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This paper studies the weighted earliness tardiness parallel machine problem where jobs have different processing times and distinct due dates. This NP hard problem arises in most just-in-time production environments. It is herein modeled as a mixed integer program, and solved using MAS^H, a deterministic heuristic based on multi-agent systems. MAS^H has three types of agents: I, G, and M. The I-agents are free jobs that need to be scheduled, whereas the G-agents are groups of jobs already assigned to machines. The M-agent acts as the system's manager of the independent intelligent I- and G-agents, which are driven by their own goals, fitness assessments, and context-dependent decision rules. The I- and G-agents employ exact and approximate approaches as part of their decisional process while the M-agent uses local search mechanisms to improve their (partial) solutions. The design of MAS^H is innovative in the way its intelligent agents determine bottleneck clusters and resolve conflicts for time slots. The numerical results provide computational evidence of the efficiency of MAS^H, whose performance on benchmark instances from the literature is superior to that of existing approaches. The success of MAS^H and its modularity make it a viable alternative to more complex manufacturing problems. Most importantly, they demonstrate the benefits of the hybridization of artificial intelligence and operations research.