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A capacitated lot-sizing model with sequence-dependent setups, parallel machines and bi-part injection moulding

APPLIED MATHEMATICAL MODELLING 2021年 100卷 805-820页

作者： Mula, Josefa Diaz-Madronero, Manuel Andres, Beatriz Poler, Raul Sanchis, Raquel Univ Politecn Valencia UPV Res Ctr Prod Management & Engn CIGIP C Alarcon 1 Alicante 03801 Spain

This paper aims to propose a model for solving a capacitated lot-sizing problem with sequence-dependent setups (CLSD) and parallel machines in a bi-part injection moulding (BPIM) context that consists of injecting two different parts or products into the same mould in separate injection cavities. These two parts require the same sequence order and available capacity at the same instant, but generate different part inventories. The addressed problem should be considered a multi-machine CLSD-BPIM one. We provide a mixed integer linear programming (MILP) model for it. The proposed model is based on a real-world case study from a second-tier supplier of the automotive sector. In sequence setup, inventory and resource setup costs terms, the benefits of the proposed multi-machine CLSD-BPIM model are validated by comparing it with a single-machine CLSD-BPIM model that replicates the current planning scenario carried out in the company under study. Additionally, random instances based on the real problem have been generated and tested. The computational results exhibit optimal solutions for the small instances with an average of 0.2-second solution time, the medium instances present average gaps of 0.47% with 2.6 h and large instances present average gaps of 4% with 6 h solution time. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： Capacitated lot-sizing Sequence-dependent setup times Injection moulding Parallel machines mixed integer linear programming Automotive industry

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Optimal delivery route planning for a fleet of heterogeneous drones: A rescheduling-based genetic algorithm approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期179卷

作者： Wang, Xudong Liu, Zeyu Li, Xueping Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA West Virginia Univ Dept Ind & Management Syst Engn Morgantown WV 26506 USA

The past decade has witnessed the rapid growth of pilot studies and the adoption of drone-assisted delivery by many e-commerce companies. In practice, delivery companies usually assemble fleets of drones consisting of heterogeneous configurations to satisfy various customer package demands, and this calls for in-depth research on how to deliver packages via drones efficiently. In this study, we extend the classic vehicle routing problem (VRP) by optimally scheduling the operations in a package depot center with a fleet of drones with different capacities, speeds, and maximum flight ranges. The drones travel multiple sorties to serve customers nearby. We formulate this problem as a mixed integer programming (MIP) model that minimizes the total delivery time for a set of packages. Due to the NP-hardness of the problem, we first develop a FIFO-based genetic algorithm (FBGA) to solve the problem. In anticipation of multiple trips, we further propose a modified, rescheduling-based genetic algorithm (RBGA) that incorporates a small-scale routing-planning optimization model that "reschedules"drone operations at the end of drone sorties, with the objective of minimizing the completion time. The RBGA features the combination of the knapsack problem, the shortest path problem, and a scheduling problem, from which near-optimal delivery plans are obtained for the fleet of drones. We conduct extensive numerical experiments with different numbers of customers and drones to evaluate the performance of the algorithms. The results suggest that compared with MIP, the proposed algorithms solve the problem efficiently for the testing instances, particularly for large instances that MIP fails to solve in time, indicating the practicality of the proposed methods for real-world implementations.

关键词： Drone delivery Vehicle routing problem mixed integer linear programming Genetic algorithm

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Modelling and heuristically solving three-dimensional loading constrained vehicle routing problem with cross-docking

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ADVANCED ENGINEERING INFORMATICS 2023年第1期57卷

作者： Cen, Xuekai Zhou, Guo Ji, Bin Yu, Samson S. Zhang, Zheng Fang, Xiaoping Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Deakin Univ Ctr Adv Design & Engn Training Sch Engn Waurn Ponds Vic 3216 Australia

Cross-docking is a logistics strategy widely applied in numerous enterprises, where vehicle scheduling is one of the most important factor in operational level for cost reduction. In sight of the widespread applications of the three-dimensional loading on the vehicle scheduling, this study addresses a vehicle routing problem with cross-docking and three-dimensional loading constraints (3L-VRPCD). A relaxation mixed integer linear programming (MILP) model is proposed for solving the 3L-VRPCD. In order to effectively solve large-scale 3LVRPCD instances, a hybrid heuristic algorithm is then proposed, which combines adaptive large neighbourhood search (ALNS) and multi-order local search packing (LSP) algorithm. In addition, a storage-pool-based strategy is integrated into the proposed algorithm for efficiency enhancement. A large number of 3L-VRPCD instances with wide-ranging properties are generated to test the feasibility and effectiveness of the proposed MILP model and heuristic method. Experimental results demonstrate remarkable advantages of the proposed heuristic over the MILP-based method with respect to solution quality and computational efficiency, especially for medium to large-scale instances. Meanwhile, the results verify that the proposed storage-pool-based strategy is capable of accelerating the search process and reducing the computational burden of the heuristic. Furthermore, the impacts of different properties (e.g., loading conditions) on the solution of 3L-VRPCD have been analysed and discussed.

关键词： Vehicle routing with cross-docking Three-dimensional packing Adaptive large neighbourhood search mixed integer linear programming

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Multi-objective solution and decision-making framework for coordinating the short-term hydropeaking-navigation-production conflict of cascade hydropower reservoirs

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JOURNAL OF CLEANER PRODUCTION 2023年第1期422卷

作者： Ma, Xiangyu Liao, Shengli Liu, Benxi Zhao, Hongye Cheng, Chuntian Su, Huaying Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China Guizhou Power Grid Co Ltd Elect Power Dispatching Control Ctr Guiyang 550002 Guizhou Peoples R China

The transformation of energy structure motivates hydropower to participate in peak shaving operations for grid stability, which conflicts with the multiple uses of cascade hydropower reservoirs. To coordinate the principal contradictory tasks of peak shaving, ship navigation, and power generation in day-ahead scheduling, a mixed integer linear programming model for the short-term multi-objective optimal scheduling is constructed. Specifically, a novel power release-based indicator is adopted to describe the navigation objective, while the minimization of the peak-valley difference of the residual load and the total water released are taken as the other two objective functions. Constraint aggregation and rectangular meshing methods are employed to handle the challenging nonlinear and nonconvex constraints. Subsequently, the Normalized Normal Constraint method, integrated with the mathematical optimization solver Gurobi, generates a set of well-distributed Pareto solutions, each representing a scheduling scheme. Finally, the Combined Weighting Technique for Order Preference by Similarity to Ideal Solution is utilized as a decision-maker to trade off and determine a compromise solution for final implementation. Case studies on the cascade hydropower reservoirs of the Wujiang River in China demonstrate that the proposed model can adequately characterize the demands of various stakeholders, and the suggested framework can obtain an even and extensive Pareto front, mediate conflicts, and make reasonable decisions. Compared to the original peak-shaving scheme, the finalized compromise scheme reduces the peak shaving effect by just 27.82% but offers simultaneous boosts of 96.54% in navigation benefits and 10.88% in power generation benefits. Consequently, the presented framework can be a promising alternative for conflict resolution of cascade hydropower reservoirs.

关键词： Multi-objective optimal scheduling Peak shaving Cascade hydropower reservoirs Normalized normal constraint mixed integer linear programming CW-TOPSIS

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Multi-objective scenario optimization of the food supply chain - Slovenian case study

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COMPUTERS & CHEMICAL ENGINEERING 2023年 172卷

作者： Drofenik, Jan Pahor, Bojan Kravanja, Zdravko Pintaric, Zorka Novak Univ Maribor Fac Chem & Chem Engn Smetanova 17 Maribor 2000 Slovenia Saubermacher Slovenia doo Sp Porcic 4a Lenart 2230 Slovenia

This paper presents the use of a process system approach to optimize the food supply chain considering three aspects of sustainability. The main objective is to highlight the opportunities to increase the share of food produced at national level, taking into account the environmental impact and economic efficiency within the framework of the Green Deal requirements established by the European Commission. A mixed-integer linear programming (MILP) model was developed that allocates the available land among different crops, calculates the mass of crops and the number of animals, and considers three groups of indicators: (a) total profit, (b) envi-ronmental indicators such as greenhouse gas emissions, and c) social criteria such as self-sufficiency ratio. Multi -objective optimization leads to a trade-off solution in which self-sufficiency increases by 45 percentage points compared to the current situation, while agricultural emissions decrease by 20% and the economic performance of the food supply chain improves significantly.

关键词： Self-sufficiency Food supply chain Multi-objective optimization mixed integer linear programming Sustainable development

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Optimal Insertion of Energy Storage Systems Considering the Economic Dispatch and the Minimization of Energy Not Supplied

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ENERGIES 2023年第6期16卷 2593页

作者： Rengel, Andres Tellez, Alexander Aguila Ortiz, Leony Ruiz, Milton Univ Politecn Salesiana Elect Engn Career Quito 170702 Ecuador

This study presents an optimal insertion model for battery storage systems in the nodes of an electrical transmission network. The proposed model is developed through mixed integer linear programming applied to the calculation of DC power flows, considering restrictions given by the characteristics of the network and by the parameters of the generation units. The proposal's main objective is to reduce the costs of operation and non-supplied energy produced, due to needing to meet the demand fully or partially. As a case study to evaluate the proposed methodology, the IEEE 24-bar test system is used. In this base case, electrical generators that depend on different primary energy resources are modeled: hydraulic, thermal, photovoltaic, and wind, in addition to potential electrical energy storage systems. These storage systems are assigned as possible analysis scenarios through the proposed optimization technique. The study is carried out in a time horizon of 24 h per day, according to a standard demand curve. With the incorporation of optimally selected storage systems in their capacity and location, it is possible to minimize dependence on the use of fossil fuels. In addition, considerable savings are obtained by reducing generation costs, and the stability of the energy supply is guaranteed. This novel proposal presents a methodology that covers all the variables of this problem, thus guaranteeing an authentic and precise study in terms of optimization. The results obtained highlight and demonstrate the benefits of stability, continuous attention to demand, reduction in dependence on exhaustible and polluting sources, and cost reduction.

关键词： power transmission systems energy storage systems renewable resources unit commitment optimization methods mixed integer linear programming electrical power systems

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A MILP model on coordinated coverage path planning system for UAV-ship hybrid team scheduling software

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JOURNAL OF SYSTEMS AND SOFTWARE 2023年 206卷

作者： Zhang, Xiaopan Zhang, Furong Tang, Zheng Chen, Xingjun Wuhan Univ Technol Wuhan Hubei Peoples R China Dalian Naval Acad Dalian Peoples R China

Shipborne unmanned aerial vehicles (UAVs) are safer and more flexible for maritime missions, but frequent recharging is needed during long-term patrols. A coordinated system for path planning between ships and electric UAVs is necessary for efficient large-area coverage. A two-stage approach is proposed to minimize the makespan overall UAVs' flight and the move distance overall ships combinationally. First, the target space is triangularized corresponding to the UAV camera field of view for generating air waypoints. Second, a MILP model is designed to connect suitable air waypoints for UAVs and marine waypoints for ship(s) to form the optimal path for them coordinating the requirements of the area coverage and the UAV recharging. The simulation experiments show the proposed model works for the scenario of either the static or the dynamic motherships in a unified way. In the static mode, the vessels are not migrated and the number of vessels and ship calling points required is the same. In the dynamic model, the ship can be repositioned to recover and recharge the drone, and the task can be accomplished simply by repositioning the ship between waypoints. Dynamic models have better interaction patterns than static models. (c) 2023 The Author(s). Published by Elsevier *** is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： UAV-ship hybrid team Coverage path planning system Shipborne electric UAV Scheduling software mixed integer linear programming

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Optimal design of hydrogen-based storage with a hybrid renewable energy system considering economic and environmental uncertainties

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ENERGY CONVERSION AND MANAGEMENT 2024年 300卷

作者： Oyewole, Oladimeji Lawrence Nwulu, Nnamdi Ikechi Okampo, Ewaoche John Univ Johannesburg Ctr Cyber Phys Food Energy & Water Syst CCP FEWS POB 524Auckland Pk ZA-2006 Johannesburg South Africa

Hydrogen and electricity derived from renewable sources present feasible alternative energy options for the decarbonisation of the transportation and power sectors. This study presents the utilisation of hydrogen generated from solar and wind energy resources as a clean fuel for mobility and backup storage for stationary applications under economic and environmental uncertainties. This is achieved by developing a detailed technoeconomic model of an integrated system consisting of a hydrogen refuelling station and an electric power generation system using mixed integer Quadratic Constrained programming (MIQCP), which is further relaxed to mixed integer linear programming (MILP). The model is implemented in the Advanced Interactive Multidimensional Modelling Software (AIMMS) and considering the inherent uncertainties in the wind resource, solar resource, costs and discount rate, the total cost of the three configurations (Hybrid PV-Wind, Standalone PV, and Standalone wind energy system) was minimised using robust optimisation technique, and the corresponding optimal sizes of the components, levelised cost of energy (LCOE), excess energy, greenhouse emission avoided, and carbon tax were evaluated. The levelised cost of the deterministic optimisation solution for all the configuration ranges between 0.0702 $/kWh to 0.0786 $/kWh, while the levelised cost of the robust optimisation solution ranges between 0.07188 $/kWh to 0.1125 $/kWh. The proposed integration has the advantages of affordable hydrogen and electricity prices, minimisation of carbon emissions and grid export of excess energy.

关键词： Hydrogen Refuelling Station Green hydrogen for mobility sector Renewable Power System Robust Optimal Sizing mixed integer linear programming mixed integer Quadratic Constrained programming

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On a bi-criteria flow shop scheduling problem under constraints of blocking and sequence dependent setup time

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ANNALS OF OPERATIONS RESEARCH 2021年第1-2期296卷 615-637页

作者： Aqil, Said Allali, Karam Univ Hassan II Casablanca Lab Math & Applicat FST POB 146 Mohammadia Morocco

In this paper, we propose a bi-criteria optimization model for a flow shop scheduling problem with permutation, blocking and sequence dependent setup time. Indeed, these constraints are the most encountered in the industrial field, which demands high command flexibility. The objective is the minimization of two criteria, in our case the makespan and the total tardiness combined in a single objective function with a weighting coefficient for each criterion. To solve this problem, we propose a mixed integer linear programming method and a set of different metaheuristics. The suggested metaheuristics are;the genetic algorithm, the iterated greedy metaheuristic and the iterative local search algorithm. This last algorithm is proposed in two ways of exploration of the neighborhood. To verify the effectiveness of our resolution algorithms, a set of instances with n jobs and m machines is randomly generated from small instances to relatively large size ones. The analysis of the suggested simulation model allowed us to note that the iterative local search algorithm gives good results compared to the iterative greedy algorithm. Moreover, it was found that the weighting parameter plays an essential role in the problem decision making. However, it was established that it is difficult to find a good solution that minimizes both criteria at once, a suitable compromise will be necessary to be adopted using the weighting coefficient.

关键词： Flow shop Sequence-dependent setup time Blocking Bi-criteria optimization mixed integer linear programming Metaheuristic

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Data-driven energy management system for flexible operation of hydrogen/ammonia-based energy hub: A deep reinforcement learning approach

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ENERGY CONVERSION AND MANAGEMENT 2023年第1期291卷

作者： Wen, Du Aziz, Muhammad Univ Tokyo Dept Mech Engn 7-3-1 HongoBunkyo Ku Tokyo 1138656 Japan Univ Tokyo Inst Ind Sci 4-6-1 Komaba Meguro Tokyo 1538505 Japan

In the context of carbon neutrality, multi-energy systems are being designed to enhance the integration of renewable energy, and the deployment of large-scale energy storage solutions is vital to ensure a flexible and cost-effective system operation. Hydrogen and ammonia are two promising energy carriers that can serve as storage media, feedstock, and fuels. This study designed an energy hub that integrates multiple energy resources and energy storage methods. Two pathways, power-to-gas-to-power (P2X2P) and biomass-to-gas-to-power (B2X2P), which use hydrogen and ammonia as energy carriers, are proposed for the energy hub. Considering the flexibility and profitability of the energy hub, scheduling of the energy hub was controlled using model-based mixed-integer linear programming and model-free deep reinforcement learning methods. A modified doubledeep Q-network framework was trained on a monthly dataset and executed on a yearly dataset. The results indicate that the B2X2P pathway outperforms the P2X2P pathway in terms of profitability, while the P2X2P pathway has greater operational flexibility. Because the optimal biomass-to-ammonia-to-power has a 550.76 MUSD net present value and a five-year discounted payback period, ammonia is best suited for mass production and storage. Moreover, the modified double deep Q network framework demonstrates superior generality for solving similar optimal scheduling problems.

关键词： Optimal planning Deep reinforcement learning mixed integer linear programming Scenario analysis Hydrogen and ammonia

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