Abdelghani Bekrar

The control of myopic behavior in semi-heterarchical production systems: A holonic framework

Heterarchical control architectures are essentially founded on cooperation and full local autonomy, resulting in high reactivity, no master/slave relationships and local information retention. Consequently, these architectures experience myopic decision-making, bringing entities towards local optimality rather than the systems overall performance. Although this issue has been identified as an important problem within heterarchical control architectures, it has not been formally studied. The aim of this paper is to identify the dimensions of myopic behavior and propose mechanisms to control this behavior. This study focuses on myopic behavior found in manufacturing control. For this particular study, we propose a holonic framework and a holonic organization that integrates specific mechanisms to control the temporal and social myopia. Our proposal was validated using simulations designed for solving the allocation problem in flexible manufacturing systems. These simulations were conducted to show the improvement by integrating the new mechanisms. These simulation results indicate that the myopic control mechanisms achieve better performance than the reactive strategies, because not only they introduce a planning horizon, but also because they balance local and global objectives, seeking a consensus.

Dynamic scheduling for multi-site companies: a decisional approach based on reinforcement multi-agent learning

In recent years, most companies have resorted to multi-site or supply-chain organization in order to improve their competitiveness and adapt to existing real conditions. In this article, a model for adaptive scheduling in multi-site companies is proposed. To do this, a multi-agent approach is adopted in which intelligent agents have reactive learning capabilities based on reinforcement learning. This reactive learning technique allows the agents to make accurate short-term decisions and to adapt these decisions to environmental fluctuations. The proposed model is implemented on a 3-tier architecture that ensures the security of the data exchanged between the various company sites. The proposed approach is compared to a genetic algorithm and a mixed integer linear program algorithm to prove its feasibility and especially, its reactivity. Experimentations on a real case study demonstrate the applicability and the effectiveness of the model in terms of both optimality and reactivity.

Coupling a genetic algorithm with the distributed arrival-time control for the JIT dynamic scheduling of flexible job-shops

In order to increase customer satisfaction and competitiveness, manufacturing systems need to combine flexibility with Just-in-Time (JIT) production. Until now, research on JIT scheduling problems has been mostly limited to high volume assembly lines rather than job-shop-like systems, due to their combinatorial complexity. In this paper, we propose a generic strategy for dynamically controlling task schedules by coupling genetic algorithms and distributed arrival-time control to optimise JIT performance. We explore two such hybrid approaches: a sequential approach where the two algorithms work separately and an integrated approach where the distributed arrival time control is embedded into the genetic algorithm. Performance of these approaches is benchmarked with quadratic linear programme solutions to get a gauge of their relative strengths in a static environment. Results from applying these approaches to a job-shop-like automated cell verify their effectiveness for JIT manufacturing under realistic dynamically changing environment.

Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns

The flexible job shop scheduling problem under machine breakdowns is considered.A two stages particle swarm optimization is proposed to solve the problem.The proposed algorithm optimizes makespan, robustness and stability of the solution.A predictive schedule witch is more robust and stable is obtained. In real-world industrial environments, unplanned events and unforeseen incidents can happen at any time. Scheduling under uncertainty allows these unexpected disruptions to be taken into account. This work presents the study of the flexible job shop scheduling problems (FJSP) under machine breakdowns. The objective is to solve the problem such that the lowest makespan is obtained and also robust and stable schedules are guaranteed. A two-stage particle swarm optimization (2S-PSO) is proposed to solve the problem assuming that there is only one breakdown. Various benchmark data taken from the literature, varying from Partial FJSP to Total FJSP, are tested. Computational results prove that the developed algorithm is effective and efficient enough compared to literature approaches providing better robustness and stability. Statistical analyses are given to confirm this performance.

A rail-road PI-hub allocation problem

The rail-road π-hub allocation problem for the newly proposed physical internet concept is studied.Both static and dynamic scenarios are considered.For static case, heuristic and Simulating Annealing based approaches are proposed.Multi-agent based approach is proposed to deal with dynamic scenarios in case of perturbations.All approaches are evaluated on simulated scenarios. This research concerns an allocation problem in the context of the physical internet aimed at improving rail-road π-hub efficiency by optimizing the distance travelled by each container to the dock, as well as the number of trucks used. To achieve this, heuristic, metaheuristic and Multi-agent-based approaches are proposed. When given the sequence of all the containers in the train, the proposed heuristic approach can assign these containers to outbound doors. Then, the Simulating Annealing (SA) method improves this allocation by minimizing the distance travelled. In addition, a multi-agent system model is proposed to generate reactive solutions which take dynamic aspects into account.The experimental results show that the proposed SA yields an improvement of about 2.42-7.67% in relation to the solution generated by the heuristic; it provides good results within a reasonable time. Conversely, the multi-agent-based approach provides good solutions in case of perturbations or unexpected events.

A Holonic Approach to Myopic Behavior Correction for the Allocation Process in Flexible-Job Shops Using Recursiveness

This chapter’s main interest is the myopic behaviour inherent to holonic control architectures. Myopic behaviour is the lack of coherence among local decision-making and system’s global goals. So far, holonic architectures use mediator entities to overcome this issue, bringing the holonic paradigms more toward hierarchy than heterarchy. Instead, this chapter explores the recursiveness characteristic of holonic manufacturing systems (HMS) as a possible way to correct myopic behaviour, by distributing decision-making over adjunct entities. The chapter explains our approach and its agent-based implementation for solving the allocation problem in a flexible job-shop. Results from simulations are compared with a mixed-integer linear program to determine its efficiency in terms of makespan and execution time. Preliminary results encourage further research in this area.

Pollux: a dynamic hybrid control architecture for flexible job shop systems

Nowadays, manufacturing control systems can respond more effectively to exigent market requirements and real-time demands. Indeed, they take advantage of changing their structural and behavioural arrangements to tailor the control solution from a diverse set of feasible configurations. However, the challenge of this approach is to determine efficient mechanisms that dynamically optimise the configuration between different architectures. This paper presents a dynamic hybrid control architecture that integrates a switching mechanism to control changes at both structural and behavioural level. The switching mechanism is based on a genetic algorithm and strives to find the most suitable operating mode of the architecture with regard to optimality and reactivity. The proposed approach was tested in a real flexible job shop to demonstrate the applicability and efficiency of including an optimisation algorithm in the switching process of a dynamic hybrid control architecture.

Navigation Scheme with Priority-Based Scheduling of Mobile Agents: Application to AGV-Based Flexible Manufacturing System

This paper proposes a new navigation approach for mobile agents in AGV (Autonomous Guided Vehicles)-based flexible manufacturing system. The navigation scheme combines a scheduled motion planner and a priority-based negotiation. The scheduled motion planner ensures the product transportation while choosing the appropriate resource among several. The priority policy is designed using a negotiation process to solve conflicts when agents navigate close to each other or towards the same resource. Some simulations are provided in order to show the pertinence of the proposed scheme as well as its feasibility when the number of mobile agents increases. They highlight the cooperation scheme, the appropriate selection of the resource during the navigation as well as the flexibility of the proposed approach.

A switching mechanism framework for optimal coupling of predictive scheduling and reactive control in manufacturing hybrid control architectures

Nowadays, manufacturing systems are seeking control architectures that offer efficient production performance and reactivity to disruptive events. Dynamic hybrid control architectures are a promising approach as they are not only able to switch dynamically between hierarchical, heterarchical and semi-heterarchical structures, they can also switch the level of coupling between predictive scheduling and reactive control techniques. However, few approaches address an efficient switching process in terms of structure and coupling. This paper presents a switching mechanism framework in dynamic hybrid control architectures, which exploits the advantages of hierarchical manufacturing scheduling systems and heterarchical manufacturing execution systems, and also mitigates the respective reactivity and optimality drawbacks. The main feature in this framework is that it monitors the system dynamics online and shifts between different operating modes to attain the most suitable production control strategy. The experiments were carried out in an emulation of a real manufacturing system to illustrate the benefits of including a switching mechanism in simulated scenarios. The results show that the switching mechanism improves response to disruptions in a global performance indicator as it permits to select the best alternative from several operating modes.

Hybrid PSO-tabu search for the optimal reactive power dispatch problem

This paper presents a new approach to solve the optimal reactive power dispatch (ORPD) problem based on hybridizing Particle Swarm Optimization (PSO) and Tabu-Search (TS) meta-heuristics (PSO-TS). The ORPD problem is formulated as a nonlinear constrained single-objective optimization problem where the real power loss is to be minimized. The proposed approach is used to find the settings of the control variables such as generator voltages, tap positions of tap changing transformers and the amount of reactive compensation devices, to optimize power transmission loss. The study was implemented on IEEE 30-bus systems, and the results were compared with non-hybridized PSO and TS and other evolutionary algorithms reported in the literature.