Vahit Kaplanoğlu

A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems

In real manufacturing environments, the control of system elements such as automated guided vehicles has some difficulties when planning operations dynamically. Multi agent-based systems, a newly maturing area of distributed artificial intelligence, provide some effective mechanisms for the management of such dynamic operations in manufacturing environments. This paper proposes a multi-agent based scheduling approach for automated guided vehicles and machines within a manufacturing system. The proposed multi-agent based approach works under a real-time environment and generates feasible schedules using negotiation/bidding mechanisms between agents. This approach is tested on off-line scheduling problems from the literature. The results show that our approach is capable of generating good schedules in real time comparable with the optimization algorithms and the frequently used dispatching rules.

An object-oriented approach for multi-objective flexible job-shop scheduling problem

Object-oriented technology contributes to building manufacturing control systems.Machine and operation associations are designed as software objects.Operation routing is represented by a class.Simulated annealing algorithm is used in the problem solution. Flexible manufacturing systems are very complex to control and it is difficult to generate controlling systems for this problem domain. Flexible job-shop scheduling problem (FJSP) is one of the instances in this domain. It is a problem which inherits the job-shop scheduling problem (JSP) characteristics. FJSP has additional routing sub-problem in addition to JSP. In routing sub-problem each operation is assigned to a machine out of a set of capable machines. In scheduling sub-problem the sequence of assigned operations is obtained while optimizing the objective function(s). In this paper an object-oriented (OO) approach is presented for multi-objective FJSP along with simulated annealing optimization algorithm. Solution approaches in the literature generally use two-string encoding scheme to represent this problem. However, OO analysis, design and programming methodology help to present this problem on a single encoding scheme effectively which result in a practical integration of the problem solution to manufacturing control systems where OO paradigm is frequently used. OO design of FJSP is achieved by using UML class diagram and this design reduces the problem encoding to a single data structure where operation object of FJSP could hold its data about alternative machines in its own data structure hierarchically. Many-to-many associations between operations and machines are transformed into two one-to-many associations by inserting a new class between them. Minimization of the following three objective functions are considered in this paper: maximum completion time, workload of the most loaded machine and total workload of all machines. Some benchmark sets are run in order to show the effectiveness of the proposed approach. It is proved that using OO approach for multi-objective FJSP contributes to not only building effective manufacturing control systems but also achieving effective solutions.

A multi-agent approach to load consolidation in transportation

The operational decisions of todays logistics sector have many difficulties such as dynamism of the order attributes and environmental changes. This sector is generally operating under unpredictable environments and having high degree of complex interactions. The dispatch officers especially have difficulties while making load/capacity/route planning and load consolidation decisions. They use numerous alternative consolidation techniques in order to be cost effective during their decisions such as time based consolidation or quantity based consolidation or both of them simultaneously. In this paper we propose a multi-agent based load consolidation decision making approach. In the proposed approach the load consolidation decisions for the less-than-truckload orders are made by the software agents. The less-than-truckload orders are assigned/consolidated to the trucks by the negotiation mechanism constructed within the model.

An application oriented multi-agent based approach to dynamic load/truck planning

A novel multi-agent system for dynamic load and truck planning.A realistic simulation with real data from a major transportation company.A practical negotiation based model for real life implementations.Detailed explanations and numerical examples are provided to enable a better understanding of the model. Truck operations decisions for transportation logistics pose challenges especially when loads are less-than-truckload (LTL). Within a dynamic business environment load planners should consider effective utilization of resources and profitability of their operations. Multi-agent based system provides effective mechanisms for the management of dynamic operations in transportation. The algorithms for transportation domain that are available in the literature are generally focusing on generation of effective solutions for planning/scheduling problems without considering real transportation systems dynamics. Multi-agent based design of the load/truck planning problems is supposed to be helpful for integration of algorithms with real-time logistics controlling systems. The cooperative structure of the multi-agent based approach is motivated by real-world third party logistics (3PL) company operations. Negotiation mechanism among the agents is used to handle the dynamic events. The proposed approach is tested via simulation by using LTL data from a 3PL logistics company. The approach generates feasible and profitable decisions under dynamic circumstances by using negotiation/bidding mechanisms. Proposed approach is implemented by using JACK?, an agent development framework. A multi-agent based dynamic load/truck control system (MABDLCS) is also developed along with this approach. MABDLCS could be used for both testing some transportation scenarios and for real time vehicle/load control purposes. The solutions obtained by using the proposed approach demonstrated that MAS is contributing on problem solution quality while generating real-time schedules.

A Multi-Agent Framework for Load Consolidation in Logistics

Abstract Logistics companies mainly provide land transportation services facing with difficulties in making effective operational decisions. This is especially the case of making load/capacity/route planning and load consolidation where customer orders are generally unpredictable and subject to sudden changes. Classical modelling and decision support systems are mostly insufficient for providing satisfactory solutions in a reasonable time solving such dynamic problems. Agent-based approaches, especially multi-agent paradigms that can be considered as relatively new members of system science and software engineering, are providing effective mechanisms for modelling dynamic systems generally operating under unpredictable environments and having a high degree of complex interactions. It seems that multi-agent paradigms have big potential for handling complex problems in land transportation logistics. Based on this motivation, the paper proposes a multi-agent based framework for load consolidation problems of...

A PRACTICAL APPROACH TO PRIORITIZE PROJECT ACTIVITIES THROUGH FUZZY RANKING

Effective management of project activities is a critical issue for project managers, especially when the projects are complex. This is more essential for projects where the number of activity attributes is numerous. In such circumstances, prioritizing of project activities is an important issue and can enable better management of projects by focusing more on the most critical activities. However, in many cases prioritizing project activities can be a difficult task because some of the activity attributes might not be expressed easily in crisp terms. In this article, a practical fuzzy rating and ranking approach is proposed to prioritize project activities with fuzzy attributes. The proposed approach is also tested through an example problem from the literature.

Route prioritisation in a multi-agent transportation environment via multi-attribute decision making

Effective management of the vehicles plays an important role in transportation sector. There are some novel approaches in software engineering which can be used to dynamically control the trucks. One of the example technologies is multi-agent systems. Multi-agent-based technologies could be used for truck dispatching in real time. The trucks in such systems have their own transportation plan and they can decide about their routes throughout their operations. Any truck agent must select a route to pickup or deliver a transportation order while doing its transportation operation. However, truck driver route preference is also crucial for the transportation performances because of morale, motivation and psychological conditions of drivers have a considerable effect on transportation operation success. In this paper, driver route preference model is incorporated into a multi-agent-based vehicle dispatching system which uses fuzzy graph theoretical-matrix permanent approach which is a novel method in multi-attribute decision making MADM. The model is also compared with fuzzy TOPSIS method. Spearman rank correlation test is used to assess the correlation between the ranks.