The optimization of waste collection in Béni Mellal aims to improve the efficiency of the municipal service by reducing the distances traveled by trucks, minimizing collection time, and optimizing resource usage. The rapid population growth and urbanization have led to a constant increase in waste, making its management more complex. This issue is further exacerbated by constraints such as traffic congestion, limited truck capacities, and the variability of waste volumes across neighborhoods and seasons.

To achieve this goal, several factors are considered: city mapping, the location of collection points, waste volumes produced, traffic schedules, and truck capacity. The integration of GPS data and the analysis of existing routes helps identify inefficiencies and adjust truck paths accordingly.

A crucial aspect of this optimization concerns periods of high waste production, notably Aïd Al-Adha and Ramadan, when increased consumption leads to a significant rise in waste. Adjusting the routes based on these variations is essential to ensure effective service and avoid overflow.

This approach helps reduce operational costs, improves citizen satisfaction by ensuring faster and more regular collections, limits environmental impact by reducing unnecessary trips, and ensures better management of human and material resources. Therefore, optimized waste collection planning contributes to making the city cleaner, more sustainable, and more pleasant to live in.

This project focuses on optimizing the cleaning routes at ENSA BM to ensure efficient coverage of all necessary areas while minimizing travel time and respecting operational constraints.

Problem Definition

The cleaning process is modeled using the Traveling Salesman Problem (TSP), where each building or cleaning area (classrooms, bathrooms, corridors) is considered a node. Travel times between these points serve as edge weights, and the goal is to determine the shortest route that ensures timely cleaning.

Optimization Approach

1️⃣ Identification of Cleaning Points

• Each cleaning area is treated as a point in the optimization problem.

• Cleaning schedules are aligned with operational needs, ensuring certain areas are cleaned at specific times (e.g., early morning, between classes).

2️⃣ Integration of Class Schedules and Occupancy Data

• Cleaning activities must not disrupt ongoing lectures or events.

• The scheduling system takes into account course timetables to optimize cleaning windows.

3️⃣ Mathematical Model Using TSP

• Buildings and cleaning areas are mapped as nodes in a weighted graph.

• Edge weights represent travel times between locations.

• The objective is to find an optimal path that minimizes travel time while ensuring cleaning happens at suitable times.

4️⃣ Operational Constraints

• A limited number of cleaning teams, each with a maximum workload.

• Some areas (e.g., bathrooms, kitchens) require more time to clean.

• Multiple teams can clean in parallel to improve efficiency.

By applying TSP and integrating occupancy constraints, this project aims to enhance cleaning efficiency at ENSA BM, reducing unnecessary movement and ensuring a cleaner environment with minimal disruption to academic activities.

This project aims to optimize the costs and time of road line marking in the province of Béni Mellal. By improving planning, resource management, and adopting innovative techniques, we seek to reduce expenses and enhance the efficiency of road marking operations.

Public lighting plays a crucial role in the safety, livability, and attractiveness of a city. In Salé, as in many other cities, the efficient management of the public lighting network is a major challenge, both operationally and economically. The proper functioning of streetlights and other equipment requires regular and well-planned maintenance to ensure quality service while controlling energy and maintenance costs.

The main objective of this project is to optimize the maintenance routes of the teams responsible for public lighting in Salé, taking into account specific logistical, human, and financial constraints. To achieve this, we have chosen to model the problem as an adapted version of the Vehicle Routing Problem (VRP), a widely used approach in managing distribution and intervention routes. This problem consists of finding optimal routes for a fleet of vehicles while respecting several constraints, such as vehicle capacity, intervention duration, and geographical priorities.

Optimizing maintenance routes aims to reduce the costs associated with vehicle travel, minimize intervention delays, while ensuring that sensitive areas, such as schools or hospitals, receive priority attention in case of a breakdown. This work is part of a continuous improvement process of public services through the use of advanced technologies and mathematical optimization tools.

In this context, the project focuses on collecting and analyzing data related to lighting equipment, modeling an adapted optimization problem, and implementing effective algorithms to solve this problem. By applying heuristic methods and modern tools such as Google OR-Tools, we aim to propose a robust and concrete solution capable of addressing the specific challenges faced by public lighting management in Salé.

This study also allows for reflection on the impact of route optimization on reducing operational costs, while ensuring timely and efficient interventions, in line with local priorities and environmental constraints.

Access to clean drinking water remains a significant challenge for many rural communities in the Beni Mellal-Khénifra region. Due to prolonged droughts, climate change, and limited infrastructure, isolated douars struggle to secure a reliable water supply. Addressing this issue is crucial to improving the quality of life and ensuring the well-being of these vulnerable populations.

Our project focuses on optimizing water distribution using water tankers, ensuring that underserved communities receive a fair and systematic supply. The strategy involves establishing communes as central distribution points, where water will be stored before being delivered to surrounding douars. Each commune will act as a starting point, creating several sub-problems of the Traveling Salesman Problem (TSP) for efficient route planning. The distribution will be based on key factors such as population size, distance from the regional water source, and the urgency of need.

A critical aspect of our approach is the implementation of optimized routing techniques to organize delivery paths efficiently. By dividing the delivery process into multiple TSPs starting from each commune, we minimize transportation costs and reduce delays, ensuring that water reaches the most affected areas in a timely manner. This structured and data-driven model not only guarantees a steady supply but also enhances the sustainability of the distribution system.

This project is designed to be a long-term solution that ensures continued access to water for rural communities, offering a sustainable approach to water distribution that can be scaled and adapted to other regions in need. Beyond immediate relief, this initiative lays the foundation for long-term water security in the region. By addressing logistical challenges and prioritizing areas in greatest need, the project contributes to resilience against climate change and strengthens water accessibility in rural Morocco. Ultimately, this effort will improve living conditions, promote sustainability, and serve as a scalable model for other regions facing similar challenges.

This project uses effective logistics methods to optimize the water connection in Douar Adouz, located in the Beni Mellal region. The primary objective is to streamline water supply operations by reducing costs, improving distribution times, and ensuring a reliable and continuous service. The project includes analyzing transportation flows, planning routes, and optimizing resource utilization to minimize water losses and maximize operational efficiency. This integrated approach seeks to provide sustainable access to drinking water while enhancing logistical processes.

This project aims to develop an intelligent ambulance assistant that optimizes emergency response during large-scale events like the World Cup in Casablanca. By leveraging algorithms inspired by the Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP), the system minimizes travel distances and response times while considering critical constraints such as patient urgency levels, maximum tolerated intervention delays, and limited ambulance availability. Through mathematical modeling, advanced optimization techniques, and real-time route visualization, the project provides a smart, technology driven solution to support medical coordination and save lives more efficiently.

The optimization of drug distribution routes is crucial to improving the efficiency and reliability of pharmaceutical supply chains. This project focuses on enhancing the delivery process for pharmacies in Beni Mellal by minimizing delivery times and optimizing transportation routes.

Using advanced algorithms based on the Traveling Salesman Problem (TSP), we aim to find the most efficient delivery paths. The project involves collecting real-world data on pharmacy locations, delivery schedules, and traffic conditions. With these inputs, we develop and test optimized routes that reduce both travel distance and delivery time.

The proposed solution utilizes mathematical modeling and optimization techniques to identify the best delivery sequences. Implementing these optimized routes can significantly improve delivery efficiency, ensuring that pharmacies receive essential medications quickly and reliably.

This study has practical applications for pharmaceutical distributors, allowing them to reduce operational costs while improving service quality. By optimizing delivery routes, we contribute to better healthcare access and faster response times for patients in the Beni Mellal region.

This project aims to manage a fleet of vehicles using the Capacity Constrained Vehicle Routing Problem (CVRP), route optimisation can reduce operational costs, reduce environmental impact and improve the customer experience.

Faced with the challenges posed by the increasing complexity of delivery networks, it is essential to adopt innovative approaches to improve distribution channels.

However, our problem will be to minimise the distances covered by a fleet's rounds, while respecting vehicle capacity constraints. This will be solved using the scenario method, in which we have worked on the case of a cement plant fleet, using Google My Maps to visualise the routes and analyse the difference between the ordinary scenario and the optimised scenario.

As a result, optimising routes using the scenario method results in a tangible reduction in transport costs, a reduction in the number of vehicles mobilised and better adaptation to the capacity constraint.

The application of this model to the cement plant's fleet has resulted in a significant improvement in operational efficiency and environmental impact.

CRITICAL SITUATION ANALYSIS :

The ENSA Beni Mellal faces severe infrastructure limitations threatening academic excellence:

• Bandwidth Bottleneck: Limited 1Gbps connection shared across multiple institutions creates congestion

• Shared Network Environment: Single network infrastructure serves both "Campus Connect" student access and critical administrative operations, creating security concerns and performance conflicts

• Exponential User Growth: Annual influx of 250-300 new students (35% growth) without corresponding infrastructure expansion

• Modern Device Proliferation: Students now connecting with multiple high-bandwidth devices (laptops, smartphones, tablets) overwhelms existing capacity

• University Learning Platform Limitations: Campus-wide online learning platform experiences frequent timeouts and poor video streaming quality

• Research Capabilities Hampered: Faculty and students cannot efficiently access or share large datasets, limiting research collaboration and publication output

• Linear Connection Chain: Single-path design where failure at any point disrupts all downstream services

• Outdated Equipment: Current hardware operating at maximum capacity with no room for growth

• Insufficient Wireless Coverage: Only 3 uncoordinated home-grade access points for 740+ users

• No Backup Connections: Complete network outages when primary connection experiences problems

TRANSFORMATION OBJECTIVES

Our project delivers a comprehensive network transformation to:

1. Create a Reliable Network comparable to modern enterprise standards

2. Increase Connection Speed to meet growing demands of online education

3. Provide Complete Wireless Coverage throughout all campus buildings

4. Support Advanced Research Activities with stable, high-speed connectivity

5. Separate Student and Administrative Traffic for improved security and performance

6. Build a Scalable Foundation for future growth and technologies

OPTIMIZED INFRASTRUCTURE SOLUTION

Our logistics-inspired design includes:

• Hub-and-Spoke Architecture: Central distribution point with direct connections to all buildings, similar to efficient transport hubs that minimize travel distances

• Redundant Connection Paths: Multiple fiber routes between critical locations, creating alternate pathways like detour routes in transportation networks

• Strategic Equipment Placement: Positioning of network devices at optimal locations to maximize coverage while minimizing cable runs and installation costs

• Network Segmentation: Separate virtual networks for administrative, student, and research traffic with appropriate security controls

• High-Density Access Points: Enterprise-grade wireless coverage designed to support 3+ devices per user

• Unified Security System: Integrated network cameras with central monitoring station and automatic alert system

• Smart Building Management: Network-connected environmental controls and access systems for improved campus operations

PRACTICAL DESIGN METHODOLOGY

Our approach uses straightforward planning tools familiar in logistics:

• Traffic Flow Analysis: Measuring actual data usage patterns to properly size network pathways

• Coverage Mapping: Building-by-building assessment to ensure proper wireless signal throughout campus

• Capacity Planning: Calculating required bandwidth based on actual user needs and applications

• Growth Forecasting: Planning for 5-year enrollment increases and evolving technology requirements

• Cost-Benefit Ratio: Balancing equipment quality against budget constraints for maximum value

• Implementation Scheduling: Phased deployment plan to minimize disruption to academic activities

TRANSFORMATIVE OUTCOMES

• Implementation will deliver immediate and measurable benefits in:

Network Availability: from 97.1% to 99.99%

Peak Throughput: from 850 Mbps to 8.5 Gbps

Wireless Coverage: from 15% to 100%

Average Latency: from 87ms to 12ms

User Satisfaction: from 42% to 95% +53%

• This transformational initiative positions ENSA Béni Mellal at the technological forefront of academic institutions while establishing the foundation for next-generation research and educational excellence.

The ENSABM Water Tower Project was conceived as a strategic initiative to ensure a sustainable and autonomous potable water supply for the ENSA Beni Mellal campus. This project responds to the increasing needs of the institution and its community, which faces frequent water cuts and unreliable access to drinking water, particularly during periods of peak demand.

Water Reservoir Design: The central structure is a cylindrical elevated water tower with a usable volume of 170 m3, built with reinforced concrete and Ø20 steel reinforcement for enhanced durability. The tank itself measures 8 meters in height and 5 meters in diameter, while the total height of the tower reaches 40 meters, ensuring adequate gravitational pressure for distribution.

Hydraulic Network Optimization: The distribution network spans 1,500 meters of PEHD pipes (DN40 and DN50). This layout was mathematically optimized using TSP (Travelling Salesman Problem) algorithms to minimize pipe length and pressure losses while maximizing efficiency in layout. The system delivers water at a gravitational pressure between 3.2 and 4 bars, enough to serve all connected infrastructures.

Water Demand and Capacity: Based on a population of 1,129 individuals, including students, administrative, and teaching staff, and an estimated daily consumption of 20 liters per person, along with an additional 44 m3 allocated for green space irrigation, the total daily water demand amounts to approximately 70,025 liters. By incorporating a 20% safety margin, the system is designed to provide two full days of water autonomy, thereby ensuring reliable supply during emergencies and uninterrupted campus operations.

Pump System: A submersible pump has been integrated to rapidly fill the reservoir. With a flow rate of 80 m3/h, operating pressure of 3.5 to 4 bars, and a power range of 10–20 kW, the pump can fill the reservoir in just 2 hours, ensuring readiness even after full depletion.

Greywater Recycling System: A complementary greywater recycling module significantly reduces dependency on potable water, cutting consumption by up to 70%.

Recycled water is reused for toilets, irrigation, and hot water storage tanks on the rooftop. This feature aligns with environmental goals and lowers utility expenses.

Maintenance Plan: The structure is designed for a service life of 50 years with a yearly maintenance cost of 2,000 MAD. A detailed schedule includes monthly inspections, quarterly valve testing, semi-annual internal assessments, and annual disinfection and leak verification.

Budget and Execution: The total implementation cost was estimated at 500,000 MAD, offering a cost-effective and scalable model for similar institutions.

Future Digital Integration: The project opens the path for smart monitoring tools, IoT integration, and real-time water analytics using digital dashboards. Future upgrades could include SCADA systems, automated leak detection, and predictive maintenance algorithms, forming a complete smart water infrastructure and supporting the digital transformation vision of the institution.

The ENSABM Water Tower Project is not merely an infrastructure upgrade but a strategic investment in water resilience, sustainability, and digital readiness. By combining robust engineering with advanced optimization techniques, the project ensures reliable water supply during periods of scarcity, supports environmental stewardship through greywater recycling, and establishes a foundation for future digital enhancements. Its scalable and cost-effective nature makes it a model for replication across similar institutions in Morocco and beyond. As ENSABM continues its digital transformation, this project stands as a flagship initiative linking physical infrastructure with smart technology to serveboth current needs and future ambitions.