Springer Science and Business Media LLC

In today’s markets, non-uniform, customized products complicate the manufacturing processes significantly. In this paper, we propose a cellular manufacturing system design model to manage product variety by integrating with the technology selection decision. The proposed model determines the product families and machine groups while deciding the technology of each cell individually. Hedging against changing market dynamics leads us to the use of flexible machining systems and dedicated manufacturing systems at the same facility. In order to integrate the market characteristics in our model, we proposed a new cost function. Further, we modified a well known similarity measure in order to handle the operational capability of the available technology. In the paper, our hybrid technology approach is presented via a multi-objective mathematical model. A filtered-beam based local search heuristic is proposed to solve the problem efficiently. We compare the proposed approach with a dedicated technology model and showed that the improvement with the proposed hybrid technology approach is greater than 100% in unstable markets requiring high product varieties, regardless of the volumes of the products.

Manufacturing flexibility is becoming a fundamental production objective, along with cost, quality, and delivery time. Current production systems face quick changes in market conditions and they need to adapt in this environment. The supply chain and industrial globalization give an important role for assembly systems. Placed at the end of the value chain, assembly systems must face those quick changes successfully to reach the expected performance. The key performance indicators are normally based on cost, quality, and delivery time objectives. Reducing costs and improving quality are almost universal goals. Delivery time is typically determined by customer demand in the supply chain, planning from make-to-stock to make-to-order, and aspiring to reach a just-in-time manufacturing system. In this context, flexibility could be the differential advantage to tackle uncertainty. Closely related to the rest of production objectives and the overall performance of the system, flexibility must be integrated in the system for successful decision-making in operations. This work presents this approach of flexibility. A brief review of flexibility concepts and measurements in the literature precedes an introduction to flexibility, defined based on the function of utility. This function represents the expectations of system performance. This approach allows the formulation of the taxonomy of operational flexibility in agreement with the classical types identified in former works. Next, an integer model is programmed to simulate the basic behavior of task planning in a make-to-order assembly system. This first application illustrates flexibility quantification based on utility evolution. The use of common industrial parameters to quantify operational flexibility will finally facilitate an integrated interpretation of system performance trends.

Flexible robotic cells combine the capabilities of robotic flow shops with those of flexible manufacturing systems. In an m-machine flexible cell, each part visits each machine in the same order. However, the m operations can be performed in any order, and each machine can be configured to perform any operation. We derive the maximum percentage increase in throughput that can be achieved by changing the assignment of operations to machines and then keeping that assignment constant throughout a lot's processing. We find that no increase can be gained in two-machine cells, and that the gain in three- and four-machine cells each is at most 14 $$\frac{2}{7}$$ %.

The selection of Reconfigurable Manufacturing Systems (RMS) configurations that include arrangement of machines, equipment selection, and assignment of operations, has a significant impact on their performance. This paper reviews the relevant literature and highlights the gaps that exist in this area of research. A novel “RMS Configuration Selection Approach” is introduced. It consists of two phases; the first deals with the selection of the near-optimal alternative configurations for each possible demand scenario over the considered configuration periods. It uses a constraint satisfaction procedure and powerful meta-heuristics, real-coded Genetic Algorithms (GAs) and Tabu Search (TS), for the continuous optimization of capital cost and system availability. The second phase utilizes integer-coded GAs and TS to determine the alternatives, from those produced in the first phase, that would optimize the degree of transition smoothness over the planning horizon. It uses a stochastic model of the level of reconfiguration smoothness (RS) across all the configuration periods in the planning horizon according to the anticipated demand scenarios. This model is based on a RS metric and a reconfiguration planning procedure that guide the development of execution plans for reconfiguration. The developed approach is demonstrated and validated using a case study. It was shown that it is possible to provide the manufacturing capacity and functionality needed when needed while minimizing the reconfiguration effort. The proposed approach can provide decision support for management in selecting RMS configurations at the beginning of each configuration period.

Tool management is recognized as a critical issue in flexible manufacturing facilities management. This article addresses the issue of tool management in a flexible system installed in an avionics components factory. The system is composed of two machining centers equipped with local tool magazines of limited capacity. A tool handling system is in charge of tool movements between the tool room and the two machines. Each machine is able to perform any operation, provided that it is equipped with the suitable tool. In this kind of installation, tool allocation must be determined, and tool movements must be synchronized in order to minimize operating costs, or, equivalently, maximize the productivity of the system. We propose an approach to production planning based on a clustering algorithm, which takes into account the tool requirements of each part program in the production batch. We also propose two different heuristics for the scheduling problem. A case study was conducted on the facility mentioned above. Two conflicting objectives can be identified for this kind of production system: the reduction of tools to be shared among machines and the reduction of workload unbalance. The tests and comparison made demonstrate how the proposed procedure leads to superior results in terms of both objectives.

The new economic challenges and recent trends in globalization have made it very difficult for Canadian forest product companies to improve their financial position without the coordinated involvement of the entire company, including their supply chains (distributed facilities, company offices, industrial customers, and distributors). Such a new level of efficiency involves their distributed facilities and offices spread around the world, and their customers. One consequence of this new reality is that forest products companies are now facing the need to re-engineer their organizational processes and business practices with their partners. To do this they must adopt new technologies to support the coordination of their planning and control efforts in a customer-centered environment. This paper first proposes a generic software architecture for development of an experimentation environment to design and test distributed advanced planning and scheduling systems. This architecture enables combination of agent-based technology and operations research-based tools in order to first take advantage of the ability of agent technology to integrate distributed decision problems, and, second, to take advantage of the ability of operations research to develop and exploit specific normative decision models. Next, this paper describes how this architecture has been configured into an advanced planning and scheduling tool for the lumber industry. Finally, we present how an application of this advanced planning tool is currently being validated and tested in a real manufacturing setting.

This paper presents an investigation of simulation packages regarding their ability to model business processes related to manufacturing systems. Three simulation packages are investigated: VS7, SIMAN/CINEMA IV, and SIMFACTORY II.5. These packages are evaluated with regard to their capabily of modeling problems related to the manufacturing systems design (MSD) framework, which involves different levels of detail: the conceptual modeling level and the detailed design level. The investigation is based on a case study related to manufacturing systems. The main objective of this investigation is to examine the manufacturing simulation packages and their ability to offer variable detail modeling. Research findings suggest that no simulation environment offers sufficiently flexible facilities for the variable detailed modeling of manufacturing systems design. The paper proposes a method for systems entity classification to increase the levels of detail in an effective manner without duplication of data collection and model building efforts.