models in production planning with decentralized decision making. Work Pap Oper Manag 8(2):22. https://doi.org/10.4995/wpom.v8i2.7858Sahin F, Robinson EPP (2002) Flow coordination and information sharing in supply chains: review, implications, and directions for future research. Decis Sci 33(4):505–535. https://doi.org/10.1111/j.1540-5915.2002.tb01654.xSilva CA, Sousa JMC, Runkler TA, Sá da Costa J (2009) Distributed supply chain management using ant colony optimization. Eur J Oper Res 199(2):349–358. https://doi.org/10.1016/j.ejor.2008.11.021Simatupang T, Sridharan R (2006) The collaboration index: a measure for supply chain collaboration. Int J Phys Distrib Logist Manag 35:44–62. https://doi.org/10.1108/09600030510577421Singh G, Ernst A (2011) Resource constraint scheduling with a fractional shared resource. Oper Res Lett 39(5):363–368. https://doi.org/10.1016/j.orl.2011.06.003Singh G, O’Keefe CM (2016) Decentralised scheduling with confidentiality protection. Oper Res Lett 44(4):514–519. https://doi.org/10.1016/j.orl.2016.05.004Sokoler LE, Standardi L, Edlund K, Poulsen NK, Madsen H, Jørgensen JB (2014) A Dantzig–Wolfe decomposition algorithm for linear economic model predictive control of dynamically decoupled subsystems. J Process Control 24(8):1225–1236. https://doi.org/10.1016/j.jprocont.2014.05.013Sridharan R, Simatupang TM (2009) Managerial views of supply chain collaboration. Gadjah Mada Int J Bus 11(2):253–273Stadtler H (2007) A framework for collaborative planning and state-of-the-art. OR Spectr 31(1):5–30. https://doi.org/10.1007/s00291-007-0104-5Stadtler H, Kilger C (2008) Supply chain management and advanced planning. In: Stadtler H, Kilger C (eds) Supply chain management and advanced planning. Concepts, models, software, and case studies. Springer, BerlinStank TP, Goldsby TJ, Vickery SK (1999) Effect of service supplier performance on satisfaction and loyalty of store managers in the fast food industry. J Oper Manag 17(4):429–447. https://doi.org/10.1016/S0272-6963(98)00052-7Taghipour A, Frayret JM (2013) An algorithm to improve operations planning in decentralized supply chains. In: 2013 international conference on advanced logistics and transport, ICALT 2013, pp 100–103. https://doi.org/10.1109/icadlt.2013.6568442Tang SH, Rahimi I, Karimi H (2016a) Objectives, products and demand requirements in integrated supply chain network design: a review. Int J Ind Syst Eng 23(2):181. https://doi.org/10.1504/IJISE.2016.076399Tang J, Zeng C, Pan Z (2016b) Auction-based cooperation mechanism to parts scheduling for flexible job shop with inter-cells. Appl Soft Comput 49:590–602. https://doi.org/10.1016/j.asoc.2016.08.046Thomas A, Singh G, Krishnamoorthy M, Venkateswaran J (2013) Distributed optimisation method for multi-resource constrained scheduling in coal supply chains. Int J Prod Res 51(9):2740–2759. https://doi.org/10.1080/00207543.2012.737955Thomas A, Venkateswaran J, Singh G, Krishnamoorthy M (2014) A resource constrained scheduling problem with multiple independent producers and a single linking constraint: a coal supply chain example. Eur J Oper Res 236(3):946–956. https://doi.org/10.1016/j.ejor.2013.10.006Thomas A, Krishnamoorthy M, Singh G, Venkateswaran J (2015) Coordination in a multiple producers–distributor supply chain and the value of information. Int J Prod Econ 167:63–73. https://doi.org/10.1016/j.ijpe.2015.05.020VICS (2004) Collaborative planning, forecasting and replenishment. Retrieved January 21, 2017, from https://www.gs1us.org/Vitasek K (2016) Strategic sourcing business models. Strateg Outsour Int J 9(2):126–138. https://doi.org/10.1108/SO-02-2016-0003Walther G, Schmid E, Spengler TS (2008) Negotiation-based coordination in product recovery networks. Int J Prod Econ 111(2):334–350. https://doi.org/10.1016/j.ijpe.2006.12.069Wang L, Pfohl HC, Berbner U, Keck AK (2016) Supply chain collaboration or conflict? Information sharing and supply chain performance in the automotive industry. In: Clausen U, Friedrich H, Thaller C, Geiger C (eds) Commercial transport. Springer, Cham, pp 303–318. https://doi.org/10.1007/978-3-319-21266-1Wenzel S, Paulen R, Krämer S, Beisheim B, Engell S (2016a) Shared resource allocation in an integrated petrochemical site by price-based coordination using quadratic approximation. In: 2016 European control conference, ECC 2016, pp 1045–1050. https://doi.org/10.1109/ecc.2016.7810427Wenzel S, Paulen R, Stojanovski G, Kraemer S, Beisheim B, Engell S (2016b) Optimal resource allocation in industrial complexes by distributed optimization and dynamic pricing. At-Automatisierungstechnik 64(6):428–442. https://doi.org/10.1515/auto-2016-0003Whang S (1995) Coordination in operat

Preliminary specification and design documentation for software components to achieve catallaxy in computational systems

This Report is about the preliminary specifications and design documentation for software components to achieve Catallaxy in computational systems. -- Die Arbeit beschreibt die Spezifikation und das Design von Softwarekomponenten, um das Konzept der Katallaxie in Grid Systemen umzusetzen. Eine Einführung ordnet das Konzept der Katallaxie in bestehende Grid Taxonomien ein und stellt grundlegende Komponenten vor. Anschließend werden diese Komponenten auf ihre Anwendbarkeit in bestehenden Application Layer Netzwerken untersucht.Grid Computing

Agent-Based Computational Economics

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.Agent-based computational economics; Autonomous agents; Interaction networks; Learning; Evolution; Mechanism design; Computational economics; Object-oriented programming.