Showing papers on "Integer programming published in 2022"
TL;DR: In this paper , a clustering-based algorithm was proposed to classify regions into clusters and obtain approximate optimal point-to-point paths for UAVs such that coverage tasks would be carried out correctly and efficiently.
Abstract: Unmanned aerial vehicles (UAVs) have been widely applied in civilian and military applications due to their high autonomy and strong adaptability. Although UAVs can achieve effective cost reduction and flexibility enhancement in the development of large-scale systems, they result in a serious path planning and task allocation problem. Coverage path planning, which tries to seek flight paths to cover all of regions of interest, is one of the key technologies in achieving autonomous driving of UAVs and difficult to obtain optimal solutions because of its NP-Hard computational complexity. In this paper, we study the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions. First, with models of separated regions and heterogeneous UAVs, we propose an exact formulation based on mixed integer linear programming to fully search the solution space and produce optimal flight paths for autonomous UAVs. Then, inspired from density-based clustering methods, we design an original clustering-based algorithm to classify regions into clusters and obtain approximate optimal point-to-point paths for UAVs such that coverage tasks would be carried out correctly and efficiently. Experiments with randomly generated regions are conducted to demonstrate the efficiency and effectiveness of the proposed approach.
82 citations
TL;DR: In this article , a Mixed-integer Linear Programming (MILP) model is proposed to find the best sequence of routes for each ambulance and minimize the latest service completion time (SCT) as well as the number of patients whose condition gets worse because of receiving untimely medical services.
Abstract: <p style='text-indent:20px;'>The shortage of relief vehicles capacity is a common issue throughout disastrous situations due to the abundance of injured people who need urgent medical aid. Hence, ambulances fleet management is highly important to save as many injured individuals as possible. In this regard, the present paper defines different patient groups based on their needs and characteristics. In order to provide the affected people with proper and timely medical aid, changes in their health status are also considered. A Mixed-integer Linear Programming (MILP) model is proposed to find the best sequence of routes for each ambulance and minimize the latest service completion time (SCT) as well as the number of patients whose condition gets worse because of receiving untimely medical services. Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to find high-quality solutions over a short time. In the end, Lorestan province, Iran, is considered as a case study to assess the model's performance and analyze the sensitivity of solutions with respect to the major parameters, which results in insightful managerial suggestions.</p>
69 citations
TL;DR: The obtained results confirm that the coordinated model can efficiently handle the P2P transactive energy trading for different energy hubs, addressing the minimum data exchange issue, and achieving the least-cost operation of the energy hubs in the system.
66 citations
TL;DR: Based on fuzzy relational inequality, a bi-level linear program optimizes the visible light brightness and operating costs of access points in a wireless transmission station system as mentioned in this paper , which has been shown to be both practical and successful.
54 citations
TL;DR: In this paper , the authors investigated a fully decentralized model for electricity trading within a transactive energy market, where the optimal operation of the energy hubs is modeled as a standard mixed-integer linear programming (MILP) optimization problem.
53 citations
TL;DR: Using a case study from Electric Reliability Council of Texas (ERCOT), it is shown that the proposed tailored Benders decomposition outperforms the nested Bender decomposition in solving GEP and TEP simultaneously.
45 citations
TL;DR: In this article, a collaborative neuro-dynamic optimization approach is presented for cardinality-constrained portfolio selection, where the expected return and investment risk in the Markowitz framework are scalarized as a weighted Chebyshev function and the cardinality constraints are equivalently represented using introduced binary variables as an upper bound.
43 citations
40 citations
TL;DR: The present work addresses the need to reduce the operating cost of multi-microgrids and improve the convergence performance of the solution algorithms applied for their optimized electric power dispatch when considering the uncertainties associated with existing loads, renewable energy sources, and electric vehicle usage by proposing a novel double-layer robust optimization dispatch model.
39 citations
TL;DR: Wang et al. as discussed by the authors investigated a new multi-period vaccination planning problem that simultaneously optimizes the total travel distance of vaccination recipients (service level) and the operational cost, and proposed a weighted-sum and an ϵ -constraint methods, which rely on solving many singleobjective MILPs and thus lose efficiency for practical-sized instances.
Abstract: This work investigates a new multi-period vaccination planning problem that simultaneously optimizes the total travel distance of vaccination recipients (service level) and the operational cost. An optimal plan determines, for each period, which vaccination sites to open, how many vaccination stations to launch at each site, how to assign recipients from different locations to opened sites, and the replenishment quantity of each site. We formulate this new problem as a bi-objective mixed-integer linear program (MILP). We first propose a weighted-sum and an ϵ -constraint methods, which rely on solving many single-objective MILPs and thus lose efficiency for practical-sized instances. To this end, we further develop a tailored genetic algorithm where an improved assignment strategy and a new dynamic programming method are designed to obtain good feasible solutions. Results from a case study indicate that our methods reduce the operational cost and the total travel distance by up to 9.3% and 36.6%, respectively. Managerial implications suggest enlarging the service capacity of vaccination sites can help improve the performance of the vaccination program. The enhanced performance of our heuristic is due to the newly proposed assignment strategy and dynamic programming method. Our findings demonstrate that vaccination programs during pandemics can significantly benefit from formal methods, drastically improving service levels and decreasing operational costs.
33 citations
TL;DR: In this paper , a two-level optimization method of scheduling and trajectory planning for connected automated vehicles (CAVs) is proposed for vehicles entering scheduling, and a multi-vehicle optimal trajectory control model is developed based on the optimal vehicle schedule from the first level.
Abstract: The large-scale application of connected automated vehicles (CAVs) provides new opportunities and challenges for the optimization and management of traffic conflict zones. To improve the traffic efficiency of conflict zones and reduce the travel delay and fuel consumption of CAVs, this paper presents a two-level optimization method of scheduling and trajectory planning for CAVs. At the first level, a 0–1 mixed-integer linear program (MILP) is proposed for vehicles entering scheduling. At the second level, a multi-vehicle optimal trajectory control model is developed based on the optimal vehicle schedule from the first level. Then, to reduce the complexity of solving the multi-vehicle optimal trajectory control model, we transform this model into non-linear programming (NLP) based on the infinitesimal method. Moreover, a rolling optimization strategy is developed to facilitate field application. Numerical simulation experiments of different traffic scenarios are conducted, and the results show that the proposed method can effectively reduce vehicle delays and fuel consumption, compared with the first-in-first-out (FIFO) method. The numerical results show that the vehicle delay can be reduced by up to 54% and fuel consumption by up to 34% under different traffic demands. Sensitivity analysis indicates that the performance of the proposed method is mainly determined by the minimum safety time interval of vehicles entering the conflict zone.
30 Aug 2022
TL;DR: In this paper , the authors study the problem of how to place VNFs on edge and public clouds and route the traffic among adjacent VNF pairs, such that the maximum link load ratio is minimized and each user's requested delay is satisfied.
Abstract: AbstractAs noted before, resource allocation problem in NFV can be fomulated as an Integer Nonlinear Programming (INLP). And the most frequent approaches to deal with NFV resource allocation include combinatorial optimization theory (e.g., randomized/LP rounding, primal-dual approximation), Deep Reinforcement Learning, Game theory, etc. In this chapter, we study the problem of how to place VNFs on edge and public clouds and route the traffic among adjacent VNF pairs, such that the maximum link load ratio is minimized and each user’s requested delay is satisfied.
TL;DR: In this article , a fault mode-assisted gated recurrent unit (FGRU) life prediction method is used to guide the predictive maintenance initiation time of all machines, and the FGRU method is more accurate than three common methods (Encoder-Decoder Recurrent Neural Network, Bidirectional Long Short-Term Memory and GRU) through two actual bearing degradation cases, and shows through three benchmark cases that the joint decision-making can effectively reduce the time cost of manufacturing enterprises.
TL;DR: In this article , a trajectory optimization approach for high-speed trains to reduce traction energy consumption and increase riding comfort is proposed, which can also achieve energy-saving effects by optimizing the operation time between stations.
Abstract: This paper proposes a trajectory optimization approach for high-speed trains to reduce traction energy consumption and increase riding comfort. Besides, the proposed approach can also achieve energy-saving effects by optimizing the operation time between stations. First, an optimization model is developed by defining the objective function as a trade-off function of the traction energy consumption and riding comfort. In addition to constraints in the classic optimal train control model, three new factors–the discrete throttle settings, neutral zones, and sectionalized tunnel resistance–are considered. Then, the model is discretized and turned into a multi-step decision optimization problem. All the nonlinear constraints are approximated using piecewise affine (PWA) functions, and the trajectory optimization problem is turned into a mixed integer linear programming (MILP) problem which can be solved by existing solvers CPLEX and YALMIP. Finally, some case studies with real-world data sets are conducted to present the effectiveness of the proposed approach. The simulation results are compared with the practical running data of trains, which shows that the proposed model and the optimization approach save energy and improve the riding comfort.
TL;DR: Results indicate that the logic-based Benders decomposition method is able to return high-quality schedules for solving seru scheduling problems.
TL;DR: An exact mixed-integer programming model is established to accurately obtain the minimum disassembly objectives: cycle time, energy consumption, and improved hazardous index and the superiority of HDA is proved by comparing the optimization results of a large-scale case with three other classic algorithms.
Abstract: To address the problem of considerable waste electromechanical product generation, a partial disassembly line balancing problem with multi-robot workstations that can synchronously disassemble multiple products (MPR-PDLBP) is investigated to improve the product capacity and efficiency of existing disassembly lines. First, an exact mixed-integer programming model is established to accurately obtain the minimum disassembly objectives: cycle time, energy consumption, and improved hazardous index. Second, compared with the conventional disassembly line balancing problem (DLBP), the solution space and optimization difficulty of MPR-PDLBP increase significantly. Thus, a multi-objective hybrid driving algorithm (HDA) based on a three-layer encoding method with a heuristic rule is proposed to effectively address MPR-PDLBP, and a driving strategy is proposed to improve the exploitation ability and convergence speed of HDA. Finally, validity of the proposed model and algorithm are verified by comparing the calculation results of GUROBI and HDA for two small-scale cases. The superiority of HDA is proved by comparing the optimization results of a large-scale case with three other classic algorithms.
TL;DR: In this paper , a flexible coordinated power system expansion planning (CPSEP) framework is proposed to minimize the summation of the expansion planning, operation and reliability costs while taking the network model based on AC optimal power flow constraints, and the reliability and flexibility considerations into account.
TL;DR: In this article, a flexible coordinated power system expansion planning (CPSEP) framework is proposed to minimize the summation of the expansion planning, operation and reliability costs while taking the network model based on AC optimal power flow constraints, and the reliability and flexibility considerations into account.
TL;DR: The mixed-integer linear programming formulation is aimed at minimizing accumulated cost and load energy unserved with optimal hardening of substations, assuming that any non-hardened substation disabled by flooding must be repaired.
TL;DR: In this article , the scheduling problem in a seru production system (SPS) is formulated as a mixed-integer programming problem and then reformulated to a set partitioning master problem and some independent subproblems by employing the logic-based Benders decomposition (LBBD) method.
TL;DR: This survey presents theoretical foundations of pseudo-polynomial arc flow formulations, by showing a relation between their network and Dynamic Programming (DP), which allows a better understanding of the strength of these formulations, through a link with models obtained by Dantzig-Wolfe decomposition.
TL;DR: An enhanced brain storm optimization algorithm with some particular strategies is designed to handle the integrated distributed production and distribution problem with consideration of time windows, in which a set of jobs needs to be assigned among factories and the jobs are processed on flow shop environments at their associated factories.
Abstract: Production and distribution are two essential activities in supply chain management. Currently, integrated production and distribution problems receive much attention because decision-makers devote to improving the operation efficiency of both stages and try to achieve an optimal solution. This work proposes an integrated distributed production and distribution problem with consideration of time windows, in which a set of jobs (i.e., customer orders) needs to be assigned among factories and the jobs are processed on flow shop environments at their associated factories. Then, the completed jobs are delivered by capacitated vehicles to customers in different regions while satisfying given time windows as much as possible. Accordingly, to optimally solve the proposed problem, a mixed integer programming model with minimizing total weighted earliness and tardiness has been established. For the optimization task, an enhanced brain storm optimization algorithm with some particular strategies is designed to handle the considered problem. To assess the performance of the proposed optimization method, several experiments by adopting a set of benchmark test problems are performed, and state-of-the-art optimizers are chosen for comparisons. The obtained optimization results exhibit that the designed algorithm significantly outperforms its rivals and can be considered as an excellent optimizer for solving the studied problem. Besides, compared with the CPLEX solver, the designed optimizer also performs much better for solving large-size problems.
TL;DR: In this paper , a mixed-integer linear programming (MILP) formulation for the GTEP problem is proposed, and three different formulations, i.e., a big-m formulation, a hull formulation, and an alternative big-M formulation, are reported for transmission expansion.
01 Feb 2022
TL;DR: In this article , an evacuation route proposal system based on a quantitative risk evaluation that provides the safest route for individual evacuees by predicting dynamic gas dispersion with a high accuracy and short calculation time was proposed.
Abstract: Evacuation planning is important for reducing casualties in toxic gas leak incidents. However, most evacuation plans are too qualitative to be applied to unexpected practical situations. Here, we suggest an evacuation route proposal system based on a quantitative risk evaluation that provides the safest route for individual evacuees by predicting dynamic gas dispersion with a high accuracy and short calculation time. Detailed evacuation scenarios, including weather conditions, leak intensity, and evacuee information, were considered. The proposed system evaluates the quantitative risk in the affected area using a deep neural network surrogate model to determine optimal evacuation routes by integer programming. The surrogate model was trained using data from computational fluid dynamics simulations. A variational autoencoder was applied to extract the geometric features of the affected area. The predicted risk was combined with linearized integer programming to determine the optimal path in a predefined road network. A leak scenario of an ammonia gas pipeline in a petrochemical complex was used for the case study. The results show that the developed model offers the safest route within a few seconds with minimum risk. The developed model was applied to a sensitivity analysis to determine variable influences and safe shelter locations.
TL;DR: In this article, an evacuation route proposal system based on a quantitative risk evaluation that provides the safest route for individual evacuees by predicting dynamic gas dispersion with a high accuracy and short calculation time was proposed.
TL;DR: In this article , a new variant of truck-drone tandem that allows the truck to stop at non-customer locations (referred to as flexible sites) for drone LARO is introduced.
Abstract: This paper deals with the problem of coordinating a truck and multiple heterogeneous unmanned aerial vehicles (UAVs or drones) for last-mile package deliveries. Existing literature on truck–drone tandems predominantly restricts the UAV launch and recovery operations (LARO) to customer locations. Such a constrained setting may not be able to fully exploit the capability of drones. Moreover, this assumption may not accurately reflect the actual delivery operations. In this research, we address these gaps and introduce a new variant of truck–drone tandem that allows the truck to stop at non-customer locations (referred to as flexible sites) for drone LARO. The proposed variant also accounts for three key decisions — (i) assignment of each customer location to a vehicle, (ii) routing of truck and UAVs, and (iii) scheduling drone LARO and truck operator activities at each stop, which are always not simultaneously considered in the literature. A mixed integer linear programming model is formulated to jointly optimize the three decisions with the objective of minimizing the delivery completion time (or makespan). To handle large problem instances, we develop an optimization-enabled two-phase search algorithm by hybridizing simulated annealing and variable neighborhood search. Numerical analysis demonstrates substantial improvement in delivery efficiency of using flexible sites for LARO as opposed to the existing approach of restricting truck stop locations. Finally, several insights on drone utilization and flexible site selection are provided based on our findings.
01 Feb 2022
TL;DR: In this article , a multi-objective hybrid driving algorithm (HDA) based on a three-layer encoding method with a heuristic rule is proposed to effectively address the partial disassembly line balancing problem with multi-robot workstations that can synchronously disassemble multiple products.
Abstract: To address the problem of considerable waste electromechanical product generation, a partial disassembly line balancing problem with multi-robot workstations that can synchronously disassemble multiple products (MPR-PDLBP) is investigated to improve the product capacity and efficiency of existing disassembly lines. First, an exact mixed-integer programming model is established to accurately obtain the minimum disassembly objectives: cycle time, energy consumption, and improved hazardous index. Second, compared with the conventional disassembly line balancing problem (DLBP), the solution space and optimization difficulty of MPR-PDLBP increase significantly. Thus, a multi-objective hybrid driving algorithm (HDA) based on a three-layer encoding method with a heuristic rule is proposed to effectively address MPR-PDLBP, and a driving strategy is proposed to improve the exploitation ability and convergence speed of HDA. Finally, validity of the proposed model and algorithm are verified by comparing the calculation results of GUROBI and HDA for two small-scale cases. The superiority of HDA is proved by comparing the optimization results of a large-scale case with three other classic algorithms.
TL;DR: In this paper , a neighborhood search simulated annealing (SA) is proposed to solve the more complex assembly line balancing problems found in the automotive industry, with the cycle time and the number of operators (humans and robots) as the primary and secondary objectives, respectively, in addition to traditional ALBP constraints, the human and robot characteristics, in terms of task times, allowing multiple humans and robots at stations, and their joint/collaborative tasks are formulated into a new mixed-integer linear programming (MILP) model.
TL;DR: In this article , a mixed-integer linear programming model is formulated to minimize a weighted sum of the tardiness costs of transport requests and travel costs of AGVs with battery constraints.
TL;DR: In this article , a mixed-integer non-linear and non-convex programming (MINL&NCP) model was proposed to solve the EBCS problem and three tailored valid inequalities were proposed to enhance the solution efficiency.
Abstract: • A mixed-integer non-linear and nonconvex programming (MINL&NCP) model, which captures the unique feature of the EBCS problem is developed. We further approximate it by two novel MILP models in a smart way. • Non-linear charging profile and battery degradation effect are considered. • Three tailored valid inequalities are proposed to enhance the solution efficiency. • Extensive numerical experiments are carried out to seek valuable managerial insights for the public transport operators. This study deals with a fundamental electric bus charging scheduling (EBCS) problem for a single public transport route by considering the nonlinear electric bus (EB) charging profile and battery degradation effect under the partial charging policy, which allows EBs to be charged any length of time and make good use of dwell times between consecutive trips. Given a group of trip tasks for an EB fleet and charger type, the problem is to minimize the total cost for a public transport operator of providing peak-hour bus services for a focal single public transport route by simultaneously determining the EB-to-trip assignment and EB charging schedule with charger type choice subject to the necessary EB operational constraints. We first build a mixed-integer nonlinear and nonconvex programming (MINL&NCP) model for the EBCS problem. To effectively solve the MINL&NCP model to global optimality, we subsequently develop two mixed-integer linear programming (MILP) models by means of linearization and approximation techniques. To accelerate the solution efficiency, we further create three families of valid inequalities depending on the unique features of the problem. A real case study based on the No.171 bus route in Singapore is conducted to demonstrate the performance of the developed models. Extensive numerical experiments are carried out to seek valuable managerial insights for public transport operators.