Advances in robust optimization approaches for scheduling under uncertainty

A new robust optimization approach for scheduling under uncertainty:: I. Bounded uncertainty

A novel robust optimization approach for an integrated municipal water distribution system design under uncertainty: A case study of Mashhad

Production is an activity carried out by a company to add use to an object or create new objects that are more useful for everyone to meet their needs. The Covid-19 pandemic has had a major impact on the textile sector. During this pandemic, demand in the local market has decreased significantly. The stock of goods owned by the local market cannot accommodate the decrease in demand. For convection or garment companies, the price of raw materials has increased by up to 30%. This increase certainly has an impact that makes it difficult to get raw materials for the production process. These results indicate that when the price of raw materials increases, there will be an increase in production, but when the price of raw materials decreases, it will make production decreases. An increase in production when the price of raw materials increases is a sign that garment business actors are still trying to meet consumer needs for garment products. This also indicates that garment entrepreneurs continue to increase production when raw material prices increase, which indirectly indicates that the garment industry has good potential to survive the COVID-19 pandemic.

The prices of raw materials necessary for production play an important role in the production decisions of farmers, and thus in shaping the level of supply of agricultural raw materials. The constant increase in the prices of raw materials for production increases its costs. Therefore, it should be noted that the rising prices of fuel and fertilizers affect the decisions and profitability of plant production. In connection with the above, this study attempts to indicate the impact of changes in fertilizer and fuel prices on the profitability of production of selected agricultural crops in Poland. The paper uses secondary data on the price level of fertilizers, fuels and selected agricultural products, as well as the structure of their production costs. Based on the sensitivity analysis, the potential impact of the increase in fuel and fertilizer prices on the profitability of the production of selected species was indicated. The results showed that the increase in the prices of the above-mentioned raw materials can significantly affect the economic efficiency of the production activity.

Minimizing energy consumption by joint radio and computing resource allocation in Cloud-RAN

Knowledge representation of mathematical optimization problems and constructs for modeling

The subject of this research is the processes of price formation for raw materials depending on the demand for end consumer products. The article reviews a mathematical model that is based on the principle of maximum utility. The proposed model is founded on the stage-by-stage determination of the production output and consumption of end products, as well as corresponding prices depending on the prices of used raw materials and semi-finished products. The prices for intermediate products and raw materials are formed depending on the need for end products output with their optimization by demand. The article provides the basic mathematical ration with regards to using principle of maximum utility applicable to the demand-supply model and its implementation in multi-stage production. The acquired results indicate weak dependence of production output and prices for end products on the cost of raw material in terms of advanced refining. With limited production capacity of raw materials, the dynamics of prices is well predicted. The results of modeling, compared to the available statistical data, indicate the adequacy of the proposed model to the unfolding economic processes. It is determined that the accuracy of price prediction for raw products with a significant volume of its subsequent processing is limited.

Along with their widespread application, lithium-ion batteries (LIBs) have recently gained growing acceptance as a sustainable and clean technology. In this regard, the present study proposes a two-phase approach based on a new hybrid multicriteria decision-making (MCDM) method and sustainable robust optimization (RO) approach to design and plan a LIBs supply chain (SC) under uncertainty. In the first phase, the most appropriate possible locations for battery manufacturing zones are determined based on various ecological, social, and technical criteria. For this purpose, the Bayesian best-worst method (BBWM) and technique for order of preference by similarity to ideal solution (TOPSIS) are combined through the hybrid MCDM analysis. In the second phase, a two-stage stochastic RO model is formulated to optimize a set of strategic and tactical decisions for LIBs SC. The proposed LIBs SC design is a sustainable mixed-integer linear programming (MILP), multiproduct, and multiperiod model that addresses environmental, social, and economic objectives. Finally, the model’s performance is evaluated by conducting an actual case study in Iran, offering key managerial insights. Findings reveal that the hybrid BBWM-TOPSIS method reduces the optimization model’s computational complexity by eliminating 45% of inappropriate locations. Furthermore, raw material supply cost and manufacturing activities account for the highest portion of the total cost and EI elements by 53% and 60%, respectively.

Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty

This study aims to identify internal factors and external factors of UD Betris, as well as to determine the strategy for developing the business of bamboo batik handicraft industry UD Betris. Betris in Meras Village. This research was conducted from April to June 2018. The data used were primary data and secondary data. Primary data is obtained through observation and interviews which are divided into two parties, namely internal parties (business owners and labor) and external parties (local government and consumers) with 8 (eight) respondents. Meanwhile, secondary data was obtained from literature studies. Analysis of the data used is by using a SWOT analysis. The results showed that internal factors include: raw materials, quality of raw materials, price of raw materials, product uniqueness, business spirit and craftsman skills, product prices, product durability and quality, not having financial records, traditional production equipment, product arrangement that has not been arranged neat, promotion is not efficient, does not have a special place of business, and there is no parking space. external factors include: high market opportunities, government support, opportunities for training, opportunities to participate in exhibitions, plastic substitute goods, interest in business successors, and the existence of competitors using modern production tools. Based on the SWOT diagram, the chosen strategy is a combination of SO, namely: 1) Maintaining product quality and continuing to develop products by innovating to create bamboo handicraft products with new designs. 2) Continue to work with the government and related agencies to develop and expand marketing areas so that products are better known to local and foreign communities through training and exhibition events. *jnkd*.

This article discusses how in the modern Russian economy domestic and export prices for basic resources—oil, gas, oil products, metals, timber, chemicals, and grain—should be related. The correlation between the level of domestic prices for raw materials and industrialization processes is shown. The need for a coherent industrial policy aimed at a more rapid development of the processing and manufacturing industries is underlined. The conclusion is that the various measures of indirect regulation of domestic prices for basic resources are a natural constituent of a reasonable economic development strategy for Russia. Some possible actions to curb the growth in domestic prices for raw materials and primary products are considered.

Coordinated operations of electricity and district heating networks offer a potential for mitigating inherent variability of renewable energy sources (RES) in the ongoing transition to smart grids. This paper proposes a two-stage distributionally robust optimization (DRO) approach to determine the optimal day-ahead unit commitment in coordinated electricity and district heating networks with variable RES power output. The proposed formulation is to minimize the worst-case expected total cost over an ambiguity set comprising a family of probability distributions with given support and moments of RES power output. As such, the proposed DRO approach can overcome the limitations of stochastic programming in its inherent dependence of exact probability distributions along with a huge computational burden, but also becomes less conservative than classical robust optimization. The pertinent DRO model is eventually reformulated as a tractable mixed-integer second-order cone (SOC) programming after employing linear decision rules and the SOC duality. Simplified affine policies are utilized to further improve computational tractability and performance. Finally, case studies are conducted based on Barry Island electricity and district heating networks. The numerical results demonstrate the decision-making superiority of the proposed method as compared with deterministic, stochastic programming, and robust optimization approaches. They also validate the computational improvement of the proposed approach by employing simplified affine policies.

Adaptive and robust radiation therapy optimization for lung cancer

Static scheduling of directed acyclic data flow graphs onto multiprocessors using particle swarm optimization

The Unmanned Aerial Vehicles (UAVs), commonly known as drones, are significant in the agriculture sphere to automate the work such as: data acquisition, crop spraying among others. This paper proposes a path planning solution for a team of UAVs that is required to whiten a greenhouse’s roof, this problem having both social-economic impact, as well as scientific-technical impact. First, the space around the roof is partitioned into cells based on a 3D cell decomposition technique, labeling the cells including the surface of the roof as regions of interest (ROIs). Based on this representation, a Petri net (PN) model captures the motion of drones, while their trajectories are returned by a Mixed Integer Linear Programming (MILP) problem which optimizes the energy consumption. An adaptable strategy is used such that the MILP problem considers only the available UAVs with enough energy to reach the ROIs. The algorithm is implemented in MATLAB and the simulation results are captured in a video link, evaluating the impact of the size of the team and the precision used for mapping the environment over the running time.