Integer programming formulations of discrete hub location problems

Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0

Flexible job shop scheduling problems &#x0028 FJSP &#x0029 have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence &#x0028 SI &#x0029 and evolutionary algorithms &#x0028 EA &#x0029 are developed, employed and improved for solving them. More than 60&#x0025 of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population and local search operators for improving convergence performance are summarized. Next, one classical hybrid genetic algorithm &#x0028 GA &#x0029 and one newest imperialist competitive algorithm &#x0028 ICA &#x0029 with variables neighborhood search &#x0028 VNS &#x0029 for solving FJSP are presented. Finally, we summarize, discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems

https://link.springer.com/content/pdf/10.1057%2Fjors.1994..pdf

Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method

An energy-efficient multi-objective optimization for flexible job-shop scheduling problem

The Dual-Ants Colony

Scheduling aircraft landing is a complex task encountered by most of the control towers. In this paper, we study the aircraft landing problem (ALP) in the multiple runway case. We present in the first part, a mathematical formulation of the problem with a linear and nonlinear objective function. In the second part, we consider the static case of the problem where all data are known in advance and we present a new heuristic for scheduling aircraft landing on a single runway, this heuristic is incorporated into an ant colony algorithm to solve the multiple runway case.

Improved Ant Colony Algorithm to Solve the Aircraft Landing Problem

Robust optimisation of the intermodal freight transport problem: Modeling and solving with an efficient hybrid approach

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

This study follows the new concept of the OnLine Electric Vehicle (OLEV) using the wireless charging technology as one of contactless electric power transfer technologies for Electric Vehicle (EV) charging. This electric transport system, which has been developed by Korea Advanced Institute of Science and Technology (KAIST), allows vehicles to charge their battery while in motion. In this paper, we used integer programming to find the minimum total investment cost that considers the OLEV system with a multiple route; afterwards, we introduced the resolution of this problem with the particle swarm optimization as the particles showed their robustness against nonlinear optimization problems.

Ahmed El Hilali Alaoui

Papers

The Dual-Ants Colony

Improved Ant Colony Algorithm to Solve the Aircraft Landing Problem

Robust optimisation of the intermodal freight transport problem: Modeling and solving with an efficient hybrid approach

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Optimal allocation of wireless power transfer system for electric vehicles in a multipath environment