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IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Sahar, Najmus Basile, Anthony Yan, Bing Rochester Inst Technol Dept Elect & Microelect Engn Rochester NY 14623 USA

ISBN: (纸本)9798350358513;9798350358520

In modern manufacturing, meeting the rising demand for customized products within tight deadlines poses significant challenges. Industry 4.0 offers opportunities to revolutionize manufacturing through automation, including adopting autonomous mobile robots. Integrating mobile robots into job shops introduces additional complexities, such as robot assignment and travel times, expanding the classical scheduling problem. In this paper, a mixed integer linear programming formulation is established to efficiently schedule mobile robots in smart job shops for on-time deliveries. It incorporates constraints on robot assignment and travel times alongside traditional machine and part-related constraints. To tackle the complexity of the problem, a systematic approach is employed to tighten the formulation, establishing linear relationships between operation and transportation sequence variables. Testing results demonstrate the effectiveness of the model and tightened constraints in achieving near-optimal solutions to meet on- time deliveries.

关键词： job shop scheduling mobile robots mixed integer linear programming formulation tightening

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Solving an Integrated Project Portfolio Selection and Contractor Selection Problem: Fuzzy Goal programming Based on Fuzzy Preference Relations

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FUZZY INFORMATION AND ENGINEERING 2025年第1期17卷 75-107页

作者： Zahedirad, Mostafa Khalili-Damghani, Kaveh Ghezavati, Vahidreza Komijan, Alireza Rashidi Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran *** Iran Islamic Azad Univ Dept Ind Engn Sci & Res Branch Tehran *** Iran

Several factors affect the flexibility and the complexity of the project selection and the contractor selection problems. Project portfolio managers are expected to select the best combination of projects and contractors considering multiple conflicting objectives in a multi-period planning horizon. In this paper, we propose an integrated project portfolio optimization and contractor selection problem. The problem is modeled through a multi-objective mixed integer linear programming (MILP) model. Three solution approaches including Goal programming (GP), Fuzzy Goal programming (FGP), and fuzzy goal programming considering a fuzzy preference relationship are proposed. All solution approaches have been applied to a real case. The computational results show the out performance of FGP considering fuzzy relations. The time complexity of the proposed models in the sense of the relation of CPU time and the number constraints and variables of the models were discussed.

关键词： integer programming Computational modeling programming Planning mixed integer linear programming Complexity theory Time complexity Portfolios Optimization project selection contractor selection multi-objective mixed integer programming goal programming fuzzy goal programming fuzzy preference relation

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Last Mile Drone Delivery: Complexity and Research Challenges 2nd

Last Mile Drone Delivery: Complexity and Research Challenges

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2nd Decision Science Alliance International Summer Conference-DSA ISC

作者： Xhafa, Fatos Dominguez, Cristian Juan, Angel A. Univ Politecn Cataluna Barcelona 08034 Spain Univ Politecn Valencia CIGIP Alcoy 03801 Spain

ISBN: (纸本)9783031782374;9783031782381

Drones or unmanned aerial vehicles technologies are experiencing a fast development pace propelled by a number of applications in diverse areas such as environmental monitoring, surveillance, military, smart cities, etc. In particular, the use of drones in smart city logistics is becoming increasingly relevant to the services of transporting small goods in urban neighborhoods. Hereby, a real life use case is the last mile delivery, which consists in completing the final phase of product delivery process from a truck to the customer's house. Prior to last mile delivery, products are shipped in a truck from a distribution center to a location in a city, which becomes the starting point of the last mile delivery. A key issue in last mile delivery is the drone route planning in order to guarantee the correct and efficient delivery service. Although the drone route planning in last mile delivery shares similar requirements with route planning in other delivery systems, there are complexities and challenges due to new requirements specific to drones such as limited flying distance, product weights, energy consumption, fleet costs, etc. In this paper, we analyze the complexity and research challenges of drone route planning in last mile drone delivery. The limitations of exact optimization methods versus heuristic methods are also highlighted through Gurobi solver results. The paper contributes to understanding the complex multi-objective nature of the drone routing in last mile delivery.

关键词： Last mile delivery Drone routing problem Heuristic solutions mixed integer linear programming Multi-objective optimization

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Multi-period Planning of Municipal Solid Waste Management: a Case Study in Qingdao

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PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2023年第1-2期7卷 107-126页

作者： Zhao, Shifen Ren, Tiantian Ma, Lei Li, Zhiwei Shanxi Jinhuankeyuan Environm Resources Technol C Taiyuan 030024 Peoples R China Shanxi Jinzhong Municipal Ecol & Environm Monitor Jinzhong 030600 Peoples R China Beijing Inst Petrochem Technol Sch New Mat & Chem Engn Beijing 102617 Peoples R China Hefei Univ Technol Sch Chem & Chem Engn Hefei 230009 Peoples R China

With the increase of population and the improvement of people's living standards, the amount of municipal solid waste (MSW) increases rapidly. It is a challenge for the cities in developing countries to deal with these MSW economically and environmentally. This paper proposed an integrated framework which combines the grey system theory and mathematical programming model to deal with MSW management. First, the factors affecting MSW generation are analyzed and screened out by grey correlation analysis. The factors with higher priority are selected to perform the regression analysis. The generation of MSW is predicted based on the grey model GM (1,1). Second, a multi-period planning model is proposed to optimize MSW management of Qingdao City, China. The model is formulated as mixed integer nonlinear programming (MINLP) model for the scenario with variable capacities of treatment plants and mixed integer linear programming (MILP) for the scenario with given capacities of treatment plants. Four different scenarios are considered: minimum cost scenario with and without variable capacities, minimum carbon emissions with and without variable capacities. The results indicate that the average cost of MSW treatment for the minimum cost scenario with and without variable capacities is 28.32$/t and 27.92 $/t, respectively. The average carbon emission for the minimum emission scenario with and without variable capacities is 0.751 t CO2/t MSW and 0.721 t CO2/t MSW, respectively. This shows the variable expansion capacities of disposal technologies could reduce the cost and carbon emission of MSW treatment compared with the scenario with given capacities. The findings are useful for the decision-maker to adopt effective policy for MSW management. It provides a systematic method for MSW management for other developing countries.

关键词： Municipal solid waste Grey model Multiple regression analysis mixed integer linear programming

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A multi-objective optimization approach for designing a sustainable supply chain considering carbon emissions

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年第5期15卷 1777-1793页

作者： Kumar, Amit Kumar, Kaushal Univ Delhi Fac Math Sci Dept Operat Res Delhi India

Greenhouse gases (GHGs) emission has been growing for decades, that has resulted in serious environmental problems. Many researches have focused on production planning in supply chains that are primarily attributed to objectives of cost minimization and profit maximization. There has been limited research concentrated on emission of GHGs (e.g. CO2, N2O, CH4) from industrial activities. The social concern of air pollution is the motivation towards the proposed study. In this study, we propose an optimization model for designing a sustainable supply chain management (SSCM) network that attempts to reduce carbon emission, improve social benefits and eventually improve customer experience. The optimization model is a multi objective mixed integer linear programming (MILP) formulation. The proposed model is a multi raw-material, multi-product, multi-period, multi-sites model that focuses on improving three dimensions of sustainability: viz. economic, environmental and social. Model performance is demonstrated through numerical experiments. The authors are currently working on solution algorithm for solving larger instances of the proposed model.

关键词： Greenhouse gas emission Sustainable supply chain Production planning Carbon emission mixed integer linear programming Multi-objective optimization

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Optimal Multi-Constrained Workflow Scheduling for Cyber-Physical Systems in the Edge-Cloud Continuum 48

Optimal Multi-Constrained Workflow Scheduling for Cyber-Phys...

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48th Annual IEEE International Computers, Software, and Applications Conference (COMPSAC) - Digital Development for a Better Future

作者： Kouloumpris, Andreas Stavrinides, Georgios L. Michael, Maria K. Theocharides, Theocharis Univ Cyprus Dept Elect & Comp Engn KIOS Res & Innovat Ctr Excellence Nicosia Cyprus

ISBN: (纸本)9798350376975;9798350376968

The emerging edge-hub-cloud paradigm has enabled the development of innovative latency-critical cyber-physical applications in the edge-cloud continuum. However, this paradigm poses multiple challenges due to the heterogeneity of the devices at the edge of the network, their limited computational, communication, and energy capacities, as well as their different sensing and actuating capabilities. To address these issues, we propose an optimal scheduling approach to minimize the overall latency of a workflow application in an edge-hub-cloud cyber-physical system. We consider multiple edge devices cooperating with a hub device and a cloud server. All devices feature heterogeneous multicore processors and various sensing, actuating, or other specialized capabilities. We present a comprehensive formulation based on continuous-time mixed integer linear programming, encapsulating multiple constraints often overlooked by existing approaches. We conduct a comparative experimental evaluation between our method and a well-established and effective scheduling heuristic, which we enhanced to consider the constraints of the specific problem. The results reveal that our technique outperforms the heuristic, achieving an average latency improvement of 13.54% in a relevant real-world use case, under varied system configurations. In addition, the results demonstrate the scalability of our method under synthetic workflows of varying sizes, attaining a 33.03% average latency decrease compared to the heuristic.

关键词： scheduling workflow cyber-physical system edge-cloud mixed integer linear programming optimization

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Job shop planning and scheduling of reconfigurable manufacturing systems

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OPERATIONS MANAGEMENT RESEARCH 2025年 1-22页

作者： Imsetif, Jad Nezamoddini, Nasim Aqlan, Faisal Oakland Univ Ind & Syst Engn 115 Lib Dr Rochester MI 48309 USA Univ Louisville Dept Ind Engn 132 Eastern Pkwy Louisville KY 40292 USA

The fourth industrial revolution (Industry 4.0) has enabled rapid product variations and technological developments such as reconfigurable manufacturing systems (RMSs). Planning and scheduling in RMS differ from traditional systems;therefore, the manufacturing industry has faced implementation barriers. This research proposes a new mixed-integer linear programming (MILP) formulation for process planning and scheduling in RMSs. The formulation, considers new aspects such as number of products, their quantity and complexity, calibration rate, work-in-process (WIP), and inventory management. Moreover, this research promotes RMS effectiveness for business and provides new insights on the effects of different factors on the overall performance. The applicability of the proposed formulation is illustrated with a case study. The results showed that RMS outperforms a traditional system with 30% savings in cost and up to a 25% increase in demand fulfillment. Sensitivity analyses are conducted to investigate the effect of different parameters on the cost-effectiveness of RMS compared to traditional manufacturing systems. Analysis of variance (ANOVA) highlighted the importance of the reconfigurability of the machines compared to other settings

关键词： Reconfigurable manufacturing systems mixed integer linear programming Optimization Sensitivity analyses Cost effectiveness

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Minimum-Cost Routing of two UAVs with Communication Constraints

Minimum-Cost Routing of two UAVs with Communication Constrai...

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AIAA SciTech Forum

作者： Moorthy, Karthik Kumar, Deepak Prakash Darbha, Swaroop Bihl, Trevor Rathinam, Sivakumar Texas A&M Univ Mech Engn 3123 TAMU College Stn TX 77843 USA Air Force Res Lab Sensing Management Branch Dayton OH 45433 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper proposes a formulation to determine the routes of two unmanned aerial vehicles (UAVs) traveling in lockstep fashion, maintaining within a specified proximity of one another, to cooperatively cover all targets in minimum time. The results of this study provide a framework for UAV mission planning or other applications where multiple UAVs are required to travel synchronously to visit a given set of targets while keeping within a distance of one another to maintain communication. A mixed integer linear Program (MILP) is formulated to determine this minimum cost tour for both UAVs. This MILP is computationally expensive to solve which makes it impractical to obtain solutions as the problem size scales up. Therefore, a local search heuristic was formulated to approximate the optimal solutions in a quicker manner. The quality of the heuristic was measured by comparing its solutions against those from a linear programming model (LP) serving as a lower bound to the optimal solution. To observe the relationship between the problem size and the heuristic solution quality, randomly generated instances of.. = 20 to.. = 100 targets were simulated in 32.. by 32.. unit grids, varying the proximity limit from 15-30 units. It was found that the heuristic consistently delivered higher-quality solutions when smaller UAV proximity value requirements were imposed. Specifically, the heuristic solutions deviated 23-35% from the LP solutions when the proximity limit imposed was 15 units.

关键词： Unmanned Aerial Vehicle mixed integer linear programming linear programming Python Robots Aerospace Engineering Heuristic Approach Military Operations Search Algorithm Greedy Algorithm

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An integrated production-distribution optimization model for multinational manufacturing corporations

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2024年第1期19卷 29-45页

作者： Bhutta, M. Khurrum S. Alhawari, Omar I. I. Mohamed, Zubair M. M. Ohio Univ Coll Business Athens OH USA Ohio Univ Ind & Syst Engn Dept Athens OH 45701 USA Bowling Green State Univ Schmidthorst Coll Business Bowling Green OH USA

In proposing an integrated production-distribution model for multinational corporations (MNC), the current research undertakes a novel, simultaneous consideration of facility location, capacity acquisition/disposal, labor, manufacturing, purchasing, exchange rates, tariffs, and container and/or individual shipping factors. Unlike prior studies that examined some such factors. Based on real-world data from a window manufacturing MNC, a mixed integer linear programming model is developed and solved using CPLEX Solver. It reveals how the different features affect the base solution. Specifically, increased demand pushes more manufacturing to US and UK facilities rather than (low-cost) Chinese facilities;it helps meets some of China's demand and leads to reduced total tariff costs. Decreased demand instead favors the Chinese facility, and the increase in tariff costs can be offset by savings in manufacturing and transportation costs. Changes to the factors have insignificant effects on the production and distribution mix, with little to no impact on total profits, because these costs are lower than capacity costs. The comprehensive proposed model provides firms with a framework for obtaining meaningful insights into how they can maintain their operations in sustainable, profitable ways. This research also should assist policymakers in making informed decisions regarding regulations on production and distribution strategies.

关键词： Multinational corporation optimization integrated modeling mixed integer linear programming container shipment supply chain network

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On the Algorithm of Cargoes Transportation Scheduling in the Transport Network

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AUTOMATION AND REMOTE CONTROL 2023年第9期84卷 993-1004页

作者： Ignatov, A. N. Moscow Inst Aviat Technol Moscow Russia

The problem of cargoes transportation scheduling in the transport network represented by an undirected multigraph is considered. Transportations between vertices are provided at predefined time intervals. The iterative algorithm to search for a solution approximate to the optimal one by criterion value is proposed in the problem under consideration. The algorithm is constructed on the base of solutions of mixed integer linear programming problems. The applicability of the algorithm is tested by the example with more than 90 million binary variables.

关键词： transport network multigraph cargoes transportation schedule mixed integer linear programming

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