Knowledge that Transforms

Cyclic inventory is the buffer following a machine that cycles over a set of products, each of which is subsequently consumed in a continuous manner. Scheduling such a machine is interesting when the changeover times from one product to another are non‐trivial—which is generally the case. This problem has a substantial literature, but the common practices of “lot‐splitting” and “maximization of utilization” suggest that many practitioners still do not fully understand the principles of cyclic inventory. This paper is a tutorial that demonstrates those principles. We show that cyclic inventory is directly proportional to cycle length, which in turn is directly proportional to total changeover time, and inversely proportional to machine utilization. We demonstrate the virtue of “maximum changeover policies” in minimizing cyclic inventory—and the difficulty in making the transition to an increased level of demand. In so doing, we explicate the different roles of cyclic inventory, transitional inventory, and safety stock. We demonstrate the interdependence of the products in the cycle—the lot‐size for one product cannot be set independently of the remaining products. We also give necessary conditions for consideration of improper schedules (i.e., where a product can appear more than once in the cycle), and demonstrate that both lot‐splitting and maximization of utilization are devastatingly counter‐productive when changeover time is non‐trivial.

We develop a stochastic programming model to aid manufacturing firms in making strategic decisions in technology acquisition. The proposed model maximizes the firm's expected profit under the condition of the uncertainty in technological progress and development. To solve this large‐scale problem, we decompose future uncertainties through scenarios and then develop an algorithm to solve the resulting non‐linear subproblems efficiently. Finally, we develop a heuristic to eliminate the infeasibility in the master problem and obtain best solutions. Numerical results show that our heuristic solutions are very close to the optimal solutions and meaningful insights are derived.

An important issue being discussed for Chilean pine plantation policies is the application of environmental protection measures when managing its timber areas. Typical measures, already in place in more developed countries, include imposing riparian strips and protecting fragile soils from the use of heavy machinery. While environmental protection measures have been considered vital for decades, so far there has been almost no attempt to quantify both the benefits and costs of these measures. This paper attempts to measure the costs associated with the main measures which can help both the forestry firms and the government evaluate the cost impact of the new environmental protection regulations being studied. The analysis for different environmental scenarios is carried out by modifying a mixed integer LP, currently used for tactical planning by one forestry firm.

Only a small set of employee scheduling articles have considered an objective of profit or contribution maximization, as opposed to the traditional objective of cost (including opportunity costs) minimization. In this article, we present one such formulation that is a market utility‐based model for planning and scheduling in mass services (MUMS). MUMS is a holistic approach to market‐based service capacity scheduling. The MUMS framework provides the structure for modeling the consequences of aligning competitive priorities and service attributes with an element of the firm's service infrastructure. We developed a new linear programming formulation for the shift‐scheduling problem that uses market share information generated by customer preferences for service attributes. The shift‐scheduling formulation within the framework of MUMS provides a business‐level model that predicts the economic impact of the employee schedule. We illustrated the shift‐scheduling model with empirical data, and then compared its results with models using service standard and productivity standard approaches. The result of the empirical analysis provides further justification for the development of the market‐based approach. Last, we discuss implications of this methodology for future research.

This paper describes the changes that are forcing property and casualty insurance firms to rethink their service system design and in particular their distribution strategies. A set of questions related to distribution that are uppermost in the minds of executives in this industry are presented along with a literature survey of models that can be used to answer some of these questions. Based on the survey, a normative framework for designing the distribution system is proposed. Qualitative and quantitative analysis based on the proposed framework is presented along with empirical data to demonstrate the usefulness of the framework. The paper concludes with an agenda for further research.

The disposal of hazardous wastes creates major economic and environmental problems. One productive use of hazardous wastes is to blend them into fuel, which mitigates damage to the environment by recycling waste into fuel and reducing fossil‐fuel consumption. Operations personnel face a daunting task of efficiently blending hazardous waste into fuel, while simultaneously maintaining environmental regulatory requirements. This research develops a goal‐programming approach to the waste‐fuel‐blending process that considers the diverse objectives of fuel managers. A realworld case study at a cement kiln illustrates the effectiveness of this approach, where the implementation followed principles of team building and quality management.

Firms capable of reducing the time required to bring new products to the marketplace realize significant competitive gains. In this context, the existing literature on capacity expansion is limited because it does not consider the lead time required to construct and operationalize new capacity. Models are introduced here that capture the capacity expansion lead time as well as two types of learning. Specifically, learning may reduce the lead time or cost required to complete an expansion. Analytic and numerical results show that the optimal solution can be significantly affected by the explicit treatment of the lead time and learning.

We study the deterministic time‐varying demand lot‐sizing problem in which learning and forgetting in setups and production are considered simultaneously. It is an extension of Chiu's work. We propose a near‐optimal forward dynamic programming algorithm and suggest the use of a good heuristic method in a situation in which the computational effort is extremely intolerable. Several important observations obtained from a two‐phase experiment verify the goodness of the proposed algorithm and the chosen heuristic method.

We investigate the revenue impact of a new Price Setting Method (PSM) and compare it with the industry standard Bid Price Method (BPM). This comparison is performed via a simulation that was validated by a major hotel chain. In 27 out of the 32 cases, the PSM outperformed the BPM based on statistically significant tests. The PSM produces an average revenue increase of 34%, which can be thought of as an upper bound on the realistic revenue increase.