Scheduling Of Multipurpose Plants Research Articles

Assessment of financial risk in the design and scheduling of multipurpose plants under demand uncertainty

ABSTRACT 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.

Simultaneous Design and Scheduling of Multipurpose Plants Using Resource Task Network Based Continuous-Time Formulations

This paper presents a general mathematical formulation for the simultaneous design and scheduling of multipurpose plants. The formulation is based on the resource task network process representation, uses a uniform time grid continuous-time representation, and can handle both short-term and periodic problems. It originates mixed-integer nonlinear programs or mixed-integer linear programs, depending on the types of tasks and objective function being considered. The performance of the formulation is illustrated through the solution of two periodic example problems that have been examined in the literature, where the selection and design of the main equipment items and their connecting pipes is considered. The results clearly show that all decisions should be part of the same model because the plant structure, operating schedule, and cycle time can all change with a change in product demand. A comparison with an earlier approach is also presented.

Improving the efficiency of discrete time scheduling formulation

The problem of production scheduling in multipurpose plants can be formulated as a mixed integer linear programming (MILP) problem based on a discrete representation of time. The mathematical difficulties associated with solving integer programs are well established, and combining this factor with the increasing complexity and size of scheduling problems, there exists a strong requirement for efficient solution algorithms. This paper attempts to address this requirement by looking at two ways to reduce the gap between the optimal solution and the solution of its relaxed LP counterpart. The first method involves generating cut constraints in problems where the presence of changeover activities tends to widen the relaxation gap. The second method uses an established reformulation technique based on variable disaggregation which exploits the lot sizing problem found embedded in many scheduling instances. The reformulation is applied to a general scheduling framework and its performance evaluated. Test examples are described for both methods, along with their numerical results.