A flexible manufacturing system (FMS) is an integrated, computer-controlled complex of automated material handling devices and numerically controlled machine tools that can simultaneously process medium-sized volumes of a variety of part types. FMSs are becoming an attractive substitute for the conventional means of batch manufacturing, especially in the metal-cutting industry. This new production technology has been designed to attain the efficiency of well-balanced, machine-paced transfer lines, while utilizing the flexibility that job shops have to simultaneously machine multiple part types. Some properties and constraints of these systems are similar to those of flow and job shops, while others are different. This technology creates the need to develop new and appropriate planning and control procedures that take advantage of the system's capabilities for higher production rates. This paper defines a set of five production planning problems that must be solved for efficient use of an FMS, and addresses specifically the grouping and loading problems. These two problems are first formulated in detail as nonlinear 0-1 mixed integer programs. In an effort to develop solution methodologies for these two planning problems, several linearization methods are examined and applied to data from an existing FMS. To decrease computational time, the constraint size of the linearized integer problems is reduced according to various methods. Several real world problems are solved in very reasonable time using the linearization that results in the fewest additional constraints and/or variables. The problem characteristics that determine which linearization to use, and the application of the linearized models in the solution of actual planning problems, are also discussed.production/scheduling: flexible manufacturing, programming: nonlinear, programming: integer, applications

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The focus of business toward increasing efficiency and reducing costs has resulted in supply chains that are efficient during normal times, but at the cost of being vulnerable to disruptions. From time to time, frequent as well as rare catastrophes also disrupt supply chain operations. We collect and compile data from many sources and show that there has been a marked increase in both the frequency and economic losses from natural and man-made catastrophes. We find that business losses constitute a major percentage of the total losses caused by these catastrophes. The statistics suggest that for terrorist attacks, the vulnerability of U.S. business interests is much higher than others. Examination of the geographical and chronological distributions of catastrophes provides useful information for managers concerned about such disruptions. We develop a catastrophe classification framework that matches different types of catastrophes to a variety of infrastructural components of supply chains. The framework also connects a variety of mitigating strategies to appropriate catastrophe types. We identify factors that can be used to assess the vulnerability of a supply chain. They can also be useful to compare possible alternative decisions based on the vulnerability they may cause in the supply chain. To manage vulnerability in supply chains, we propose strategies that can be implemented by a company to decrease the possibility of occurrence, provide advance warning, and cope after a disturbance. We reveal potential benefits from mitigating strategies during normal times, which indicate that well-developed strategies can also result in better efficiency. We identify many future research areas concerning disruption handling in supply chains.

R etailers often face a newsvendor problem. Advance selling helps retailers to reduce demand uncertainty. Consumers, however, may prefer not to purchase in advance unless given a discount because they are uncertain about their valuation for the product in advance. It is then unclear whether or when advance selling to pass some uncertainty risk to consumers is optimal for the retailer. This paper examines the advance selling price and inventory decisions in a two-period setting, where the first period is the advance selling period and the second is the selling (and consumption) period. We find that an advance selling strategy is not always optimal, but is contingent on parameters of the market (e.g., market potential and uncertainty) and the consumers (e.g., valuation, risk aversion, and heterogeneity). For example, we find that retailers should sell in advance if the consumers' expected valuation exceeds consumers' expected surplus when not buying early by a certain threshold. This threshold increases with the degree of risk aversion but decreases with stock out risk. If the degree of risk aversion varies across consumers, then a retailer should sell in advance if the probability for a consumer to spot buy is less than a critical fractile.

The evidence is clear that a lack of attention to structured tool management has resulted in the poor performance of many manufacturing systems. Plant tooling systems affect product design options, machine loading, job batching, capacity scheduling, and real-time part routing decisions. With increasing automation in manufacturing systems, there is a growing need to integrate tool management more thoroughly into system design, planning and control. This paper critically evaluates various tool management approaches, identifying the operational tradeoffs and analyzing the models developed to address management decisions involving tooling. These decisions range from selecting the optimal machining parameters and the most economic processing rate for a particular operation, to the loading of tools and jobs on machines and the determination of the optimal tool-mix inventories needed for a particular production schedule. We present an integrated conceptual framework for resource planning to examine how tool management issues, depending upon their scope, can be classified into tool-level, machine-level, and system-level concerns. This framework specifies how decisions made at one level constrain those at lower levels, and how information from lower levels feeds back to higher level decisions. The framework structures our critical evaluation of the modeling approaches found in the academic literature and points to promising directions for future research.flexible manufacturing systems, production planning, CIM, performance evaluation, automated manufacturing systems