A Parallel Approach to Optimize the Supply Chain Management

  • Otman Abdoun
  • Yassine MoumenEmail author
  • Ali Daanoun
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 913)


The worldwide economic progression in the last century and the Demographic growth has given rise to a huge consumption in the market of goods and services, while globalization decreased the cost of shipping and transportation. The production, transportation, storage and consumption of all these goods, however, have created big environmental problems. Nowadays, global warming, created by large-scale emissions of greenhouse gasses, is a top environmental concern. In this matter, the number of organizations planning to integrate the environmental practices into their future strategic plans is continuously increasing to counter this threat. The environmental benefits of the trend are clear: fewer vehicles burning fuel, crowding urban streets, and taking up valuable parking areas. However, the problem with transportation is that it can be so difficult to choose the perfect path for the vehicle to take if there is many stops to be taking in consideration. Due to the complexity of real world problems, such as supply chain management, finding a good path for vehicles with traditional ways (by using human capabilities) require a long time to satisfy all constraints. Even with machines, this particular problem needs a huge computational power (in term of processing power and memory usage) as well as time to solve. Actually, Parallelism is an approach that not only reduce the resolution time but also improve the quality of the provided solutions. The purpose of this paper is to evaluate the Travelling Salesman Problem (TSP) as a function of forming and optimizing transport networks using an efficient parallelization strategy for the Ant Colony Optimization (ACO) taking the maximum advantage of the parallel architecture offered by NVidia’s Graphics Processing Units (GPUs).


GPU Parallel Ant Colony Optimization Sequential Ant Colony Optimization Travelling Salesman Problem CUDA 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science, Polydisciplinary FacultyAbdelmalek Essaadi UniversityLaracheMorocco

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