The increasing coupling of planning and scheduling between different companies leads to novel challenges in devising and implementing effective decision support systems. In this paper, we describe a hard decentralized scheduling problem with heterogeneous machines and competing job sets that belong to different self-interested stakeholders (agents). These agents want to minimize their costs that consist of individual tardiness cost as well as their share of the machine operating cost. The determination of a beneficial solution, i.e., a respective contract in terms of a common schedule, is particularly difficult due to information asymmetry and self-interested behavior of the involved agents. To solve this coordination problem, we present two automated negotiation protocols with a set of optional building blocks. In the first protocol, new solutions are iteratively generated as mutations of a single provisional contract and proposed to the agents, while feasible rules with quotas restrict the acceptance decisions of the agents and, thus, the successive adaptation of the provisional contract. The second protocol is based on a population of contracts and mimics evolutionary processes. For evaluation purposes, we built a simulation testbed and conducted computational experiments. The computational study shows that the protocols can achieve high quality solutions very close to results from centralized multi-criteria procedures. Particular building block configurations yield improved outcomes, e.g., in case that the agents are also allowed to make contract proposals. Thus, the presented approach contributes to the methodology and practice of collaborative decision making.