Single- and multiple-processor models for minimizing completion time variance

This article concerns the scheduling of n jobs around a common due date, so as to minimize the average total earliness plus total lateness of the jobs. Optimality conditions for the problem are developed, based on its equivalence to an easy scheduling problem. It seems that this problem inherently has a huge number of optimal solutions and an algorithm is developed to find many of them. The model is extended to allow for the availability of multiple parallel processors and an efficient algorithm is developed for that problem. In this more general case also, the algorithm permits great flexibility in finding an optimal schedule.     

Optimal job splitting on a multi‐slot machine with applications in the printing industry

In this article, we define a scheduling/packing problem called the Job Splitting Problem, motivated by the practices in the printing industry. There are n types of items to be produced on an m‐slot machine. A particular assignment of the types to the slots is called a “run” configuration and requires a setup cost. Once a run begins, the production continues according to that configuration and the “length” of the run represents the quantity produced in each slot during that run. For each unit of production in excess of demand, there is a waste cost. Our goal is to construct a production plan, i.e., a set of runs, such that the total setup and waste cost is minimized. We show that the problem is strongly NP‐hard and propose two integer programming formulations, several preprocessing steps, and two heuristics. We also provide a worst‐case bound for one of the heuristics. Extensive tests on real‐world and randomly generated instances show that the heuristics are both fast and effective, finding near‐optimal solutions. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Using real options analysis to value reoptimization options in the shifting bottleneck heuristic

The reoptimization procedure within the shifting bottleneck (SB) involves reevaluation of all previously scheduled toolgroup subproblems at each iteration of the SB heuristic. A real options analysis (ROA) model is developed to value the option to reoptimize in the SB heuristic, such that reoptimization only occurs when it is most likely to lead to a schedule with a lower objective function. To date, all ROA models have sought to value options financially (i.e., in terms of monetary value). The ROA model developed in this paper is completely original in that it has absolutely no monetary basis. The ROA methodologies presented are shown to greatly outperform both full and no reoptimization approaches with respect to both computation time and total weighted tardiness. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

A nonidentical parallel processor scheduling problem

The problem considered in this article is a generalization of the familiar makespan problem, in which n jobs are allocated among m parallel processors, so as to minimize the maximum time (or cost) on any processor. Our problem is more general, in that we allow the processors to have (a) different initial costs, (b) different utilization levels before new costs are incurred, and (c) different rates of cost increase. A heuristic adapted from the bin-packing problem is shown to provide solutions which are close to optimal as the number of iterations is allowed to increase. Computational testing, over a large number of randomly generated problem instances, suggests that heuristic errors are, on average, very small.     

Evergreening and operational risk under price competition

“Evergreening” is a strategy wherein an innovative pharmaceutical firm introduces an upgrade of its current product when the patent on this product expires. The upgrade is introduced with a new patent and is designed to counter competition from generic manufacturers that seek to imitate the firm's existing product. However, this process is fraught with uncertainty because the upgrade is subject to stringent guidelines and faces approval risk. Thus, an incumbent firm has to make an upfront production capacity investment without clarity on whether the upgrade will reach the market. This uncertainty may also affect the capacity investment of a competing manufacturer who introduces a generic version of the incumbent's existing product but whose market demand depends on the success or failure of the upgrade. We analyze a game where capacity investment occurs before uncertainty resolution and firms compete on prices thereafter. Capacity considerations that arise due to demand uncertainty introduce new factors into the evergreening decision. Equilibrium analysis reveals that the upgrade's estimated approval probability needs to exceed a threshold for the incumbent to invest in evergreening. This threshold for evergreening increases as the intensity of competition in the generic market increases. If evergreening is optimal, the incumbent's capacity investment is either decreasing or nonmonotonic with respect to low end market competition depending on whether the level of product improvement in the upgrade is low or high. If the entrant faces a capacity constraint, then the probability threshold for evergreening is higher than the case where the entrant is not capacity constrained. Finally, by incorporating the risk‐return trade‐off that the incumbent faces in terms of the level of product improvement versus the upgrade success probability, we can characterize policy for a regulator. We show that the introduction of capacity considerations may maximize market coverage and/or social surplus at incremental levels of product improvement in the upgrade. This is contrary to the prevalent view of regulators who seek to curtail evergreening involving incremental product improvement. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 71–89, 2016     

A note on efficient sequences with respect to total flow time and number of tardy jobs

For the single‐machine scheduling problem with the objective of simultaneously minimizing total flow time and number of tardy jobs, a lower bound on the number of efficient sequences is known. However, the proof thereof, which makes use of a modified version of Smith's algorithm, is unduly lengthy and sophisticated. Adopting a totally new point of view, we present in this short article a much simpler proof based on the naive idea of pairwise interchange. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 346–348, 2016     

Variance of the number of units produced on a transfer line with buffer inventories during a period of length T

The transfer-line models in the literature are planning models rather than operational models. That is, they are very useful for planning or designing the transfer line, but are less useful for controlling the daily operation of the line. The performance measure used in these models is the efficiency of the line A. The expected number of units produced during a period of length T cycles is AT. In this article a procedure is presented for calculating the variance of the number of units produced by the transfer line during a period of length T cycles. These two performance measures can be used to construct an interval estimate for, say, the number of units produced during a shift. This interval estimate is an operational guide for the production manager.     

Facility location when demand is time dependent

In this article we investigate the problem of locating a facility among a given set of demand points when the weights associated with each demand point change in time in a known way. It is assumed that the location of the facility can be changed one or more times during the time horizon. We need to find the time “breaks” when the location of the facility is to be changed, and the location of the facility during each time segment between breaks. We investigate the minisum Weber problem and also minimax facility location. For the former we show how to calculate the objective function for given time breaks and optimally solve the rectilinear distance problem with one time break and linear change of weights over time. Location of multiple time breaks is also discussed. For minimax location problems we devise two algorithms that solve the problem optimally for any number of time breaks and any distance metric. These algorithms are also applicable to network location problems.     

Interactive optimization methodology for fleet scheduling

This article addresses the problem of scheduling the United States Navy's Atlantic Fleet to satisfy overseas strategic requirements. An integer programming formulation is developed but results in a model with prohibitive size. This fact and the qualitative nature of additional secondary objectives and constraints suggest an interactive optimization approach. A system that solves a natural relaxation of the integer program within an interactive environment is discussed.