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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]

Maintenance activities are inevitable and costly in integrated mining operations. Conducting maintenance may require the whole system, or sub-units of the system, to be shut down temporarily. These maintenance activities not only disrupt the unit being shut down, but they also have consequences for inventory levels and product flow downstream. In this paper, we consider an interconnected mining system in which there are complicated maintenance relationships and stock accumulation at intermediate nodes. We propose a time-indexed mixed-integer linear programming formulation to optimize the long-term integrated maintenance plan and maximize the total throughput. We also devise an algorithm, which combines Benders decomposition and Lagrangian relaxation, to accelerate the computational speed. To validate our mathematical model, we perform simulations for a real-world case study in the iron ore industry. The results show that our method can yield better solutions than CPLEX optimization solver alone in faster time. [ABSTRACT FROM AUTHOR]

This paper presents a customized local energy trading approach using a comprehensive set of performance indices (i.e. Community Savings, Self-Consumption, Self-Sufficiency, Fairness Index, Individual Household Savings) for evaluation of the trading practices. The simulations performed on historical data sets compared dispatch mechanisms based on Mixed Integer Linear Programming (MILP) and techniques originating from Auction-Based approaches. The intent was to use performance metrics in building Local Energy Market (LEM) trading solutions based on a given set of limited inputs and use them to optimize pricing or trading dispatch to get the best consumer returns. Modeling results indicate that the Auction-Based Model performed better than the MILP methods in terms of all performance indices and had potential to provide participants with higher levels of savings.

Microgrid resource sizing problems typically include the analysis of a combination of value streams such as peak shaving, load shifting, or load scheduling, which support the economic feasibility of the microgrid deployment. However, microgrid benefits can go beyond these, and the ability to provide ancillary grid services such as frequency regulation or spinning and non-spinning reserves is well known, despite typically not being considered in resource sizing problems. This paper proposes the expansion of the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state-of-the-art microgrid resource sizing model, to include revenue streams resulting from the participation in ancillary service markets. Results suggest that participation in such markets may not only influence the optimum resource sizing, but also the operational dispatch, with results being strongly influenced by the exact market requirements and clearing prices.

Improper waste disposal by humans has created significant environmental issues in the marine ecosystem, including endangering aquatic life and accelerating the extinction of certain marine species. Due to the floating nature of the marine debris, the coordinates for collecting activities must be estimated in advance. In this article, GNOME software is used to estimate the coordinates of debris, and then a fleet of several ships is used to collect them. Also, a mixed integer linear programming model is presented for the routing optimization of debris collection fleets. The proposed optimization model formulates the objective function based on numerous factors, including labor cost, rent, and ship insurance, and considers constraints on fuel tank capacity, the time window, and the ship's cargo capacity. A new hybrid algorithm combining the Puma algorithm and neighborhood search is proposed to address the problem. Metropolis acceptance is used in the simulated annealing algorithm to avoid the local optima and greedy selection. Numerical examples of the marine survey and the port of Rotterdam are used to test the proposed approach, which has been proven effective in several scenarios. Results achieved from the proposed hybrid method demonstrate considerable performance improvement in solving the problem. This approach has decreased total fuel and labor costs by 10–15% compared to conventional methods, with minimized time window violation reaching 25%. These results show a significant reduction in total operational costs with proper scheduling and route planning. [ABSTRACT FROM AUTHOR]

Wildfires are natural recurrent events, that may be devastating if not addressed correctly. In these situations, where quick and accurate decisions are needed, Operational Research can be helpful for providing fast and robust solutions. This paper focuses on the response actions taken during the suppression stage of a wildfire. A mixed integer linear programming model is proposed to obtain a wildfire suppression strategy, including the wildfire behaviour changes induced by the solution. The selected wildfire suppression strategy is modelled in detail, pointing out which locations to control and their timing, based on available paths between them, avoiding engagement in dangerous situations. A computational study is carried out to determine the most suitable solver to provide exact solutions of the model. Also, a two-stage version of the model is proposed to deal with the multicriteria nature of the problem. A case study is also included to validate the model’s applicability, which is solved using the two proposed versions of the model and an iterative approach to compare their performance. [ABSTRACT FROM AUTHOR]

This paper proposes an online energy-efficient path planning approach for UAVs in complex environments. The path planning problem is formulated as a minimization optimization problem based on Mixed Integer Linear Programming (MILP), where a cost function is designed to minimize energy consumption while ensuring terrain obstacle avoidance within a limited detection range. To achieve this, we apply a Receding Horizon Control (RHC) and optimization approach. The entire path is divided into segments or sub-paths, with constraints in place to prevent collisions with obstacles. This proposed optimization approach enables fast navigation through dense environments, ensuring a collision-firee path. For further optimizing the path for energy, a path smoothing strategy is introduced to reduce energy consumption caused by sharp turns. The results demonstrate the effectiveness and accuracy of the proposed approach in dense environments with a high risk of collisions with obstacles. [ABSTRACT FROM AUTHOR]

Model predictive control is a promising approach to robustly control complex energy systems, such as hybrid battery-hydrogen energy storage systems that enable seasonal storage of renewable energies. However, deriving a mathematical model of the energy system suitable for model predictive control is difficult due to the unique characteristics of each energy system component. This work introduces mixed integer linear programming models to describe the nonlinear multidimensional operational behavior of components using piecewise linear functions. Furthermore, this paper develops a new approach for deriving a strategy for seasonal storage of renewable energies using cost factors in the objective function of the optimization problem while considering degradation effects. An experimentally validated simulation model of the PHOEBUS Energy System is utilized to compare the performance of two model predictive controllers with a hysteresis band controller such as utilized for the real-world system. Furthermore, the sensitivity of the model predictive controller to the prediction horizon length and the temporal resolution is investigated. The prediction horizon was found to have the highest impact on the performance of the model predictive controller. The best-performing model predictive controller with a 14-day prediction horizon and perfect foresight increased the total energy stored at the end of the year by 18.9% while decreasing the degradation of the electrolyzer and the fuel cell. [ABSTRACT FROM AUTHOR]

Geospatial data conflation involves matching and combining two maps to create a new map. It has received increased research attention in recent years due to its wide range of applications in GIS (Geographic Information System) data production and analysis. The map assignment problem (conceptualized in the 1980s) is one of the earliest conflation methods, in which GIS features from two maps are matched by minimizing their total discrepancy or distance. Recently, more flexible optimization models have been proposed. This includes conflation models based on the network flow problem and new models based on Mixed Integer Linear Programming (MILP). A natural question is: how are these models related or different, and how do they compare? In this study, an analytic review of major optimized conflation models in the literature is conducted and the structural linkages between them are identified. Moreover, a MILP model (the base-matching problem) and its bi-matching version are presented as a common basis. Our analysis shows that the assignment problem and all other optimized conflation models in the literature can be viewed or reformulated as variants of the base models. For network-flow based models, proof is presented that the base-matching problem is equivalent to the network-flow based fixed-charge-matching model. The equivalence of the MILP reformulation is also verified experimentally. For the existing MILP-based models, common notation is established and used to demonstrate that they are extensions of the base models in straight-forward ways. The contributions of this study are threefold. Firstly, it helps the analyst to understand the structural commonalities and differences of current conflation models and to choose different models. Secondly, by reformulating the network-flow models (and therefore, all current models) using MILP, the presented work eases the practical application of conflation by leveraging the many off-the-shelf MILP solvers. Thirdly, the base models can serve as a common ground for studying and writing new conflation models by allowing a modular and incremental way of model development. [ABSTRACT FROM AUTHOR]

This research compares SERU manufacturing systems to traditional assembly lines, focusing on the impact of uncertainty in task processing time on production output. The study considers worker skill levels and team identity, using a stochastic mixed integer linear programming approach to model uncertainty and optimize workforce allocation. Discrete event simulation is then integrated to evaluate performance using five key performance indicators (KPIs). Results show that SERU systems outperform traditional lines in terms of throughput when uncertainty is considered. The integrated approach provides more reliable performance data than deterministic optimization alone. The study also highlights the advantages of SERU systems when worker skill levels and team identity are factored in. This research fills a gap in the literature by proposing a stochastic optimization approach that considers uncertainty and worker skill levels, and by integrating stochastic optimization with simulation for comprehensive analysis. This approach provides valuable guidance for production managers in optimizing production systems. [ABSTRACT FROM AUTHOR]

Mixed integer linear programming (MILP) is known as a type of programming that can combine continuous variables, integer variables, and (0-1) variables in the same algorithm and generate fitting results for the data. Using this technique, it is possible to model and solve complex problems in many different fields such as economics, biology, engineering, etc. In the present study, a regional planning model was developed using MILP technique for the conversion of manure from dairy and beef cattle into biogas and electrical energy. For this regional planning study, considering the locations of future facilities, data on dairy and beef cattle in the Isparta province of Türkiye were used. According to the model written and solution outputs, to utilize all manure obtained from dairy and beef cattles in Isparta, 5 biogas plants with a total manure processing capacity of approximately 522,000 tons should be built in different districts. It is possible to produce a total of approximately 21,000,000 m3 of biogas and 38,500 MW of electricity per year in these biogas plants. This electrical energy obtained can meet 3.83% of the annual electricity consumption of Isparta province. [ABSTRACT FROM AUTHOR]

Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real time. Through precise prediction of the load and the photovoltaic (PV) and wind power outputs, the proposed approach guarantees optimum scheduling. The precise predictions of the load, PV, and wind power levels are achieved by employing a predictor of the Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational costs of the hybrid power plants and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the feasibility of the proposed dispatch engine. Real-time measurement data pertaining to load and the wind and PV power outputs are obtained from different locations in the Sultanate of Oman. The real-time data are utilized to predict the future levels of power output from PV and from the wind farm over the course of 24 h. The predicted power levels are then used in combination with a PV–Wind–DG–ESS–Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed FFNN's powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce a plant's running expenses. [ABSTRACT FROM AUTHOR]

The telecommunications industry in Indonesia faces numerous challenges in allocating resources for network development, including network complexity, technological changes, market competition, and customer expectations. To achieve optimal financial outcomes and customer satisfaction, investments must be well-managed, well-placed, and well-executed. This study analyses network investment portfolio selection outcomes at Telkomsel; Indonesia's largest cellular operator, comparing conventional scenarios (financial and network) with an optimized scenario using Mixed Integer Linear Programming (MILP). The research evaluates these scenarios based on total portfolio score, incremental revenue, and Internal Rate of Return (IRR). Financial indicators such as net present value (NPV), IRR, EBIT margin, incremental revenue, and network indicators such as capacity and customer satisfaction (download/upload throughput, latency, packet loss, jitter) assess investment feasibility. The MILP optimization scenario strongly correlates with incremental revenue, NPV, and IRR, indicating higher financial performance. Sites selected in the MILP optimization outperscenario formed others in total score (22% better than the financial scenario, 63% better than the network scenario), incremental revenue (3.5% better than the financial scenario, 90.2% better than the network scenario), and portfolio IRR (4% better than the financial scenario, 70% better than the network scenario). [ABSTRACT FROM AUTHOR]

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems. [ABSTRACT FROM AUTHOR]

Biorefinery plays a crucial role in the decarbonization of the current economic model, but its high investments and costs make its products less competitive. Identifying the best technological route to maximize operational synergies is crucial for its viability. This study presents a new superstructure model based on mixed integer linear programming to identify an ideal biorefinery configuration. The proposed formulation considers the selection and process scale adjustment, utility selection, and heat integration by heat cascade integration from different processes. The formulation is tested by a study where the impact of new technologies on energy efficiency and the total annualized cost of a sugarcane biorefinery is evaluated. As a result, the energy efficiency of biorefinery increased from 50.25% to 74.5% with methanol production through bagasse gasification, mainly due to its high heat availability that can be transferred to the distillery, which made it possible to shift the bagasse flow from the cogeneration to gasification process. Additionally, the production of DME yields outcomes comparable to methanol production. However, CO2 hydrogenation negatively impacts profitability and energy efficiency due to the significant consumption and electricity cost. Nonetheless, it is advantageous for surface power density as it increases biofuel production without expanding the biomass area. [ABSTRACT FROM AUTHOR]

This study introduces a new policy for managing parking scarcity called Parking Allocation and Ride-Sharing System (PARS). In PARS, a centralized algorithm allocates parking spacesto drivers who are willing to participate in a coordinated carpool. The algorithm is used to optimize the creation of carpools going to and returning from a particular venue and simultaneously reserve parking for these carpools at the venue. An efficient mixed integer linear programming (MIP) formulation is presented and two heuristics, namely Ride Decomposition (RD) and Quick Converge (QC), are proposed and compared via internally generated experiments. Experimental results show that a commercial solver is able to solve the MIP with thousands of individuals to optimality in minutes. For larger instances, the RD and QC heuristic algorithms can solve the problem, on average, 42.23% and 86.39% faster than the commercial solver and provide solutions that are 3.61% and 3.49% from optimal, respectively. [ABSTRACT FROM AUTHOR]

The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost, consisting of the price of raw materials, purchasing set-up costs, and inventory costs, and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch and bound algorithm while solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution. [ABSTRACT FROM AUTHOR]

The time of optimal planning and operation of islanded microgrid (IMG) can be minimized by modeling it as non-iterative mixed integer linear problem (MILP). In this respect, conventional non-linear power flow model needs to be transformed to its linearized form without loss of accuracy and robustness. In the present work, a linearized droop based power flow model for IMG has been developed which takes the advantage of topology of the network by modeling it in form of matrices to reduce computation time and complexity. The proposed method is compared to earlier developed non-linear power flow models for IMG. Further, linear power flow equations are formulated as MILP for optimal dump load allocation problem to resolve V and f fluctuations in an IMG. The results of proposed MILP formulation are compared with another proposed MINLP based non-iterative method and an already existing MINLP based iterative method which involves non-linear power flow and heuristic techniques. The IEEE 33-node and 69-node radial distribution networks (DNs) have been modified as droop controlled IMGs and used as test beds. The work has been conducted in GAMS/MATLAB.

Mixed integer linear programming (MILP) is known as a type of programming that can combine continuous variables, integer variables, and (0-1) variables in the same algorithm and generate fitting results for the data. Using this technique, it is possible to model and solve complex problems in many different fields such as economics, biology, engineering, etc. In the present study, a regional planning model was developed using MILP technique for the conversion of manure from dairy and beef cattle into biogas and electrical energy. For this regional planning study, considering the locations of future facilities, data on dairy and beef cattle in the Isparta province of Türkiye were used. According to the model written and solution outputs, to utilize all manure obtained from dairy and beef cattles in Isparta, 5 biogas plants with a total manure processing capacity of approximately 522,000 tons should be built in different districts. It is possible to produce a total of approximately 21,000,000 m3 of biogas and 38,500 MW of electricity per year in these biogas plants. This electrical energy obtained can meet 3.83% of the annual electricity consumption of Isparta province.

The production process of precast components is labor-intensive, involving various manual tasks. The physically demanding tasks usually result in fatigue and overexertion of workers, leading to increased occupational health risks and reduced productivity. An appropriate work–rest strategy is recognized to effectively promote both workers' health and productivity, while it has rarely been studied in the field of the construction industry. To narrow this gap, this study developed a mixed-integer linear programming approach to optimize the work–rest schedule by integrating workers' overexertion. The objective is to maximize the productive time affected by the workers' accumulative fatigue and recovery. Also, the optimized work–rest strategy can be highly customized by considering personalized factors and task characteristics. Experimenting with a case study compared the default rest schedule provided by the superintendent onsite with the optimal solution solved from the developed model. Results suggested that up to 20% improvement in productive time can be achieved, especially for the task with a relatively higher workload. Computational experiments were conducted to evaluate the sensitivity of total productive time to various personalized and task-specific factors. The proposed method provides superintendents with an applicable strategy to improve workers' productivity and reduce their occupational risks resulting from overexertion. This study can promote the implementation of personalized occupational health management and support the improvement of regulations on the required rest with quantified evidence, thereby contributing to more reliable scheduling and sustainable workforce development for the construction industry. The research scope was limited to the precast production process, and further investigation on broader applications will be conducted. [ABSTRACT FROM AUTHOR]

This research proposes a multi-period multiple parts mixed-integer linear programming optimization model for the trade-off analysis of spare parts supply through computer numerical control (CNC) manufacturing and additive manufacturing (AM). The multiple spare parts have different characteristics such as volume, shape size, and geometry complexity. The model focuses on minimizing lead times and thus reducing downtime costs. Scenario analyses are developed for some parameters to assess the robustness of the model. The analysis shows that the mix between AM-based spare parts and CNC-based spare parts is sensitive to changes in demand. For the given data, the findings demonstrate that AM is cost-effective with spare parts having high geometry complexity while CNC-based manufacturing is economically feasible for spare parts with low geometry complexity and large sizes. The proposed model can support decision-makers in selecting the optimal manufacturing method for multiple spare parts having different characteristics and attributes. The paper concludes with limitations and future directions. [ABSTRACT FROM AUTHOR]

This study aims at developing the Hydrogen Economics and InfRAstructure optimization model (HERA) to support the technical-economic assessment of hydrogen supply chain infrastructures. It is developed at an hourly scale, in Mixed Integer Linear Programming (MIP), and includes the hydrogen production and delivery via road transportation or pipelines, as well as hydrogen and electricity storage options. To be tested, HERA was applied in a case study, to compare two different locations for hydrogen production. Findings show that the use of HERA can support the technical-economic assessment of a hydrogen infrastructure, highlighting the impact of the distance between the hydrogen production site and the demand, and the use of batteries and complementary renewable resources, to smooth the variability, on total costs. Besides, the proposed model was able to find out the optimal equipment combination for the supply chain by considering the modular (integer) nature of the problem to be solved. [Display omitted] • Development of HERA, an integer optimization model for hydrogen supply chain. • HERA designs the configuration of the components while addressing intermittency. • The case study shows the impacts of parameters on the system's design and costs. • The use of batteries and complementary renewable resources reduced the LCOH. • The quality of renewable resources can be overshadowed by the distance to the demand. [ABSTRACT FROM AUTHOR]

The integration of an energy storage system into an integrated energy system (IES) enhances renewable energy penetration while catering to diverse energy loads. In previous studies, the adoption of a battery energy storage (BES) system posed challenges related to installation capacity and capacity loss, impacting the technical and economic performance of the IES. To overcome these challenges, this study introduces a novel design incorporating a compressed CO2 energy storage (CCES) system into an IES. This integration mitigates the capacity loss issues associated with BES systems and offers advantages for configuring large-scale IESs. A mixed integer linear programming problem was formulated to optimize the configuration and operation of the IES. With an energy storage capacity of 267 MWh, the IES integrated with a CCES (IES–CCES) system incurred an investment cost of MUSD 161.9, slightly higher by MUSD 0.5 compared to the IES integrated with a BES (IES–BES) system. When not considering the capacity loss of the BES system, the annual operation cost of the IES–BES system was 0.5 MUSD lower than that of the IES–CCES system, amounting to MUSD 766.6. However, considering the capacity loss of the BES system, this study reveals that the operation cost of the IES–BES system surpassed that of the IES–CCES system beyond the sixth year. Over the 30-year lifespan of the IES, the total cost of the IES–CCES system was MUSD 4.4 lower than the minimum total cost of the IES–BES system. [ABSTRACT FROM AUTHOR]

Productivity and concerns regarding the well-being of workers exposed to vibrations stand as significant topics within labor-intensive sectors. In particular, this study contributes to the existing research by analyzing the problem with linkages among worker skill level, production rates, and vibration exposure. A bi-objective mixed integer linear programming model was employed to optimize both productivity and the exposure to hand-arm vibration in the manufacturing workplace. A sensitivity analysis was carried out to examine the impact of key parameters on the trade-off between productivity and vibration exposure. The results demonstrate the model's effectiveness in determining the best job rotation schedules by achieving optimal productivity and vibration exposure for low and medium problem sizes. Moreover, the numerical case study points out that strengthening the workforce by adding more proficient skilled workers can maintain a good level of productivity with a decreased likelihood of excessive vibration exposure. [ABSTRACT FROM AUTHOR]

Due to a wide variety of real-world constraints, proper project portfolio selection is a critical issue for project-oriented organizations. In this paper, a bi-objective stochastic mixed-integer linear programming model is developed to cope with the project selection and scheduling problem in the presence of greenhouse gas emissions, and non-hazardous/hazardous wastes regulatory restrictions. Moreover, reinvesting proceeds of projects as well as loans are allowed to finance projects over the planning horizon. The proposed model maximizes the net present value of the expected project portfolio's terminal wealth under uncertain conditions, as well as the sustainability score of the project portfolio, simultaneously. The sustainability score is calculated by one of the recent multi-criteria decision-making methods, SECA, based on seven qualitative sustainability indicators and by solving a non-linear optimization model. To assess the performance of the proposed model, a case study of eighteen industrial projects is applied. Since the duration of industrial projects is usually uncertain, the proposed model is reformulated as a scenario-based stochastic programming model. Furthermore, the CPLEX solver and Branch and Benders algorithm are used to solve the problem. Results show that the Branch and Benders algorithm is much more efficient than the CPLEX solver. Results show that increasing the carbon and landfill tax rates is not always an appropriate decision made by policymakers to control various types of emissions. Such decisions may not only make the projects less attractive for investment but also, do not significantly reduce the negative environmental effects, which decreases sustainability in both economic and environmental dimensions. This highlights the importance of considering each problem's attitudes for setting regulations where copying does not always create the same solutions for sustainability issues. [ABSTRACT FROM AUTHOR]

Energy management systems (EMSs) play a central role in improving the performance of microgrids by ensuring their efficient operation while minimizing operational costs and environmental impacts. This paper presents a comprehensive study of mixed integer linear programming (MILP) based EMSs developed and implemented in MATLAB 2021a using YALMIP software for the energy management of a new positive energy district in the city of Savona, Italy, as part of the Interreg Alpine Space Project ALPGRIDS. The main objective of this research is to optimize the functioning of the microgrid, focusing on cost efficiency and environmental sustainability. In pursuit of this objective, the EMS undergoes comprehensive testing and analysis, replicating actual conditions and addressing the diverse demands of end-users across typical days throughout the year, considering real electricity selling and purchase prices. The EMS also accounts for the reactive power capabilities of the various technologies integrated into the microgrid. The levelized cost of electricity (LCOE) serves as a metric for assessing curtailment costs, while penalties related to reactive power absorption from the distribution network are appraised in alignment with prevailing regulatory guidelines. The case study provides valuable insights into the practical implementation of EMS technology in microgrids and demonstrates its potential for sustainable energy management in complex urban energy districts. In all scenarios, the battery energy storage system (BESS) and combined heat and power (CHP) are pivotal for load satisfaction and microgrid resilience. BESSs balance supply and demand, which are crucial in periods of low renewable energy availability, while the versatile CHP efficiently addresses energy demands, contributing significantly to overall microgrid effectiveness. Their synergy ensures reliable load satisfaction, showcasing the dynamic and adaptive nature of microgrid energy management across diverse scenarios. [ABSTRACT FROM AUTHOR]

In proposing an integrated production–distribution model for multinational corporations (MNC), the current research undertakes a novel, simultaneous consideration of facility location, capacity acquisition/disposal, labor, manufacturing, purchasing, exchange rates, tariffs, and container and/or individual shipping factors. Unlike prior studies that examined some such factors. Based on real-world data from a window manufacturing MNC, a mixed integer linear programming model is developed and solved using CPLEX Solver. It reveals how the different features affect the base solution. Specifically, increased demand pushes more manufacturing to US and UK facilities rather than (low-cost) Chinese facilities; it helps meets some of China's demand and leads to reduced total tariff costs. Decreased demand instead favors the Chinese facility, and the increase in tariff costs can be offset by savings in manufacturing and transportation costs. Changes to the factors have insignificant effects on the production and distribution mix, with little to no impact on total profits, because these costs are lower than capacity costs. The comprehensive proposed model provides firms with a framework for obtaining meaningful insights into how they can maintain their operations in sustainable, profitable ways. This research also should assist policymakers in making informed decisions regarding regulations on production and distribution strategies. [ABSTRACT FROM AUTHOR]

The increasing share of renewables in electricity grids comes with a challenge of energy surpluses and deficits, which needs be handled by demand side management (DSM) and storage options. Within this approach, wastewater treatment plants (WWTPs), with flexible energy consumption and production processes and storage units, can contribute to stabilizing the grids and integrating more renewables. In this study, the operation of a real WWTP was optimized by mixed integer linear programming (MILP) to minimize its indirect carbon dioxide (CO2) emissions. The operation of the WWTP was shown to be flexible in following the CO2 emission factor of the electricity grid, which was possible with the utilization of the WWTP's storage units and flexible co-substrate feeding. As a result, by changing only the operational behavior of the WWTP, its indirect CO2 emissions decreased by 4.8% due to the higher share of renewables in the electricity grid. The CO2 emissions were shown to decrease further up to 6.9% by adding virtual storage units. [ABSTRACT FROM AUTHOR]

The collection of Municipal Solid Waste (MSW) represents the most important challenge in MSW management as the economy and efficiency of the whole MSW management system is affected by the quality of the collection operations. The methodology proposed in this paper is based on the development of an algorithm for the optimization of MSW collection systems where the objective is to minimize the distances and the number of vehicles required as well as the optimal siting of the depot, considering vehicles of different capacities operated in multi-trips and the possibility of the use of multi depots. Therefore, the algorithm analyzes the validity of the collection system from the environmental and economic perspectives. The algorithm is performed in a sequence of phases defined by vehicle capacities in which each phase is composed of three major steps including partitioning, optimization of collection configurations and vehicle allocation. The algorithm was developed using Mixed Integer Linear Programming (MILP) and is applied for the municipality of Tiznit showing its applicability and efficiency. A sensitivity analysis was applied demonstrating the importance of vehicle capacities for collection optimization in the case of high waste generation rates. [ABSTRACT FROM AUTHOR]

In times of crisis and war, the supply and delivery of relief items to the front line is of utmost importance. The locations of support forces play a key role in facilitating these deliveries. Evaluating the efficiency of these locations can help improve logistics processes and enhance the speed and accuracy of relief services. The aim of this research is to assess the efficiency of support force locations to identify their strengths and weaknesses, analyze the impacts of geographical position on the delivery process of relief items, and provide suggestions for optimizing these locations. In this research, a mathematical modeling approach is presented to determine efficient locations for deploying support forces using Data Envelopment Analysis (DEA). Additionally, a mixed-integer linear programming model is proposed for routing prioritized support items. The proposed model allows for the adjustment of manageable inputs to improve outputs according to the principle of managerial accessibility, while also maintaining the current levels of unmanageable inputs if they cannot be reduced based on the principle of natural accessibility. Subsequently, routing for the distribution of these prioritized support items is provided using a mixedinteger linear programming model. The proposed model has been used to evaluate 25 potential locations prepared to provide ground support services to assist friendly forces in contested areas, with the aim of ending the conflict in favor of friendly forces. Sixteen viable support locations have been identified. Finally, routing for the distribution of support items to these 16 locations has been presented. The results of this research are of significant importance for the decision-making of commanders in future battles. Finally, implementing an optimal routing system for sending prioritized support items can help improve the efficiency of military operations, increase the ability to respond to changing battlefield conditions, and preserve the lives of ground forces. As a strategic tool, this system can play a key role in the success of future battles. [ABSTRACT FROM AUTHOR]

Microgrid resource sizing problems typically include the analysis of a combination of value streams such as peak shaving, load shifting, or load scheduling, which support the economic feasibility of the microgrid deployment. However, microgrid benefits can go beyond these, and the ability to provide ancillary grid services such as frequency regulation or spinning and non-spinning reserves is well known, despite typically not being considered in resource sizing problems. This paper proposes the expansion of the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state-of-the-art microgrid resource sizing model, to include revenue streams resulting from the participation in ancillary service markets. Results suggest that participation in such markets may not only influence the optimum resource sizing, but also the operational dispatch, with results being strongly influenced by the exact market requirements and clearing prices.

Inadequate lighting plans for the operations performed at night on construction sites can affect the quality of the implemented work, labor productivity, safety, as well as the project's overall cost. Accordingly, to avoid inadequate lighting conditions, the location of the luminaries utilized to illuminate the chosen construction area and its configuration, including luminaire height and luminary angle, are crucial choices in the design of the lighting plan. Besides making sure that the chosen installation pattern is cost-effective and as much as possible meets the lighting levels needed for each point according to the nature of the implemented work and safety considerations, In this way, a maximal coverage location model using LINGO software is developed in order to investigate the best locations of luminaires, taking into account gradual and cooperative covering. The objective is to ensure that the selected allocation of luminaires minimizes the summation of the received illuminance by the points that exceed the demand and the received illuminance that is below the demand. The optimized results refer to that smaller luminary angles lead to large coverage and minimizes the objective function. Making sure that the selected luminaire height leads to the desired illuminance levels according to the inverse relationship between the height and the illuminance.

Intense global competition, dynamic product variations, and rapid technological developments force manufacturing systems to adapt and respond quickly to various changes in the market. Such responsiveness could be achieved through new paradigms such as Reconfigurable manufacturing systems (RMS). In this paper, the problem of configuration design for a scalable reconfigurable RMS that produces different products of a part family is addressed. In order to handle demand fluctuations of products throughout their lifecycles with minimum cost, RMS configurations must change as well. Two different approaches are developed for addressing the system configuration design in different periods. Both approaches make use of modular reconfigurable machine tools (RMTs), and adjust the production capacity of the system, with minimum cost, by adding/removing modules to/from specific RMTs. In the first approach, each production period is designed separately, while in the second approach, future information of products' demands in all production periods is available in the beginning of system configuration design. Two new mixed integer linear programming (MILP) and integer linear programming (ILP) formulations are presented in the first and the second approaches respectively. The results of these approaches are compared with respect to many different aspects, such as total system design costs, unused capacity, and total number of reconfigurations. Analyses of the results show the superiority of both approaches in terms of exploitation and reconfiguration cost. [ABSTRACT FROM AUTHOR]

Over the past decade, the significance of optimizing green supply chain management (GSCM) has gained unprecedented attention from both scholars and industry professionals. This surge in interest has led researchers to employ diverse modeling approaches in the pursuit of enhancing green supply chain networks. In this systematic review, we analyze 159 recent GSCM optimization papers published from 2017 to 2022 and identify the recent trends in mathematical modeling, multi-objective optimization, and the modeling/solver tools utilized. We find that the primary green focus is on minimizing carbon emissions (n = 44), reflecting the increasing concern for environmental sustainability. Among the modeling approaches employed, mixed-integer linear programming has emerged as the most popular choice (n = 51), followed by game theory-based modeling (n = 30). When it comes to multiobjective optimization, the ε-constraint approach is the most widely used. Evolutionary algorithms have emerged as the dominant meta-heuristic optimization approach. Additionally, the widely utilized solver in this domain is CPLEX with the most popular modeling/solver combination being GAMS/CPLEX. Moreover, the Journal of Cleaner Production was the leading outlet for research in this domain (n = 35). In addition to these findings, this study also discusses some other research trends and future research directions. Finally, we discuss the theoretical, managerial, and policy implications of this study. By providing GSCM researchers and practitioners with the latest trends in GSCM optimization approaches, this study contributes to the further advancement of the field. [ABSTRACT FROM AUTHOR]

• We studied the advanced air mobility aircraft scheduling problem at vertiports. • We considered separation rules at touchdown and lift-off pads and blocking constraints. • Two mixed integer linear programming formulations are presented for optimally solving small instances. • We propose two heuristic algorithms for solving real-life sized instances. • The computational results provide insights into vertiport operations. Recent progress in electric vertical take-off and landing (eVTOL) vehicles suggests that soon these vehicles could safely and efficiently transport people and cargo in urban areas. Therefore, advanced air mobility vehicles could become an alternative means of transport to overcome traffic congestion in cities in the upcoming years. There has been enormous interest from companies and governments in recent years in developing such technologies and enabling markets for new air transportation services. Despite the interest in the topic, little research has been done to address the aircraft scheduling problem in advanced air mobility take-off and landing areas (vertiports). The vertiports serve as the airports of eVTOL vehicles and could experience congestion problems similar to those of airports. This work proposes two optimization models for scheduling departing and landing aircraft at the vertiports' common ground taxi routes (taxiways), gates, and touchdown and lift-off (TLOF) pads. The mathematical models include advanced air mobility features such as separation rules and blocking constraints. As scheduling objectives, the first model maximizes the vertiport throughput, and the second model minimizes the deviation from the expected take-off/landing time. In addition, as a solution methodology, we developed two heuristic algorithms that use scheduling rules to assign and sequence the aircraft to the vertiport components. Computational results show that the optimization models find optimal schedules for small-sized instances of up to 10 aircraft, while the heuristic algorithms provide good results in terms of solution quality and computational time for large instances. [ABSTRACT FROM AUTHOR]

To enhance the utilization of renewable energy and the economic efficiency of energy system's planning and operation, this study proposes a hybrid optimization configuration method for battery/pumped hydro energy storage considering battery‐lifespan attenuation in the regionally integrated energy system (RIES). Moreover, a two‐layer optimization model was established for integrated energy system planning and operation based on the combination of the Salp Swarm algorithm and mixed‐integer linear programming. Considering wind and solar energies and multiple loads, such as electricity, cooling, and heating, the first step in this paper involved the construction of a model for the RIES incorporating hybrid energy storage and various energy‐conversion devices. Then, given a synergy among different energy sources in the system, the long‐term impact of battery‐lifespan attenuation is introduced by including battery‐replacement costs. Based on the optimization results obtained from daily operations, a hybrid energy storage‐based optimization configuration model is established to minimize the annual operational and energy‐storage investment costs. The results show that, compared to the systems with a single pumped hydro storage or battery energy storage, the system with the hybrid energy storage reduces the total system cost by 0.33% and 0.88%, respectively. Additionally, the validity of the proposed method in enhancing the economic efficiency of system planning and operation is confirmed. Furthermore, a comparative analysis is conducted of the impact of battery‐lifespan degradation on the system's economic efficiency. The results show that during the system's operation phase, the total system cost is reduced by 9.97% considering battery‐lifespan degradation than that without considering the degradation. [ABSTRACT FROM AUTHOR]

In this paper, a bi-objective mixed-integer linear programming model is constructed to manage the pharmaceutical supply chain of a hospital. The proposed model aims to concurrently reduce the overall cost of obtaining drugs from several vendors and choose the best suitable source. The suggested model takes into account supplier distance, inventory management, and multi-product and multi-period. The major assumptions of the proposed model are product storage for future periods of decreased demand and supplier capacity. The results indicate that the ideal approach can minimize hospital supply and pharmaceutical planning expenses. The Best-Worst and TOPSIS methods determine which pharmaceutical supplier should be selected for future orders. The suggested model identifies human resource capability as an essential factor that might significantly affect the system's total cost. The results of applying the model and the sensitivity analysis validate the efficacy and validity of the suggested mathematical model and solution strategy. [ABSTRACT FROM AUTHOR]

As stationary hybrid energy‐storage systems (HESS) for power systems applications have recently drawn interest due to their enhanced performance and decreasing cost, developing systematic approaches for HESS design while considering controls is gaining traction. Herein, a method is presented to optimally design hybrid battery storage by proposing a mathematical modeling framework, formulated as a mixed integer linear programming model. The optimization is capable of handling multiple subsystems of batteries, considering their economic and technological performance. Decisions involve sizing of the batteries, optimal temporal and strategic dispatch to end uses, and energy sources for charging each battery. The applicability of the model is tested on four case studies for three battery chemistries representing distinct objectives: high‐power, high‐energy, and second life. Compared to traditionally designed battery storage with a homogeneous battery, optimally designed hybrid systems can save 12%–26% of system costs, depending on the nature of the dispatch profile. Findings point to design preference toward the second life battery supplemented with some high‐power or high‐energy battery capacity, or both. With the utilized electricity price structure, customers can experience approximately 10%–35% reduction in their bills. [ABSTRACT FROM AUTHOR]

Ergonomic aspects have a crucial role in manual assembly systems. They impact on the workers' health, final product quality and productivity. For these reasons, there is the necessity to integrate them into the assembly line balancing phase as, whereas, only time and cost variables are considered. In this study, human energy expenditures are considered as ergonomic aspects and we integrate them, for the first time, into the assembly line balancing problem type 2 through the rest allowance evaluation. We consider as an objective function the minimization of the smoothness index. Firstly, a new optimal method based on mixed integer linear programming and a new linearization methodology are proposed. Then, a heuristic approach is introduced. To complete the study, a computational experimentation is presented to validate the mathematical model and to compare the methodologies proposed in terms of computational time, complexity and solution. Additionally, we provide a detailed analysis of the impact that rest allowance evaluation can have on productivity comparing the results obtained, taking into account the rest allowance integration before, during and after the assembly balancing process. [ABSTRACT FROM AUTHOR]

As the Internet grows in capacity, the energy consumption of Information and Communication Technologies (ICT) are significantly increasing. Significant research efforts on energy conservation have been devoted to devise different technological solutions to address raised concerns surrounding the power consumption of networking equipment and its impact firstly on the emission of greenhouse gases and secondly on electricity bills. In this work, we investigate energy-efficient physical topologies for NSF IP over wavelength-division multiplexing (WDM) network for the purpose of minimizing energy consumption by redesigning its current physical connectivity. We implement different network topologies, such as implementing the small-world, scale-free (SFN), and random networks on the NSF network, then evaluate and compare its physical properties and network power consumption with the current NSF topology design using a mixed-integer linear programming model, all with the aim of minimizing the network total power consumption. The evaluation shall optimize and minimize the embodied energy consumption of network equipment in the IP and optical layers. Results have demonstrated that the implementation of the proposed energy-minimized topology designs can significantly improve the node's clustering coefficient, reduce network's diameter, and reduce energy consumption of the NSF IP over WDM network to 28% if compared with the current design implementation. [ABSTRACT FROM AUTHOR]

Heterogeneous Multi-Processor System-on-Chip architectures are prevalent in modern embedded system applications that target high-performance needs. This work comprises two parts to optimize the multiprocessor system structure concerning system speed, power, and area requirements. The first part includes the game model concept, which is remarkably advantageous in generating strategic decisions on incoming signals and minimizes traffic delays coming from the input portion of the system. Secondly, an improved moth search optimization is proposed to explore the design space and optimize the system space and power consumption. This research is implemented in the Field Programmable Gate Array (FPGA) Xilinx Zynq and Virtex platforms and evaluated by ExPRESS benchmarks. The Game Model-combined improved Moth Search Optimization (GMMSO) technique showed performance improvements of 69.34% in delay measurement, 46% in data rate and 36.34% in static power consumption compared with the traditional HMILP (Heuristic Mixed Integer Linear Programming) methods. [ABSTRACT FROM AUTHOR]

Quality improvement is the most effective activity for the process and product development cycle, while minimizing the process and product variation. For this particular purpose, robust design models are proposed to reduce the process and product variance. However, the majority of robust design models in the literature deals with certain situations. This article has four objectives. First, an experimental design matrix is generated for both qualitative and quantitative input variables under uncertainty. Secondly, triangular fuzzy numbers are used to measure the values of a response variable while dealing with an α-level cut strategy. Fitted fuzzy mean, standard deviation and variance response functions are also obtained. Thirdly, a fuzzy mixed-integer robust design optimization model is proposed to obtain the optimum operating conditions of input variables under uncertainty. Finally, a numerical example is presented to show the effectiveness of the proposed fuzzy-based methodology development for an uncertain environment. [ABSTRACT FROM AUTHOR]

This research focuses on the home health care optimization problem that involves staff routing and scheduling problems. The considered problem is an extension of multiple travelling salesman problem. It consists of finding the shortest path for a set of caregivers visiting a set of patients at their homes in order to perform various tasks during a given horizon. Thus, a mixed-integer linear programming model is proposed to minimize the overall service time performed by all caregivers while respecting the workload balancing constraint. Nevertheless, when the time horizon become large, practical-sized instances become very difficult to solve in a reasonable computational time. Therefore, a new Learning Genetic Algorithm for mTSP (LGA-mTSP) is proposed to solve the problem. LGA-mTSP is composed of a new genetic algorithm for mTSP, combined with a learning approach, called learning curves. Learning refers to that caregivers' productivity increases as they gain more experience. Learning curves approach is considered as a way to save time and costs. Simulation results show the efficiency of the proposed approach and the impact of learning curve strategy to reduce service times. [ABSTRACT FROM AUTHOR]

Featured Application: Supply Chain Management. Synchromodality is an emerging concept in supply chain management. A synchromodal supply chain can be defined as a multimodal transportation planning system, wherein the different agents work in an integrated and flexible way that enables them to dynamically adapt the transport mode based on real-time information from stakeholders, customers, and the logistic network. The potential of synchromodality for the fast-moving consumer goods (FMCG) industry is related to the nature of business. The FMCG market is characterized by relatively low margins and high turnover, which is especially important in export supply chains. However, for a company, it may be challenging to objectively evaluate the costs and benefits, not to mention the design of a synchronized supply chain. In order to facilitate the adoption of the concept and guide the practitioners, our study put forward the following research questions: What should be considered in incorporating synchromodality in the export supply chain for FMCG? How should companies approach tradeoffs among factors affecting the supply chain? To answer these questions, we propose an adaptable framework, which should be considered a primary contribution of our study. The framework incorporates the center of gravity model, mixed integer linear programming, and sensitivity analysis. The framework is validated using a real-world problem from a multinational FMCG company. The problem involves the optimal volume allocation and the selection of the most efficient transportation mode for inland freight. Our study demonstrates that incorporating synchromodality in the export supply chain could reduce the overall cost by 9% and enhance flexibility by allowing multiple modes of transportation. [ABSTRACT FROM AUTHOR]