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In this work we investigate the integrated planning, scheduling and control (iPSC) of process systems under uncertain conditions throughout the three levels of decision making. The planning problem is explored in a rolling horizon fashion coupled with demand forecasts. Proactive and reactive approaches are employed to handle the effect of stochastic variations. Depending on the nature of the uncertain parameters robust optimisation and chance constrained programming are employed. For the closed-loop implementation of the control, novel multi-parametric controllers are designed. The proposed framework is tested on the iPSC of a polymerisation process. Finally, Monte Carlo simulations are conducted to highlight the benefits of the “uncertainty-aware” solutions when compared to the deterministic ones.

This paper addresses the planning and scheduling problem of a multiproduct multistage continuous plant by three novel MILP-based (mixed integer linear programming) models. These models combine a TSP (traveling salesman problem) formulation with the main ideas of general precedence and unit-specific general precedence concepts to provide hybrid discrete/continuous time representations of the system. Also, an efficient solution approach involving rolling horizon and iterative-improvement algorithm is derived for solving medium-size instances of the problem. Results analyses for different model’s parameters demonstrate the benefits of the new formulations and the effectiveness of the solution approach presented in this work.

A novel continuous time MILP formulation for a class of industrial scheduling problems is presented in this work. This TSP/precedence-based formulation allows coupling sequence-dependent issues in a multiproduct batch process with parallel units and resource limitations. Maintenance operations are also taken into account based on the performance decay of production units. A motivating example is shown to demonstrate the applicability of this new formulation to industrial scheduling problems.

Flowshop scheduling problems have been extensively studied by several authors using different approaches. A typical flowshop process consists of successive manufacturing stages arranged in a single production line where different jobs have to be processed following a predefined production recipe. In this work, the scheduling of a complex flowshop process involving Automated Wet-Etch Station (AWS) from Semiconductor Manufacturing Systems, requires a proper synchronization of processing and transport operations, due to stringent storage policies and fixed transfer times between stages. Robust hybrid solution strategies based on mixed integer linear programming (MILP) formulations and heuristic-based approaches, such as aggregation and decomposition methods, are proposed and illustrated on industrial scale problems. The results show significant improvements in solution quality coupled with a reduced computational effort compared to other existing methodologies. Fil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina Fil: Castro, Pedro. UMOSE/LNEG; Portugal

This work presents efficient algorithms based on Mixed-Integer Linear Programming (MILP) and heuristic strategies for complex job-shop scheduling problems raised in Automated Manufacturing Systems. The aim of this work is to find alternative a solution approach of production and transportation operations in a multi-product multi-stage production system that can be used to solve industrial-scale problems with a reasonable computational effort. The MILP model developed must take into account; heterogeneous recipes, single unit per stage, possible recycle flows, sequence-dependent free transferring times and load transfer movements in a single automated material-handling device. In addition, heuristic-based strategies are proposed to iteratively find and improve the solutions generated over time. These approaches were tested in different real-world problems arising in the surface-treatment process of metal components in the aircraft manufacturing industry.

The automated wet-etch station (AWS) is one of the most critical stages of a modern semiconductor manufacturing system (SMS), which has to simultaneously deal with many complex constraints and limited resources. Due to its inherent complexity, industrial-sized automated wet-etch station scheduling problems are rarely solved through full rigorous mathematical formulations. Decomposition techniques based on heuristic, meta-heuristics and simulation-based methods have been traditionally reported in literature to provide feasible solutions with reasonable CPU times. This work introduces an improvement MILP-based decomposition strategy that combines the benefits of a rigorous continuous-time MILP (mixed integer linear programming) formulation with the flexibility of heuristic procedures. The schedule generated provides enhanced solutions over time to challenging real-world automated wet etch station scheduling problems with moderate computational cost. This methodology was able to provide more than a 7% of improvement in comparison with the best results reported in literature for the most complex problem instances analyzed. Fil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico Para la Industria Química; Argentina Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Gutierrez, Gloria Maribel. Universidad de Valladolid; España Fil: de Prada, Cesar. Universidad de Valladolid; España

Automated Wet-Etch Station (AWS) is a complex flow shop operation process in Semiconductor Manufacturing Systems. In this station, automated material-handling robots are used to move wafer lots across a lineal configuration of chemical and water baths. In every bath, limited processing times and complex storage policies must be assured. In this work, an optimization-based framework is developed to improve the operations of AWS. To do this, a sequential procedure based on mixed-integer linear programming (MILP) formulations is proposed. The aim of this work is to provide a robust approach to generate near-optimal results to industrial AWS scheduling problems with modest CPU time.

This work addresses the short-term scheduling of one of the most critical stages in the semiconductor industry, the automated wet-etch station (AWS). An efficient MILP-based computer-aided tool is developed in order to achieve a proper synchronization between the activities of sequential chemical and water baths and limited automated wafer's lot transfer devices. The major goal is to find the optimal integrated schedule that maximizes the whole process productivity without generating wafer contamination. Several examples are successfully solved to illustrate the capabilities of the proposed method.

This article presents a new algorithm for industrially sized problems that, because of the large number of tasks to schedule, are either intractable or result in poor solutions when solved with ful...

This paper analyses the operational decision making procedures required to address the simultaneous management of energy supplies and requests in a microgrid scenario, in order to best accommodate arbitrary energy availability profiles resulting from an intensive use of renewable energy sources, and to extensively exploit the eventual flexibility of the energy requirements to be fulfilled. The optimization of the resulting short term scheduling problem in deterministic scenarios is addressed through a MILP (Mixed-Integer Linear Programming) mathematical model, which includes a new hybrid time formulation developed to take profit of the advantages of the procedures based on discrete time representations, while maintaining the ability to identify solutions requiring a continuous time representation, which might be qualitatively different to the ones constrained to consider a fixed time grid for decision-making. The performance of this new time representation has been studied, taking into account the granularity of the model and analyzing the associated trade-offs in front of other alternatives. The promising results obtained with this new formulation encourage further research regarding the development of decision-making tools for the enhanced operation of microgrids. Fil: Silvente, Javier. Universitat Politecnica de Catalunya; España Fil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina Fil: Zamarripa, Miguel A.. Universitat Politecnica de Catalunya; España Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina Fil: Graells, Moisés. Universitat Politecnica de Catalunya; España Fil: Espuña, Antonio. Universitat Politecnica de Catalunya; España

A new optimization model is presented for the short-term management of the energy supply and demand in smart grids. The detailed model includes a flexible demand profile in order to manage the energy requirements by incorporating penalizations in the economic objective function for delays in satisfying energy demand. The MILP model for the optimization of deterministic scenarios is reformulated in order to incorporate discrete and hybrid time representations. This approach allows considering a different granularity of the problem. Finally, the improved performance of the hybrid approach introduced is shown by comparing the performance of these two time representations.

This work extends the use of a Mixed Integer Linear Programming (MILP) model, devised to optimize the Supply Chain planning problem, for decision making in cooperative and/or competitive scenarios, by integrating these models with the use of the Game Theory. The system developed is tested in a case study based in previously proposed Supply Chain, adapted to consider the operation of two different Supply Chains (multi-product production plants, storage centers, and distribution to the final consumers); two different optimization criteria are used to model both the Supply Chains benefits and the customer preferences, so both cooperative and non-cooperative way of working between both Supply Chains can be considered. Fil: Zamarripa, Miguel A.. Universidad Politecnica de Catalunya; España Fil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico Para la Industria Química; Argentina Fil: Espuña, Antonio. Universidad Politecnica de Catalunya; España

A general MILP-based model is presented for the scheduling of multiple products in Automated Manufacturing Systems. The proposed model addresses the scheduling of multiple processing operations in several stages considering recycle flows and sequence-dependent transfer times in a single automated-material handling robot. A real-world industrial application problem is solved to demonstrate the effectiveness of the proposed approach.

This work develops a multi-objective MILP (Mixed Integer Linear Programming) model, devised to optimize the planning of supply chains using Game Theory optimization for decision making in cooperative and/or competitive scenarios. Three different optimization criteria are considered (total cost, tardiness and expenses of the buyers for the competitive problem). The multi objective problem has been solved using the Pareto frontier solutions, and both cooperative and non cooperative scenarios between supply chains are considered, so multiple optimization tools/techniques have been combined to analyze the different trade-offs associated to the resulting decision making: Game Theory, MILP based approach and Pareto frontiers. The resulting model is tested in a case study, based on the operation of two different supply chains in both competitive and cooperative situations.

An efficient MILP-based approach is presented to solve flow shop scheduling problems in automated manufacturing systems. The problem comprises complex production constraints and stringent storage policies in a multiple product multistage batch process. Automated transfers accross the entire system, recicle flows and different production recipes are also considered in the process scheme. The proposed decomposition approach was able to solve the entire problem in a sequential manner by dividing it into two major stages that are iteratively solved to find near optimal results with an acceptable CPU time. The effectiveness of this method is tested and compared with a monolithic MILP formulation by solving a real-world problem.

Track systems have been increasingly utilized in many different stages in the Semiconductor Industry. Most of the manufacturing processes, particularly those executed in the wafer fabrication, require these transportation devices to perform the transfers of wafers lots through several process steps. Since these process steps only produce one lot at a time in a single bath, the number of transfer operations between consecutive baths increases drastically with the number of steps and lots in the system. Therefore, the problem to be tackled in this work aims at finding the efficient structure and operation strategy of a particular track system working in an Automated Wet-Etch Station (AWS). A rigorous mathematical formulation (MILP) for the simultaneous scheduling and design of the AWS is developed in order to minimize the residence time of wafer lots in the system, providing at the same time a better utilization of track's resources.

This work addresses the development of a multi-objective MILP (Mixed Integer Linear Programming), devised to optimize the planning of supply chains introducing the use of game theory for decision making in cooperative and/or competitive scenarios. The model developed is tested in a real-world case study, based on the operation of two different supply chains; three different optimization criteria are consider, and both cooperative and non cooperative way of working between supply chain's is considered.

This work addresses the short-term scheduling of one of the most critical stages in the semiconductor industry, the automated wet-etch station (AWS). An efficient MILP-based computer-aided tool is developed in order to achieve a proper synchronization between the activities of sequential chemical and water baths and limited automated wafer's lot transfer devices. The major goal is to find the optimal integrated schedule that maximizes the whole process productivity without generating wafer contamination.

This work proposes to improve the tactical decision-making of a supply chain (SC) under an uncertain competition scenario through the use of different optimization criteria, which allows to manage not only the specific objectives of the SC of interest, but also the way how its clients address their selection between different potential suppliers, identifying best market share for the SC of interest and the strategy to attain it. The resulting multi-objective optimization problem has been solved using the ɛ-constraint method in order to approximate the Pareto space of non-dominated solutions while a framework based on game theory is used as a reactive decision making support tool to deal with the uncertainty of the competitive scenario. The use of the proposed system is illustrated through its application to a multi-product, multi-echelon supply chain case study, which is intended to cooperate or to compete with another SC of similar characteristics. Fil: Zamarripa, Miguel A.. Universidad Politecnica de Catalunya; España Fil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina Fil: Espuña, Antonio. Universidad Politecnica de Catalunya; España

Background This study examined the daily surgical scheduling problem in a teaching hospital. This problem relates to the use of multiple operating rooms and different types of surgeons in a typical surgical day with deterministic operation durations (preincision, incision, and postincision times). Teaching hospitals play a key role in the health-care system; however, existing models assume that the duration of surgery is independent of the surgeon’s skills. This problem has not been properly addressed in other studies. We analyze the case of a Spanish public hospital, in which continuous pressures and budgeting reductions entail the more efficient use of resources. Methods To obtain an optimal solution for this problem, we developed a mixed-integer programming model and user-friendly interface that facilitate the scheduling of planned operations for the following surgical day. We also implemented a simulation model to assist the evaluation of different dispatching policies for surgeries and surgeons. The typical aspects we took into account were the type of surgeon, potential overtime, idling time of surgeons, and the use of operating rooms. Results It is necessary to consider the expertise of a given surgeon when formulating a schedule: such skill can decrease the probability of delays that could affect subsequent surgeries or cause cancellation of the final surgery. We obtained optimal solutions for a set of given instances, which we obtained through surgical information related to acceptable times collected from a Spanish public hospital. Conclusions We developed a computer-aided framework with a user-friendly interface for use by a surgical manager that presents a 3-D simulation of the problem. Additionally, we obtained an efficient formulation for this complex problem. However, the spread of this kind of operation research in Spanish public health hospitals will take a long time since there is a lack of knowledge of the beneficial techniques and possibilities that operational research can offer for the health-care system. Electronic supplementary material The online version of this article (doi:10.1186/1472-6963-14-464) contains supplementary material, which is available to authorized users.

The Vehicle Routing Problem with Stochastic Demands (VRPSD) has attracted the attention of the research community over the last decades by introducing the random behavior of the demand into the traditional routing problem. Many related works were focusing on providing suitable approaches of this large combinatorial problem for many different cases of uncertainly demand. Moreover, exact approaches that were developed up to now provide reliable results for specific demand values, e.g. using the highest demand value or the most expected value, but these solutions do not consider the concurrent effect of many possible scenarios into the objective function. So, the real necessity of more efficient and reliable approaches for this problem that provides optimal solutions for small and medium size cases in a reasonable time and also that response consistently to the random behavior of the demand has been clearly appeared in the last years ( Novoa and Storer, 2009 ). In this work a robust MILP-based formulation for the VRPSD problem is developed. The main goal of this method is to find a reliable solution that provides an optimal result considering the occurrence of many possible scenarios in simultaneous.