Simultaneous Scheduling of Pump and Ready Mixed Concrete Trucks Using Mathematical Programming

نوع: Type: Thesis

مقطع: Segment: masters

عنوان: Title: Simultaneous Scheduling of Pump and Ready Mixed Concrete Trucks Using Mathematical Programming

ارائه دهنده: Provider: Hosna-Sadat Mousavi-Nasab

اساتید راهنما: Supervisors: Dr. Mohsen Babaei

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Dr. Hosseinian, Dr. Taheri-Nezhad

زمان و تاریخ ارائه: Time and date of presentation: 2025

مکان ارائه: Place of presentation: 61

چکیده: Abstract: The increasing complexity and scale of modern construction projects have heightened the need for accurate and optimized planning systems in ready-mixed concrete (RMC) delivery and pumping operations. Timely and coordinated concrete delivery—subject to technical, temporal, and operational constraints—is essential for the success of casting activities. This study presents a comprehensive mathematical model for the simultaneous scheduling and routing of concrete mixer trucks and pumps. The model accounts for operational limitations, equipment capacities, cost structures, and quality requirements, enabling project managers to make informed decisions and optimize resource utilization. Building upon a baseline framework, the proposed model incorporates additional variables and constraints for concrete pumps and their interactions with truck operations. Formulated as a Mixed Integer Linear Programming (MILP) model, it was implemented in GAMS and solved using CPLEX. Real-world validation was conducted using data from urban construction projects associated with “Ekbatan Concrete and Construction Factory” in Hamedan, Iran. The model introduces several key innovations, including independent pump scheduling, sequencing of activities, prevention of cold joints, optimal resource allocation under restricted time windows, and simultaneous modeling of multiple interacting resources. Numerical experiments demonstrate that the model achieves a 23% reduction in pump requirements and a 13% reduction in truck requirements, without compromising construction quality. Across diverse scenarios and sensitivity analyses, the model exhibited structural stability and high adaptability in decision-making. For example, under varying cost coefficients or time restrictions, resource allocation patterns changed intelligently, leading to more balanced workloads. Despite the higher complexity of its formulation, the model remained computationally efficient, delivering solutions within practical runtimes (below 1000 seconds). From an operational perspective, the proposed model offers substantial benefits, including reduced resource consumption, enhanced coordination among equipment, lower waiting times, improved scheduling of multi-site projects, and greater customer satisfaction. Furthermore, extended analyses confirmed that the model is applicable not only to small-scale cases but also to large-scale urban projects, making it a reliable decision-support tool for project managers and concrete suppliers. The model also shows flexibility to adapt to changes in working hours, equipment capacities, or cost structures. Overall, this research effectively integrates mathematical optimization with real-world project data, operational constraints, and multi-resource interactions. It provides a robust scientific and practical tool that contributes to reducing transportation costs, improving resource productivity, enhancing project scheduling, and elevating service quality in the RMC industry. Future research directions include multi-plant environments, time-dependent travel, and direct measures of customer satisfaction, paving the way for more advanced models in construction management. Ultimately, this study highlights how properly integrated mathematical tools can bridge the gap between theoretical modeling and real-world complexity, providing effective operational decision support.

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