Electric vehicles (EVs) are witnessing a surge in demand and production for their environmental benefits. This study reviews the development of electric vehicle routing problem (EVRP) in transport ...logistics. The review found that few existing EVRP models considered integrally the unique characteristics of EVs, such as the nonlinear charging function, nonlinear electricity-consumption function, and dynamic driving ranges. This study develops a new comprehensive model of the EVRP that considers a general energy/electricity consumption function for EVs that factor in energy losses (aerodynamic, tire friction, drivetrain, ancillary, and kinetic/potential losses), nonlinear charging function with the piecewise linearization technique, efficient visits to charge stations, and continuous decision variables for speed, payload, travel time, recharging, etc. The newly developed model is much closer to reality by integrating most of the unique characteristics of EVs known so far, and therefore it advances the state-of-the-art in EVRP. Computational experiments on Solomon's instances were done to exhibit the effectiveness and efficiency of the developed model under the effects of air conditioner, battery capacity, and travel speed. These experiments demonstrated that the model can obtain more practical logistics plans for an EV fleet with a lower total emissions cost and higher energy utilization.
•A systematic literature survey of the EVRP is provided.•A general energy consumption function of EVRP is established.•A new comprehensive EVRP model is presented with energy recharging and consumption.•Computational experiments were done to analyze the model characteristics.
Quantum annealing is a heuristic algorithm for solving combinatorial optimization problems, and hardware for implementing this algorithm has been developed by D-Wave Systems Inc. The current version ...of the D-Wave quantum annealer can solve unconstrained binary optimization problems with a limited number of binary variables. However, the cost functions of several practical problems are defined by a large number of integer variables. To solve these problems using the quantum annealer, integer variables are generally binarized with one-hot encoding, and the binarized problem is partitioned into small subproblems. However, the entire search space of the binarized problem is considerably larger than that of the original integer problem and is dominated by infeasible solutions. Therefore, to efficiently solve large optimization problems with one-hot encoding, partitioning methods that extract subproblems with as many feasible solutions as possible are required. In this study, we propose two partitioning methods and demonstrate that they result in improved solutions.
The flexible operation of alkaline water electrolyzers enables power-to-x plants to react efficiently to different energy scenarios. In this work, a novel scheduling model for alkaline water ...electrolysis is formulated as a mixed-integer linear program. The model is constructed by implementing operational states (production, standby, idle) and transitions (cold/full startup, shutdown) as integer variables, while the power loading and hydrogen flowrate are set as continuous variables. The operational characteristics (load range, startup time, ramp rates) are included as model constraints. The proposed model allows finding optimal number of electrolyzers and production schedules when dealing with large data sets of intermittent energy and electricity price. The optimal solution of the case study shows a balance between hydrogen production, energy absorption, and operation and investment costs. The optimal number of electrolyzers to be installed corresponds to 54% of the ones required to absorb the highest energy peak, being capable of loading 89.7% of the available energy during the year of operation, with an overall plant utilization of 93.7% and 764 startup/shutdown cycles evenly distributed among the units.
•A novel scheduling model for alkaline water electrolysis (AEL) has been developed.•The model introduces AEL states, transitions, and operational characteristics.•The model allows to find optimal number of electrolyzers and production schedules.•The model is suitable to handle large data sets of fluctuating energy and prices.•The MILP solution provides a balance between production, energy absorption and costs.
•Study hub network design problems with profit-oriented objectives.•Simultaneous optimization of collected profit, setup and transportation costs.•A Lagrangian function is used to efficiently obtain ...bounds at nodes of the tree.•Numerical results confirm the superiority of our framework over commercial solver.
This paper studies hub network design problems with profits. They consider a profit-oriented objective that measure the tradeoff between the revenue due to served commodities and the overall network design and transportation costs. An exact algorithmic framework is proposed for two variants of this class of problems, where a sophisticated Lagrangian function that exploits the structure of the problems is used to efficiently obtain bounds at the nodes of an enumeration tree. In addition, reduction tests and partial enumerations are used to considerably reduce the size of the problems and thus help decrease the computational effort. Numerical results on a set of benchmark instances with up to 100 nodes confirm the efficiency of the proposed algorithmic framework. The proposed methodology can be used as a tool to solve more complex variants of this class of problems as well as other discrete location and network design problems involving servicing decisions.
In this paper, we define a new class of generalized higher-order (F,η)-invexity functions, higher-order (F,η)-pseudo invexity functions and higher-order (F,η)-quasi invexity functions. Under the new ...generalized invexity, the Wolfe dual model for a class of multiobjective fractional programming problem is established, and several weak duality, strong duality and strict converse duality theorems are obtained and proved.
Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from ...the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.
In this paper we survey the primary research, both theoretical and applied, in the area of robust optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the ...modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multistage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.
Small‐molecule drugs are of significant importance to human health. The use of efficient model‐based de novo drug design method is an option worth considering for expediting the discovery of drugs ...with satisfactory properties. In this article, a deep learning model is first developed for identifications of protein‐ligand complexes with high binding affinity, where the Mol2vec descriptor, the convolutional neural network, and the gate augmentation‐based Attention mechanism are used for the model construction. Then, an optimization‐based de novo drug design framework is established by integrating the deep learning model into a Mixed‐Integer NonLinear Programming (MINLP) model for drug candidate design. The optimal solution of the MINLP model is further verified by the physics‐based methods of molecular docking and molecular dynamics simulation. Finally, two case studies involving the design of anticoagulant and antitumor drug candidates are presented to highlight the wide applicability and effectiveness of the MINLP‐based de novo drug design framework.
The adoption of a reconfigurable intelligent surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs ...for both the transmit power allocation and the phase shifts of the surface reflecting elements subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to 300% higher energy efficiency in comparison with the use of regular multi-antenna amplify-and-forward relaying.
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