In this work, a novel optimisation-simulation based on the Recursive Optimisation-Simulation Approach (ROSA) methodology is developed to provide effective decision-support for integrated production planning and scheduling. The proposed iterative approach optimises production plans while satisfying complex scheduling constraints, such as robots' allocation in collaborative tasks. The plans are determined through a two-level MILP model and are iteratively evaluated by a detailed discrete-event simulation model to guarantee capacity-feasible solutions at the scheduling level. Through an industrial case study of a multistage assembly line design collaboratively operated by humans and mobile shared robots, near-optimal solutions comprise lot-sizing decisions, the release schedule of production orders, the allocation of tasks to humans or robots, and the number of robots per period. Moreover, by addressing a set of propositions to assess the methodology, the results highlight the advantages of the hybrid approach to converge into optimised operational decisions and analyse the process dynamics.

Industrial companies are seeking for highly flexible strategic and operational solutions to face the requirements of current dynamic markets. The aim of this work is to provide a decision support assessment for the design and scheduling of a multipurpose plant under demand uncertainty, allowing the assessment of alternative risk profile solutions. A general two-stage mixed-integer linear programming (MILP) model is proposed with the goal to maximise the annualised profit of the plant operation under a set of scenarios while minimising the associated financial risk. Considering the long-term investment perspective, the Conditional Value at Risk (CVaR) measure is used to evaluate the likelihood that a specific loss or gain will exceed a certain value at risk. A bi-objective model is formulated using the augmented ε-constraint method to generate an approximation to the Pareto-optimal curve, illustrating the trade-offs between plant profit (with the corresponding design and scheduling decisions) and the associated financial risk. Addressing a set of propositions regarding a case-study, the conclusions highlight the advantages of the risk measure integration in support of the decision-making process, discussing the managerial insights in the assessment of diverse financial outcomes for the solution optimisation.