Traditionally, the short-term production and distribution activities have been addressed with a decoupled and sequential methodology. Although this approach simplifies the problem, there are several environments where it generates inefficiencies or is simply not applicable. Consequently, the integration of both problems is very valuable in a variety of industrial applications, especially in industries where final products must be delivered to customers shortly after production. This paper presents a mixed-integer linear optimization model that simultaneously solves the production and distribution scheduling in a single-stage multi-product batch facility with multiple non-identical units operating in parallel, where transportation operations are carried out with a heterogeneous fleet of vehicles. As operations are performed in a batch environment, the production and distribution problems also integrate decisions related to the number and size of batches required to meet the demand for multiple products. The capabilities of the proposed approach are illustrated through several cases of study. Finally, these examples are solved with a two-stage approach and the superiority of the solutions using the integrated approach is demonstrated.

This paper presents a novel hybrid model that integrates predictive and optimization techniques to enhance the scheduling and management of electricity generation in large-scale power systems, with a focus on the variability of photovoltaic (PV) energy. By combining a long short-term memory (LSTM) neural network with an optimization framework, the model forecasts PV power generation over a one-month horizon using historical data, validated against actual production. The optimization component, built on a refined large-scale power system model, incorporates these predictions using a block representation approach to simulate diverse generation technologies, including natural gas, fossil fuel-based thermal units, hydroelectric, PV, nuclear, and wind power plants. This integrated approach addresses the stochastic nature of renewable sources, distinguishing it from prior studies that focus solely on prediction or optimization. The Argentine Interconnection System (SADI) serves as the case study, leveraging over a decade of time-series data to evaluate the model’s performance. Results demonstrate reliable prediction and scheduling capabilities, achieving a low prediction error of approximately 0.01% for key PV sources. Implemented in Python within the Spyder environment, with TensorFlow and Keras for LSTM predictions and PYOMO for optimization, the model offers a practical and effective solution for system operators to optimize resource allocation in renewable-heavy power systems.

Climate change demands clean energy solutions, and renewable sources such as solar and wind are prime candidates. However, their variability poses challenges for their integration into large-scale power systems. This paper addresses this issue by proposing a novel hybrid mathematical model. The proposal integrates both fossil and renewable sources, considering real-world constraints such as system demand, reserves, and transmission dynamics. The model combines several approaches. By using a novel block composition technique, the computational complexity is reduced, making the model applicable to large-scale systems. A neural network is also developed to improve the forecasting of renewable energy production, which is crucial for managing its intermittency. The effectiveness of the proposed model is tested by considering the large Argentinean electricity system, demonstrating its practical applicability. The results show that acceptable forecasts can be obtained for the generation and transmission scheduling of the whole system.

Over time, the number of smart grids installed worldwide is gradually increasing. However, the major portion of the required electricity is still being produced by traditional large-scale and centralized power systems. The main requirement, then, is to study and develop mathematical methods that attend the integration between the two systems previously announced. In this paper, a novel model that addresses this issue is presented. The model minimizes the total operating cost of the large-scale system considering the participation of the smart grid as a dynamic entity, entailing a close relationship between both systems. This approach distinguishes the novel proposal from others that solve similar situations by taking into account the two systems in isolation. Besides, the models that represent the most common organizational structures of the smart grids are also presented in this paper. They are needed to develop the integrated model. Many similar problems in the literature are solved by implementing decomposition techniques, which might obtain a local optimum different from the global one. By contrast, problems with this proposal are solved by using mixed-integer linear programming models that ensure the reaching of a global optimum. The real test case is the integrated Argentine large-scale system and the Armstrong smart grid. Results indicate that the novel model can reach solutions that are 5% lower in comparison with the traditional techniques of considering in isolation. Efficient CPU times enable the possibility of promptly obtaining solutions if there is any change in the parameters. In addition, other benefits, apart from the economical reductions, are also achieved. Operating information closer to the reality of both systems is obtained because it considers the effects of the smart grid in large-scale system solving.

Electricity systems are currently expanding towards more efficient forms of production. Several expansionary strategies are being developed to cover increases in future electricity demand. Goals such as reducing greenhouse gas emissions, increasing the efficiency of operations, and achieving more equitable participation of the actors in charge of the investments are set. Following this premise, this paper presents a multi-objective model that helps in decision-making on the problem of expanding electricity generation. The model considers more realistic views than other works in the literature. The vast majority of the stakeholders in the studied field are satisfied with the present proposal. Investment costs, greenhouse emissions, and investment contribution rates are considered. Also, the actual procedures of the generation and transmission stages are rigorously studied. This means obtaining solutions that are closer to reality. The case study is the electricity system of Argentina. The results obtained indicate that the recommended solutions are the most convenient from all points of view. They constitute a mix of the generation with renewable and non-renewable technologies. The case study reveals emission reductions of up to 25% and it can be achieved that the most vulnerable social groups do not have to finance future system expansions.

Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off-peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem-specific operators are devised, and a set of state-of-the-art greedy algorithms aim to optimize different criteria. The proposed resolution algorithms are tested over a set of realistic instances built using real-world energy consumption data, Time-of-Use prices from an electricity company, and user preferences estimated from historical information and sensor data. The results show that the evolutionary algorithm is able to improve upon the greedy algorithms both in terms of the electricity costs and user satisfaction and largely outperforms to a large extent the current strategy without planning implemented by users.

In multisite production environments, the appropriate management of production resources is an activity of fundamental relevance to optimally respond to market demands. In particular, each production facility can operate with different policies according to its objectives, prioritizing the quality and standardization of the product, customer service, or the overall efficiency of the system; goals which must be taken into account when planning the production of the entire complex. At the operational level, in order to achieve an efficient operation of the production system, the integrated problem of batching and scheduling must be solved over all facilities, instead of doing it for each plant separately, as has been usual so far. Then, this paper proposes a mixed-integer linear programming model for the multisite batching and scheduling problems, where different operational policies are considered for multiple batch plants. Through two examples, the impact of policies on the decision-making process is shown.

Transportation planning in forest industry is a challenging activity since it involves complex decisions about raw material allocation, vehicle routing and scheduling of trucks arrivals to both harvest areas and the plants. In the Argentine context, specifically in the Argentinean Northeast (NEA) region, the forest industry plays essential role for the economic development and, among the included activities, the transportation is the key element considering the volumes that must be moved and the distances to be traveled. Therefore, enhancing efficiency in the transportation activity improves significantly the performance of this industry. In this work, a Mixed Integer Linear Programming (MILP) model is presented, where raw material allocation, vehicle routing and scheduling of trucks arrivals are simultaneously addressed. Since the resolution times of the proposed integrated MILP model are prohibitive for large instances, a hierarchical approach is also presented. The considered decomposition approach involves two stages: in the first phase, the raw material allocation and vehicle routing problems are solved through a MILP model, while in the second phase, fixing the route for each truck according to the results of the previous step, the scheduling of truck arrivals to both the harvest areas and the plants is solved through a new MILP model. The obtained results show that the proposed approach is very effective and could be easily applied in this industry.

Nowadays, oil companies have to deal with an increasingly competitive environment. In this sense, the optimization of operational processes to enhance efficiency is crucial. This article addresses the design of a decision support tool for the inland upstream transport logistics in the oil industry based on a case of study in Argentina. This problem is traditionally difficult to solve for managers due to the large number of demand facilities scattered on a large geographic area that have to be served and the consideration of several operational requirements, such as maximum allowable travel times for vehicles, availability of a limited fleet size with a small number of drivers, plus the usual demand constraints as well as those arising from security risks derived from the incompatibility of chemical products. A novel mathematical formulation and a constructive heuristic are proposed in order to address this problem. The results allow to reduce the time that the company spends for obtaining a feasible distribution plan that minimizes the time horizon of the distribution schedule provided to the clients and enhances customer satisfaction.

The literature on flow shop scheduling has extensively analyzed two classes of problems: permutation and non-permutation ones (PFS and NPFS). Most of the papers in this field have been just devoted on comparing the solutions obtained in both approaches. Our contribution consists of analyzing the structure of the critical paths determining the makespan of both kinds of schedules for the case of 2 jobs and m machines. We introduce a new characterization of the critical paths of PFS solutions as well as a decomposition procedure, yielding a representation of NPFS solutions as sequences of partial PFS ones. In structural comparisons we find cases in which NPFS solutions are dominated by PFS solutions. Numerical comparisons indicate that a wider dispersion of processing times improves the chances of obtaining optimal non-permutation schedules, in particular when this dispersion affects only a few machines.