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Mostafa Mohammadi

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Mostafa Mohammadi

Postdoctoral Fellow
Department of Industrial Economics and Technology Management

mostafa.mohammadi@ntnu.no
1351, Sentralbygg 1 Gløshaugen, Trondheim
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About Publications

About

Mostafa Mohammadi is a Postdoctoral Fellow at the Department of Industrial Economics and Technology Management at NTNU. He graduated with a Master’s degree and a Ph.D. from Sapienza University of Rome, both with cum laude honors. His doctoral thesis, titled Stochastic Programming for Optimizing Sustainable Urban Logistics, addresses various challenges in the dynamic environment of routing problems with stochastic information.

With a foundation in transportation systems engineering and industrial engineering, he specializes in quantitative modeling and analysis using a range of Operations Research techniques. He applies data-driven and stochastic dynamic optimization methods to develop decision-support solutions for complex challenges in inventory management, transportation, urban logistics, and strategic logistics planning. His research aims to enhance the resilience and efficiency of logistics systems, combining academic depth with practical applications to address both theoretical and industry needs.

Competencies

  • Operations Research
  • Production Planning
  • Sequential Decision Making
  • Supply Chain Management
  • Transportation Science
  • Urban Logistics
  • Vehicle Routing Problem

Publications

Heuristic approaches to address vehicle routing problem in the Iot-based waste management system

This study develops a two-echelon waste management system that optimizes waste collection using a combination of heuristics and metaheuristics which heuristics generate initial solutions to enhance metaheuristic efficiency.

An allocation-routing optimization model for integrated solid waste management

This study proposes a multi-objective ISWM model to optimize waste management under uncertainty, maximizing probabilistic profit and minimizing costs by chance-constrained programming and advanced metaheuristics to address stochastic factors.

A dynamic approach for the multi-compartment vehicle routing problem in waste management

This study develops a dynamic, IoT-enabled approach for multi-compartment vehicle routing. Discrete choice models is used to reoptimize routing by considering traffic, bin priorities, and waste thresholds.

Designing a multi-period dynamic electric vehicle production-routing problem in a supply chain considering energy consumption

This study proposes a multi-period dynamic production-routing model for electric vehicles, optimizing supply chain operations by considering variable travel speeds and energy consumption.

Industry 4.0 in waste management: An integrated IoT-based approach for facility location and green vehicle routing

This study introduces a green, multi-objective location-allocation model for integrated reverse logistic. Combining IoT-enabled bins and a split-pickup vehicle routing model, it minimizes costs, CO₂ emissions, and visual pollution.

Integrated location and routing for cold chain logistics networks with heterogeneous customer demand

A multi-objective model for cold chain logistics is developed to optimize facility location and vehicle routing to ensure quality in temperature-sensitive deliveries by considering mixed commodities and temperature constraints in transportation phase.

An integrated temporal and spatial synchronization for two-echelon vehicle routing problem in waste collection system

This study introduces a two-echelon vehicle routing problem (2E-VRP) for urban waste collection, incorporating spatial and temporal synchronization with meeting point temporal capacity constraints to minimize delays.
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