Operations & Supply Chain Management

We consider a general class of queueing systems with multiple job types and a flexible service facility. The arrival times and sizes of incoming jobs are random, and correlations among the sizes of arriving job types are allowed. By choosing among a finite set of configurations, the facility can dynamically control the rates at which it serves the various job types. We define system work at any given time as the minimum time required to process all jobs currently in the backlog.

We give a unified probabilistic analysis for a general class of bin packing problems by directly analyzing corresponding mathematical programs. In this general class of packing problems, objects are described by a given number of attribute values. (Some attributes may be discrete; others may be continuous.) Bins are sets of objects, and the collection of feasible bins is merely required to satisfy some general consistency properties.

We propose and analyze a heuristic that uses region partitioning and an aggregation scheme for customer attributes (load size, time windows, etc.) to create a finite number of customer types. A math program is solved based on these aggregated customer types to generate a feasible solution to the original problem. The problem class we address is quite general and defined by a number of general consistency properties.

A firm has inventories of a set of components that are used to produce a set of products. There is a finite horizon over which the firm can sell its products. Demand for each product is a stochastic point process with an intensity that is a function of the vector of prices for the products and the time at which these prices are offered. The problem is to price the finished products so as to maximize total expected revenue over the finite sales horizon. An upper bound on the optimal expected revenue is established by analyzing a deterministic version of the problem.

We consider a single-item, periodic-review inventory model with uncertain demands in which each period's production volume is limited by a capacity level. The demand distributions, capacity levels, and cost parameters vary according to a periodic pattern. We prove that modified base-stock policies are optimal for the finite-horizon planning model and for both the infinite-horizon discounted and undiscounted cost criterion. We further show that the optimal base-stock levels can be calculated via a simple but efficient value-iteration method.

In this paper we consider single machine scheduling problems with a common due-date for all jobs, arbitrary monotone earliness and tardiness costs and arbitrary breakdown and repair processes. We show that the problem is equivalent to a deterministic one without breakdowns and repairs and with an equivalent cost function of a job's completion time. A V-shaped schedule without idle times is shown to be optimal, if this equivalent cost function is quasi-convex.

We consider the problem of scheduling N jobs on M parallel machines so as to minimize the maximum earliness or tardiness cost incurred for each of the jobs. Earliness and tardiness costs are given by general (but job-independent) functions of the amount of time a job is completed prior to or after a common due date. We show that in problems with a nonrestrictive due date, the problem decomposes into two parts. Each of the M longest jobs is assigned to a different machine, and all other jobs are assigned to the machines so as to minimize their makespan.

We propose using a modification of the simple peak hour approximation (SPHA) for estimating peak congestion in multiserver queueing systems with exponential service times and time-varying periodic Poisson arrivals. This lagged pointwise stationary approximation (lagged PSA) is obtained by first estimating the time for the actual peak congestion by the time of peak congestion in an infinite server model and then substituting the arrival rate at this tiem int he corresponding stationary finite server model.

We consider multimachine scheduling problems with earliness and tardiness costs. We first analyze problems in which the cost of a job is given by a general nondecreasing, convex function F, of the absolute deviation of its completion time from a (common) unrestrictive due-date, and the objective is to minimize the sum of the costs incurred for all N jobs. (A special case to which considerable attention is given to the completion time variance problem.)