An Agent-Based Model Driven Decision Support System for Reactive Aggregate Production Scheduling in the Green Coffee Supply Chain

• Published in: Applied sciences Vol. 9; no. 22; p. 4903
• Main Authors: Pérez-Salazar, María, Aguilar-Lasserre, Alberto, Cedillo-Campos, Miguel, Posada-Gómez, Rubén, del Moral-Argumedo, Marco, Hernández-González, José
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2019
• Subjects: agent based modeling and simulation; Agent-based models; Agribusiness; Agricultural production; Behavior; Breakdowns; Coffee; Customer services; Decision making; decision support system; Decision support systems; Demand; Food; Food chains; Food supply; Fuzzy logic; green coffee supply chain; Inference; Integer programming; Knowledge; Operations management; Parchment; Process planning; Production planning; Production scheduling; Raw materials; Schedules; Scheduling; Simulation; Supply chains; Wholesalers
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9224903

The aim of this paper is to contribute to the thread of research regarding the need for logistic systems for planning and scheduling/rescheduling within the agro-industry. To this end, an agent-based model driven decision support system for the agri-food supply chain is presented. Inputs in this research are taken from a case example of a Mexican green coffee supply chain. In this context, the decision support agent serves the purposes of deriving useful knowledge to accomplish (i) the decision regarding the estimation of Cherry coffee yield obtained at the coffee plantation, and the Parchment coffee sample verification decision, using fuzzy logic involving an inference engine with IF-THEN type rules; (ii) the production plan establishment decision, using a decision-making rule approach based upon the coupling of IF-THEN fuzzy inference rules and equation-based representation by means of mixed integer programming with the aim to maximize customer service level; and (iii) the production plan update decision using mathematical equations once the customer service level falls below the expected level. Three scenarios of demand patterns were considered to conduct the experiments: increasing, unimodal and decreasing. We found that the input inventory and output inventory vary similar over time for the unimodal demand pattern, not the case for both the increasing and decreasing demand patterns. For the decreasing demand pattern, ten tardy orders for the initial production schedule, an 88% service level, and nineteen tardy orders from the estimated production results, a 77% service level. This value falls below the expected level. Consequently, the updated aggregate production schedule resulted in ten tardy orders and an 88% service level.