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A dual population collaborative genetic algorithm for solving flexible job shop scheduling problem with AGV

SWARM AND EVOLUTIONARY COMPUTATION 2024年 86卷

作者： Han, Xiaoqing Cheng, Weiyao Meng, Leilei Zhang, Biao Gao, Kaizhou Zhang, Chaoyong Duan, Peng Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

With the increase in labor costs and the development of manufacturing automation technology, automatic guided vehicle (AGV) is widely used in various flexible workshop scenarios. The integrated scheduling of processing machines and AGV is of great significance in real -world workshop production. This article studies the integration problem of flexible job shop scheduling problem (FJSP) and AGV with minimizing the makespan, and proposes a novel mixed integer linear programming (MILP) model and a dual population collaborative genetic algorithm (DCGA). In DCGA, a two-layer encoding strategy based on machine selection and operation sequencing is used. Two decoding methods are designed to determine AGV selection, and each population uses a decoding method. Moreover, a population collaboration operation is designed. The feasibility and effectiveness of the MILP model and DCGA are verified through experimental simulation. Specifically, the DCGA improves 18 current best solutions for benchmarks in the existing studies.

关键词： Flexible job shop scheduling problem Automatic guided vehicle Genetic algorithm Dual population collaboration mixed integer linear programming

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Integration of sales, inventory, and transportation resource planning by dynamic-demand joint replenishment problem with time-varying costs

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 188卷

作者： Suemitsu, Issei Miyashita, Naoko Hosoda, Junko Shimazu, Yoshihito Nishikawa, Takahiro Izui, Kazuhiro Hitachi Ltd Res & Dev Grp 1-280Higashi Koigakubo Kokubunji Tokyo 1858601 Japan Logisteed Ltd DX STRATEGY Headquarters 2-9-2Kyo BashiChuo Ku Tokyo 1048350 Japan Kyoto Univ Katsura Kyoto Univ Dept Micro Engn Nishikyo Ku Kyoto 6158540 Japan

This paper addresses the joint replenishment problem in the context of sales and operations planning. Unlike traditional ones that assume static demands, unit costs, and transportation capacity, we consider dynamic-deterministic demands from sales plans and time-varying unit costs and transportation capacity to deal with the recent logistics conditions. We then introduce the capacitated dynamic-demand joint replenishment problem with time-varying costs. To efficiently solve this problem, a three-phase approach is proposed: (1) simplifying the problem and determining ideal inventory quantities using mixed integer linear programming, (2) estimating policy variables for each item using the covariance matrix adaptation evolution strategy, and (3) updating the ideal inventory quantities based on evaluated shortages until all demands are satisfied. Our method outperforms conventional approaches with at least 12 times faster solution runtimes in tests with up to 100 items. We also obtain the insight that the cost-efficient replenishment plan changes according to the increase rate of the unit transportation cost.

关键词： Supply chain management Sales & operations planning Joint replenishment problem mixed integer linear programming

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Optimal hydrogen infrastructure planning for heat decarbonisation

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2024年 204卷 121-136页

作者： Efthymiadou, Margarita E. Charitopoulos, Vassilis M. Papageorgiou, Lazaros G. Univ Coll London UCL Sargent Ctr Proc Syst Engn Dept Chem Engn Torrington Pl London WC1E 7JE England

Energy decarbonisation is essential to achieve Net -Zero emissions goal by 2050. Consequently, investments in alternative low-carbon pathways and energy carriers for the heat sector are required. In this study, we propose an optimisation framework for the transition of heat sector in Great Britain focusing on hydrogen infrastructure decisions. A spatially-explicit mixed-integer linear programming (MILP) evolution model is developed to minimise the total system's cost considering investment and operational decisions. The optimisation framework incorporates both long-term planning horizon of 5-year steps from 2035 to 2050 and typical days with hourly resolution. Aiming to alleviate the computational effort of such multiscale model, two hierarchical solution approaches are suggested that result in computational time reduction. From the optimisation results, it is shown that the installation of gas reforming hydrogen production technologies with CCS and biomass gasification with CCS can provide a cost-effective strategy achieving decarbonisation goal. What-if analysis is conducted to demonstrate further insights for future hydrogen infrastructure investments. Results indicate that, as cost is highly dependent on natural gas price, Water Electrolysis capacity increases significantly when gas price rises. Moreover, the introduction of carbon tax policy can lead to lower CO2 net emissions.

关键词： Energy systems modelling Hydrogen infrastructure mixed integer linear programming What-if analysis Heat decarbonisation Net-Zero

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Agri-food supply chain optimization through a decentralized production process in the olive oil industry

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 192卷

作者： Urra-Calfunir, Bryan A. Monardes-Concha, Carlos A. Miranda-Gonzalez, PabloA. Univ Catolica Norte Sch Engn Coquimbo Chile Univ Catolica Norte Dept Ind Engn Antofagasta Chile

A tactical/operational model is proposed to assist decision-making in harvest planning and olive oil production planning of the different producers in the rural sector, considering allocation and transportation decisions of the kilograms of olives to the production plant with temporary relocation decisions of the mobile olive oil mills through decentralized production activities in the agri-food supply chain. A mixed integer linear programming model is presented that maximizes the profit of small farmers in the production process of single-varietal extra virgin olive oil. The decisions are based on the geographical dispersion of the different olive growers and the oil concentration of the olive varieties in a certain period, which depends on the climatic conditions of each sector where the olives are cultivated. The mathematical programming model is applied in Coquimbo, Chile, using actual data collected from previous harvest seasons. The computational results of the optimization model indicate that transportation decisions impact olive harvesting and oil production decisions. It is concluded that cooperatives of small producers in the rural sector increase overall profits and reduce transportation costs and the number of kilometers traveled by at least 41% through a decentralized production process with mobile production plants.

关键词： OR in agriculture mixed integer linear programming Agri-food supply chain Olive oil Mobile production

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Multi-Objective Resource Allocation for UAV-Assisted Air-Ground Integrated MC-NOMA Networks

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IEEE ACCESS 2024年 12卷 141000-141012页

作者： Wang, Tong Hubei Univ Econ Sch Informat Engn Wuhan 430205 Hubei Peoples R China

We consider a Multi-UAV multicarrier non-orthogonal multiple access (MC-NOMA) downlink network in which each UAV serves a group of ground users within its designated cell. Our goal is to maximize the total downlink rate for each cell by simultaneously optimizing the subcarrier assignment and power control. However, the need to maximize the downlink rate for each individual UAV cell introduces conflicts because the optimization objectives for different cells are inherently at odds with each other, making this a Multi-Objective Optimization Problem (MOOP). We applied the weighted Tchebycheff method to convert the MOOP into a Single-Objective Optimization Problem (SOOP). The resulting SOOP remains a mixed-integer Nonlinear programming (MINLP) problem. To address this, we first relax the combinatorial subcarrier assignment variables into continuous variables and then apply a penalty method to enforce binary constraints. To handle the nonconvexity of the objective function and constraints, we utilize the Big-M method and Successive Convex Approximation (SCA) to decouple the product terms and deal with nonconvexity. We then employed an iterative approach to obtain a suboptimal solution and continued the process until convergence was achieved. Simulations demonstrate that this method effectively balances the trade-offs among different cells, achieves significant performance improvements and successfully approximates Pareto optimal solutions.

关键词： NOMA Autonomous aerial vehicles Resource management Optimization Downlink Power control programming mixed integer linear programming Non-orthogonal multiple access (NOMA) power control subcarrier assignment multi-objective optimization problem (MOOP) mixed integer nonlinear problem (MINLP) big-m method successive convex approximation (SCA)

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Simultaneous scheduling of jobs and transporters in a hybrid flow shop with collision-free transporter routing: A novel parallel heuristic

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COMPUTERS & OPERATIONS RESEARCH 2024年 168卷

作者： Amirteimoori, Arash Mohamed, Moataz Kia, Reza London Sch Econ & Polit Sci LSE Dept Math London England McMaster Univ Dept Civil Engn Hamilton ON Canada Sultan Qaboos Univ Coll Econ & Polit Sci Dept Operat Management & Business Stat Muscat Oman

A computationally potent two-stage parallel heuristic (TSPH) and a mixed integer linear programming (MILP) are suggested to address the simultaneous scheduling of jobs and transporters in a hybrid flow shop system, wherein multiple transporters, stage omission, transporter eligibility, machine eligibility, and collision-free transporter routing are assumed. To the best of our knowledge, the significance of transporter collision-free routing has not been spotlighted in the literature on flow shop systems. Collision-free routing of transporters is a requisite technical attribute as transport means may collide on routes and break down the whole system. Our MILP and TSPH assume collision-free routing to impede the issue. Being equipped with parallel computing, TSPH can deal with large problems in a considerably short time. To support, TSPH is analogized against the suggested MILP, two-step MILP (TSMILP), and an efficient parallel meta-heuristic (i.e., parallel particle swarm optimization and genetic algorithm (PPSOGA)) that outperformed many literarily prominent meta-heuristics in the literature. The benchmark results uncover that TSPH outclasses both TSMILP and PPSOGA in the quality of solutions. Eventually, utilizing the convergence plot and Nemenyi's post-hoc procedure for Friedman's test, it is revealed that the outcomes of TSPH are remarkably more desirable than those of TSMILP and PPSOGA.

关键词： Hybrid flow shop Transporter routing Parallel computing Heuristic mixed integer linear programming

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Multi-Stage Planning of Distribution Network With High Penetration Renewable Energy Considering Reliability Index

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第2期60卷 2344-2356页

作者： Liu, Jie Sun, Kaiqi Ding, Zhaohao Li, Ke-Jun Sun, Yuanyuan Shandong Univ Sch Elect Engn Jinan 250061 Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China

In this study, a multi-stage planning model of the distribution network with high photovoltaic (PV) penetration considering operational reliability is presented. The operation reliability cost consisting of three modified reliability indices is imposed in the objective function in the proposed distribution systems planning model. Additionally, the distribution network reconfiguration including microgrid is considered at the operation level to improve system operation reliability. Nonlinear expressions of modified reliability indices aggravate the complexity of the proposed planning model. To address the computational burden, the planning model is simplified as a mixed integer linear programming (MILP) model, which have an efficient computational behavior with exiting MILP solvers. The benefits of imposing operation reliability cost in the objective function and network reconfiguration including microgrid as operation strategies, are discussed. The effectiveness of the proposed model is tested by numerical results.

关键词： Planning Costs Distribution networks Power system reliability Reliability engineering Indexes Investment Distribution network multi-stage planning reliability index photovoltaic mixed integer linear programming

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Path Planning Optimization Based Interference Awareness for Mobile Robots in mmWave Multi Cell Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第9期73卷 13639-13650页

作者： Ren, Yijing Friderikos, Vasilis Kings Coll London Dept Engn London WC2R 2LS England

The emerging beyond fifth-generation (B5G) and envisioned sixth-generation (6G) wireless networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). The scenario under investigation in this paper relates to mobile robots that autonomously roam on an industrial floor and perform a variety of tasks at different locations, whilst utilizing high directivity beamformers in millimeter wave (mmWave) small cells. In such scenarios, the potential close proximity of mobile robots connected to different base stations may cause excessive levels of interference having as a net result a decrease in the overall achievable data rate in the network. To resolve this issue, a novel MR optimal path planning scheme via a mixed integer programming formulation is proposed where robots' trajectory is considered jointly with the interference level at different beam sectors. To combat the curse of dimensionality, a geographical division clustering based MR path planning heuristic scheme is proposed to enable scalability and real-time decision making. The proposed heuristic aims to find a low interference path for each mobile robot whilst achieving a near-optimal performance. A wide set of numerical investigations reveal that the proposed optimal and heuristic path planning schemes for the mmWave connected mobile robots can improve the overall achievable throughput by up to 93% compared to an interference oblivious scheme without penalizing the total travel time.

关键词： Interference Robots Millimeter wave communication Optimization Mobile robots Service robots Trajectory Beamforming beyond 5G industry 4.0 mixed integer linear programming mobile robots path planning

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A multi-road quasi network flow model for the vertical alignment optimization of a road network

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ENGINEERING OPTIMIZATION 2024年第7期56卷 1140-1163页

作者： Ayman, Khandoker Md Hare, Warren Lucet, Yves Univ British Columbia Dept Comp Sci Okanagan Campus Kelowna BC Canada Univ British Columbia Dept Math Okanagan Campus Kelowna BC Canada

Minimizing earthwork costs is crucial in the optimization of vertical alignment. In this article, the previous quasi network flow model for the vertical alignment optimization of a single road is extended to a multi-road network. A 'one-at-a-time' method is first examined to solve the optimization problem and it is noted that this method is effectively what is currently used in practice. Two academic test problems are used to demonstrate that this method is not optimal. This leads to the development of three novel methods to solve the optimization problem: no-flow, with-flow and divide-and-conquer. It is noted that the with-flow method guarantees global optimality. All methods are compared on two real-world case studies. On small road networks, the with-flow method decreases costs by 7.4%, and achieves a 20 times speedup over the one-at-a-time method. On long road networks, the divide-and-conquer method is faster than all other methods without any notable cost difference. However, the divide-and-conquer method is an heuristic and cannot guarantee that it returns a global minimum.

关键词： Road network optimization vertical alignment mixed integer linear programming quasi network

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Long-term maintenance optimization for integrated mining operations

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OPTIMIZATION AND ENGINEERING 2024年第4期25卷 1817-1848页

作者： Yang, Yingying Loxton, Ryan Rohl, Andrew L. Bui, Hoa T. Curtin Univ ARC Training Ctr Transforming Maintenance Data Sc Perth WA Australia

Maintenance activities are inevitable and costly in integrated mining operations. Conducting maintenance may require the whole system, or sub-units of the system, to be shut down temporarily. These maintenance activities not only disrupt the unit being shut down, but they also have consequences for inventory levels and product flow downstream. In this paper, we consider an interconnected mining system in which there are complicated maintenance relationships and stock accumulation at intermediate nodes. We propose a time-indexed mixed-integer linear programming formulation to optimize the long-term integrated maintenance plan and maximize the total throughput. We also devise an algorithm, which combines Benders decomposition and Lagrangian relaxation, to accelerate the computational speed. To validate our mathematical model, we perform simulations for a real-world case study in the iron ore industry. The results show that our method can yield better solutions than CPLEX optimization solver alone in faster time.

关键词： Long-term maintenance planning Optimization mixed integer linear programming Integrated network Mining industry

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