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Food-energy-water nexus considerations in optimal greenhouse farming systems design and operation

APPLIED ENERGY 2025年 379卷

作者： Di Martino, Marcello Namany, Sarah Mahmood, Farhat Al-Ansari, Tareq Linke, Patrick Pistikopoulos, Efstratios N. Texas A&M Univ Artie McFerrin Dept Chem Engn 3122 TAMU College Stn TX 77843 USA Texas A&M Univ Texas A&M Energy Inst 1617 Res Pkwy College Stn TX 77845 USA Hamad Bin Khalifa Univ Qatar Fdn Coll Sci & Engn POB 34110 Doha Qatar Texas A&M Univ Qatar Dept Chem Engn Texas A&M Engn BldgPOB 23874 Doha Qatar

Greenhouse farming systems have the potential to sustainably relieve the stresses on food supply systems caused by a globally increasing population, together with the reduction of available agricultural land due to urbanization and soil degradation. However, literature regarding the sustainable design and operation optimization of greenhouse process systems remains scarce. This work focuses on the optimal planning and scheduling of a greenhouse farming system dependent on the utilized farming technologies, the available crops, and the selected geographic location. Ad extremum, the derived greenhouse optimization framework enables the generic trade-off analysis among completely isolated and transparent greenhouses, as well as energy and water saving greenhouses. Planning and scheduling decisions include the cover material transmissivity and isolation, cooling, heating, wetting and drying technologies, multi-crops farming strategies, irrigation, as well as artificial lighting and a dynamic shading system. To derive sustainable greenhouse system solutions, this work follows a food-energy-water nexus approach by analyzing not only an economic objective, but also resource-use objectives and a societal benefit objective, in the form of the nutritional value of the produced food basket, over one year of operation at an hourly timescale. Accordingly, this approach results in a multi-objective multi-scale mixed-integer linear programming optimization problem of large size. Various solution strategies to reduce the computational burden and solve this optimization problem to global optimality are discussed. The Pareto-front envelope for Doha, Qatar is characterized by a best-possible solution vector of $2.949M/year, 144 MW/year, 124 m3/year, and farming of carrot, lettuce, tomato, and spinach. In turn, the best trade-off solution for farming this nutrition optimal food basket consist of an annualized system cost of between $3.2M and $3.5M, energy-use between 186 and 189 MW, an

关键词： Greenhouse farming Planning and scheduling Modular operation mixed-integer linear programming Multi-objective optimization Food-energy-water nexus

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Two-echelon van-robot routing problem with sharing-curbside satellites

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JOURNAL OF HEURISTICS 2025年第1期31卷 1-35页

作者： Li, Hongqi Wang, Feilong Xiong, Hanxi Wang, Zhiqi Beihang Univ Sch Transportat Sci & Engn 37 Xueyuan Rd Beijing 100191 Peoples R China

High population density and commercial-activity density in urban areas make land use for urban logistics systems even more challenging. Herein, a concept named sharing-curbside satellite (SS) is involved in two-echelon city logistics systems. Traditional vans are deployed in the 1st-echelon network, whereas ground-based robots are employed in the 2nd-echelon network. As a type of nondedicated satellites, the SS shares curbside spaces with the local traffic flow, and each SS can have multiple time windows for direct transshipment between vans and robots. The SS can provide a new mode for urban deliveries through temporary and nondedicated satellites at the neighborhood level. In this study, the two-echelon van-robot routing problem with SSs (2ERP-SS) is defined. The SS synchronization involves vans being used as part of SSs, each SS has multiple time windows, cargoes are transshipped directly between vans and robots, and the available transshipment capacity decreases over time. We develop a mixed-integer linear programming model. We provide a large neighborhood search (LNS) combined with a beam search algorithm, and employ an adaptive LNS (ALNS) for comparison. The effectiveness of the mathematical formulation and heuristics are evaluated through computational experiments, and practical management insights are elucidated.

关键词： Routing Two-echelon Van and robot Sharing-curbside satellite mixed-integer linear programming Large neighborhood search

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Optimal planning and management of the energy-water-carbon nexus in hybrid AC/DC microgrids for sustainable development of remote communities

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APPLIED ENERGY 2025年第PartB期377卷

作者： Valencia-Diaz, Alejandro Toro, Eliana M. Hincapie, Ricardo A. Univ Tecnol Pereira Fac Ingn Programa Ingn Elect Pereira Colombia Univ Tecnol Pereira Fac Ciencias Empresariales Programa Ingn Ind Pereira Colombia

This paper presents a two-stage stochastic mixed-integer nonlinear programming model, which is linearized to a mixed-integer linear programming (MILP) form, for optimizing the energy-water-carbon (EWC) nexus in remote AC/DC microgrids aimed at sustainable community development. The model optimizes the selection, location, and operation of diesel generators, voltage source converters, photovoltaic systems, wind turbines, and battery energy storage systems while managing CO2 emissions. It accurately models the AC/DC microgrid's operation, determining optimal voltages, currents, and converter states (inverter or rectifier). Uncertainties in power demand, water consumption, solar irradiation, and wind speed are modeled using stochastic scenarios generated via k-means clustering. The MILP model is implemented in GAMS and solved with the commercial solver CPLEX, ensuring global optimization. Results show that integrating distributed energy resources, compared to a case without these elements, reduces CO2 emissions of the diesel generators and the operational costs of the EWC nexus, highlighting the proposed approach's environmental and economic benefits. These findings underline the importance of incorporating distributed energy resources in the sustainable development of microgrids.

关键词： AC/DC microgrid Energy-water-carbon nexus Sustainable development goals Distributed energy resources mixed-integer linear programming Two-stage stochastic programming

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Production routing decisions in a two-echelon supply chain with multiple delivery modes

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2025年第0期

作者： Benfedel, Rachida Belkaid, Faycal Brahimi, Nadjib Univ Tlemcen Mfg Engn Lab Tlemcen 22 Rue Abi Ayed Abdelkrim Fg PasteurBP 119 Tilimsen 13000 Algeria Rennes Sch Business 2 Rue Robert Arbrissel F-35065 Rennes France

We study an original two-echelon production routing problem with multiple delivery modes (2E-PRP-MDM). In the first echelon, the primary production facility is tasked with satisfying the demands of two distinct entities: a set of warehouses and a set of customers through direct shipments. In the second echelon, warehouses become delivery hubs, meeting the needs of another group of customers with the possibility of delivering several customers using the same vehicle, which entails routing decisions. The use of two different delivery modes is justified by the segmentation of customers into very important customers with high volumes (first echelon) and low-volume customers (second echelon). This supply chain structure requires efficient decisions on production planning, routing, and practical resource allocation across both echelons. In order to address this challenging optimization problem, we have developed a mixed-integer linear programming (MILP) formulation and two heuristics, a three-phase iterative (3PI) approach, and a simulated annealing (SA) algorithm with path relinking. Extensive numerical experiments showed that the simulated annealing heuristic with path relinking (PR) outperforms the 3PI approach, which in turn outperforms the branch-and-cut procedure of the solver when run on the MILP formulation. In terms of managerial insights, we compared scenarios with and without delivery flexibility from the manufacturing to warehouses to the customers. Our findings provide valuable insights for managers seeking to reduce costs, based on the analysis of the supply chain of an Algerian soft drink company. The results show that an integrated approach achieves a cost reduction of over 2.73% compared to the current practice of the company. Additionally, they reveal that increasing the number of warehouses does not always lead to lower total costs.

关键词： production planning local search mixed-integer linear programming two-echelon production routing problem

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Destination-to-gate assignment to mitigate congestion-related risks in oversaturated metro lines: A new passenger flow control strategy

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 196卷

作者： Zhong, Linhuan Xu, Guangming Liu, Wei Liu, Yang Liu, Xinyi Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore Singapore Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore

The accumulation of large passenger flows at metro stations often poses congestion risks to urban rail systems, including an increased likelihood of accidents (e.g., slips, trips, and falls) and train departure delays. However, during peak hours, the priority boarding rights of passengers at upstream stations often lead to congestion and overcrowding at downstream popular stations. We propose a new passenger flow control strategy to address this issue, namely destination- to-gate assignment. This approach assigns specific gates to destinations served by the station, enabling passengers to board the appropriate train carriages. This strategic allocation facilitates a more even distribution of passengers, reducing congestion and enhancing spatial equity in passenger travel, thereby mitigating operational risks associated with overcrowding. For the problem of interest, we propose a nonlinear integer programming model to optimize the destination-to-gate assignment, aiming to simultaneously minimize risks related to passenger crowding and waiting times. The model adopts a first-come, first-served (FCFS) boarding rule to accurately capture the dynamic nature of passenger flow while considering the capacity limitations of train carriages. Leveraging the model's characteristics, we employ a set of linearization methods to equivalently transform it into a mixed-integer linear programming (MILP) model. To address the computational challenges posed by real-world scale, we develop a customized heuristic algorithm that uses Variable Neighborhood Search (VNS) combined with passenger flow simulation to efficiently generate high-quality solutions. Finally, we conduct a series of numerical experiments using data from Guangzhou Metro Line 9 to demonstrate the effectiveness of our proposed approach. The results show that the proposed destination- to-gate assignment strategy effectively alleviates congestion-related risks across all stations and promotes spatial equity in passenger tra

关键词： Oversaturated metro line Passenger flow control Destination-to-gate assignment Congestion-related risk mixed-integer linear programming

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Global and Preference-Based Optimization with mixed Variables Using Piecewise Affine Surrogates

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2025年第2期204卷 1-39页

作者： Zhu, Mengjia Bemporad, Alberto IMT Sch Adv Studies Lucca Piazza San Francesco 19 I-55100 Lucca Italy

Optimization problems involving mixed variables (i.e., variables of numerical and categorical nature) can be challenging to solve, especially in the presence of mixed-variable constraints. Moreover, when the objective function is the result of a complicated simulation or experiment, it may be expensive-to-evaluate. This paper proposes a novel surrogate-based global optimization algorithm to solve linearly constrained mixed-variable problems up to medium size (around 100 variables after encoding). The proposed approach is based on constructing a piecewise affine surrogate of the objective function over feasible samples. We assume the objective function is black-box and expensive-to-evaluate, while the linear constraints are quantifiable, unrelaxable, a priori known, and are cheap to evaluate. We introduce two types of exploration functions to efficiently search the feasible domain via mixed-integer linear programming solvers. We also provide a preference-based version of the algorithm designed for situations where only pairwise comparisons between samples can be acquired, while the underlying objective function to minimize remains unquantified. The two algorithms are evaluated on several unconstrained and constrained mixed-variable benchmark problems. The results show that, within a small number of required experiments/simulations, the proposed algorithms can often achieve better or comparable results than other existing methods.

关键词： Derivative-free optimization Preference-based optimization mixed-integer linear programming Surrogate models

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Compact MILP models for double row layout problem with pairwise clearance

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EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2025年第1期19卷 45-67页

作者： Alaimo, Richard Lim, Churlzu UNC Charlotte Ind & Syst Engn Dept 9201 Univ City Blvd Charlotte NC 28223 USA

Double row layout problem (DRLP) seeks for an optimal arrangement of departments along both sides of a central corridor to minimise the total material flow cost. This study considers a variant of DRLP where pairwise minimum clearance requirements between departments are enforced when they are assigned to the same side. This problem accounts for additional interaction that exists between departments during the layout planning process. Two mixed-integer linear programming formulations are proposed with the motivation that using fewer binary variables compared to the existing formulation in the literature helps reduce the solution time. Noting the NP-hardness of the problem, symmetry-breaking constraints are investigated in an effort to further alleviate the computational burden. The efficacy of the proposed models is demonstrated via a computational study using a set of test problem instances.

关键词： facility layout double row layout problem mixed-integer linear programming combinatorial optimisation clearance requirements

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A cutting-plane algorithm for an orienteering problem with mandatory visits and simultaneous production

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年

作者： Wang, Shijin Zhang, Hanyu Chu, Feng Fang, Kan Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China Univ Paris Saclay Univ Evry Lab IBISC Evry France Tianjin Univ Coll Management & Econ Tianjin Peoples R China

This work investigates an orienteering problem with mandatory visits and simultaneous production (denoted by OPMV-SP) in additive manufacturing, where some nodes must be visited and others are optional, and the objective is to maximise the total collected scores. The problem is firstly formulated as a mixed-integer linear programming (MILP) model. Four sets of valid inequalities are then introduced to enhance the MILP. Based on the MILP, an exact method namely cutting-plane algorithm (CPA) is developed and evaluated on both benchmark and simulated instances. The results of benchmarks demonstrate that CPA is competitive, proving the optimality for 191 out of 202 feasible instances. Additionally, the performance of different combinations of valid inequalities are evaluated. Sensitivity analysis on the effects of mandatory visits and waiting times offers managerial insights for production and delivery strategies. The results of simulated instances of real-world cases further demonstrate CPA's efficiency and effectiveness.

关键词： Orienteering problem cutting-plane algorithm additive manufacturing simultaneous production and transportation mixed-integer linear programming

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Techno-Economic Feasibility and Optimal Design Approach of Grid-Connected Hybrid Power Generation Systems for Electric Vehicle Battery Swapping Station

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ENERGIES 2025年第5期18卷 1208-1208页

作者： Nyamayoka, Lumbumba Taty-Etienne Masisi, Lesedi Dorrell, David Wang, Shuo Univ Witwatersrand Fac Engn & Built Environm FEBE Sch Elect & Informat Engn EIE ZA-2050 Johannesburg South Africa Univ Turku Dept Mech & Mat Engn Turku 20520 Finland Beijing Inst Technol Natl Engn Res Ctr Elect Vehicles Beijing 100081 Peoples R China

Fossil fuel depletion, environmental concerns, and energy efficiency initiatives drive the rapid growth in the use of electric vehicles. However, lengthy battery charging times significantly hinder their widespread use. One proposed solution is implementing battery swapping stations, where depleted electric vehicle batteries are quickly exchanged for fully charged ones in a short time. This paper evaluates the techno-economic feasibility and optimal design of a grid-connected hybrid wind-photovoltaic power system for electric vehicle battery swapping stations. The aim is to evaluate the viability of this hybrid power supply system as an alternative energy source, focusing on its cost-effectiveness. An optimal control model is developed to minimize the total life cycle cost of the proposed system while reducing the reliance on the utility grid and maximizing system reliability, measured by loss of power supply probability. This model is solved using mixed-integer linear programming to determine key decision variables such as the power drawn from the utility grid and the number of wind turbines and solar photovoltaic panels. A case study validates the effectiveness of this approach. The simulation results indicate that the optimal configuration comprises 64 wind turbines and 402 solar panels, with a total life cycle cost of ZAR 1,963,520.12. These results lead to an estimated energy cost savings of 41.58%. A life cycle cost analysis, incorporating initial investment, maintenance, and operational expenses, estimates a payback period of 5 years and 6 months. These findings confirm that the proposed hybrid power supply system is technically and economically viable for electric vehicle battery swapping stations.

关键词： electric vehicle battery swapping station grid-connected hybrid renewable power supply systems multi-objective optimization mixed-integer linear programming life cycle cost analysis

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MILP Modeling and Optimization of Multi-Objective Three-Stage Flexible Job Shop Scheduling Problem With Assembly and AGV Transportation

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IEEE ACCESS 2025年 13卷 25369-25386页

作者： Yang, Shiming Meng, Leilei Ullah, Saif Zhang, Biao Sang, Hongyan Duan, Peng Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Univ Engn & Technol Dept Ind Engn Taxila 47080 Pakistan

In the real manufacturing environment, the machining stage of the jobs and the assembly stage of the products are often completed in different workshops. In addition, automatic guided vehicle (AGV) plays an indispensable role in the transportation of jobs from machining workshop to assembly workshop. This paper studies multi-objective three-stage flexible job shop scheduling problem (FJSP-T-A) with minimizing both the makespan and the total energy consumption. In FJSP-T-A, jobs are first machined in flexible job shop, then are transported to assembly workshop by AGVs, and finally are assembled in assembly workshop. To solve this problem, a mixed-integer linear programming model (MILP) is developed and the optimal Pareto front for small-scale instances are solved by using the $\varepsilon $ -method. FJSP-T-A is NP-hard, and an efficient multi-population co-evolutionary algorithm (MPCEA) is proposed to efficiently solve large-scale instances. In the MPCEA, we design a strategy to select relatively high-quality individuals to enhance the algorithm's convergence speed, and design a multi-objective variable-neighborhood search (MOVNS) method to improve the local search ability. Experiments are conducted to prove the effectiveness of the MILP model and the MPCEA.

关键词： Assembly Transportation Energy consumption Conferences Machining Job shop scheduling Sequential analysis Systematic literature review Genetic algorithms Optimization Three-stage flexible job shop scheduling problem mixed-integer linear programming co-evolutionary algorithm variable-neighborhood search

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