Developing production schedules which balance part usage and smooth production loads for just-in-time production systems

A mixed-model multilevel manufacturing facility running under a just-in-time (JIT) production system is controlled by setting the production schedule for the highest level in the facility, which is usually a mixed-model final assembly line. The schedule is set to achieve the goals of the organization, which under JIT are (1) to keep a constant rate of part usage, and (2) to maintain a smooth production load. In this article we extend earlier work in the literature, which focused on the first goal, by developing scheduling procedures which satisfy both goals. Properties of the resulting production schedules are analyzed and illustrative examples are presented.     

Book reviews

America's Secret Power: the CIA in a Democratic Society. By Loch K. Johnson. Oxford University Press, New York (1989), ISBN 0–19–505490–3, $24.95

The Bundeswehr and Western Security. Edited by Stephen F. Szabo. Houndmills, Basingstoke, and Macmillan, London (1990), ISBN 0–333–49880–1, £45.00

Symbolic Defense: the Cultural Significance of the Strategic Defense Initiative. By Edward Tabor Linenthal. University of Illinois Press, Chicago, IL (1989), ISBN 0–252–01619‐X, $19.95

Rethinking European Security. Edited by Furio Cerutti and Rodolfo Ragionieri. Crane Russak, New York (1990), £29.00

Alternative Conventional Defense Postures in the European Theater, Vol. 1: The Military Balance and Domestic Constraints. Edited by Hans Günter Brauch and Robert Kennedy, Crane Russak, New York (1990), £32.00

The Gulf War. Edited by Hanns Maull and Otto Pick. Pinter, London (1989), ISBN 0–86187–763–2, £36.00     

Radar pulse interleaving for multi‐target tracking

In a multifunction radar, the maximum number of targets that can be managed or tracked is an important performance measure. Interleaving algorithms developed to operate radars exploit the dead‐times between the transmitted and the received pulses to allocate new tracking tasks that might involve transmitting or receiving pulses, thus increasing the capacity of the system. The problem of interleaving N targets involves a search among N! possibilities, and suboptimal solutions are usually employed to satisfy the real‐time constraints of the radar system. In this paper, we present new tight 0–1 integer programming models for the radar pulse interleaving problem and develop effective solution methods based on Lagrangian relaxation techniques. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Dynamic proximity calculations for situation awareness

We apply dynamic proximity calculations (density and clustering) from dynamic computational geometry to a military application. The derived proximity information serves as an abstract view of a current situation in the battlefield that can help a military commander achieve situation awareness. We employ Delaunay triangulation as a computational tool in our framework, and study its dynamic update in depth. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Preservation of stochastic orders for random minima and maxima,with applications

It is shown, in this note, that the right spread order and the increasing convex order are both preserved under the taking of random maxima, and the total time on test transform order and the increasing concave order are preserved under the taking of random minima. Some inequalities and preservation properties in reliability and economics are given as applications. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Appointment scheduling at a multidisciplinary outpatient clinic using stochastic programming

The purpose of this paper is to investigate the problem of constructing an appointment template for scheduling patients at a specific type of multidisciplinary outpatient clinic called an integrated practice unit (IPU). The focus is on developing and solving a stochastic optimization model for a back pain IPU in the face of random arrivals, an uncertain patient mix, and variable service times. The deterministic version of the problem is modeled as a mixed integer program with the objective of minimizing a weighted combination of clinic closing time (duration) and total patient waiting time (length of stay). A two‐stage stochastic program is then derived to account for the randomness and the sequential nature of the decisions. Although it was not possible to solve the two‐stage problem for even a limited number of scenarios, the wait‐and‐see (WS) problem was sufficiently tractable to provide a lower bound on the stochastic solution. The introduction of valid inequalities, limiting indices, and the use of special ordered sets helped to speed up the computations. A greedy heuristic was also developed to obtain solutions much more quickly. Out of practical considerations, it was necessary to develop appointment templates with time slots at fixed intervals, which are not available from the WS solution. The first to be derived was the expected value (EV) template that is used to find the expected value of the EV solution (EEV). This solution provides an upper bound on the objective function value of the two‐stage stochastic program. The average gap between the EEV and WS solutions was 18%. Results from extensive computational testing are presented for the EV template and for our adaptation of three other templates found in the literature. Depending on the relative importance of the two objective function metrics, the results demonstrate the trade‐off that exists between them. For the templates investigated, the “closing time” ranged from an average of 235 to 275 minutes for a 300‐minute session, while the corresponding “total patient time in clinic” ranged from 80 to 71 minutes.