An Improved RTN Continuous-Time Formulation for the Short-term Scheduling of Multipurpose Batch Plants

An Integrated Optimization Model for Industrial Energy System Retrofit with Process Scheduling, Heat Recovery, and Energy Supply System Synthesis

The urgent need for CO2 reduction is calling upon the industry to contribute. However, changes within local energy supply systems including efficiency enhancement are bound to several economical and technical constraints, which results in interfering trade-offs that make it difficult to find the optimal investment option for CO2 mitigation. In this article, a new optimization model is presented that allows to optimize the design and operation of a supply and heat recovery system and production scheduling simultaneously. The model was used for retrofitting of a small brewery’s local energy system to identify decarbonization measures for eight potential future scenarios with different technical, economical and ecological boundary conditions. The results show that the proposed cost-optimized changes to the current energy system only slightly reduce carbon emissions if decarbonization is not enforced since the optimal solutions prioritize integration of photo voltaic (PV) modules that mainly substitute electricity purchase from grid, which is already assumed to be carbon free. However, enforcing decarbonization rates of 50% for the assumed future boundary conditions still results in cost savings compared to the current energy system. These systems contain heat pumps, thermal energy storages, electric boilers, and PV. Battery storages are only part of the optimal system configuration if low to moderate decarbonization rates below 50% are enforced. An analysis of marginal costs for units not considered in the optimal solutions shows that solar thermal collectors only require small decreases in collector cost to be selected by the solver.

A new approach for scheduling of multipurpose batch processes with unlimited intermediate storage policy

The increasing demand of goods, the high competitiveness in the global marketplace as well as the need to minimize the ecological footprint lead multipurpose batch process industries to seek ways to maximize their productivity with a simultaneous reduction of raw materials and utility consumption and efficient use of processing units. Optimal scheduling of their processes can lead facilities towards this direction. Although a great number of mathematical models have been developed for such scheduling, they may still lead to large model sizes and computational time. In this work, we develop two novel mathematical models using the unit-specific event-based modelling approach in which consumption and production tasks related to the same states are allowed to take place at the same event points. The computational results demonstrate that both proposed mathematical models reduce the number of event points required. The proposed unit-specific event-based model is the most efficient since it both requires a smaller number of event points and significantly less computational time in most cases especially for those examples which are computationally expensive from existing models.

Optimization-Based Scheduling for the Process Industries: From Theory to Real-Life Industrial Applications

Scheduling is a major component for the efficient operation of the process industries. Especially in the current competitive globalized market, scheduling is of vital importance to most industries, since profit margins are miniscule. Prof. Sargent was one of the first to acknowledge this. His breakthrough contributions paved the way to other researchers to develop optimization-based methods that can address a plethora of process scheduling problems. Despite the plethora of works published by the scientific community, the practical implementation of optimization-based scheduling in industrial real-life applications is limited. In most industries, the optimization of production scheduling is seen as an extremely complex task and most schedulers prefer the use of a simulation-based software or manual decision, which result to suboptimal solutions. This work presents a comprehensive review of the theoretical concepts that emerged in the last 30 years. Moreover, an overview of the contributions that address real-life industrial case studies of process scheduling is illustrated. Finally, the major reasons that impede the application of optimization-based scheduling are critically analyzed and possible remedies are discussed.

Integration of Production Planning and Scheduling Based on RTN Representation under Uncertainties

Production planning and scheduling are important bases for production decisions. Concerning the traditional modeling of production planning and scheduling based on Resource-Task Network (RTN) representation, uncertain factors such as utilities are rarely considered as constraints. For the production planning and scheduling problem based on RTN representation in an uncertain environment, this paper formulates the multi-period bi-level integrated model of planning and scheduling, and introduces the uncertainties of demand and utility in planning and scheduling layers respectively. Rolling horizon optimization strategy is utilized to solve the bi-level integrated model iteratively. The simulation results show that the proposed model and algorithm are feasible and effective, can calculate the consumption of utility in every period, decrease the effects of uncertain factors on optimization results, more accurately describe the uncertain factors, and reflect the actual production process.

Advances in mixed-integer programming methods for chemical production scheduling

The goal of this paper is to critically review advances in the area of chemical production scheduling over the past three decades and then present two recently proposed solution methods that have led to dramatic computational enhancements. First, we present a general framework and problem classification and discuss modeling and solution methods with an emphasis on mixed-integer programming (MIP) techniques. Second, we present two solution methods: (a) a constraint propagation algorithm that allows us to compute parameters that are then used to tighten MIP scheduling models and (b) a reformulation that introduces new variables, thus leading to effective branching. We also present computational results and an example illustrating how these methods are implemented, as well as the resulting enhancements. We close with a discussion of open research challenges and future research directions.