Francesco Nucci

A new algorithm to rank temporal fuzzy sets in fuzzy discrete event simulation

In this paper fuzzy set theory was applied to discrete event simulation to model uncertainty in input data. Various approaches to fuzzy simulation have been proposed in the literature, even if many of the problems are still to be solved. The key points are how to manage the simulation event list and how to update the fuzzy simulation clock. These two tasks are mainly based on the ranking algorithm. In the following, the classical algorithms were applied to rank temporal fuzzy sets and the results obtained were compared with the ones obtained by the proposed ranking algorithm. The comparison was performed by analyzing a simple case study in the manufacturing field. The results show how the new ranking algorithm can be very useful in a fuzzy simulation environment.

New policy to manage tools in flexible manufacturing systems using network part programs

The high investment related to the acquisition of Flexible Manufacturing Systems forces firms to a better utilization of the machines. Different actions can be taken in order to avoid idle times of the machines: reduction of the unproductive times (time dedicated to rapid movements, tool exchange, pallet exchange, etc.), improvement of machines and, not last, a better management of the resources. The paper proposes a new policy for the management of tool operations in parallel machine FMS to minimize the idle times due to the lack of tools. The proposed policy uses new opportunities in manufacturing technology related with the use of network part programs in NC machines. It is already known in literature the potentiality of network part programs, more flexible than traditional sequential part programs that execute simply the rigid list of operations. Network part programs allow the different alternative ways to process each part. The way in which network part programs are executed by machines depends on the state of the tools and availability of the tools. The proposed method has been compared with other existing ones based on a real test case, a parallel machine FMS with two machines and a tool carrier.

System Analysis and Assessment by Fuzzy Discrete Event Simulation

The integration of uncertainty notions into numerical simulation modeling tools is an interesting research field to cope with the lack of models able to deal with non-probabilistic uncertainty. The goal may be reached by exploiting fuzzy sets theory which allows analyzing problems from the non-probabilistic point of view. In this work, the problems related with the integration of fuzzy sets in discrete event simulation theory are analyzed. New methods are presented to extend simulation to fuzzy simulation. The proposed approach soundness is demonstrated by assuring the possibility to reproduce any possible feasible evolution of a system. A numerical case proposed in literature is used to to benchmark the improvement respect to the state-of-the-art.

Simulation and mathematical programming for a multi-objective configuration problem in a hybrid flow shop

This paper introduces an application of simulation-based multi-objective optimization to solve a system configuration problem in a hybrid flow shop system. The test case is provided by a firm that manufactures mechanical parts for the automotive sector. We present an architecture that uses both discrete-event simulation and mathematical programming tools in order to solve the problem. The multiple-objective nature of the problem is preserved throughout the proposed approach, using Pareto-dominance concepts both to eliminate inefficient solutions within the proposed solution algorithm and to provide the user with efficient solutions. Mathematical programming is used to cull the required number of simulation runs. Computational results obtained using a real-world case study are reported. The proposed approach is benchmarked against a general purpose simulation-optimization engine in order to prove its effectiveness.

Long-term planning in manufacturing production systems under uncertain conditions

Nowadays, the frequency of decisions related to the configuration and capacity evaluation of manufacturing production systems is increasing in more and more industrial sectors, especially in the automotive field. This is due to a variety of factors, such as the reduction of the life cycle of the product, increasing competition, etc. In such a context, decision makers have to take their actions in shorter times than they ever did in the past: as an example, they typically need to take quick decisions about different production system alternatives. This specific problem has increased in complexity because of the necessity to take into account all the sources of variability and each related level of uncertainty in the available data definition. Two main aspects lead to such difficulties: the lack of a proper decision support system and the need to contextually model the uncertain data. This paper presents the first step in this direction. In particular, a decision support system (DSS) has been developed to help decision makers take productive capacity planning decisions according to the uncertain characterisation of the market evolution. First, a strategy evaluation tool allows the decision maker to specify several productive capacity expansion policies and, then, uses a fuzzy discrete event simulation paradigm (Fuzzy-DEVS) to compare them, providing the possibility of choosing between the different alternatives according to performance indicators. A strategy design tool helps the decision maker by inferring the best expansion policy on the basis of the system analysis conducted in the first step. Finally, our approach has been validated by means of an industrial test case in the automotive sector.

System Performance Simulation and Analysis

The performance evaluation of different system architectures and the development of tailored methods to manage FFMSs at operational level are the final decision activities of the design approach presented in this book. In this chapter a simulation theory-based tool is presented. The proposed tool is able to automatically simulate a set of different scenarios and to provide the necessary capability to compare the performance of FFMSs versus FMSs. Moreover, tailored methods to optimize the performance at operational level are introduced in the simulated supervisor of the FFMSs architecture. The methods allow to split the execution of the part program among different machining centers and to manage the opportunity to share more than one pallet transport system on the same route. The methods are validated through simulation experiments.

A fuzzy linear programming approach to mix product selection problem

In this paper, we consider linear programming problems with fuzzy objective function coefficients. In this case, the optimal solution set is defined as a fuzzy set. A new method to find the most significant solutions of the fuzzy problem, together with their importance degrees, has been developed. The new method works directly on the main LP problem by readapting the simplex algorithm to fuzzy arithmetic and using a proper fuzzy ranking criterion. The main advantages, compared with the state-of-the-art, consist in solving the original (fuzzy) LP problem, rather than a set of simplified versions, and generating a list of the most relevant solutions with their importance degree, instead of a set of solutions obtained with different defuzzification methods. A literature case study of mix product selection problem is analyzed as benchmark.

The multi-shift single-vehicle routing problem under fuzzy uncertainty

In this paper, we study the single-vehicle routing problem (VRP) with time windows, multi-shift, and fuzzy uncertainty. In this problem, one vehicle is used repeatedly to serve demand over a planning horizon of several days. The problem is inspired by a routing problem in maintenance activities, where one maintenance crew uses a vehicle to perform jobs in various locations. Crew operates in shifts and always returns to depot before shift ends (no overtime is allowed). The objective consists in completing all the maintenance jobs in various locations minimizing the number of shifts the crew is used, and reducing the shift duration time. We study the effect of uncertainty in travel and job processing time on KPI. We develop an Artificial Immune Heuristic to solve the addressed problem. A numerical real test study is performed in order to investigate the problem. We propose a framework to evaluate the uncertainty effect. Results show important time saving can be obtained with the proposed approach.

Minimization of Energy Consumptions by Means of an Intelligent Production Scheduling

Nowadays achieving overall sustainability in industrial activities is the natural consequence of diminishing non-renewable resources and stricter regulations related to environment and occupational safety/health.In industrial sector, CO2 emissions derive both from direct and indirect emissions. The second type is due to the use of electricity and currently represents more than thirty percent of global amount. For this reason energy consumption reduction is critical aspect in several industrial environments. Power consumption reduction is possible by modifying manufacturing conditions, utilizing alternative technologies and increasing resource utilization rate.The current market demand is characterized by request of small lots with different characteristics, which requires a complex management of the manufacturing production flow.Production planning and scheduling models, arising in flexible manufacturing environments, allows to combine several aspects such as: technological questions (e.g.: minimize manufacturing times and costs) economic criteria (e.g.: maximize production rate) and environmental prospective (e.g.: emissions reduction). A good manufacturing scheduling allows to saturate the system, avoiding bottlenecks, by means of the adaptation of the plant productivity to the request one.In this paper, authors describe an optimization framework focused on the minimization of energy and production costs by means of an intelligent production scheduling. In order to assess the performances, different real case production scenarios, in which the manufacturing activity is mainly based on machining operations, have been analyzed. In this work, several technologies, with various capabilities, have been taken into account in order to perform production activities. In addition, the scheduling has the possibility of using production technologies with low environmental impact and lower productivity, where the increase of the activity duration does not deteriorate the system performance. In this way several production schedules are feasible and the main scheduling aim focuses in obtaining the required productivity to fulfill demand and minimize energy consumption.

A simulation-based lean production approach at a low-volume parts manufacturer with part combining

Lean Production approach provides a framework to limit source of variability and to improve performance of production systems. If production units characterized by low-volume and part combining are considered, lean approach has to be tuned in order to provide the correct limitation of work-in-progress and the suitable sequencing of parts. In such a case, a discrete event simulation study is necessary to illustrate the control-element operations and indicate the applicability of the elements. A case study in the field of earth-moving machine is considered. A simulation study proved that the implementation of lean elements lead to a significant performance improvement.