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Article
Publication date: 6 March 2009

Elias K. Xidias, Andreas C. Nearchou and Nikos A. Aspragathos

The purpose of this paper is to develop an efficient method for solving a vehicle scheduling problem (VSP) in 2D industrial environments. An autonomous vehicle is requested to…

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Abstract

Purpose

The purpose of this paper is to develop an efficient method for solving a vehicle scheduling problem (VSP) in 2D industrial environments. An autonomous vehicle is requested to serve a set of work centers in the shop floor providing transport and delivery tasks while avoiding collisions with obstacles during its travel. The objective is to find a minimum in length, collision‐free vehicle routing schedule that serves timely as many as possible work centers in the shop floor.

Design/methodology/approach

First, the vehicle's environment is mapped into a 2D B‐Spline surface embedded in 3D Euclidean space using a robust geometric model. Then, a modified genetic algorithm is applied on the generated surface to search for an optimum legal route schedule that satisfies the requirements of the vehicle's mission.

Findings

Simulation experiments show that the method is robust enough and can determine in a reasonable computation time a solution to VSP under consideration.

Originality/value

There is a gap in the literature for methods that face VSP in shop‐floor environments. This paper contributes to filling this gap by implementing a practical method that can be easily programmed and included in a modern service delivery system.

Details

Industrial Robot: An International Journal, vol. 36 no. 2
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 12 May 2023

İnci Sarıçiçek, Ahmet Yazıcı and Özge Aslan

This study aims to propose a novel method for the conflict detection and eradication of autonomous vehicles which has predetermined routes to establish multi pickup and delivery…

179

Abstract

Purpose

This study aims to propose a novel method for the conflict detection and eradication of autonomous vehicles which has predetermined routes to establish multi pickup and delivery tasks according to task priorities and vehicle capacity status on each pickup and delivery nodes in assembly cells in the automotive production.

Design/methodology/approach

In the designed system, the routing of autonomous vehicles (AVs) and scheduling of pickup and delivery tasks are established in production logistics. Gantt chart is created according to vehicle routes, and conflicts are detected using the proposed conflict-sweep algorithm. The proposed conflict-solving algorithm eliminates conflicts on intersections and roads by considering vehicle routes and task priorities.

Findings

In many production systems, there is a need to obtain flexible routes in each pickup delivery task group that changes during day, week, etc. Proposed system provides remarkable advantages in obtaining conflict-free routes for pre-scheduled multi transport tasks of vehicles by considering efficiency in production systems.

Originality/value

A novel method is proposed for the conflict detection and eradication of AVs. Proposed system eliminates conflicts on intersections and roads by considering pre-planned vehicle routes for a fleet of heterogeneous AVs. Unlike most of the other conflict-free algorithms, in which conflicts are solved between two points, proposed system also considers multi pickup and delivery points for AVs. This is pioneering paper that addresses conflict-free route planning with backhauls and scheduling of multi pickup and delivery tasks for AVs.

Details

Robotic Intelligence and Automation, vol. 43 no. 2
Type: Research Article
ISSN: 2754-6969

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Article
Publication date: 25 June 2024

Elias Xidias and Paraskevi Zacharia

A fleet of mobile robots has been effectively used in various application domains such as industrial plant inspection. This paper proposes a solution to the combined problem of…

50

Abstract

Purpose

A fleet of mobile robots has been effectively used in various application domains such as industrial plant inspection. This paper proposes a solution to the combined problem of task allocation and motion planning problem for a fleet of mobile robots which are requested to operate in an intelligent industry. More specifically, the robots are requested to serve a set of inspection points within given service time windows. In comparison with the conventional time windows, our problem considers fuzzy time windows to express the decision maker’s satisfaction for visiting an inspection point.

Design/methodology/approach

The paper develops a unified approach to the combined problem of task allocation and motion planning for a fleet of mobile robots with three objectives: (a) minimizing the total travel cost considering all robots and tasks, (b) balancing fairly the workloads among robots and (c) maximizing the satisfaction grade of the decision maker for receiving the services. The optimization problem is solved by using a novel combination of a Genetic Algorithm with pareto solutions and fuzzy set theory.

Findings

The computational results illustrate the efficiency and effectiveness of the proposed approach. The experimental analysis leverages the potential for using fuzzy time windows to reflect real situations and respond to demanding situations.

Originality/value

This paper provides trade-off solutions to a realistic combinatorial multi-objective optimization problem considering concurrently the motion and path planning problem for a fleet of mobile robots with fuzzy time windows.

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