Journal of the Operational Research Society

An interactive method for the vehicle routeing problem has been developed, which attempts to refine routes produced by existing methods. Improvements have been found for some of the standard problems, and the new shortest-distance solutions are reported here.

Decision environments involve the need to solve problems with varying degrees of uncertainty as well as multiple, potentially conflicting objectives. Chance constraints consider the uncertainty encountered. Codes incorporating chance constraints into a mathematical programming model are not available on a widespread basis owing to the non-linear form of the chance constraints. Therefore, accurate linear approximations would be useful to analyse this class of problems with efficient linear codes. This paper presents an approximation formula for chance constraints which can be used in either the single- or multiple-objective case. The approximation presented will place a bound on the chance constraint at least as tight as the true non-linear form, thus overachieving the chance constraint at the expense of other constraints or objectives.

Computer packages for short-term forecasting and statistical stock control usually offer largely self-contained systems with no facility for including special management or market knowledge directly in the control system. This paper describes an empirical solution developed under the limitations on time and resources commonly experienced in the commercial environment. It was successfully implemented in applications in the volume engineering and food industries. Possible applications to the shoe industry are also considered. The basic model uses a Box-Jenkins predictor with transformation factors for seasonality, forward pattern of promotions and known step increases in demand.

In the situation where a number of items must pass through a number of processes in the same order, it is suggested that the optimum (minimum total time) scheduling may be approximated by giving priority to items according to the extent to which process time increases with process number in order; this criterion is put in arithmetical form. This is confirmed by (a) a simple geometrical argument; (b) comparison with the known optimum schedules of a number of published three-process cases; (c) experiments on larger cases; it is not practical here to find the exact optimum. Methods are given for obtaining lower bounds which all scheduling times must equal or exceed; besides placing limits on the amount of effort to be expended, these bounds support the suggested approximate method.

This paper describes an approach to the valuation of draft positions for the annual National Football League and National Hockey League entry drafts. We then use this valuation to evaluate and construct exchanges of draft positions.

A review of the methods and purposes of optimization in system dynamics models is undertaken. It is suggested that, given the typical client set involved, an optimization strategy embracing search-based techniques is preferable to one based on optimal control methods. When conducting an optimization, various tactical problems may need to be resolved and ways of doing this are described. The resolution of the problems is independent of the particular software tool being used for search-based optimization. A range of studies is discussed and these studies both calibrate and exemplify the adoption of a search routine to optimize a system dynamics model.