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Background: Peripheral Nerve Stimulation (PNS) is an established therapy for chronic neuropathic pain of peripheral origin, typically following nerve injury. However, there is a paucity of Randomized Controlled Trials (RCTs) demonstrating the therapeutic benefits of PNS. The goals of the current study (COMFORT Study) are to document the safety and efficacy of the Nalu Neurostimulation in a PNS RCT, compared to conventional medical management (CMM). Methods/Design: This is a prospective, multicenter, RCT evaluating the treatment of neuropathic pain with PNS therapy. One of the following four regions will be targeted for treatment: low back, shoulder, knee or foot/ankle. Consented subjects will undergo a baseline evaluation, after which they are randomized 2:1 (PNS+CMM arm to CMM arm). Subjects randomized to PNS+CMM arm will undergo a trial implant period using best clinical practices. Subjects who pass the trial phase, by showing a ≥ 50% reduction in pain relative to baseline, will receive the permanent implant. All subjects receiving a permanent implant will be followed for a total of 36 months. At the 3-month primary end point, subjects in CMM arm will be given the option to crossover into PNS+CMM arm, beginning with a trial implant. The study duration is expected to be 5.5 years from first enrollment to last follow-up of last subject and subsequent study closure. Adverse events will be captured throughout the study. Discussion: The COMFORT study, described here, has the potential to demonstrate the efficacy and safety of the Nalu Neurostimulation System in the treatment of peripheral neuropathy. Results of this study will be the first Level-I evidence, out to 36 months, validating the use of this PNS system in the treatment of chronic pain. This study is designed to enroll the largest cohort, to date, of subjects comparing PNS+CMM vs CMM alone. Plain Language Summary: Peripheral nerve stimulation (PNS) has been used for decades to treat neuropathic pain of peripheral origin. This therapy typically involves the placement small (~1 mm diameter) cylindrical electrodes (leads) near the nerve(s) in question, which is then followed by the delivery mild electrical pulses to the target, thereby blocking the pain signal from reaching the central nervous system. Despite the clinical success of this approach, there are few randomized controlled trials (RCTs) demonstrating PNS efficacy in the treatment of peripheral neuralgia/neuropathy. This may be, in large part, due to a paucity of PNS devices that are small enough to deliver this therapy at multiple locations in the extremities and the torso. For example, most implantable pulse generators (IPGs) range in size from 14 to 40 cm3 in volume. The purpose of this RCT is to demonstrate the safety and efficacy of an externally powered micro-IPG (< 1.5 cm3 in volume), in the delivery of PNS to treat peripheral neuropathic pain. Active Arm subjects will receive therapy with the micro-IPG and continue to use conventional medical management (CMM); Control Arm subjects will be treated with CMM only. The primary endpoint is the responder rate at 3-months, in both arms, defined as the percentage of subjects with ≥ 50% pain reduction from baseline following implantation of the micro-IPG. Control Arm subjects will be given the option to crossover to the Active Arm at 3-months. Study subjects in both arms are followed out to 36 months. [ABSTRACT FROM AUTHOR]

Price setting process through sealed bids mechanism for subcontracts of project activities is analyzed. A parsimonious stochastic model is used to compare expected price bids for three types of subcontracts, fixed price, cost plus, and menu contracts.

The problem of implementing an order-up-to-R replenishment policy in a periodic review inventory system with regular and emergency supply modes is examined under the assumption that the purchasing cost per unit is the same for both modes. Consequently, a model for solving the order-up-to level and the indifference inventory level is developed. It is suitable in situations where demand variance is high, the unit shortage cost is large, or the fixed order set-up cost for emergency supplies is small.

Uncapacitated network design problems subject to input data uncertainty were considered. Specifically, the concept of robustness was applied to the problem within a mixed integer programming framework to facilitate the development of algorithms based on extensions to the Benders decomposition method. The algorithms, which were implemented in VS FORTRAN, generated robust solutions for randomly generated problems.

Many decisions must be made before relevant uncertainties are revealed; hence robustness approaches to decision making are often considered. There are two major approaches to robustness in the literature: (i) maximizing the value of the worst outcome and (ii) minimizing the maximum ex post regret of the decision. While the first approach seeks the best worst‐case outcome, it may result in substantial opportunity losses relative to the ex post optimal decision; this is particularly critical in new product design decisions as it leaves the leading firm exposed to a late entrant who designs its products after uncertainties are resolved. The second approach seeks to minimize these ex post opportunity losses, but it may expose the decision maker to greater downside risk, that is, lower outcome in the worst case. Most studies have examined one of these approaches in isolation; in contrast, in this study, we explore the trade‐offs between protecting upside regret and downside risk with a focus on a product line design (PLD) problem, which relies greatly on conjoint analysis data. The PLD problem may suffer from significant uncertainty in the partworth utilities which define the attribute‐level consumer preferences on a product for different population segments. Our numerical experiments, based on a real conjoint data set, show that neither robustness approaches dominates the other and hence our proposed approach uses both. We construct a Min‐Value vs. Max‐Relative‐Regret efficient frontier to narrow down the PLD choice and propose a metric to quantify the trade‐offs and finalize the selection. [ABSTRACT FROM AUTHOR]

We study an instance of a general production problem called the `matching problem', which involves extremely tight mating tolerances for assembly. Situations exist in industry where the production equipment used to produce components cannot meet the tolerances required for assembly. In these situations components are often sorted into different size classes and then `meted' with other components of the same size class. As a specific instance of the general matching problem, we focus on the production control of components (needles and bodies) used in fuel injectors in an actual automobile subassembly production facility. In this setting the manufacturer sorts components into class sizes such that a needle of class i can be matched with a body of class i to satisfy the tolerance requirements. We study dynamic control rules that attempt to optimize the operation of this production facility within reasonable computational limits; we further develop a bound on optimal system performance. In the application studied, the performance of the recommended heuristic is within 0.2% of a bound on optimal performance., 1. Introduction We consider a production problem involving extremely tight mating tolerances for assembly, where components are sorted into different size classes and then mated with other components of the [...]

An algorithm to create robust manufacturing system layout designs was developed. This approach for layout decision making was found to satisfy the requirements of a manufacturing systems designer. Moreover, the robustness approach involves long planning horizons and inaccurate information on production volumes, thus motivating the designer search for cost-effective layouts. The algorithm was also applied to problems with single period and multiple period planning horizons.

In this paper, we address the flowpath design issue of automated guided vehicle systems (AGVSs). In particular we concentrate on the design of unidirectional flowpaths (i.e. vehicles are restricted to travel only in one direction along a given segment of the flowpath). We have developed five different heuristics for the design of unidirectional AGVSs. We have also developed simulated annealing algorithms for the above problem. Our extensive computational results indicate that a composite heuristic (i.e. one that combines the most successful of our five heuristics) yields solutions of comparable quality in a fraction of the time required by simulated annealing. For large-size flowpath design problems, we advocate the usage of composite heuristics over simulated annealing, and in particular for the cases in which inaccurate estimates for the input data in our design problem exist. [ABSTRACT FROM AUTHOR]