Mixed Integer Linear Programming Optimization Research Articles

A peer-to-peer energy trading market framework for single buyer model: An integration of decentralised finance instruments and cooperative game model

Single Buyer (SB) model has gained traction in many Southeast Asia, Middle Eastern, and North African countries for its blend of centralisation and controlled competition. The semi-liberalised regulatory environment has hindered the integration of cogeneration or combined heat and power (CHP) as a prosumer within the electricity value chain, despite its pivotal role in enhancing energy efficiency. There is a disconnection in the existing literature as electric supply configurations and enablement of peer-to-peer (P2P) electricity trading are individually studied. There also lacks the studies addressing electricity as a special commodity and its compatibility with the market. This paper bridges the research gaps by proposing a P2P energy trading market framework that integrates decentralised finance (DeFi) instruments and a cooperative game model. Malaysia, which implements the SB model in its electric supply structure, is selected for a case study. The comprehensive framework develops (1) a stableswap hybrid function automated market maker (HFMM) decentralised exchange (DEX) for P2P trading, (2) a mixed-integer linear programming (MILP) optimisation model to determine the optimal operational strategy with minimised total annualised cost (TAC) of the system, and (3) an allocation scheme based on a cooperative game model to gauge the individual and coalitional cost savings allocations. Overall, this study presents a novel approach to addressing the challenges with innovating the SB model, while also fostering competitions among prosumers. The SB structure of Malaysia is briefly inspected, and the proposed framework is expected to contribute to the growth of macro-level CHP integration in the nation's energy planning.

Stochastic optimization framework for hybridization of existing offshore wind farms with wave energy and floating photovoltaic systems

Due to the complementarity of renewable energy sources, there has been a focus on technology hybridization in recent years. In the area of hybrid offshore power plants, the current research projects mostly focus on the combinational implementation of wind, solar, and wave energy technologies. Accordingly, considering the already existing offshore wind farms, there is the potential for the implementation of hybrid power plants by adding wave energy converters and floating photovoltaics. In this work, a stochastic sizing model is developed for the hybridization of existing offshore wind farms using wave energy converters and floating photovoltaics considering the export cable capacity limitation. The problem is modeled from an investor perspective to maximize the economic profits of the hybridization, while the costs and revenues regarding the existing units and the export cable are excluded. Furthermore, to tackle the uncertainties of renewable energy generation, as well as the energy price, a scenario generation method based on copula theory is proposed to consider the dependency structure between the different random variables. Altogether, the hybridization study is modeled in a mixed integer linear programming optimization framework considering the net present value of the project as the objective function. The results showed that hybrid-sources-based energy generation provided the highest economic profit in the studied cases in the different geographical locations. Furthermore, the technical specifications of the farms have also been considerably improved providing more stable energy generation, guaranteeing a minimum level of power in a high share of the time, and with a better utilization of the capacity of the cable while the curtailment of energy is maintained within the acceptable range.

Impact of tariff structures on energy community and grid operational parameters

This paper presents a mixed-integer linear programming optimization model of a renewable energy community comprised of members with local generators, battery energy storage systems, electric vehicles, and heat pumps and thermal energy storage, thus representing a local multi-energy system. The goal of the paper is to analyze the impact of different tariff structures for incentivizing energy sharing within energy communities on the distribution grid operational parameters. To achieve this, a comprehensive analysis of a hypothetical renewable energy community is conducted using the presented optimization model. Various tariff structures are evaluated, including flat and dynamic tariffs, reflecting the day-ahead market prices for energy and supply, as well as various network tariff designs, such as regulated charge reduction, time-of-use network tariffs and capacity payments. The results indicate that sharing energy within communities reduces energy losses in the grid, peak loads and reverse power flows and results in a smoother voltage profile. It is also found that tariffs that include dynamic electricity and supply costs can further increase these benefits, while including capacity payments as an incentive mechanism yields most favourable results for all analyzed criteria.

Potentials of Mixed-Integer Linear Programming (MILP)-Based Optimization for Low-Carbon Hydrogen Production and Development Pathways in China

Hydrogen (H2) is considered one of the main pillars for transforming the conventional “dark” energy system to a net-zero carbon or “green” energy system. This work reviewed the potential resources for producing low-carbon hydrogen in China, as well as the possible hydrogen production methods based on the available resources. The analysis and comparison of the levelized cost of hydrogen (LCOH) for different hydrogen production pathways, and the optimal technology mixes to produce H2 in China from 2020 to 2050 were obtained using the mixed-integer linear programming (MILP) optimization model. The results were concluded as three major ones: (a) By 2050, the LCOH of solar- and onshore-wind-powered hydrogen will reach around 70–80 $/MWh, which is lower than the current H2 price and the future low-carbon H2 price. (b) Fuel costs (>40%) and capital investments (~20%) of different hydrogen technologies are the major cost components, and also are the major direction to further reduce the hydrogen price. (c) For the optimal hydrogen technology mix under the higher renewable ratio (70%) in 2050, the installed capacities of the renewable-powered electrolysers are all more than 200 GW, and the overall LCOH is 68.46 $/MWh. This value is higher than the LCOH (62.95 $/MWh) of the scenario with higher coal gasification with carbon capture and the storage (CG-CCS) ratio (>50%). Overall, this work is the first time that hydrogen production methods in China has been discussed comprehensively, as well as the acquisition of the optimal H2 production technology mix by the MILP optimization model, which can provide guidance on future hydrogen development pathways and technology development potential in China.

Multi-objective optimization of straw-based bio-natural gas supply chains considering cost, CO2 emission, and safety

Bio-natural gas (BNG) has received recent interest as a renewable alternative to natural gas (NG). BNG supply chains (BSC), including production, transportation, and utilization, are compatible and can overlap with existing NG infrastructure. The goal is to develop a set of models that achieve the various trade-offs between economic, environmental and safety risks by combining life cycle assessment (LCA), mixed-integer linear programming (MILP) and fuzzy optimization, expanding the utilization of BSC while quantifying the risk of the whole process, so as to improve the safety awareness of the project operation. BNG technologies were considered: anaerobic digestion (AD), gasification, and syngas methanation. This model can analyze the changes in the different objectives of each module and the distribution of biomass mass flow by comparing different straw-based harvesting rates. Results indicate that AD technology possesses the lowest economic cost, and BNG as transportation fuel will generate the most benefits. The unit cost of BNG for different technologies ranges from 0.36 to 0.67 $/m³，the emission reduction is from 5.18 kg/m³ to 10.28 kg/m³，and the maximum safety risk value is 1.96. The proposed model was applied to a case study in China to illustrate the optimization of a regional straw-based BSC. The results illustrate how BNG supply chains can be systematically planned as part of an integrated decarbonization policy while mitigating safety risks that may occur from handling this fuel.

Quantum Circuit Reconstruction from Power Side-Channel Attacks on Quantum Computer Controllers

The interest in quantum computing has grown rapidly in recent years, and with it grows the importance of securing quantum circuits. A novel type of threat to quantum circuits that dedicated attackers could launch are power trace attacks. To address this threat, this paper presents first formalization and demonstration of using power traces to unlock and steal quantum circuit secrets. With access to power traces, attackers can recover information about the control pulses sent to quantum computers. From the control pulses, the gate level description of the circuits, and eventually the secret algorithms can be reverse engineered. This work demonstrates how and what information could be recovered. This work uses algebraic reconstruction from power traces to realize two new types of single trace attacks: per-channel and total power attacks. The former attack relies on per-channel measurements to perform a brute-force attack to reconstruct the quantum circuits. The latter attack performs a single-trace attack using Mixed-Integer Linear Programming optimization. Through the use of algebraic reconstruction, this work demonstrates that quantum circuit secrets can be stolen with high accuracy. Evaluation on 32 real benchmark quantum circuits shows that our technique is highly effective at reconstructing quantum circuits. The findings not only show the veracity of the potential attacks, but also the need to develop new means to protect quantum circuits from power trace attacks. Throughout this work real control pulse information from real quantum computers is used to demonstrate potential attacks based on simulation of collection of power traces.

Closed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming

The derivation of an efficient operational strategy for storing intermittent renewable energies using a hybrid battery-hydrogen energy storage system is a difficult task. One approach for deriving an efficient operational strategy is using mathematical optimization in the context of model predictive control. However, mathematical optimization derives an operational strategy based on a non-exact mathematical system representation for a specified prediction horizon to optimize a specified target. Thus, the resulting operational strategies can vary depending on the optimization settings.This work focuses on evaluating potential improvements in the operational strategy for a hybrid battery-hydrogen energy storage system using mathematical optimization. To investigate the operation, a simulation model of a hybrid energy storage system and a tailor-made mixed integer linear programming optimization model of this specific system are utilized in the context of a model predictive control framework. The resulting operational strategies for different settings of the model predictive control framework are compared to a rule-based controller to show the potential benefits of model predictive control compared to a conventional approach. Furthermore, an in-depth analysis of different factors that impact the effectiveness of the model predictive controller is done. Therefore, a sensitivity analysis of the effect of different electricity demands and resource sizes on the performance relative to a rule-based controller is conducted. The model predictive controller reduced the energy consumption by at least 3.9 % and up to 17.9% compared to a rule-based controller. Finally, Pareto fronts for multi-objective optimizations with different prediction and control horizons are derived and compared to the results of a rule-based controller. A cost reduction of up to 47 % is achieved by a model predictive controller with a prediction horizon of 7 days and perfect foresight.

Comparative modeling of cost-optimal energy system flexibility for Swedish and Austrian regions

This study develops a reproducible method for estimating the cost-efficient flexibility potential of a local or regional energy system. Future scenarios that achieve ambitious climate targets and estimate the cost-efficient flexibility potential of demonstration sites were defined. Flexible potentials for energy system assessment are upscaled from the demonstration sites in Eskilstuna (Sweden) and Lower Austria (Austria). As heat pumps (HPs) and district heating (DH) are critical for future heat demand, these sites are representative types of DH networks in terms of size and integration with the electricity grid. In both regions a TIMES model is used for energy system optimization, while for upscaling, Eskilstuna uses the building-stock model ECCABS, whereas Lower Austria uses a mixed integer linear programming optimization model, and the BALMOREL power system model. According to the modeling, HPs will dominate Eskilstuna’s heating sector by 2040. In Lower Austria, DH becomes more prevalent, in combination with wood biomass and HPs. These findings are explained by the postulated technological-economic parameters, energy prices, and CO2 prices. We conclude that future electricity prices will determine future heating systems: either a high share of centralized HPs (if electricity prices are low) or a high share of combined heat-and-power (if electricity prices are high). Large-scale energy storage and biomass can be essential solutions as may deliver increased cost-effectiveness, if available and under certain conditions.

A three-stage reliability-centered framework for critical feeder identification, failure modes prioritization, and optimal maintenance strategy assignment in power distribution system

In this paper, a three-stage reliability-centered maintenance (RCM) framework for critical feeder identification, failure modes prioritization, and optimal maintenance strategy assignment is presented for the power distribution system. This three-stage methodology addresses a notable gap in the literature by incorporating these three stages together. The first stage ranks critical feeders using the BWM and TOPSIS methods. In the second stage, the failure modes are prioritized using feeder rank, severity, occurrence, and detection factors and are assigned a score representing their priority. Finally, in the third stage, a maintenance strategy assignment, formulated as a mixed-integer linear programming (MILP) optimization problem is proposed. The optimization problem considers three strategies, cold-line, hot-line, and run-to-failure (RTF), assigned to each failure mode. Besides, the possibility of implementing cold-line repairs overlapping with the upstream grid's scheduled outages and the availability of several maintenance crews are taken into account. The objective function is to minimize the total maintenance costs, which include operation, equipment, energy not supplied (ENS), expected energy not supplied (EENS), and future corrective maintenance costs. The proposed approach is implemented on a real distribution system. The results indicate that the proposed RCM framework leads to a 26.32 % reduction in the EENS compared to selecting the RTF strategy for all components. Besides, the proposed optimization-based method leads to a 29 % lower objective function than the business-as-usual maintenance planning, and the proposed three-stage RCM framework outperforms a previously published state-of-the-art method by decreasing the objective function and the EENS.

The Optimal Size of a Heterogeneous Air Taxi Fleet in Advanced Air Mobility: A Traffic Demand and Flight Scheduling Approach

Introducing Advanced Air Mobility (AAM) as a novel transportation mode poses unique challenges due to limited practical and empirical data. One of these challenges involves accurately estimating future passenger demand and the required number of air taxis, given uncertainties in modal shift dynamics, induced traffic patterns, and long-term price elasticity. In our study, we use mobility data obtained from a Dresden traffic survey and modal shift rates to estimate the demand for AAM air taxi operations for this regional use case. We organize these operations into an air taxi rotation schedule using a Mixed Integer Linear Programming (MILP) optimization model and set a tolerance for slight deviations from the requested arrival times for higher productivity. The resulting schedule aids in determining the AAM fleet size while accounting for flight performance, energy consumption, and battery charging requirements tailored to three distinct types of air taxi fleets. According to our case study, the methodology produces feasible and high-quality air taxi flight rotations within an efficient computational time of 1.5 h. The approach provides extensive insights into air taxi utilization, charging durations at various locations, and assists in fleet planning that adapts to varying, potentially uncertain, traffic demands. Our findings reveal an average productivity of 12 trips per day per air taxi, covering distances from 13 to 99 km. These outcomes contribute to a sustainable, business-focused implementation of AAM while highlighting the interaction between operational parameters and overall system performance and contributing to vertiport capacity considerations.

A Mixed‐Integer Linear Programming Framework for Optimization of Water Network Operations Problems

AbstractWater distribution systems (WDSs) are critical infrastructure used to convey water from sources to consumers. The mathematical framework governing the distribution of flows and heads in extended period simulations of WDSs lends itself to application in a wide range of optimization problems. Applying the classical mixed integer linear programming (MILP) approach to model WDSs hydraulics within an optimization framework can contribute to higher solution accuracy with lower computational effort. However, adapting WDSs models to conform to a MILP formulation has proven challenging because of the intrinsic non‐linearity of system hydraulics and the complexity associated with modeling hydraulic devices that influence the state of the WDS. This paper introduces MILPNet, an adjustable framework for WDSs that can be used to build and solve an extensive array of MILP optimization problems. MILPNet includes constraints that represent the mass balance and energy conservation equations, hydraulic devices, control rules, and status checks. To conform to MILP structure, MILPNet employs piece‐wise linear approximation and integer programming. MILPNet was implemented and tested using Gurobi Python API. Modeling accuracy was shown to be comparable to EPANET, a public domain software for hydraulic modeling, and sensitivity analyses were conducted to examine the impacts of the modeling assumptions on the performance of MILPNet. Additionally, application of the framework was demonstrated using pump scheduling optimization examples in single and rolling horizon scenarios. Our results show that MILPNet can facilitate the construction and solution of optimization problems for a range of applications in WDSs operations.

Fertilizer Logistics in Brazil: Application of a Mixed-Integer Programming Mathematical Model for Optimal Mixer Locations

Background: Brazil is one of the largest consumers of fertilizers and is highly dependent on the international market to meet its demand for agricultural production inputs. The complexity of the fertilizer supply chain motivated us to carry out this study on redesigning the fertilizer logistics chain and evaluate strategies for reducing logistics costs by redesigning the fertilizer mixing network in Brazil, a country that is heavily dependent on imported fertilizers for agriculture. Methods: We introduce a multi-product mixed-integer linear programming optimization model encompassing the logistics network, from import ports to mixing factories and agricultural fertilizer supply centers. This model includes logistics infrastructure and taxes, accounting for greenhouse gas emissions (specifically carbon dioxide) in fertilizer logistics. Results: The results indicate that expanding the port capacity for fertilizer importation can significantly reduce logistics costs and greenhouse gas emissions by up to 22.5%, decreasing by 23.9% compared to the baseline. We also observed that removing taxes on fertilizer importation can reduce logistics costs by approximately 11%, but it increases greenhouse gas emissions by 2.25% due to increased reliance on road transport. We identified 15 highly resilient regions for establishing mixing factories, evaluated various scenarios and determined the importance of these locations in optimizing the fertilizer supply network in the country. Moreover, the results suggest a significant potential to enhance the role of Brazil’s Northern Arc region in fertilizer import flows. Conclusions: Public policies and private initiatives could be directed toward encouraging the establishment of mixing factories in the identified regions and increasing transport capacity in the Northern Arc region. Improving the logistical conditions of the fertilizer network would contribute to food security by reducing the costs of essential inputs in food production and promoting sustainability by reducing greenhouse gas emissions.

Analysis of Flexibility Potential of a Cold Warehouse with Different Refrigeration Compressors

The research into new approaches to shift from fossil fuels to renewable energy sources (RES) has surged as environmental issues are on the rise, and fossil fuel sources are becoming scarce. The flexibility potential of cold supply systems has been discussed widely in the literature, firstly due to their high share of electricity consumption worldwide and secondly because of their potential to store thermal energy in the form of cold energy. However, finding a clear definition of flexibility and a concise approach for its quantification is still under progress. In this work, a comprehensive definition of the flexibility of energy systems and a novel methodology for its quantification are introduced. The methodology was applied on a cold warehouse with real data regarding its cold energy demand. The cold warehouse was first modeled via oemof, which is a modular open source framework developed in Python 3.8 using a mixed integer linear programming (MILP) optimization approach. The operation optimization of the cold warehouse was conducted for three goals, namely “minimization of electricity costs”, “minimization of CO2 emissions”, and “minimization of maximum used electric power (peak load minimization)”. Additionally, the effect of using different types of refrigeration compressors on the optimized operation of the cold warehouse was investigated. The results suggest that a cold warehouse possesses a high level of flexibility potential, which can be taken advantage of to reduce the electricity cost by up to 50%, the CO2 emissions between 25% to 30%, and the maximum used electric power by 50%. Different compressor types produced very similar results, although their flexibility level may vary.

Veggies and PV: Optimization of Building-Integrated Agriculture in an Energy Hub

Building-integrated Agriculture (BIA) is the concept of utilizing façade surfaces for crop production. The potential is to reduce land-use and to increase the utility of built-up area. If outdoor building surfaces were to be used for farming, it results in a competition for sunlight between crops, solar renewable energy (such as photovoltaics, PV), and daylight access to illuminate indoor spaces. We therefore propose a coupled BIA and multi-energy systems model that can represent various energy sources (such as sunlight) and their conversion and storage (such as façade based crop production, PV, or electric batteries) in order to optimally meet demands for building energy and food. It is formulated as a mixed integer linear program (MILP) optimization model that describes the energy flows as an annual hourly time series. The model conducts a bi-objective minimization of monetary cost and carbon emissions, resulting in a Pareto front of optimal solutions. The model meets building energy demands for cooling, heating and electricity by an optimized energy technology portfolio, as well as nutritional demands for leafy vegetables of all occupants by either BIA or supermarket purchases. We apply our model to a residential case study in Singapore, which serves as an example for a high density city with already numerous community-driven and practiced BIA initiatives ongoing. Our results show that when minimizing cost, utilizing PV is more cost efficient on highly exposed surfaces such as the roof than BIA. However, for more shaded façade surfaces, crop production can be a cost and environmentally efficient addition to building design, as the annual vegetables demand of all occupants can be covered entirely by self-grown produce.

Monitoring the Italian transposition of the EU regulation concerning renewable energy communities and the relevant policies for battery storage

This paper deals with the transposition of the European Union (EU) Directive concerning Renewable Energy Communities (RECs) into the Italian legislation. The methodology aims at identifying those national provisions which do not comply with the EU legislation. Consequently, long and onerous infringement procedures may ultimately limit the development of RECs. Moreover, the proposed methodology quantifies the economic value of national policies concerning the participation of storage assets in RECs. Three policy options are compared against the case without flexible assets. To do so, ad-hoc mixed-integer linear programming optimization problems are developed. The assessment is based on the formulation of three key performance indicators, concerning the revenues and costs of the REC. The main data sources in the work are the actual Italian Wholesale Market prices and the price incentives introduced by relevant Italian authorities to support the operation of the RECs. Results indicate an incorrect transposition of the fundamental rights of the EU law. These concern a significant alteration of the membership criteria for prospective RECs and the introduction of external third-parties, which may bypass fundamental requirements set out in the EU law. Numerical results call for a profound revision of the policy options for battery storage in RECs since the envisaged configurations introduce structural limits to the REC's revenues This work can support researchers and policy makers at both national and EU-level in developing policy options and techno-economic measures that foster the development of new RECs.

Optimal decarbonization strategies for an industrial port area by using hydrogen as energy carrier

This article discusses possible strategies for decarbonizing the energy systems of an existing port. The approach consists in creating a complete superstructure that includes the use of renewable and fossil energy sources, the import or local production of hydrogen, vehicles and other equipment powered by Diesel, electricity or hydrogen and the associated refuelling and storage units. Two substructures are then identified, one including all these options, the other considering also the addition of the energy demand of an adjacent steel industry. The goal is to select from each of these two substructures the most cost-effective configurations for 2030 and 2050 that meet the emission targets for those years under different cost scenarios for the energy sources and conversion/storage units, obtained from the most reliable forecasts found in the literature. To this end, the minimum total cost of all the energy conversion and storage units plus the associated infrastructures is sought by setting up a Mixed Integer Linear Programming optimization problem, where integer variables handle the inclusion of the different generation and storage units and their activation in the operational phases. The comprehensive picture of possible solutions set allows identifying which options can most realistically be realized in the years to come in relation to the different assumed cost scenarios. Optimization results related to the scenario projected to 2030 indicate the key role played by Diesel hybrid and electric systems, while considering the most stringent, or much more stringent, scenarios for emissions in 2050, almost all vehicles energy demand and industry hydrogen demand is met by hydrogen imported as ammonia by ship.

A CVaR-based stochastic framework for storm-resilient grid, including bus charging stations

This paper proposes a two-stage stochastic framework for improving distribution system resilience against storms, in which the uncertainties associated with load demands, solar irradiance after a storm event, and maximum gust wind speed are considered. In the first stage, the available idle electric buses (EBs) are optimally allocated to the charging stations prior to storm arrival. In the second stage, critical loads are restored through island formation after the storm. In this framework, the solar-powered charging stations of EBs and the distributed generators (DGs) are part of the electric energy resources. The solar charging stations contribute to supplying the critical loads in two ways: 1- The electricity generation through rooftop-installed photovoltaic modules 2- By discharging the batteries of the pre-allocated EBs. The objective of this optimization model is to minimize the expected priority-weighted curtailed energy. At the same time, the risk imposed by the uncertain parameters is taken into account through the conditional value-at-risk (CVaR) index. The proposed framework is expressed in terms of a stochastic mixed-integer linear programming (MILP) optimization problem. This framework is implemented on the 33-node distribution test system, and its superior resilient performance is demonstrated through four case studies.

Scheduling aerial resource operations for the extinction of large-scale wildfires

The significant increase in large-scale wildfire events in recent decades, caused primarily by climate change, has resulted in a growing number of aerial resources being used in suppression efforts. Present-day management lacks efficient and scalable algorithms for complex aerial resource allocation and scheduling for the extinction of such fires, which is crucial to ensuring safety while maximizing the efficiency of operations. In this work, we present a Mixed Integer Linear Programming (MILP) optimization model tailored to large-scale wildfires for the daily scheduling of aerial operations. The main objective is to achieve a prioritized target water flow over all areas of operation and all time periods. Minimal target completion across individual areas and time periods and total water output are also maximized as secondary and ternary objectives, respectively. An efficient and scalable multi-start heuristic, combining a randomized greedy approach with simulated annealing employing large neighborhood search techniques, is proposed for larger instances. A diverse set of problem instances is generated with varying sizes and extinction strategies to test the approaches. Results indicate that the heuristic can achieve (near)-optimal solutions for smaller instances solvable by the MILP, and gives solutions approaching target water flows for larger problem sizes. The algorithm is parallelizable and has been shown to give promising results in a small number of iterations, making it applicable for both night-before planning and, more time-sensitive, early-morning scheduling.

Last mile delivery Optimisation model for drone-enabled Vehicle Routing Problem

In light of e-commerce's exponential growth trajectory over recent years, it has never been more critical to evaluate the last-mile delivery segment for its efficiency and cost-effectiveness. Autonomous vehicles (drones) offer considerable promise as potential solutions with ongoing investigations into emerging technologies in this context. To address problems related to last mile-delivery in logistics operations, the practicality of adopting a hybrid truck-drone delivery system is examined through this study. The researchers utilized Mixed-integer linear programming (MILP)and Gurobi optimization solvers both for optimizing performance as well as facilitating execution. While testing a drone dataset of a well-known logistics company as part of their research using an optimization model, the findings suggested that were remarkable competitive advantages including significant gains in reduction of timing. Nevertheless, there are several constraints like maintenance, recharging, difficult weather conditions & traffic congestion-necessitating focused innovative AI-based approaches. In spite of these impediments, a hybrid truck-drone’s potential applicability can remarkably boost the efficiency of last-mile delivery operations.

Active BIM system for optimized multi-project ready-mix-concrete delivery

PurposeReady-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery.Design/methodology/approachThe presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures.FindingsThe system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants.Originality/valueThe suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.