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To address the rising demand for hydrogen energy and its reliance with the water sector, this study presents an optimal scheduling framework for a multi-energy microgrid (MEMG) that integrates electric, thermal, water, and hydrogen energy networks. To this end, a mixed-integer linear programming (MILP) model is formulated to minimize both operational costs and emissions. A bi-variate piecewise McCormick envelope technique is utilized to manage the non-linear constraints associated with the water network. The model also incorporates the transportation sector, including electric and hydrogen vehicles (EVs, HVs), with vehicle-to-grid (V2G) technology, and models their associated uncertainties using Monte Carlo simulation (MCS). Additionally, the sale of oxygen as a by-product of the hydrogenation process is also considered. The case study shows significant economic and environmental benefits, with a 29.66% cost reduction and 22.26% emissions decrease from water network integration. Oxygen sales further reduce costs by 14.19%, and V2G technology contributes an additional 2.35% cost and 6.01% emissions reduction. The proposed linear approximation method achieved superior performance, with a root mean square error (RMSE) of 0.72 and a relative error of 2.132%. • A MILP-based model is proposed for hydrogen MEMG under the water-energy nexus. • The advanced McCormick envelope method is proposed for linear approximation. • Uncertainties in the transportation sector are addressed using Monte Carlo simulation. • The comparative analysis is presented through the case study. [ABSTRACT FROM AUTHOR]

Phasor measurement units (PMUs) provide synchronized measurements to enhance power system monitoring, strategically placed to achieve full network observability with minimal cost. In this paper, the PMU placement problem for critical buses is addressed using integer linear programming, taking into account both PMU contingencies and the impact of zero-injection buses. The primary contribution is the development of a multi-stage approach to place PMUs on critical buses. Moreover, it is demonstrated that considering PMU contingencies inherently accounts for line contingencies. Furthermore, a new performance metric, the Bus Coverage Index (BCI), is proposed to evaluate the effectiveness of the placement strategy. This index overcomes the limitations of existing indices, such as the Sum of Redundancy Index (SORI) and Bus Observability Index (BOI). The results are tested on various IEEE benchmark systems under four different cases, showing significantly improved results in terms of network observability and minimized number of PMUs. In Case 1, SORI values improved significantly for the IEEE 7 and IEEE 118 bus systems, while in Case 2, enhancements were observed in the IEEE 30 and IEEE 118 systems. Case 3 demonstrated consistency in results across systems. Notably, in Case 4, the number of required PMUs was reduced in the IEEE 30, IEEE 57, IEEE 118, and New England 39 bus systems, with complete network observability. [ABSTRACT FROM AUTHOR]

EU regulations get stricter from 2028 on by imposing net-zero energy building (NZEB) standards on new residential buildings including on-site renewable energy integration. Heat pumps (HP) using thermal building mass, and Model Predictive Control (MPC) provide a viable solution to this problem. However, the MPC potential in NZEBs considering the impact on indoor comfort have not yet been investigated comprehensively. Therefore, we present a co-simulative approach combining MPC optimization and IDA ICE building simulation. The demand response (DR) potential of a ground-source HP and the long-term indoor comfort in an NZEB located in Vorarlberg, Austria over a one year period are investigated. Optimization is performed using Mixed-Integer Linear Programming (MILP) based on a simplified RC model. The HP in the building simulation is controlled by power signals obtained from the optimization. The investigation shows reductions in electricity costs of up to 49% for the HP and up to 5% for the building, as well as increases in PV self-consumption and the self-sufficiency ratio by up to 4% pt., respectively, in two distinct optimization scenarios. Consequently, the grid consumption decreased by up to 5%. Moreover, compared to the reference PI controller, the MPC scenarios enhanced indoor comfort by reducing room temperature fluctuations and lowering the average percentage of people dissatisfied by 1% pt., resulting in more stable indoor conditions. Especially precooling strategies mitigated overheating risks in summer and ensured indoor comfort according to EN 16798-1 class II standards. [ABSTRACT FROM AUTHOR]

This paper introduces a mean profit- conditional value-at-risk (CVaR) model for purchasing electricity on the day-ahead market (DA) by electric vehicles fleet aggregator (EVA). EVA controls electric vehicles (EVs) during their workplace parking, enabling smart charging and cost savings by accessing market prices that are potentially lower than flat rates available during home charging. The proposed stochastic linear programming model leverages market price scenarios to optimize aggregated charging schedules, which serve as templates for constructing effective DA bidding curves. It integrates an aspiration/reservation-based formulation of the mean profit-risk criteria, specifically Conditional Value at Risk (CVaR) to address the EVA's risk aversion. By incorporating interactive analysis, the framework ensures adaptive and robust charging schedules and bids tailored to the aggregator's risk preferences. Its ability to balance profitability with risk is validated in case studies. This approach provides a practical and computationally efficient tool for EV aggregators of global companies that can benefit from the workplace charging their fleets thanks to buying energy in the DA market. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) offers an effective solution for flexible management of Wireless Sensor Networks (WSNs) by separating control logic from sensor nodes. This paper tackles the challenge of timely recovery from SDN controller failures and proposes a game theoretic model for multi-domain controllers. A game-enhanced autonomous fault recovery algorithm for SDN controllers is proposed, which boasts fast fault recovery and low migration costs. Taking into account the remaining capacity of controllers and the transition relationships between devices, the target controller is first selected to establish a controller game domain. The issue of mapping the out-of-control switches within the controller game domain to the target controller is transformed into a linear programming problem for solution. A multi-population particle swarm optimization algorithm with repulsive interaction is employed to iteratively evolve the optimal mapping between controllers and switches. Finally, migration tasks are executed based on the optimal mapping results, and the role transition of the target controller is completed. Comparative experimental results demonstrate that, compared to existing SDN controller fault recovery algorithms, the proposed algorithm can balance the migration cost of switches and the load pressure on controllers while reducing propagation delay in SDN controllers, significantly decreasing the fault recovery time. [ABSTRACT FROM AUTHOR]

Motivated by make-to-order applications with committed delivery dates in a variety of industries, we investigate the integrated multi-plant collaborative production, inventory, and hub–spoke delivery problem in a complex production–distribution network. This network includes multi-location heterogeneous plants, distribution centers, and customers, for producing customized and splittable orders with one or more general-size multi-type jobs. Completed jobs are transported from plants to distribution centers, and then the orders whose all constituent jobs have arrived are delivered from distribution centers to customer sites. The objective is to make integrated scheduling decisions for production, inventory, and delivery, for minimizing total cost composed of production, transportation, tardiness, and inventory. We first formulate this problem as a mixed-integer programming model, and analyze its intractability by proving that the problem is NP-hard and no approximation algorithms exist with a constant worst-case ratio. We then reformulate this problem as a binary integer linear programming model to select a feasible schedule for each job, and propose a combined column generation and two-layer column enumeration algorithm to solve it. Through extensive numerical experiments, we demonstrate that our proposed algorithm is capable of generating optimal or near-optimal solutions expeditiously and outperforms four benchmark approaches, and gain valuable managerial insights for practitioners. [ABSTRACT FROM AUTHOR]

Water utility operators prioritize timely repairs of leakages, an activity that refers to a challenging task by the spatial localization of multiple existing leakages within a water distribution network (WDN). In the literature, genetic algorithms (GAs) are applied to find the best combinations of leakages, typically in combination with a reduced number of candidate leakage nodes to reduce computational effort. To address this limitation, a graph-based GA is proposed in this work by considering the topological relationship between the leakage candidate nodes and sizes for the creation of the offspring. In greater detail, each gene in the graph-based GA consists of a leakage place and leakage size, and two random genes are selected based on the roulette wheel selection for the creation of the offspring. Afterward, possible offspring nodes are selected within the shortest path between these two leakage places or in spatial proximity connected to the shortest path, where the leakage size is set to a random value between the leakage sizes of the two genes. The developed approach was tested on part of a WDN with 100 different leakage scenarios and varying number of leakages, sizes, and locations. As the results showed, the graph-based GA significantly improved leakage localization compared with a classic GA and linear programming solver, with a median distance of 44 m between suspected and actual leakage locations given perfect conditions while also being computationally efficient. However, the achievable performance was strongly affected by measurement errors, model uncertainties, and partially unknown nodal demands and was more accurate for localizing leakage places near exactly measured locations and with larger leakage sizes. [ABSTRACT FROM AUTHOR]

The university course timetabling is a tactical level problem that almost all academic departments encounter before each semester. Nowadays, in order to meet some standards, set by educational accreditation agencies within the scope of higher education quality assurance, it is required to generate efficient course timetables, which ensure optimal distribution of basic capabilities. Actually, these capabilities should be first specified based on the learning outcomes defined by lecturers for each course that are also associated with program outcomes and are aimed to be acquired by all of the students. Based on this motivation, a multi-objective mixed-integer non-linear programming model is developed for a novel capability-based course timetabling problem. Its validity and practicality are tested on a real-life application in an Industrial Engineering Department. When the balanced solutions provided by compromise and fuzzy goal programming techniques were compared with the existing schedules of the previous years, it was revealed that significant improvements could be achieved in terms of several conflicting objectives (i.e., optimal capability distribution over the timetable, acquisition of maximum number/variety of different capabilities by the students, meeting expectations of lecturers by minimizing total temporal difference between the periods his/her courses are assigned, total penalty cost related to soft constraints). [ABSTRACT FROM AUTHOR]

Lean production is a set of methods aimed at increasing efficiency and flexibility within a firm by eliminating waste and accurately defining value. For this reason, it is interested in eliminating activities that do not add value to the product to reduce costs. Within the scope of lean production, different methods are used based on certain priorities and simultaneously. In this study, the Lean Production Leveling-Heijunka approach, a lean production method applied in a mold sponge manufacturing firm, is discussed. In the accessible literature, it has been seen that the heijunka method has been studied little before and no linear model has been found for its application. Therefore, within the scope of this study, a multi-objective linear model that includes production leveling, sequencing and job stability elements is proposed. As the proposed model is multi-objective, the weighted sum method was used as the aim aggregation method to solve it. The problem of the firm was solved using the proposed model in GAMS/CPLEX. To evaluate the performance of the model, first a small-size sample problem was solved and then the main problem of the firm where the application was used was solved. The effectiveness of the model was evaluated by comparing the results obtained after solving the firm problem with the current case. In addition, Heijunka values for both cases were calculated to demonstrate the success of the proposed model's solution. According to the calculations, the Heijunka ratio was four in the solution obtained by applying the proposed model, while it was two in the current situation. These results demonstrate that a successful Heijunka method was implemented in the firm where the application was conducted, and the production process was made more balanced. [ABSTRACT FROM AUTHOR]

New product or technology innovation is always facing many uncontrollable factors, which lead to the uncertainty of the success probability of an innovation project and its developing time. Multi-agent collaboration is an effective way to improve the success probability and reduce the developing time of an innovation project. In this paper, we employ the knowledge sharing model and the success probability model to formulate a differential game for the multi-agent collaborative innovation (MACI) mechanism under uncertainty. Firstly, the equilibrium policy for each agent is obtained by applying the optimal control theory. Secondly, we observe that the derived equilibrium policy can not only effectively reduce the developing time of the innovation procedure, but also improve the success probability of the innovation project. Thirdly, we show that the optimal profit allocation contract for the collaborative mechanism can be obtained by solving a linear programming problem. Finally, this paper investigates how governments' subsidy policies affect the equilibrium of the collaborative mechanism. Numerical experiments are conducted to test the performance of different collaborative innovation mechanisms. This proposed model and its derived managerial insights provide new theoretical guidance for establishing and implementing the multi-agent collaborative mechanism of new product or technology innovations under uncertain environments. [ABSTRACT FROM AUTHOR]

A key problem in mathematical imaging, signal processing and computational statistics is the minimization of non-convex objective functions that may be non-differentiable at the relative boundary of the feasible set. This paper proposes a new family of first- and second-order interior-point methods for non-convex optimization problems with linear and conic constraints, combining logarithmically homogeneous barriers with quadratic and cubic regularization respectively. Our approach is based on a potential-reduction mechanism and, under the Lipschitz continuity of the corresponding derivative with respect to the local barrier-induced norm, attains a suitably defined class of approximate first- or second-order KKT points with worst-case iteration complexity O (ε - 2) (first-order) and O (ε - 3 / 2) (second-order), respectively. Based on these findings, we develop new path-following schemes attaining the same complexity, modulo adjusting constants. These complexity bounds are known to be optimal in the unconstrained case, and our work shows that they are upper bounds in the case with complicated constraints as well. To the best of our knowledge, this work is the first which achieves these worst-case complexity bounds under such weak conditions for general conic constrained non-convex optimization problems. [ABSTRACT FROM AUTHOR]

China is struggling to facilitate the application of renewable portfolio standards to realize sustainable economic growth. As such, improving the current distribution mechanism is crucial. In this paper, the context‐dependent data envelopment analysis and multi‐objective linear programming are combined to allocate the renewables quota for each province. This integrated approach can maximize total electricity generation while minimizing the total CO2 emission with considering the disparity of production technology level. Then, the extended Gini coefficient is employed to assess the fairness of new quota mechanism. We find that (1) the eastern region is the most efficient during the power generation process. During 2016–2019, the efficiency in the western region presents an upward trend. (2) The allocation results indicate that Inner Mongolia and Qinghai have the greatest pressure to absorb renewable energy electricity, while Guangdong and Guizhou can instead reduce the most. Shandong and Inner Mongolia face the greatest burden in conserving non‐renewable electricity. (3) Compared to 2020, the newly allocated scheme can mitigate inequality, with the Gini coefficient changing from 0.264 in 2020 to 0.248 after the allocation. Meanwhile, the reallocation reduces the Gini coefficient related to renewable electricity, non‐renewable electricity, and CO2 emissions by 0.003, 0.028, and 0.073, respectively at the 2020 level. [ABSTRACT FROM AUTHOR]

The aircraft landing problem (ALP) is an important issue of assigning an airport's runways to the arrival aircrafts as well as to schedule the landing time of these aircrafts in practice. A large number of the extant studies have tried to address such a practical problem with using various algorithms for one or more runways. For a static single-runway of the ALP, this paper proposes a new approach to develop an alternative powerful algorithm. For a given sequence of planes, we develop a faster algorithm for solving the ALP with the running time O (n log n) , where n is the number of aircrafts in the schedule. Alternatively, we reduce the proposed problem of minimizing the total cost by determining the landing times for a given landing sequence into a min-cost flow problem. We conduct a set of experimental studies to compare the performance of our near-linear time algorithm to the quadratic time algorithm whose time complexity is O (n 2) , for computing the optimal landing times. The computational results show that the proposed heuristic based on our algorithm could be much faster than both such quadratic time algorithm and the one using linear programming. [ABSTRACT FROM AUTHOR]

Digital delivery of songs has radically changed the way people can enjoy music, the sort of music available for listening, and the manner by which rights holders are compensated for their contributions to songs. Subscribers can enjoy an unlimited potpourri of songs and sounds, uniquely free of incremental acquisition or switching costs. This shift reveals listening patterns governed by affinity, boredom, attention budgets, etc. Listening patterns can be driven instantaneously, dynamically, organically or programmatically (playlists, for example). Listening demand is in a new paradigm, with a commensurate change in revenue implications. These new listening phenomena deprecate past orthodoxy around content curation in which a listener made a single purchase of a song. This point-of-sale model is now insufficient: demand revenue is proportional to song affinity—e.g., by how often a song is listened to within a time interval—and can be modeled as a time dependent process. We explore modeling digital on-demand demand and employ a fully Bayesian probabilistic model that: (1) yields estimators for multi-level effects on song demand and (2) naturally joins with multi-stage Linear Optimization scheme to optimize the same. [ABSTRACT FROM AUTHOR]

Suppose you have a collection of objects in some exotic metric space and you'd like to see what they would look like if they were instead in Euclidean space. Or suppose these objects aren't even points in a metric space, they are just entities for which you have some rough, intuitive notion of distance between each pair of them—one that need not satisfy any official mathematical properties like the triangle inequality. There is a linear algebraic optimization procedure called multidimensional scaling that embeds these objects in Euclidean space in a manner that approximates their original distances. This uncovers the Euclidean geometry hidden in data. This article explores how it works and what it's useful for, particularly in a data science context. [ABSTRACT FROM AUTHOR]

Over the years, a number of methods have been proposed to forecast the unknown inner-cell values of a set of related RxC contingency tables when only their margins are known. This is a classical problem that emerges in many areas, from economics to quantitative history, being particularly ubiquitous when dealing with electoral data in sociology and political science. However, the two current major algorithms to solve this problem, based on Bayesian statistics and iterative linear programming depend on adjustable (hyper-)parameters and do not yield a unique solution: their estimates tend to fluctuate (when convergence is reached) around a stationary distribution. Within the linear programming framework, this paper proposes a new algorithm (lclphom) that always converges to a unique solution, having no adjustable parameters. This characteristic makes it easy to use and robust to claims of hacking. Furthermore, after assessing lclphom with real and simulated data, lclphom is found to yield estimates of (almost) similar accuracy to the current major solutions, being more preferable to the other lphom-family algorithms the more heterogeneous the row-fraction distributions of the tables are. Interested practitioners can easily use this new algorithm as it has been programmed in the R-package lphom. [ABSTRACT FROM AUTHOR]

Facing the future development needs of standardization, intelligence, and serviceability in space equipment, the orbital replaceable unit (ORU) standard interface has attracted much attention from researchers. As the main structure of the standard interface, the mechanical interface (MI) not only needs to reserve space for other functional interfaces such as electrical, thermal, fluid, and data on its contact surface, but also needs to reserve sufficient internal space to complete functional expansion. In contrast to previous studies, this paper starts from the perspective of interface internal space utilization and proposes a cost-effectiveness prioritized design strategy and process. Combining operations research ideas, the interface design is transformed into multiple linear/nonlinear programming problems, ultimately improving the structural indicators of the MI. Subsequently, Recurdyn and Workbench were used for dynamics analysis of the docking state of the mechanical interface and for static analysis of the interface after locked, respectively. Finally, the future trends and development of MI are outlined. • A cost-effectiveness prioritized design strategy to maximize the mechanical interface universality. • A linear programming method to guide the design and gear selection of the mechanical interface transmission device. • A two-stage planning method for solving the 4-bar linkage design problem under complex and nonlinear constraints. [ABSTRACT FROM AUTHOR]

In this research paper, a novel branch-and-bound algorithm within the outcome-space is introduced for the generalized sum-of-linear-ratios problem. Initially, an equivalent problem of the original problem is derived by applying constraints to the numerator and denominator of the objective function, leveraging the reciprocal characteristics of logarithmic and exponential functions. Subsequently, the linear relaxation programming problem for this equivalent problem is constructed through two distinct approaches, utilizing the geometric attributes of these functions. By integrating the branch-and-bound framework with the linear relaxation problem, a comprehensive branch-and-bound algorithm is proposed for globally solving the sum-of-linear-ratios problem, and its convergence is established. Finally, numerical experiments are conducted to confirm the viability and efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: A circular economy (CE) is an economic system that tries to eliminate waste and continually use resources. Due to growing environmental concerns, supply chain (SC) design should be based on the CE considerations. In addition, responding and satisfying customers are the challenges managers constantly encounter. This study aims to improve the design of an agile closed-loop supply chain (CLSC) from the CE point of view. Design/methodology/approach: In this research, a new multi-stage, multi-product and multi-period design of a CLSC network under uncertainty is proposed that aligns with the goals of CE and SC participants. Recycling of goods is an important part of the CLSC. Therefore, a multi-objective mixed-integer linear programming model (MILP) is proposed to formulate the problem. Besides, a robust counterpart of multi-objective MILP is offered based on robust optimization to cope with the uncertainty of parameters. Finally, the proposed model is solved using the e-constraint method. Findings: The proposed model aims to provide the strategic choice of economic order to the suppliers and third-party logistic companies. The present study, which is carried out using a numerical example and sensitivity analysis, provides a robust model and solution methodology that are effective and applicable in CE-related problems. Practical implications: This study shows how all upstream and downstream units of the SC network must work integrated to meet customer needs considering the CE context. Originality/value: The main goal of the CE is to optimize resources, reduce the use of raw materials, and revitalize waste by recycling. In this study, a comprehensive model that can consider both SC design and CE necessities is developed that considers all SC participants. [ABSTRACT FROM AUTHOR]

This paper investigates the best control method of the lowest specific fuel consumption (SFC) to reduce the specific fuel consumption of the triple-bypass variable cycle engine. Specific fuel consumption is the ratio of fuel flow to thrust. First, the Kriging model of the engine near the supersonic cruise and subsonic cruise state points was extracted using the component-level model of the triple-bypass variable cycle engine, and the PSM was obtained close to the steady-state point. The contribution of each control variable to the engine's specific fuel consumption was computed using the PSM and, at the same time, due to the linear characteristics of the PSM, it was easy to deal with various constrained linear optimization problems, and the steady-state points with the smallest specific fuel consumption under the constraints could be obtained through the linear optimization algorithm; however, the surge margin and pre-turbine temperature of the optimized point were limited in the optimization process, the method of direct switching inevitably brought the problem of overshoot of the controlled quantity, and the actual controlled quantity could still exceed the safe operation boundary of the engine in the process of change. Moreover, the performance optimization control itself is premised on sacrificing the surge margin of the engine, and its operating boundary is closer to the surge line, so the limitation protection problem in the transition state cannot be ignored in the process of performance optimization control. In this paper, a multivariable steady-state controller was designed based on Model Predictive Control (MPC) to meet the needs of engine optimization control mode switching. The simulation results of the supersonic cruise mode show that the minimum fuel consumption control can reduce the fuel consumption of the engine by 2.6% while the thrust remains constant. [ABSTRACT FROM AUTHOR]

Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing visibility, and verifying sustainable supplier practices. Initiatives leading the implementation of traceability technologies must choose the least-costly set of firms—or seed set—to target for early adoption. Choosing this seed set is challenging because firms are part of supply chains interlinked in complex networks, yielding an inherent supply chain effect: benefits obtained from traceability are conditional on technology adoption by a subset of firms in a product's supply chain. We prove that the problem of selecting the least-costly seed set in a supply chain network is hard to solve and even approximate within a polylogarithmic factor. Nevertheless, we provide a novel linear programming-based algorithm to identify the least-costly seed set. The algorithm is fixed-parameter tractable in the supply chain network's treewidth, which we show to be low in real-world supply chain networks. The algorithm also enables us to derive easily computable bounds on the cost of selecting an optimal seed set. We leverage our toolbox to conduct large-scale numerical experiments that provide insights into how the supply chain network structure influences diffusion. These insights can help managers optimize their technology diffusion strategy. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01759. [ABSTRACT FROM AUTHOR]

We consider a linear sum-of-ratios fractional programming problem that arises from a broad range of applications and is known to be NP-hard. In this paper, we first develop a successive linear optimization (SLO) method for the linear sum-of-ratios problem and show that it converges to a KKT point of the underlying problem. Second, we propose a new branch-and-cut algorithm for globally solving the linear sum-of-ratios fractional program by integrating the SLO method, the linear optimization (LO) relaxation, branch-and-bound framework and branch-and-cut rule. We establish the global convergence of the algorithm and estimate its complexity. Numerical results are reported to illustrate the effectiveness of the proposed algorithm in finding a global optimal solution to large-scale instances of linear sum-of-ratios problem. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of television advertising, optimizing ad schedules to maximize viewer engagement and revenue has become significant. Traditional methods often operate in silos, limiting the potential insights gained from broader data analysis due to concerns over privacy and data sharing. This article introduces a novel approach that leverages Federated Learning (FL) to enhance TV ad schedule optimization, combining the strengths of local optimization techniques with the power of global Machine Learning (ML) models to uncover actionable insights without compromising data privacy. It combines linear programming for initial ads scheduling optimization with ML—specifically, a K-Nearest Neighbors (KNN) model—for predicting ad spot positions. Taking into account the diversity and the difficulty of the ad-scheduling problem, we propose a prescriptive-predictive approach in which first the position of the ads is optimized (using Google's OR-Tools CP-SAT) and then the scheduled position of all ads will be the result of the optimization problem. Second, this output becomes the target of a predictive task that predicts the position of new entries based on their characteristics ensuring the implementation of the scheduling at large scale (using KNN, Light Gradient Boosting Machine and Random Forest). Furthermore, we explore the integration of FL to enhance predictive accuracy and strategic insight across different broadcasting networks while preserving data privacy. The FL approach resulted in 8750 ads being precisely matched to their optimal category placements, showcasing an alignment with the intended diversity objectives. Additionally, there was a minimal deviation observed, with 1133 ads positioned within a one-category variance from their ideal placement in the original dataset. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the domain of optimization, linear programming (LP) is recognized as an exceptionally effective method for ensuring the most favorable outcomes. Within the context of LP, the minimum cost flow (MCF) problem is fundamental, with its primary objective being to reduce the transportation costs for a single item moving through a network, under the constraints related to capacity. This network is made up of supply nodes, directed arcs, and demand nodes and each arc has an associated cost and capacity constraint, these factors are certain. However, in practical scenarios, these factors are susceptible to variation due to causal uncertainty. The neutrosophic set theory has surfaced as a challenging approach to tackle the uncertainty that is often encountered in optimization processes. In this manuscript, our primary objective is to address the minimal cost flow (MCF) problem while accounting for the uncertainty inherent in the neutrosophic set. We specifically focus on the cost aspect as SVTN numbers and introduce a new approach based on a customized ranking function handmade for the MCF problem a pioneering endeavor within the field of neutrosophic sets. Additionally, we present numerical example to validate the effectiveness and robustness of our model. [ABSTRACT FROM AUTHOR]

In the context of the "dual carbon" goals, to address issues such as high energy consumption, high costs, and low power quality in the rapid development of electrified railways, this study focused on the China Railways High-Speed 5 Electric Multiple Unit and proposed a mathematical model and capacity optimization method for an on-board energy storage system using lithium batteries and supercapacitors as storage media. Firstly, considering the electrical characteristics, weight, and volume of the storage media, a mathematical model of the energy storage system was established. Secondly, to tackle problems related to energy consumption and power quality, an energy management strategy was proposed that comprehensively considers peak shaving and valley filling and power quality by controlling the charge/discharge thresholds of the storage system. The capacity optimization adopted a bi-level programming model, with the series/parallel number of storage modules as variables, considering constraints imposed by the Direct Current to Direct Current converter, train load, and space. An improved Particle Swarm Optimization algorithm and linear programming solver were used to solve specific cases. The results show that the proposed onboard energy storage system can effectively achieve energy savings, reduce consumption, and improve power quality while meeting the load and space limitations of the train. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to deal with intra and inter-cell layout problems in cellular manufacturing systems. The model is organized to minimize the total handling cost, i.e. intra and inter-cell handling costs in a continuous environment. Design/methodology/approach: The research was conducted by developing a mixed integer mathematical model. Due to the complexity and NP-hard nature of the cellular manufacturing layout problem, which mostly originated from binary variables, a "graph-pair" representation is used for every machine set and cells each of which manipulates the relative locations of the machines and cells both in left-right and below-up direction. This approach results in a linear model as the binary variables are eliminated and the relative locations of the machines and cells are determined. Moreover, a genetic algorithm as an efficient meta-heuristic algorithm is embedded in the resulting linear programming model after graph-pair construction. Findings: Various numerical examples in both small and large sizes are implemented to verify the efficiency of the linear programming embedded genetic algorithm. Originality/value: Considering the machine and cell layout problem simultaneously within the shop floor under a static environment enabled managers to use this concept to develop the models with high efficiency. [ABSTRACT FROM AUTHOR]

Purpose: With the changing landscape of the globalised business world, business-to-business supply chains face a turbulent ocean of disruptions. Such is the effect that supply chains are disrupted to the point of failure, supply is halted and its adverse effect is seen on the consumer. While previous literature has extensively studied risk and resilience through mathematical modelling, this study aims to envision a novel supply chain model that integrates blockchain to support visibility and recovery resilience strategies. Design/methodology/approach: The stochastic bi-objective (cost and shortage utility) optimisation-based mixed-integer linear programming model integrates blockchain through a binary variable, which activates at a particular threshold risk-averse level of the decision-maker. Findings: Firstly, visibility is improved, as identified by the average reduction of penalties by 36% over the different scenarios. Secondly, the average sum of shortages over different scenarios is consequently reduced by 36% as the recovery of primary suppliers improves. Thirdly, the feeling of shortage unfairness between distributors is significantly reduced by applying blockchain. Fourthly, unreliable direct suppliers resume their supply due to the availability of timely information through blockchain. Lastly, reliance on backup suppliers is reduced as direct suppliers recover conveniently. Research limitations/implications: The findings indicate that blockchain can enhance visibility and recovery even under high-impact disruption conditions. Furthermore, the study introduces a unique metric for measuring visibility, i.e. penalty costs (lower penalty costs indicate higher visibility and vice versa). The study also improves upon shortages and recoveries reported in prior literature by 6%. Finally, blockchain application caters to the literature on shortage unfairness by significantly reducing the feeling of shortage unfairness among distributors. Practical implications: This study establishes blockchain as a pro-resilience technology. It advocates that organisations focus on investing in blockchain to enhance their visibility and recovery, as it effectively reduces absolute shortages and feelings of shortage unfairness while improving recovery and visibility. Originality/value: To the best of the authors' knowledge, this is a unique supply chain model study that integrates a technology such as blockchain directly as a binary variable in the model constraint equations while also focusing on resilience strategies, costs, risk aversion and shortage unfairness. [ABSTRACT FROM AUTHOR]

In this paper, we conduct an analysis of a matrix game using rough interval payoffs, which we refer to as the rough interval matrix game (RIMG). We employ three methods to address the problem at hand: linear programming, graphical method, and algebraic method. The proposed approach offers several key advantages over existing methods, which we highlight. We introduce algorithms associated with each of the three mentioned methods to obtain optimal solutions for the RIMG. To validate the effectiveness of our proposed techniques, we present two numerical examples and apply the methodologies to solve them. In conclusion, we summarize the findings of our study and discuss potential avenues for future research based on the insights provided in this paper. [ABSTRACT FROM AUTHOR]

Risk-parity is one of the most recent and interesting strategies in the portfolio selection area. Considering the mean-standard-deviation risk measure, this paper studies the risk-parity problem under the uncertainty of the covariance matrix. Assuming that the uncertainty is represented by a finite set of scenarios, the problem is formulated as a scenario-based stochastic programming model. Then, since the occurrence probabilities of scenarios are not known with certainty, two ambiguity sets of distributions are considered, and corresponding to each one, a distributionally robust optimization model is presented. Computational experiments on real-world instances taken from the literature confirm the importance of the proposed models in terms of stability, volatility and Sharpe-ratio. [ABSTRACT FROM AUTHOR]

This paper presents a resilience-based approach for critical load restoration in distribution networks using microgrids during extreme events when the main supply is disrupted. Reconfiguration of the distribution network using graph theory is investigated, for which Dijkstra's algorithm is first used to determine the shortest paths between microgrids and critical loads, and then the feasible restoration trees are established by combining the restorable paths. A mixed-integer linear programming (MILP) model is then used to find the optimal selection of feasible restoration trees to make a restoration scheme. The service restoration is implemented with the objectives of maximizing the energy delivered to the critical loads and minimizing the number of switching operations. The limited fuel storage of the generation sources in microgrids, the operational constraints of the network and microgrids, as well as the radiality constraint of the restored sub-networks, are considered the constraints of the optimization problem. The presented method can be used for optimal restoration of critical loads including the number of switching operations which is essential for the ease of implementation of a restoration plan. The results of simulations on a 118-bus distribution network demonstrate the efficiency of the procedure. [ABSTRACT FROM AUTHOR]

Interior-point methods (IPMs) for linear programming (LP) are generally based on the logarithmic barrier function. Peng et al. (J. Comput. Technol. 6: 61–80, 2001) were the first to propose non-logarithmic kernel functions (KFs) for solving IPMs. These KFs are strongly convex and smoothly coercive on their domains. Later, Bai et al. (SIAM J. Optim. 15(1): 101–128, 2004) introduced the first KF with a trigonometric barrier term. Since then, no new type of KFs were proposed until 2020, when Touil and Chikouche (Filomat. 34(12): 3957–3969, 2020; Acta Math. Sin. (Engl. Ser.), 38(1): 44–67, 2022) introduced the first hyperbolic KFs for semidefinite programming (SDP). They established that the iteration complexities of algorithms based on their proposed KFs are O (n 2 3 log n ϵ) and O (n 3 4 log n ϵ) for large-update methods, respectively. The aim of this work is to improve the complexity result for large-update method. In fact, we present a new parametric KF with a hyperbolic barrier term. By simple tools, we show that the worst-case iteration complexity of our algorithm for the large-update method is O (n log n log n ϵ) iterations. This coincides with the currently best-known iteration bounds for IPMs based on all existing kind of KFs. The algorithm based on the proposed KF has been tested. Extensive numerical simulations on test problems with different sizes have shown that this KF has promising results. [ABSTRACT FROM AUTHOR]

This paper addresses a job shop scheduling problem with peak power constraints, in which jobs can be processed once or multiple times on either all or a subset of the machines. The latter characteristic provides additional flexibility, nowadays present in many manufacturing systems. The problem is complicated by the need to determine both the operation sequence and starting time as well as the speed at which machines process each operation. Due to the adherence to renewable energy production and its intermittent nature, manufacturing companies need to adopt power‐flexible production schedules. The proposed power control strategies, that is, adjusting processing speed and timing to reduce peak power requirements may impact production time (makespan) and energy consumption. Therefore, we propose a bi‐objective approach that minimizes both objectives. A linear programming model is developed to provide a formal statement of the problem, which is solved to optimality for small‐sized instances. We also proposed a multi‐objective biased random key genetic algorithm framework that evolves several populations in parallel. Computational experiments provide decision and policymakers with insights into the implications of imposing or negotiating power consumption limits. Finally, the several trade‐off solutions obtained show that as the power limit is lowered, the makespan increases at an increasing rate and a similar trend is observed in energy consumption but only for very small makespan values. Furthermore, peak power demand reductions of about 25% have a limited impact on the minimum makespan value (4–6% increase), while at the same time allowing for a small reduction in energy consumption. [ABSTRACT FROM AUTHOR]

In a recent paper published in this journal, Alonso‐Pecina et al. collect several sets of benchmark instances for the label printing problem from the literature and they also propose their own instances. Due to the intractability of the problem, no optimal solutions were declared for most of these instances. In this paper, we propose an integer linear programming model for the problem. We obtain optimal solutions or show that the solutions provided in the literature are already optimal for most of these instances based on the model. For some of the rest instances, we provide better solutions compared to the previous best solutions in the literature. [ABSTRACT FROM AUTHOR]

In epidemiological studies, evaluating the health impacts stemming from multiple exposures is one of the important goals. To analyze the effects of multiple exposures on discrete or time‐to‐event health outcomes, researchers often employ generalized linear models, Cox proportional hazards models, and machine learning methods. However, observational studies are prone to unmeasured confounding factors, which can introduce the potential for substantial bias in the multiple exposure effects. To address this issue, we propose a novel outcome model‐based sensitivity analysis method for non‐Gaussian and time‐to‐event outcomes with multiple exposures. All the proposed sensitivity analysis problems are formulated as linear programming problems with quadratic and linear constraints, which can be solved efficiently. Analytic solutions are provided for some optimization problems, and a numerical study is performed to examine how the proposed sensitivity analysis behaves in finite samples. We illustrate the proposed method using two real data examples. [ABSTRACT FROM AUTHOR]

Background: Discontinuous transcription allows coronaviruses to efficiently replicate and transmit within host cells, enhancing their adaptability and survival. Assembling viral transcripts is crucial for virology research and the development of antiviral strategies. However, traditional transcript assembly methods primarily designed for variable alternative splicing events in eukaryotes are not suitable for the viral transcript assembly problem. The current algorithms designed for assembling viral transcripts often struggle with low accuracy in determining the transcript boundaries. There is an urgent need to develop a highly accurate viral transcript assembly algorithm. Results: In this work, we propose Cov-trans, a reference-based transcript assembler specifically tailored for the discontinuous transcription of coronaviruses. Cov-trans first identifies canonical transcripts based on discontinuous transcription mechanisms, start and stop codons, as well as reads alignment information. Subsequently, it formulates the assembly of non-canonical transcripts as a path extraction problem, and introduces a mixed integer linear programming to recover these non-canonical transcripts. Conclusion: Experimental results show that Cov-trans outperforms other assemblers in both accuracy and recall, with a notable strength in accurately identifying the boundaries of transcripts. Cov-trans is freely available at https://github.com/computer-Bioinfo/Cov-trans.git. [ABSTRACT FROM AUTHOR]

This article is concerned with quadrature/cubature rules able to deal with multiple subspaces of functions, in such a way that the integration points are common for all the subspaces, yet the (nonnegative) weights are tailored to each specific subspace. These subspace‐adaptive weights cubature rules can be used to accelerate computational mechanics applications requiring efficiently evaluating spatial integrals whose integrand function dynamically switches between multiple pre‐computed subspaces. One of such applications is local hyperreduced‐order modeling (HROM), in which the solution manifold is approximately represented as a collection of basis matrices, each basis matrix corresponding to a different region in parameter space. The proposed optimization framework is discrete in terms of the location of the integration points, in the sense that such points are selected among the Gauss points of a given finite element mesh, and the target subspaces of functions are represented by orthogonal basis matrices constructed from the values of the functions at such Gauss points, using the singular value decomposition (SVD). This discrete framework allows us to treat also problems in which the integrals are approximated as a weighted sum of the contribution of each finite element, as in the energy‐conserving sampling and weighting method of C. Farhat and co‐workers. Two distinct solution strategies are examined. The first one is a greedy strategy based on an enhanced version of the empirical cubature method (ECM) developed by the authors elsewhere (we call it the subspace‐adaptive weights ECM, SAW‐ECM for short), while the second method is based on a convexification of the cubature problem so that it can be addressed by linear programming algorithms. We show in a toy problem involving integration of polynomial functions that the SAW‐ECM clearly outperforms the other method both in terms of computational cost and optimality. On the other hand, we illustrate the performance of the SAW‐ECM in the construction of a local HROMs in a highly nonlinear equilibrium problem (large strains regime). We demonstrate that, provided that the subspace‐transition errors are negligible, the error associated to hyperreduction using adaptive weights can be controlled by the truncation tolerances of the SVDs used for determining the basis matrices. We also show that the number of integration points decreases notably as the number of subspaces increases, and that, in the limiting case of using as many subspaces as snapshots, the SAW‐ECM delivers rules with a number of integration points only dependent on the intrinsic dimensionality of the solution manifold and the degree of overlapping required to avoid subspace‐transition errors. The Python source codes of the proposed SAW‐ECM are openly accessible in the public repository https://github.com/Rbravo555/localECM. [ABSTRACT FROM AUTHOR]

We propose a new logistics concept for rail-based container transportation where, instead of booking individual slots on specific trains (which is the current state of the art), customers only specify from where to where they want their containers to be delivered, as well as appropriate time windows for pickup and delivery. A containers actual transportation route is then chosen by a freight forwarder based on the availability and compatibility of freight trains. We provide an optimization algorithm based on an integer linear programming formulation that can be used to find globally optimal transportation schedules for a given set of containers. Our computational experiments show that this new paradigm can significantly reduce the number of trains required for serving a given container transportation demand, improving the logistic systems' overall environmental and economic efficiency. [ABSTRACT FROM AUTHOR]

The rapid development of intelligent warehouse systems is resulting in the realization of automation in warehouse activities and raising awareness of decarbonization, particularly the need to reduce carbon emissions from electricity consumption. Driven by the decarbonization trend, microgrid systems with rooftop photovoltaic panels are becoming more popular in warehouses and are providing zero-carbon electricity for warehouse operations. How to make better use of microgrid systems and reduce the consumption of electricity generated from traditional energy sources is becoming increasingly important in warehouse systems. This paper investigates an operational problem in a warehouse system equipped with a shuttle-based storage and retrieval system, in which a microgrid system acts as the main electricity source. Power-load management is applied to avoid peaks of energy consumption, and a mixed linear programming model is developed to optimize task sequencing and scheduling with decarbonization awareness. To solve the proposed problem, a data-driven variable neighbourhood search algorithm is built. Numerical experiments are conducted to validate the model and algorithm. Sensitivity analysis shows the effectiveness of power-load management and the influence of system configuration on energy consumption. [ABSTRACT FROM AUTHOR]

It is necessary to select appropriate active parameters to ensure both the accuracy and computation efficiency before the global analysis of chemical kinetic model. This paper proposes a new method for selecting the active parameters on the base of the combination of sensitivity analysis and linear programming. Compared with the usual methods for selecting active parameters, such as the local sensitive analysis, the characteristics of the proposed method is preliminary visualization of the possible influence of the selected active parameters on the model outputs in the process of parameter selection, ensuring the reliability of the selected active parameters. Considering the computation efficiency, the number of selected active parameters can be controlled in a suitable size through combining with dichotomy or other screening techniques. In the study, the pre-exponential factors of the Arrhenius equations in the USC-Mech II model were considered as the candidate parameters and the uncertainties of the pre-exponential factors were set. Taking the ignition of ethylene for example, the 10 reactions that can increase the ignition time of ethylene under a wide range of conditions with equivalence ratio of 1, 0.1 ~ 1 MPa and 1000 K ~ 1500 K were successfully selected using the proposed method. Then, the 10 active parameters were tested in each condition. The results showed that the selected active parameters can make the ignition delay time close to the target for each condition, which reflects the reliability of active parameter selection. [ABSTRACT FROM AUTHOR]

To improve the assembly accuracy of intelligent assembly process, a 3D surface reconstruction system based on laser scanning for assembly targets and assembly positions was designed. The light source uses a line laser to provide structured light, enabling rapid scanning of the assembly target surface on a single line. Calculate the spatial relationship between the assembly target and the assembly position through the calibration board. A position inversion optimization algorithm has been proposed. By completing the transformation of feature points from local coordinates to global coordinates. The energy distribution in the central area of the CCD image plane was simulated and analyzed under different baseline lengths, and the optimal baseline position of 35 mm was determined. The experiment is divided into two parts, and the reconstruction of the three-dimensional surface of the free-form surface target based on laser scanning was completed in the laboratory. The optimized target 3D point cloud data is evenly distributed, with almost no scattered points on the main two local surfaces, which well reflects the surface structure of the target. In the actual outdoor assembly experiment, the system cooperated with the assembly vehicle to achieve the correct assembly of the assembly. The reconstruction accuracy deviation of most test points is very small, with over 85% of test points having an error of less than 0.54 mm. The average deviation of these points before and after optimization is 0.67 mm. This system has higher application value in the field of large-scale intelligent assembly. [ABSTRACT FROM AUTHOR]

We propose a framework designed to tackle a multi-objective optimization challenge related to the placement of applications in fog computing, employing a deep reinforcement learning (DRL) approach. Unlike other optimization techniques, such as integer linear programming or genetic algorithms, DRL models are applied in real time to solve similar problem situations after training. Our model comprises a learning process featuring a graph neural network and two actor-critics, providing a holistic perspective on the priorities concerning interconnected services that constitute an application. The learning model incorporates the relationships between services as a crucial factor in placement decisions: Services with higher dependencies take precedence in location selection. Our experimental investigation involves illustrative cases where we compare our results with baseline strategies and genetic algorithms. We observed a comparable Pareto set with negligible execution times, measured in the order of milliseconds, in contrast to the hours required by alternative approaches. [ABSTRACT FROM AUTHOR]

Collapse risk assessment is an important basis for the prevention and control of geological disasters in mountainous areas. The existing research on collapse hazard is less, and there is still no further advancement in the evaluation of collapse hazard for the traditional indicator assignment method for the diversification of the assignment results of the indicators and the comprehensive evaluation method that cannot consider the ambiguity and randomness of the indicator data at the same time. In this paper, we utilize the respective advantages of the linear programming theory and the cloud model from the prevention and control point of view, and evaluate the collapse samples. Firstly, the weight interval of evaluation index is determined by improved analytic hierarchy process, entropy weight method and coefficient of variation method. Secondly, the linear programming algorithm is used to select the specific weight of each collapse sample when the risk is the largest in the interval. Finally, a comprehensive evaluation model of cloud model is constructed to determine the risk level of collapse. In this paper, 20 collapse samples counted by predecessors in G4217 Wenchuan-Lixian section are taken as research cases. The evaluation results of 20 collapse samples are compared with other evaluation methods and field survey conditions to prove the reliability and rationality of the method. The evaluation results show that 13 of the 20 collapse samples are extremely dangerous, 2 are highly dangerous, 4 are moderately dangerous, and 1 is lowly dangerous. Among them, the extremely dangerous collapse samples account for 65% of the total number of collapses. Compared with other methods, this method is more in line with the actual situation. [ABSTRACT FROM AUTHOR]

Utilizing wind power alongside flexible resources such as power‐to‐gas technology, gas‐fuel generator, demand response (DR) program, grid‐enhancing technologies, and carbon capture and storage can help to low carbon operation of integrated electricity‐gas systems (IEGSs). Accordingly, this paper proposes a low‐carbon economic dispatch model for the IEGS, in which gas‐fuel generators, DR, and gas‐fuel generator are considered to realize the economic and environmentally friendly operation of these systems. Also, the flexible AC transmission system device as one of the grid‐enhancing technologies is innovatively included in IEGSs to guarantee that wind power is deliverable entire the electricity system. Besides, power‐to‐gas equipped with hydrogen storage is used to absorb the excess wind power to produce CH4. On the other hand, to capture the inherent flexibility of the gas network, the gas‐storing characteristic of pipelines is shown by line pack modelling. To manage uncertainties associated with wind power and DR program, the proposed model is formulated as an IGDT‐stochastic problem. For efficient computation purposes, the present work follows the mixed‐integer linear programming framework. Different case studies are performed on an integrated test system. Numerical simulation results show that the proposed model leads to reducing the total cost, carbon emissions, and wind curtailment by 29%, 16.4%, and 100%, respectively. It can be seen that the proposed low‐carbon ED model is environmentally friendly and has economic benefits. [ABSTRACT FROM AUTHOR]

Matheuristics are problem independent frameworks that use mathematical programming tools to obtain high quality heuristic solutions. They are structurally general enough to be applied to different problems with little adaptation to their abstract structure, so they can be considered as new or hybrid metaheuristics based on components derived from the mathematical model of the problems of interest. In this survey, we emphasize the mathematical tools and describe how they can be used to design heuristics. We focus on mixed-integer linear programming and report representative examples from the literature of how it has been used for effective heuristic optimization. References to contributions to matheuristics deriving from neighboring research areas such as Artificial Intelligence or Quantum Computing are also included. We conclude with some ideas for possible future developments. This paper extends an original version published in 4OR with new sections on CMSA, Incremental Core, AI hybrids and Quantum Heuristics, and includes references to several recent publications. [ABSTRACT FROM AUTHOR]

Linear programming has had a tremendous impact in the modeling and solution of a great diversity of applied problems, especially in the efficient allocation of resources. As a result, this methodology forms the backbone of introductory courses in operations research. What students, and others, may not appreciate is that linear programming transcends its linear nomenclature and can be applied to an even wider range of important practical problems. The objective of this article is to present a selection, and just a selection, from this range of problems that at first blush do not seem amenable to linear programming formulation. The exposition focuses on the most basic models in these selected applications, with pointers to more elaborate formulations and extensions. Thus, our intent is to expand the modeling awareness of those first encountering linear programming. In addition, we hope this article will be of interest to those who teach linear programming and to seasoned academics and practitioners, alike. [ABSTRACT FROM AUTHOR]

Over the past twenty years, camera networks have become increasingly popular. In response to various demands imposed on these networks, several coverage models have been developed in the scientific literature, such as area, trap, barrier, and target coverage. In this paper, a new type of coverage task, the Maximum Target Coverage with k-Barrier Coverage (MTCBC-k) problem, is defined. Here, the goal is to cover as many moving targets as possible from time step to time step while continuously maintaining k-barrier coverage over the region of interest (ROI). This approach is different from independently solving the two tasks and then merging the results. An Integer Linear Programming (ILP) formulation for the MTCBC-k problem is presented. Additionally, two types of camera clustering methods have been developed. This approach allows for solving smaller ILPs within clusters, and combining their solutions. Furthermore, a polynomial-time greedy algorithm has been introduced as an alternative to solve the MTCBC-k problem. An example was also provided of how the aforementioned methods can be modified to handle a more realistic scenario, where only the targets detected by the cameras are known, rather than all the targets within the ROI. The simulations were run with both dense and sparse camera placements, convincingly supporting the usefulness of the clustering and greedy methods. [ABSTRACT FROM AUTHOR]

The food industry faces significant challenges in managing operational costs due to its high energy intensity and rising energy prices. Industrial food-processing facilities, with substantial thermal capacities and large demands for cooling and heating, offer promising opportunities for demand response (DR) strategies. This study explores the application of deep reinforcement learning (RL) as an innovative, data-driven approach for DR in the food industry. By leveraging the adaptive, self-learning capabilities of RL, energy costs in the investigated plant are effectively decreased. The RL algorithm was compared with the well-established optimization method Mixed Integer Linear Programming (MILP), and both were benchmarked against a reference scenario without DR. The two optimization strategies demonstrate cost savings of 17.57% and 18.65% for RL and MILP, respectively. Although RL is slightly less efficient in cost reduction, it significantly outperforms in computational speed, being approximately 20 times faster. During operation, RL only needs 2ms per optimization compared to 19s for MILP, making it a promising optimization tool for edge computing. Moreover, while MILP's computation time increases considerably with the number of binary variables, RL efficiently learns dynamic system behavior and scales to more complex systems without significant performance degradation. These results highlight that deep RL, when applied to DR, offers substantial cost savings and computational efficiency, with broad applicability to energy management in various applications. [ABSTRACT FROM AUTHOR]