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.
This paper proposes a multi-agent modelling approach that supports supply network configuration decisions towards sustaining operations excellence in terms of economic, business continuity and environmental performance. Two types of agents are employed, namely, physical agents to represent supply entities and auxiliary agents to deal with supply network configuration decisions. While using the evolutionary algorithm, Non-dominated Sorting Genetic Algorithm-II to optimize both cost and lead time at the supply network level, agents are modelled with an architecture which consists of decision-making, learning and communication modules. The physical agents make decisions considering varying situations to suit specific product-market profiles thereby generating alternative supply network configurations. These supply network configurations are then evaluated against a set of performance metrics, including the energy consumption of the supply chain processes concerned and the transportation distances between supply entities. Simulation results generated through the application of this approach to a refrigerator production network show that the selected supply network configurations are capable of meeting intended sustainable goals while catering to the respective product-market profiles.
In this paper, we develop a novel multi-objective modeling approach to support supply network configuration decisions, while considering varying demand profiles. In so doing, we illustrate how such an approach could contribute to building supply network robustness and resilience. The proposed model entails two key objectives; minimizing lead time and cost across the supply network. The solution approach first employs a bidding mechanism to select a set of supply network entities that match with a given demand profile from a candidate pool of entities. It then applies the popular technique known as N on-dominated Sorting Genetic Algorithm-II to generate a set of Pareto-optimal solutions representing alternative supply network configurations. The proposed model is tested on a case study of a refrigerator supply network to draw delivery time and cost comparisons under static and dynamic demand profiles.
Zero hunger is one of the top three goals of Sustainable Development Goals which is achievable by reducing the postharvest losses of the food supply chain and improving food security. In developing countries approximately 40% of fruit harvest goes to waste due to not having proper mechanisms, coordination and best practices and poor post-harvest management. A pilot study has found post-harvest losses of fresh fruits and vegetables occur in 2.29%, 1.57%, 6.22% and 7.89% at farmer, collection center, wholesaler and retailer respectively, emphasizing the need of a reconfiguration. Following good practices in handling, introducing suitable bulk packing methods, vehicle upgrades and development of different supply chain configurations are some approaches in mitigating post-harvest losses. Therefore, it is timely to change the product flow of supply chain by reconfiguration. The existing configuration of fruit and vegetable supply chain is simulated as an agent based simulation model taking banana supply chain as a case study. Short supply chain branches were introduced as suggestions to avoid the congestion and banana getting exposed to mechanical damages. The reconfigured supply chain emitted 10% less GHG than the existing banana supply chain while achieving the efficiency in distribution flow.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.