Liquid bulk is one of the largest industries of the modern world, and efficient scheduling is needed for liquid bulk terminals to remain competitive. A common solution for the planning efficiency problem is to apply planning algorithms, rather than human planners for creating schedules. However, replacing human planners by algorithms is often a difficult problem and its implementation is often obstructed both by management and the planning departments. In order to still achieve efficient schedules, attention has shifted from planning algorithms intended to replace the human planner, to scheduling support systems which aim to assist the planner in making efficient schedules. A core aspect of both automated scheduling systems as well as scheduling support systems is an optimization engine, to allow the human planner to quickly generate alternative schedule options. In recent years, literature has emerged introducing the concept of Multiagent systems for planning and scheduling. Multiagent scheduling seems to be a suitable approach for scheduling in liquid bulk terminals since the agents provide a natural metaphor for the scheduling problem in a liquid bulk terminal. This is mainly because a terminal serves vessels which are owned by a set of customers which are largely uninterested in the vessels of other customers. Multiagent systems can, in such a scenario, offer flexibility with regards to optimality criteria which traditional scheduling systems cannot. The main goal of this study was to determine whether Multiagent Systems (MAS) could be effectively applied for proposing schedule allocation options to assist schedulers in liquid bulk terminals. The research started with an extensive background study, in which it was found that there are a number of stakeholders involved in the scheduling process, which have partially conflicting goals. The main relevant stakeholders are the terminal and its customers, both consisting of several independent departments or entities. The main goals of the terminal are achieving a high terminal utilization, having a low impact on surroundings, having low pressure on operations, and not paying demurrage costs. The main goals from the customer side are achieving short turnaround times, and maintaining product quality. By combining the stakeholder requirements with information regarding the processes, infrastructure, and products at liquid bulk terminals, it was found that the scheduling problem resembles a specific version of the job-shop problem: $JMPT | pmtn, prec, r_i, s_{ij} | multi$. Since this problem is strongly $\NP$-hard it is generally infeasible to find an optimal solution as the size of the problem instance grows, and efficient algorithms are needed to find solutions. After formally defining the scheduling problem as present in liquid bulk terminals, this thesis moved on to determine the applicability of several common Multiagent scheduling solutions based on four dimensions: whether the solution provides a good analogy to the scheduling problem, whether the solution could deal with the arrival and departure of vessels over time, whether the solution could incorporate shared infrastructure between routes and whether terminal preferences could be modelled. It was found that Combinatorial Auctions provided the best fit on the scheduling problem. Due to existing contract structures between terminals and their customers it proved impossible to incorporate an additional payment structure in the scheduling problem, making it infeasible to design the auction to be truthful and strategy-proof. Nonetheless, this is not considered a problem as strategic behaviour can easily be verified. For incorporating multi-attribute optimizations several design directions were explored including Pareto-optima, hard constraints and discrete choice models. It was found that only discrete choice models offered the flexibility needed in this case. Two types of discrete choice models were proposed and implemented: a Utility Maximization model, as well as a Regret Minimization model. Empirical studies have shown that in certain cases, a regret minimizing model provides a significantly better fit on the preference data, meaning that they are better able to capture the trade-offs made by humans when comparing alternatives. As the goal of this scheduling system is to assist the planner in making allocation choices, it can benefit greatly from these features. A major drawback of the regret model is that finding the regret for all alternatives is an $O(n^2)$ operation, rather than an $O(n)$ operation. For solution methods where this posed a bottleneck, a hybrid regret / utility model was proposed and implemented. The auction structure was implemented using a minor modification on the Contract Net Protocol. The final Multiagent Systems consists of three agent types: a Route Finder agent, a Vessel agent, and a Planner agent. The Route Finder agent is responsible for finding routes for specific vessels and products on the terminal, as well as for making accurate estimations on the processing times on that route. This is implemented using a combination of Yen's algorithm and a modification of Dijkstra's shortest path algorithm. The Vessel agent uses te routes from the Route Finder agent to determine a valuation on the routes and submits these valuations as bids to the Planner agent. The Planner agent retrieves all bids and uses these to create final schedule proposals. This is done by solving the Winner Determination Problem (WDP) of the auction. Several algorithms were implemented and tested for solving the WDP, including a Branch and Bound algorithm, informed search methods and two Simulated Annealing approaches. It was found that Simulated Annealing, implemented as a search among alternative complete solutions, was the most feasible solution strategy. Typically, it provided solutions which, on average, achieve a 50\% higher utility model value than a First-Come First-Served Heuristic in three minutes. Furthermore, the Simulated Annealing process can be stopped at any moment and still provide a better alternative solution. All algorithms were implemented using a utility-maximising as well as a regret-minimising strategy. It was found that the top ten results from these different models typically differed from each other with an edit distance of five. Further empirical studies would be needed to determine whether regret-based models provide better accepted solutions in practice than their utility-based counterparts.