The Moroccan TSP Competition

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The Moroccan TSP Competition Contact information, map and directions, contact form, opening hours, services, ratings, photos, videos and announcements from The Moroccan TSP Competition, Education Website, centre, Beni Mellal.

The Moroccan TSP Competition is an annual event in 2 categories:
1️⃣ TSP(s), in which participants aim to find the shortest path linking all destinations once, and returning to the start.
2️⃣ Real-life Logistics and transport applications in Morocco.

11/09/2026

Seismic disasters create massive, immediate humanitarian needs that conventional supply chains cannot meet due to the partial destruction of road infrastructure, the diversity of demands, and the multiplicity of aid depots. This chapter presents SeismoRoute, an intelligent post-earthquake logistics optimization system developed for the MTSPC26 competition. The core problem is formulated as a Multi-Depot Vehicle Routing Problem with a Heterogeneous Fleet and Time Windows (MDVRPTW-HF), augmented by a novel arc accessibility constraint ($R_{ij}$) that models the physical impact of seismic damage on the road network. The weighted bi-objective function simultaneously minimizes total logistics costs and the urgency penalty associated with delivery delays in priority disaster zones. Google's OR-Tools 9.12 solver is deployed within a nine-step algorithmic pipeline, combining the Clarke-Wright heuristic for initial route construction with the Guided Local Search metaheuristic for optimization. The SeismoRoute platform, built using a full-stack architecture (React, Node.js, FastAPI, MongoDB), covers 182 Moroccan localities distributed across 12 regions and 12 logistics depots. On a test instance from the Marrakech region (16 nodes, 4 vehicles), the solver achieves an OPTIMAL status in 28 seconds, with an objective score of $Z = 412.5$ and a total distance of 340 km. These results demonstrate the system's feasibility and effectiveness in facilitating rapid, precise, and traceable humanitarian aid during a seismic crisis.

This project was conducted by :
Ilyas GHACHIMI; Fouad BANAN
under the supervision of Mohamed El Alaoui

📍ENSA Beni Mellal
🗓 18 April 2026

To keep in touch
LinkedIn: https://www.linkedin.com/company/the-moroccan-tsp-competition
Instagram: https://www.instagram.com/the_moroccan_tsp_competition
Facebook: https://web.facebook.com/The.Moroccan.TSP.Competition/
YouTube: https://www.youtube.com/
Tiktok: https://www.tiktok.com/.moroccan.tsp
Website: https://sites.google.com/usms.ma/mtspc
email: [email protected]

04/09/2026

Communication plays a vital role in natural disaster management, particularly for alerting populations, coordinating relief efforts, and rapidly disseminating critical information. However, during major events such as floods, wildfires, or extreme weather waves, conventional communication infrastructure can fail, disrupting information exchange in vulnerable areas. This study proposes an optimization and planning approach for an emergency communication system tailored to the Béni Mellal-Khénifra region. The research utilizes Geographic Information Systems (GIS)—specifically QGIS—to simultaneously analyze mobile network coverage, natural hazards, and territorial vulnerability. A map of cellular towers was created using OpenCelliD data, identifying "dead zones" that account for 4.1% of the region's territory. A multi-hazard map was then developed, incorporating risks associated with floods, cold snaps, heatwaves, and wildfires. By overlaying these geographic layers, the study scored 40 localities and identified critical priority zones. Finally, several alternative communication solutions adapted to the geographic context and risk levels were proposed, including FM radio, MANET-enabled drones, and mobile GSM antennas. The results demonstrate the value of an integrated geospatial approach in enhancing communication resilience during crisis situations.

This project was conducted by :
Fatima Ezzahra IGHIL; Salma CHAHBI
under the supervision of Mohamed El Alaoui

📍ENSA Beni Mellal
🗓 18 April 2026

To keep in touch
LinkedIn: https://www.linkedin.com/company/the-moroccan-tsp-competition
Instagram: https://www.instagram.com/the_moroccan_tsp_competition
Facebook: https://web.facebook.com/The.Moroccan.TSP.Competition/
YouTube: https://www.youtube.com/
Tiktok: https://www.tiktok.com/.moroccan.tsp
Website: https://sites.google.com/usms.ma/mtspc
email: [email protected]

29/08/2026

This work is part of an initiative to improve urban mobility in Rabat in preparation for the 2030 World Cup. The primary objective is to model and optimize urban transport network routes by applying the Traveling Salesman Problem framework to the transport sector.
Using a real-world dataset from the bus network, we applied optimization methods to minimize travel distances, reduce travel times, and enhance user satisfaction.
An eco-friendly approach was also incorporated, taking into account the reduction of CO₂ emissions. Finally, multimodal connectivity scenarios were proposed to improve integration with other modes of transport.

This project was conducted by :
Intissar Ben Hammou; Mustafa AL HAMMOUDI and Ikram NEMAT
under the supervision of Hajar RAJI from EST Beni Mellal

📍ENSA Beni Mellal
🗓 18 April 2026

To keep in touch
LinkedIn: https://www.linkedin.com/company/the-moroccan-tsp-competition
Instagram: https://www.instagram.com/the_moroccan_tsp_competition
Facebook: https://web.facebook.com/The.Moroccan.TSP.Competition/
YouTube: https://www.youtube.com/
Tiktok: https://www.tiktok.com/.moroccan.tsp
Website: https://sites.google.com/usms.ma/mtspc
email: [email protected]

23/08/2026

Frequent snowfall in Moroccan mountainous areas severely disrupts traffic flow and increases accident risk for road users. The Beni Mellal-Khenifra region—characterized by challenging terrain and infrastructure vulnerable to adverse weather—relies on largely empirical logistical management for salting and snow-clearing operations. This study aims to replace manual methods with a decision-support platform based on operations research algorithms. The adopted approach models the road network as a directed graph, assigning multidimensional parameters to each segment: criticality (access to emergency services), accumulated snowfall, gradient, and sinuosity. The problem is treated as a variant of the Heterogeneous Fleet Chinese Postman Problem (k-CPP), incorporating four vehicle categories with distinct capabilities. A heuristic combining Dijkstra's algorithm (for minimum distances) with a load-balancing procedure was implemented within a comprehensive web application (using Next.js, PostgreSQL, and Leaflet). Experimental results demonstrate a significant reduction in distances traveled and effective prioritization of strategic routes, all while adhering to driving-time constraints. In conclusion, the proposed solution offers real-time operational support—adaptable to other winter contexts—and highlights the value of quantitative methods in managing critical infrastructure.

This project was conducted by :
Asmaa BARKOK; Nawal EL AOURFE and Majda EL-ABBADY
under the supervision of Mohamed El Alaoui

📍ENSA Beni Mellal
🗓 18 April 2026

To keep in touch
LinkedIn: https://www.linkedin.com/company/the-moroccan-tsp-competition
Instagram: https://www.instagram.com/the_moroccan_tsp_competition
Facebook: https://web.facebook.com/The.Moroccan.TSP.Competition/
YouTube: https://www.youtube.com/
Tiktok: https://www.tiktok.com/.moroccan.tsp
Website: https://sites.google.com/usms.ma/mtspc
email: [email protected]

14/08/2026

This work presents a study aimed at determining the nutritional and water requirements of populations living in disaster-stricken villages to support the planning of emergency food interventions. Following a review of the fundamental principles of human nutrition—specifically energy requirements and recommended intakes of macronutrients, micronutrients, and water—a methodology for estimating these needs was developed.
The study draws on demographic data from the 2024 General Census of Population and Housing. Population figures were broken down by s*x and key age groups to estimate the average energy requirements for each population category. Based on these requirements, the distribution of energy intake among carbohydrates, proteins, and fats was determined in accordance with nutritional guidelines.
Minimum water requirements were also assessed, taking into account reference standards applicable to emergency situations. Finally, examples of daily food rations and proposals for varied menus were developed to meet estimated nutritional needs while accounting for the logistical constraints inherent in a humanitarian context.
This approach provides a technical and methodological basis for assessing food needs and preparing assistance programs for populations affected by disasters.

This project was conducted by: Ayoub SIMOURI under the supervision of Salwa TSOULI SARHIR

📍ENSA Beni Mellal
🗓 18 April 2026

To keep in touch
LinkedIn: https://www.linkedin.com/company/the-moroccan-tsp-competition
Instagram: https://www.instagram.com/the_moroccan_tsp_competition
Facebook: https://web.facebook.com/The.Moroccan.TSP.Competition/
YouTube: https://www.youtube.com/
Tiktok: https://www.tiktok.com/.moroccan.tsp
Website: https://sites.google.com/usms.ma/mtspc
email: [email protected]

10/08/2026

This project focuses on optimizing the energy efficiency of the electrical grid at the ENSA Béni Mellal campus. An initial assessment revealed an inefficient radial architecture causing significant Joule losses due to the cable length (1,107 m).
By applying the Traveling Salesperson Problem (TSP) model, we optimized the circuit layout connecting all buildings, reducing the total length to 924 m.
This approach significantly reduces electrical resistance and Joule losses while improving the overall efficiency of the distribution network.

This project was conducted by: Houssame ANTER; Salma Hadaji and Nouhaila SALIHI under the supervision of Oussama ACHKARI BEGDOURI

📍ENSA Beni Mellal
🗓 18 April 2026

To keep in touch
LinkedIn: https://www.linkedin.com/company/the-moroccan-tsp-competition
Instagram: https://www.instagram.com/the_moroccan_tsp_competition
Facebook: https://web.facebook.com/The.Moroccan.TSP.Competition/
YouTube: https://www.youtube.com/
Tiktok: https://www.tiktok.com/.moroccan.tsp
Website: https://sites.google.com/usms.ma/mtspc
email: [email protected]

04/08/2026

Waste management poses a major challenge for rapidly growing cities like Béni Mellal. The issue is that collection routes are often planned rigidly, without accounting for the shortest distances or real-world conditions, resulting in inefficient and costly waste collection. This problem relates to optimization models such as the Traveling Salesman Problem and the Vehicle Routing Problem. Solving this issue is crucial, given its economic, environmental, and organizational impact. Our objective is therefore to optimize collection routes—reducing travel distance and transport costs while improving response times—by employing machine learning techniques, heuristic algorithms, and combinatorial operations research.

This project was conducted by :
Youssef LAASSEL and Rihab HAZIM
under the supervision of Hajar RAJI from EST Beni Mellal

📍ENSA Beni Mellal
🗓 18 April 2026

To keep in touch
LinkedIn: https://www.linkedin.com/company/the-moroccan-tsp-competition
Instagram: https://www.instagram.com/the_moroccan_tsp_competition
Facebook: https://web.facebook.com/The.Moroccan.TSP.Competition/
YouTube: https://www.youtube.com/
Tiktok: https://www.tiktok.com/.moroccan.tsp
Website: https://sites.google.com/usms.ma/mtspc
email: [email protected]

28/07/2026

Recurrent natural disasters in Morocco — most notably the Al Haouz earthquake (September 8, 2023, magnitude 6.8, 2,960 casualties, more than 300,000 people left homeless) and the 2024 floods in the Béni Mellal-Khénifra region — expose three persistent weaknesses of humanitarian distribution operations: information fragmentation, the lack of scientifically grounded prioritization of stricken areas, and fragile communication in isolated territories. This work presents NAJDA (Arabic for “relief”), an integrated, securityhardened decision-support platform for humanitarian logistics. Its core is a three-stage decision pipeline that couples (1) the Analytic Hierarchy Process, which elicits consistent criteria weights (consistency ratio CR = 0.043 inferior to 0.10); (2) the TOPSIS method, which ranks affected villages (douars) by closeness to the ideal solution; and (3) a multi-depot capacitated Vehicle Routing Problem solver built on Google OR-Tools with Guided Local Search, driven by a tri-criteria objective. The platform combines a NestJS backend with an immutable PostgreSQL audit trail, a React dashboard with Leaflet mapping, and an offline-first mobile application secured by biometric authentication and Keycloak single sign-on. Experiments on a pilot instance of six real douars — drawn from the platform’s full regional repository of 2,485 census villages — validate the pipeline end to end: the TOPSIS ranking departs sharply from the naive population-based heuristic (Spearman ρs = −0.49), a ±20 % sensitivity analysis shows that the set of four priority douars is invariant — a finding corroborated by a cross-method comparison with SAW and VIKOR —, Guided Local Search reduces routing cost by 16.5 % over a greedy baseline (268 km versus 312 km), and the full decision cycle completes in under four minutes. The work further positions the AHP–TOPSIS–VRP pipeline against the main multi-criteria and metaheuristic alternatives and defines a statistical validation protocol for regional-scale deployment.

This project was conducted by :
Youssef AIT KARROUM and Azddine EL HAMDAOUI
under the supervision of Mohammed ENNAHBAOUI

📍ENSA Beni Mellal
🗓 18 April 2026

To keep in touch
LinkedIn: https://www.linkedin.com/company/the-moroccan-tsp-competition
Instagram: https://www.instagram.com/the_moroccan_tsp_competition
Facebook: https://web.facebook.com/The.Moroccan.TSP.Competition/
YouTube: https://www.youtube.com/
Tiktok: https://www.tiktok.com/.moroccan.tsp
Website: https://sites.google.com/usms.ma/mtspc
email: [email protected]

24/07/2026

Faced with intensifying heatwaves in the Béni Mellal-Khénifra region, local authorities lacked the tools to anticipate climate risks or organize rapid logistical responses for the most vulnerable populations. This study presents BeniMeteo, a comprehensive decision-support architecture that transforms weather forecasts into operational response plans. The approach rests on four pillars: forecasting future temperatures using a hybrid SARIMA-XGBoost model trained on twelve years of historical data; calculating a territorial risk score (Ri) that integrates heat hazard, demographic exposure, social vulnerability, and medical isolation for each *douar* (rural settlement); automatically generating four-level alerts based on hybrid seasonal thresholds calibrated against official climate norms; and optimizing logistics via the HRW-LRP model, which plans the location of response centers and mobile team routes by prioritizing critical areas. Results show forecast accuracy reaching an R² of 0.98 for short-term horizons, a 100% validation rate for alert thresholds in test cases, and the identification of 78 *douars* at a critical risk level during a heatwave simulation. BeniMeteo demonstrates that it is possible—using open data and free tools—to build a rigorous, geographically targeted, and immediately operational decision-support system to protect vulnerable populations from climate risks.

This project was conducted by :
Loubna ECH-CHOKHMANY; Ikram EL-MAZINI and N’Guémawen N’DIJINILA
under the supervision of Mohamed El Alaoui

📍ENSA Beni Mellal
🗓 18 April 2026

To keep in touch
LinkedIn: https://www.linkedin.com/company/the-moroccan-tsp-competition
Instagram: https://www.instagram.com/the_moroccan_tsp_competition
Facebook: https://web.facebook.com/The.Moroccan.TSP.Competition/
YouTube: https://www.youtube.com/
Tiktok: https://www.tiktok.com/.moroccan.tsp
Website: https://sites.google.com/usms.ma/mtspc
email: [email protected]

16/07/2026

In Morocco, and particularly in the province of Béni Mellal, forest fires pose a serious threat to natural heritage. Response operations are complex because resources—such as fire trucks and Canadair aircraft—are costly and limited, and response time is critical. To address this emergency, this study presents a Decision Support System (DSS) developed using QGIS and Python.

Our methodological approach begins with the collection, structuring, and cleaning of real-world geospatial data, including road networks, fire stations, strategic water sources, and forest areas. At the system's core, a mathematical model acts as an "intelligent manager," evaluating a weighted objective function that combines response time and total cost. Leveraging actual travel speeds across road segments, Dijkstra's algorithm is applied to calculate the fastest routes—prioritizing time efficiency over mere distance. The system also manages logistical constraints: fire trucks deploy fully loaded for immediate action, while Canadair aircraft refuel at the nearest water source.

Finally, the numerical simulation within QGIS automates resource deployment. For each fire, based on the area burned at the time of reporting and the fire's rate of spread, the system generates a single, optimal solution, specifying exactly which vehicles to dispatch and the estimated response time.

This tool drastically reduces response times and transforms risk management through an anticipatory, optimized approach.

This project was conducted by :
Fatima Ezzahra SIMOUR and Najat RAFIKI
under the supervision of Mohamed El Alaoui

📍ENSA Beni Mellal
🗓 18 April 2026

To keep in touch
LinkedIn: https://www.linkedin.com/company/the-moroccan-tsp-competition
Instagram: https://www.instagram.com/the_moroccan_tsp_competition
Facebook: https://web.facebook.com/The.Moroccan.TSP.Competition/
YouTube: https://www.youtube.com/
Tiktok: https://www.tiktok.com/.moroccan.tsp
Website: https://sites.google.com/usms.ma/mtspc
email: [email protected]

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