Miquel Angel Piera

Optimization of Logistic and Manufacturing Systems through Simulation: A Colored Petri Net-Based Methodology

Simulation models have proved to be useful for examining the performance of different system configurations and/or alternative operating procedures for complex logistic or manufacturing systems. However, when applying simulation techniques to increase the performance of those systems, several limitations arise due to their inability to evaluate more than a fraction of the immense range of options available. Simulation-optimization is one of the most popular approaches to improve the use of simulation models as a tool to obtain the best (optimal or quasi-optimal) decision variable values that minimize a certain objective function. However, despite the success of several simulation-optimization packages, many technical barriers still remain. The authors describe a new approach to integrate evaluation (simulation) methods with search methods (optimization) based on not only simulation results but also information from the simulation model.

Deadlock-Free Scheduling Method for Flexible Manufacturing Systems Based on Timed Colored Petri Nets and Anytime Heuristic Search

This paper addresses the deadlock (DL)-free scheduling problem of flexible manufacturing systems (FMS) characterized by resource sharing, limited buffer capacity, routing flexibility, and the availability of material handling systems. The FMS scheduling problem is formulated using timed colored Petri net (TCPN) modeling where each operation has a certain number of preconditions, an estimated duration, and a set of postconditions. Based on the reachability analysis of TCPN modeling, we propose a new anytime heuristic search algorithm which finds optimal or near-optimal DL-free schedules with respect to makespan as the performance criterion. The methodology tackles the time-constrained problem of very demanding systems (high diversity production and make-to-order) in which computational time is a critical factor to produce optimal schedules that are DL-free. In such a rapidly changing environment and under tight customer due-dates, producing optimal schedules becomes intractable given the time limitations and the NP-hard nature of scheduling problems. The proposed anytime search algorithm combines breadth-first iterative deepening A* with suboptimal breadth-first heuristic search and backtracking. It guarantees that the search produces the best solution obtained so far within the allotted computation time and provides better solutions when given more time. The effectiveness of the approach is evaluated on a comprehensive benchmark set of DL-prone FMS examples and the computational results show the superiority of the proposed approach over the previous works.

Revisiting state space exploration of timed coloured petri net models to optimize manufacturing system's performance

Due to constant fluctuations in market demands, nowadays scheduling of flexible manufacturing systems is taking great importance to improve competitiveness. Coloured Petri Nets (CPN) is a high level modelling formalism which have been widely used to model and verify systems, allowing representing not only the system’s dynamic behaviour but also the information flow. One approach that focuses in performance optimization of industrial systems is the one that uses the CPN formalism extended with time features (Timed Coloured Petri Nets) and explores all the possible states of the model (state space) looking for states of particular interest under industrial scope. Unfortunately, using the time extension, the state space becomes awkward for most industrial problems, reason why there is a recognized need of approaches that could tackle optimization problems such as the scheduling of manufacturing activities without simplifying any important aspect of the real system. In this paper a timed state space approach for properties verification and systems optimization is presented together with new algorithms in order to get better results when time is used as a cost function for optimizing the makespan of manufacturing systems. A benchmarking example of a job-shop is modelled in CPN formalism to illustrate the improvements that can be achieved with the proposed implementations.

A Methodology for Solving Logistic Optimization Problems through Simulation

In this paper we present a methodological approach designed to automate the decision-making in logistic systems, with deterministic time, by solving optimization problems. The Colored Petri Net (CPN) formalism has been used as a base to develop a methodology that integrates the features of operational research, artificial intelligence and simulation fields. At the same time, it combines the modeling of discrete event systems with simulation, analysis and system optimization, transforming a conceptual model into a simulation model, and a decision problem into a search problem. The use of the CPN formalism has allowed the integration of all of these different research fields into a unique decision support tool.

A causal model to explore the ACAS induced collisions

Current Air Traffic Management research programs (i.e. Single European Sky ATM (air traffic management) Research (SESAR), next generation air transportation system (NextGen)), try to overcome airside capacity shortages while improving cost-efficient operations and safety. An increment in the airspace traffic density can lead to congested traffic scenarios for which it becomes necessary to develop new safety procedures that address multithread threats. This paper considers some of the difficulties in establishing validation of the airborne collision avoidance system (ACAS), which constitutes the last-resort for reducing the risk of near mid-air collision between aircraft in a multithread scenario. A causal model that is specified in Colored Petri Net (CPN) formalism is presented as a key approach to analyze the state space of a congested traffic scenario in which the events that could transform a conflict into a collision are identified, providing a challenging tool not only for validation but also for the implementation of a new ACAS logic. The InCAS EuroControl simulator has been used to illustrate the importance of cause–effect analysis for the relationships between various encounters that arise in a multithread scenario, in which TCAS II v. 7.1 fails to avoid a collision when two Resolution Advisories are issued without consideration of the downstream effects.

A time stamp reduction method for state space exploration using colored Petri nets

In this paper we present a state space exploration method implemented as part of a Decision Support Tool designed to improve the performance of logistics and production systems. An efficient depth-first search strategy, together with a reduction method known as the ‘Old Node Method’, is introduced to improve the descendant path choice by means of an evaluation function that allows the selection of the most promising node to be expanded at any level of the path. The sub-set of the state space representing the most promising scenarios holds a set of feasible and quasi-optimal solutions.

Improvement of Lagrangian Relaxation Convergence for Production Scheduling

It is widely accepted that new production scheduling tools are playing a key role in flexible manufacturing systems to improve their performance by avoiding idleness machines while minimizing set-up times penalties, reducing penalties for do not delivering orders on time, etc. Since manufacturing scheduling problems are NP-hard, there is a need of improving scheduling methodologies to get good solutions within low CPU time. Lagrangian Relaxation (LR) is known for handling large-scale separable problems, however, the convergence to the optimal solution can be slow. LR needs customized parametrization, depending on the scheduling problem, usually made by an expert user. It would be interesting the use of LR without being and expertise, i.e., without difficult parameters tuning. This paper presents innovative approaches on the LR method to be able to develop a tool capable of solve scheduling problems applying the LR method without requiring a deep expertise on it. First approach is the improvement of an already existing method which use Constraint Programming (CP) to obtain better primal cost convergence. Second approach is called Extended Subgradient Information (ESGI) and it speed up the dual cost convergence. Finally, a set of step size rules for the Subgradient (SG) method are compared to choose the most appropriate rule depending on the scheduling problem. Test results demonstrate that the application of CP and ESGI approaches, together with LR and the selected step size rule depending on the problem, generates better solutions than the LR method by itself.

A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles

To achieve a significant improvement in the overall performance of a flexible manufacturing system, the scheduling process must consider the interdependencies that exist between the machining and transport systems. However, most works have addressed the scheduling problem as two independent decision making problems, assuming sufficient capacity in the transport system. In this paper, we study the simultaneous scheduling (SS) problem of machines and automated guided vehicles using a timed coloured Petri net (TCPN) approach under two performance objectives; makespan and exit time of the last job. The modelling approach allows the evaluation of all the feasible vehicle assignments as opposed to the traditional dispatching rules and demonstrates the benefits of vehicle-controlled assignments over machine-controlled for certain production scenarios. In contrast with the hierarchical decomposition technique of existing approaches, TCPN is capable of describing the dynamics and evaluating the performance of the SS problem in a single model. Based on TCPN modelling, SS is performed using a hybrid heuristic search algorithm to find optimal or near-optimal schedules by searching through the reachability graph of the TCPN with heuristic functions. Large-sized instances are solved in relatively short computation times, which were a priori unsolvable with conventional search algorithms. The algorithm’s performance is evaluated on a benchmark of 82 test problems. Experimental results indicate that the proposed algorithm performs better than the conventional ones and compares favourably with other approaches.

A compact timed state space approach for the analysis of manufacturing systems: key algorithmic improvements

The state space (SS) analysis of a timed coloured Petri net (TCPN) has been used traditionally for validation and verification of system properties. Performance modelling using TCPN has also received the widespread attention of researchers in recent years as a promising alternative to improve productivity and competitiveness of present flexible manufacturing systems. In this article, a new computationally effective approach is introduced for designing efficient decision support tools based on the analysis of SSs, in which the computational time is an important requirement to deal with optimal scheduling, routing or planning policies. The SS analysis of a system specified in the TCPN formalism is faced with an algorithm in two stages, key implementation algorithmic aspects are considered to improve the time consuming tasks (transition evaluation, data management and information search). In order to provide good benchmarking results when applied to the optimisation of industrial scheduling problems, some examples are given and future work is addressed at the end of the article.

Analysis of induced Traffic Alert and Collision Avoidance System collisions in unsegregated airspace using a Colored Petri Net model

For rigorously safe aviation, it is of great significance to conduct an analysis of the current and new operations, providing a global perspective of the scenario dynamics and a better understanding of the potential collision occurrence for risk assessment. The objective of this study is to consider a number of the difficulties that are involved when establishing a validation of the Traffic Alert and Collision Avoidance System (TCAS), which constitutes the last resort for reducing the risk of near mid-air collisions between aircraft in a multithread scenario, and to analyze the TCAS logic using a causal model. In this paper, the causal model that is specified in the Colored Petri Net (CPN) formalism is employed as a key approach to analyze quantitatively the state space of a congested traffic scenario in which the events could transform a conflict into a potential collision. It offers a rigorous tool not only for TCAS validation but also for the analysis of a wide range of properties of the TCAS behavior. This CPN model assumes unrestrained initial positions and TCAS II-equipped aircraft; it is demonstrated to be extremely effective for generating all possible future TCAS failure end-states from the current locations, and the Interactive Collision Avoidance Simulator EuroControl simulator is used to illustrate the collision process of a three-aircraft scenario.