Multiagent Optimization Approach to Supply Network Configuration Problems With Varied Product-Market Profiles

Abstract

This paper demonstrates the application of a novel multi-agent modelling approach to support supply network configuration (SNC) decisions towards addressing several challenges reported in the literature. These challenges include: enhancing supply network (SN)-level performance in alignment with the goals of individual SN entities; addressing the issue of limited information sharing between SN entities; and sustaining competitiveness of SNs in dynamic business environments. To this end, a multi-stage, multi-echelon SN consisting of geographically dispersed SN entities catering to distinct product-market profiles was modelled. In modelling the SNC decision problem, two types of agents, each having distinct attributes and functions, were used. The modelling approach incorporated a reverse-auctioning process to simulate the behaviour of SN entities with differing individual goals collectively contributing to enhance SN-level performance, by means of setting reserve values generated through the application of a genetic algorithm. A set of Pareto-optimal SNCs catering to distinct product-market profiles was generated using Non-dominated Sorting Genetic Algorithm II.Further evaluation of these SNCs against additional criteria, using a rule-based approach, allowed the selection of the most appropriate SNC to meet a broader set of conditions. The model was tested using a refrigerator SN case study drawn from the literature. The results reveal that a number of SNC decisions can be supported by the proposed model, in particular, identifying and evaluating robust SNs to suit varied product-market profiles, enhancing SC capabilities to withstand disruptions and developing contingencies to recover from disruptions. Keywords -supply network dynamics, supply chain design, supply network configurationManagerial relevance: Compared to the existing SNC decision support tools, the proposed modelling approach addresses three key challenges faced by decision makers. First, it ensures optimal SN-level performance when individual SN entities still aiming to satisfy their local goals such as organisation-specific competitive priorities. Second, it facilitates SNC decisionmaking leading to optimal SN-level performance with minimal information sharing among SN entities, which reflects the real-world situation of organisations' reluctance to disclose commercially sensitive information. Third, the model can be useful in facilitating SNC decisions to sustain competitiveness of SNs in a dynamic business environment, which is characterised by changing consumer requirements, disruptions and other forms of uncertainty.Overall, the proposed multi-agent optimisation model can be used to enhance SNC decisions by any SN entity, as well as other parties such as supply chain analysts or consultants.

Integrated modeling of extended agro-food supply chains: A systems approach

“…Methods such as SD, ABM are able to simulate the intangible aspects of the SCM effectively, including interactions among different SC stages, learning over time for SC partners involved, and continuous feedback on key decisions in the presence of limited information. However, studies employing simulation modeling (e.g., ABM, SD) in the area have been few and far between, as reported in the recent study by Dharmapriya, Kiridena and Shukla (2019) . In fact, there are even less studies reported on modeling consumer behaviors in the SC using simulation modeling ( Taghikhah et al, 2019 ).…”

Digitalization of Supply Chain and Its Impact on Cost, Firm Performance, and Resilience: Technology Turbulence and Top Management Commitment as Moderator

“…These three technology-oriented drivers of supply chains can transform SCM from traditional to digitalized by integrating ecosystems that possess transparent interfaces. It has been observed that through a digitalized SCM system, firms can improve their customer and supplier management systems along with their supply chain agility and responsiveness [43] helpful to corelate demand and supply. Studies demonstrate that supply chain agility will help firms to have a flexible and effective supply chain system for their products [44,45].…”

Enterprise Information Product Configuration Management System Based on Data Analysis Algorithm

“…It means that after a product is put into the market, it is welcomed by users, and through market analysis, the enterprise responds quickly and makes a variant design on the basis of the original product [5].…”