Carlo Meloni

Design and optimization of integrated E-supply chain for agile and environmentally conscious manufacturing

An agile and environmentally conscious manufacturing paradigm refers to the ability to reconfigure a flexible system quickly, economically, and environmentally responsibly. In modern manufacturing enterprises, e-supply chains integrate Internet and web-based electronic market and are promising systems to achieve agility. A key issue in the strategic logistic planning of integrated e-supply chains (IESCs) is the configuration of the partner network. This paper proposes a single- and multiobjective optimization model to configure the network of IESCs. Considering an Internet-based distributed manufacturing system composed of different stages connected by material and information links, a procedure is presented to select the appropriate links. A set of performance indices is associated with the network links. Single-criterion and multicriteria optimization models are presented under structural constraint definitions. The integer linear programming (ILP) problem solution provides different network structures that allow to improve supply chain (SC) flexibility, agility, and environmental performance in the design process. The proposed optimization strategy is applied to two case studies describing two networks for desktop computer production.

A multi-level approach for network design of integrated supply chains

Integrated e-supply chains are distributed manufacturing systems composed of various resources belonging to different companies and integrated with streamlined material, information and financial flow. The configuration of the supply-chain network is essential for business to pursue a competitive advantage and to meet the market demand. This paper proposes a three-level hierarchical methodology for a supply chain network design at the planning-management level. The integrated supply chain network is described as a set of consecutive stages connected by communication and transportation links, and the configuration aim consists in selecting the actors of the stages on the basis of transportation connection and information flow. More precisely, the first level evaluates the performance of the entities candidate to join the network and singles out efficient elements. The second level solves a multi-criteria integer linear optimization problem to configure the network. Finally, the third level is devoted to evaluating and validating the solution proposed in the first two levels. The overall decision process is the result of the interaction of the modules that are dedicated to each decision level. The paper presents some optimization techniques to synthesize the first two levels and illustrates the hierarchical decision process by way of a case study.

A Rollout Metaheuristic for Job Shop Scheduling Problems

In this paper we deal with solution algorithms for a general formulation of the job shop problem, called alternative graph. We study in particular the job shop scheduling problem with blocking and/or no-wait constraints. Most of the key properties developed for solving the job shop problem with infinite capacity buffer do not hold in the more general alternative graph model. In this paper we report on an extensive study on the applicability of a metaheuristic approach, called rollout or pilot method. Its basic idea is a look-ahead strategy, guided by one or more subheuristics, called pilot heuristics. Our results indicate that this method is competitive and very promising for solving complex scheduling problems.

Kriging metamodel management in the design optimization of a CNG injection system

This paper deals with the use of Kriging metamodels in multi-objective engineering design optimization. The metamodel management issue to find the tradeoff between accuracy and efficiency is addressed. A comparative analysis of different strategies is conducted for a case study devoted to the design of a component of the injection system for Compressed Natural Gas (CNG) engines. The computational results are reported and analyzed for a performance assessment conducted with a data envelopment analysis approach.

Set-up coordination between two stages of a supply chain

In the material flow of a plant, parts are processed in batches, each having two distinct attributes, say shape and color. In one department, a set-up occurs every time the shape of the new batch is different from the previous one. In a downstream department, there is a set-up when the color of the new batch is different from the previous one. Since a unique sequence of batches must be established, the problem consists in finding such a common sequence optimizing an overall utility index. Here we consider two indices, namely the total number of set-ups and the maximum number of set-ups between the two departments. Both problems are shown to be NP-hard. An efficient heuristic approach is presented for the first index which allows to solve a set of real-life instances and performs satisfactorily on a large sample of experimental data.

Robust simulation-optimization using metamodels

Optimization of simulated systems is the goal of many methods, but most methods assume known environments. In this paper we present a methodology that does account for uncertain environments. Our methodology uses Taguchis view of the uncertain world, but replaces his statistical techniques by either Response Surface Methodology or Kriging metamodeling. We illustrate the resulting methodology through the well-known Economic Order Quantity (EOQ) model.

A decomposition approach for the combined master surgical schedule and surgical case assignment problems

This research aims at supporting hospital management in making prompt Operating Room (OR) planning decisions, when either unpredicted events occur or alternative scenarios or configurations need to be rapidly evaluated. We design and test a planning tool enabling managers to efficiently analyse several alternatives to the current OR planning and scheduling. To this aim, we propose a decomposition approach. More specifically, we first focus on determining the Master Surgical Schedule (MSS) on a weekly basis, by assigning the different surgical disciplines to the available sessions. Next, we allocate surgeries to each session, focusing on elective patients only. Patients are selected from the waiting lists according to several parameters, including surgery duration, waiting time and priority class of the operations. We performed computational experiments to compare the performance of our decomposition approach with an (exact) integrated approach. The case study selected for our simulations is based on the characteristics of the operating theatre (OT) of a medium-size public Italian hospital. Scalability of the method is tested for different OT sizes. A pilot example is also proposed to highlight the usefulness of our approach for decision support. The proposed decomposition approach finds satisfactory solutions with significant savings in computation time.

Autonomous agents architectures and algorithms in flexible manufacturing systems

This paper investigates possible implementations of the autonomous agents concept in flexible manufacturing control. The implementation issues and the effectiveness of different control architectures and algorithms are analyzed by means of a simulation model of a flexible job shop. Extensive experimental results are reported, allowing the evaluation of the trade-off between the degree of autonomy and system performance.

Minimizing and balancing setups in a serial production system

This paper addresses a problem arising in the coordination between two consecutive manufacturing departments of a production system, in which parts are processed in batches, and each batch is characterized by two distinct attributes. Due to limited interstage buffering between the two stages, the two departments have to follow the same batch sequence. In the first department, a setup occurs every time the first attribute of the new batch is different from the previous one. In the downstream department, there is a setup when the second attribute of the new batch changes. The problem consists of finding a common batch sequence optimizing some global utility index. Here we propose a metaheuristic approach to a bi-criteria version of the problem considering two indices, namely the total number of setups paid for by the two departments and the maximum number of setups paid for by either department.

A new class of greedy heuristics for job shop scheduling problems

In this paper we introduce a new class of greedy heuristics for general job shop scheduling problems. In particular we deal with the classical job shop, i.e. with unlimited capacity buffer, and job shop problems with blocking and no-wait. The proposed algorithm family is a simple randomized greedy family based on a general formulation of the job shop problem. We report on an extensive study of the proposed algorithms, and comparisons with other greedy algorithms are presented.