A multi-agent system for chemical supply chain simulation and management support

Abstract. Modern chemical production is customer-driven and the desired delivery time for the products is often shorter than their campaign length. In addition, the raw materials supplying time is often long. These features make it desirable to provide tools to support collaborative supply chain decision making, preferably over the Internet, and where there are conflicts, compromise decisions can be quickly reached and the effects of the decisions can be quantitatively simulated. This paper des cribes such a multi-agent system (MAS) that can be used to simulate the dynamic behaviour and support the management of chemical supply chains over the Internet. Geographically distributed retailers, logistics, warehouses, plants and raw material suppliers are modelled as an open and re-configurable network of co-operative agents, each performing one or more supply chain functions. Communication between agents is made through the common agent communication language KQML (knowledge query message language). A t the simulation layer, the MAS allows distributed simulation of the chain behaviour dynamically, so that compromise decisions can be rapidly and quantitatively evaluated. Because in a chemical supply chain the scheduling of the plant often dominates the chain performance, an optimum scheduling system for batch plants is integrated into the MAS. The functions of the system are illustrated by reference to a case study for the supply and manufacture using a multi-purpose batch plant of paints and coatings.

[1]  David F. Pyke,et al.  Inventory management and production planning and scheduling , 1998 .

[2]  Tony J. Van Roy,et al.  Multi-Level Production and Distribution Planning with Transportation Fleet Optimization , 1989 .

[3]  Horst Tempelmeier Inventory service-levels in the customer supply chain , 2000, OR Spectr..

[4]  Eb Erik Diks,et al.  Computational results for the control of a divergent N-echelon inventory system , 1999 .

[5]  W. D. Gibson Getting a grip on the supply chain , 1998 .

[6]  Douglas J. Thomas,et al.  Coordinated supply chain management , 1996 .

[7]  Matthieu van der Heijden,et al.  Inventory control in multi-echelon divergent systems with random lead times , 1996 .

[8]  H. Van Dyke Parunak,et al.  DASCh: Dynamic Analysis of Supply Chains , 1999 .

[9]  Paul H. Zipkin,et al.  Competitive and Cooperative Inventory Policies in a Two-Stage Supply Chain , 1999 .

[10]  Paul H. Zipkin,et al.  Estimating the Performance of Multi-Level Inventory Systems , 1988, Oper. Res..

[11]  Sven Axsäter,et al.  A joint replenishment policy for multi-echelon inventory control , 1999 .

[12]  Eb Erik Diks,et al.  Multi-echelon systems: A service measure perspective , 1996 .

[13]  Sungwon Jung,et al.  Optimal reorder decision utilizing centralized stock information in a two-echelon distribution system , 2002, Comput. Oper. Res..

[14]  Denis Royston Towill,et al.  The seamless supply chain - the predator's strategic advantage , 1997 .

[15]  Ignacio E. Grossmann,et al.  Dynamic Modeling and Decentralized Control of Supply Chains , 2001 .

[16]  Costas D. Maranas,et al.  Mid-term supply chain planning under demand uncertainty: customer demand satisfaction and inventory management , 2000 .

[17]  J. P. Howard A COLOURFUL PROSPECT , 1999 .

[18]  Moshe Dror,et al.  Agent-based project scheduling , 2000 .

[19]  Jayashankar M. Swaminathan,et al.  Modeling Supply Chain Dynamics: A Multiagent Approach , 1998 .

[20]  Marshall L. Fisher,et al.  Coordination of production and distribution planning , 1994 .

[21]  Gintaras V. Reklaitis,et al.  Risk and uncertainty in managing chemical manufacturing supply chains , 2000 .

[22]  Ming Liang Lu,et al.  An agent-based environment for operational design , 1997 .

[23]  Ton G. de Kok,et al.  Optimal control of a divergent multi-echelon inventory system , 1998, Eur. J. Oper. Res..

[24]  Joseph F. Pekny,et al.  A model predictive framework for planning and scheduling problems: a case study of consumer goods supply chain , 2000 .

[25]  C. Pantelides,et al.  Optimal Campaign Planning/Scheduling of Multipurpose Batch/Semicontinuous Plants. 1. Mathematical Formulation , 1996 .

[26]  Marc Goetschalckx,et al.  Strategic production-distribution models: A critical review with emphasis on global supply chain models , 1997 .

[27]  Sunwon Park,et al.  Supply chain optimization in continuous flexible process networks , 2000 .

[28]  C. McGreavy,et al.  A Concurrent Engineering Environment for Chemical Manufacturing , 1995 .

[29]  R. Sargent,et al.  A general algorithm for short-term scheduling of batch operations—II. Computational issues , 1993 .

[30]  Walter Ukovich,et al.  Dynamic routing-and-inventory problems: a review , 1998 .

[31]  Youngsoo Kim,et al.  A framework of concurrent process engineering with agent-based collaborative design strategies and is application on plant layout problem , 2000 .

[32]  N. Shah,et al.  Transfer Prices for Multienterprise Supply Chain Optimization , 2001 .

[33]  Josef Kallrath,et al.  Optimal planning in large multi-site production networks , 2000, Eur. J. Oper. Res..

[34]  Nicholas R. Jennings,et al.  ADEPT: an agent-based approach to business process management , 1998, SGMD.

[35]  Christoph Haehling von Lanzenauer,et al.  Coordinating supply chain decisions: an optimization model , 2002, OR Spectr..

[36]  Prem Vrat,et al.  An integrated production-inventory-distribution model for manufacture of urea: a case , 1991 .

[37]  W. C. Benton,et al.  Supply chain partnerships: Opportunities for operations research , 1997 .

[38]  Parag C. Pendharkar,et al.  A computational study on design and performance issues of multi-agent intelligent systems for dynamic scheduling environments , 1999 .

[39]  Morris A. Cohen,et al.  Optimal material control in an assembly system with component commonality , 2001 .

[40]  Timothy W. Finin,et al.  A negotiation-based Multi-agent System for Supply Chain Management , 1999 .

[41]  Yun Peng,et al.  Agent-Based Approach for Manufacturing Integration: The Ciimplex Experience , 1999, Appl. Artif. Intell..

[42]  Dieter Stoye,et al.  Paints, coatings, and solvents , 1998 .

[43]  X. Wang,et al.  Agent-based information flow for process industries' supply chain modelling , 2000 .

[44]  Marcus Brandenburg,et al.  An integrated system solution for supply chain optimization in the chemical process industry , 2002, OR Spectr..

[45]  Ben Hua,et al.  Supply chain optimization of continuous process industries with sustainability considerations , 2000 .

[46]  Craig C. Sherbrooke,et al.  Metric: A Multi-Echelon Technique for Recoverable Item Control , 1968, Oper. Res..

[47]  S. Engell,et al.  Planning and Scheduling in the Process Industry , 2022 .

[48]  Mark S. Fox,et al.  Agent-Oriented Supply-Chain Management , 2000 .

[49]  Timothy W. Finin,et al.  A Proposal for a new KQML Specification , 1997 .