A hierarchical model for e‐supply chain coordination and optimisation

Abstract

Purpose

The integration of e‐business and supply chain enables seamless information flow from suppliers to customer service network via the internet. It also enables better‐coordinated materials flow from customer order to production, storage, distribution and delivery. The purpose of this paper is to describe the work that leads to the realisation of a hierarchical model for e‐supply chain coordination and optimisation.

Design/methodology/approach

The model is based on an e‐business information flow network in order to respond rapidly to the dynamics of e‐supply chain and market. It can be used to realize management level strategies, and facilitate the planning and control of detailed operation schedules of supply chain units in an e‐supply chain environment. Three main modules are discussed. They are routing and sequence optimiser (RSO) with the aid of a GA and TS‐based multiple population search strategy (MPSS); supply chain virtual clustering (SCVC) based on fuzzy virtual clustering; and supply chain order scheduling (SCOS) using an agent‐based distributed scheduling system.

Findings

These modules enable: the generation of preferred routings, transportation modes and work order plan under such constraints as customer service level, cycle time and cost; the formation of supply chain's unit‐transportation‐work order families using a clustering approach to down‐size supply chain problems and increase computation efficiency; and the integration of scheduling with supply chain optimisation in order to facilitate the control of a supply chain with the aid of an agent‐based distributed scheduling approach.

Originality/value

This paper proposed a hierarchical model for e‐supply chain coordination and optimisation that is capable to solve large‐scale problems. A MPSS using GA and TS was depicted in detail to expedite the RSO.

Citation

Feng Yin, X. and Pheng Khoo, L. (2007), "A hierarchical model for e‐supply chain coordination and optimisation", Journal of Manufacturing Technology Management, Vol. 18 No. 1, pp. 7-24. https://doi.org/10.1108/17410380710717616