Jean-Paul Arnaout

Ant colony optimization algorithm to solve for the transportation problem of cross-docking network

This paper addresses the transportation problem of cross-docking network where the loads are transferred from origins (suppliers) to destinations (retailers) through cross-docking facilities, without storing them in a distribution center (DC). We work on minimizing the transportation cost in a network by loading trucks in the supplier locations and then route them either directly to the customers or indirectly to cross-docking facilities so the loads can be consolidated. For generating a truck operating plan in this type of distribution network, the problem was formulated using an integer programming (IP) model and solved using a novel ant colony optimization (ACO) algorithm. We solved several numerical examples for verification and demonstrative purposes and found that our proposed approach finds solutions that significantly reduce the shipping cost in the network of cross-docks and considerably outperform Branch-and-Bound algorithm especially for large problems.

ACO for the Surgical Cases Assignment Problem

This paper addresses the Surgical Case Assignment Problem with an objective of minimizing the total unexploited and operating cost. A two-stage ant colony optimization (ACO) algorithm is introduced and its performance is evaluated by comparing its solutions to the solutions of Branch and Bound and a global solver. The results show that ACO outperformed the other algorithms and reached better solutions in a faster computational time.

Ant colony optimization algorithm for the Euclidean location-allocation problem with unknown number of facilities

This paper addresses the Euclidean location-allocation problem with an unknown number of facilities, and an objective of minimizing the fixed and transportation costs. This is a NP-hard problem and in this paper, a three-stage ant colony optimization (ACO) algorithm is introduced and its performance is evaluated by comparing its solutions to the solutions of genetic algorithms (GA). The results show that ACO outperformed GA and reached better solutions in a faster computational time. Furthermore, ACO was tested on the relaxed version of the problem where the number of facilities is known, and compared to existing methods in the literature. The results again confirmed the superiority of the proposed algorithm.

Maximizing the utilization of operating rooms with stochastic times using simulation

This paper addresses a surgery rooms scheduling problem. The problem is modeled as a parallel machine scheduling problem with sequence dependent setup times and an objective of minimizing the makespan. This is a NP-hard problem and in this paper, a solution heuristic is developed and compared to existing ones using simulation. The results and analysis obtained from the computational experiments proved the superiority of the proposed algorithm LEPST over the other algorithms presented.

Ant colony optimization algorithm to parallel machine scheduling problem with setups

This paper addresses the non-preemptive unrelated parallel machine scheduling problem with machine-dependent and job sequence-dependent setup times. All jobs are available at time zero, all times are deterministic, and the objective is to minimize the makespan. This is a NP-hard problem and in this paper, a two-stage ant colony optimization (ACO) algorithm is introduced and its performance is evaluated by comparing its solutions to the solutions of Tabu Search and an existing heuristic for the same problem. The results show that ACO outperformed the other algorithms.

Exploring the effects of cooperative adaptive cruise control on highway traffic flow using microscopic traffic simulation

This paper examines the impact of having cooperative adaptive cruise control (CACC) embedded vehicles on traffic flow characteristics of a multilane highway system. The study identifies how CACC vehicles affect the dynamics of traffic flow on a complex network and reduce traffic congestion resulting from the acceleration/deceleration of the operating vehicles. An agent-based microscopic traffic simulation model (Flexible Agent-based Simulator of Traffic) is designed specifically to examine the impact of these intelligent vehicles on traffic flow. The flow rate of cars, the travel time spent, and other metrics indicating the evolution of traffic congestion throughout the lifecycle of the model are analyzed. Different CACC penetration levels are studied. The results indicate a better traffic flow performance and higher capacity in the case of CACC penetration compared to the scenario without CACC-embedded vehicles.

Metaheuristic for the Multiple Level Warehouse Layout Problem

In this paper, we introduce an ant colony optimization (ACO) algorithm for solving the NP-hard Multiple Level Warehouse Layout Problem (MLWLP). The problem description consists of a warehouse made up of several levels, each divided into a known number of cells, and different product types need to be allocated while respecting the capacity constraints. There is one I/O port located at different horizontal distances from each cell, and the objective is to minimize the total horizontal and vertical costs. The ACO comprises two stages, and its performance is evaluated by comparing its solutions to that of Branch and Bound (B&B) and Genetic Algorithm (GA). Experimental results show that ACO attains optimal solutions for small problems, and superior solutions to B&B and GA for larger problems.

Worm optimization for the multiple level warehouse layout problem

In this paper, the NP-complete multiple level warehouse layout problem is addressed. The problem consists of assigning items to cells and levels with the objective of minimizing transportation costs. A worm optimization algorithm (WO) is introduced, based on the foraging behaviors of Caenorhabditis elegans (Worms), and its performance was assessed by comparing with a genetic algorithm (GA), ant colony optimization (ACO), and an exact solution (B&B) for small problems. The computational results reflected the superiority of WO in large problems, with a marginally better performance than ACO and GA in smaller ones, while solving the tested problems within a reasonable computational time. Furthermore, WO was able to attain most of the known optimal solutions.

Worm Optimization for the Traveling Salesman Problem

In this research, a new metaheuristic called Worm Optimization (WO) is proposed, based on the foraging behaviors of Caenorhabditis elegans (Worms). In particular, the algorithm will mimic the behaviors of worms including finding food, avoiding toxins, interchanging between solitary and social foraging styles, alternating between food exploiting and seeking, and entering a stasis stage. WO effectiveness is illustrated on the traveling salesman problem (TSP), a known NP-hard problem, and compared to well-known naturally inspired algorithms using existing TSP data. The computational results reflected the superiority of WO in all tested problems. Furthermore, this superiority improved as problem sizes increased, and WO attained the global optimal solution in all tested problems within a reasonable computational time.

Bio-inspired algorithm for the two-machine scheduling problem with a single server

Within the arena of Swarm Intelligence, this research introduces a bio-inspired ant colony optimization (ACO) algorithm for solving the NP-hard Two-Machine Scheduling Problem with a Single Server. The problem consist of a given set of jobs to be scheduled on two identical parallel machines, where each job must be processed on one of the machines, and prior to processing, the job is set up on its machine using one server; the latter is shared between the two machines. ACO performance was compared to the exact solution (B&B), as well as Genetic Algorithm, and the computational results reflected the superiority of ACO in all tested problems. Furthermore, this superiority improved as problem sizes increased, while solving the tested problems within a reasonable computational time.