Elliott N. Weiss

Models for Determining Estimated Start Times and Case Orderings In Hospital Operating Rooms

Abstract In this paper, we address a number of scheduling problems that are often faced in a hospitals operating room (OR). The operating room can be modeled as a one machine job shop where the surgical procedures are thought of as the jobs and the operating room itself is the machine. The procedure (job) times are stochastic and the operating room scheduler exerts control over the schedule of jobs. The situation can also be thought of as a D/GI/1 queueing system, where the arrival times of the customers are a decision variable. Initially we address the problem where the scheduler is given the sequence of jobs and must determine the estimated starting times of the procedures in order that the surgeons may plan their personal schedules with respect to hospital rounds and office visits. The costs that must be balanced are (1) the idle time costs if the estimated starting time is later than the actual available start time, and (2) the surgeons waiting time if the estimated starting time is before the actua...

Local search techniques for the generalized resource constrained project scheduling problem

In this article we address the problem of scheduling a single project network with both precedence and resource constraints through the use of a local search technique. We choose a solution definition which guarantees precedence feasibility, allowing the procedure to focus on overcoming resource infeasibility. We use the 110-problem data set of Patterson to test our procedure. Our results indicate a significant improvement over the best heuristic results reported to date for these problems (Bell and Han [1]). Two major advantages of the local search algorithm are its ability to handle arbitrary objective functions and constraints and its effectiveness over a wide range of problem sizes. We present a problem example with an objective function and resource constraints which include nonlinear and non-continuous components, which are easily considered by the procedure. The results of our algorithm are significantly better than random solutions to the problem.

Guidelines for setup-cost reduction programs to achieve zero inventory

Abstract Historically, academics and managers have approached inventory management by assuming setup cost is constant. However, recently, particularly through the experience of the Japanese, we have seen that setup time and setup cost are by no means fixed. For example, one approach to reduce setup, SMED (single minute exchange of die) suggests setups can be reduced from days to minutes. Furthermore, setup reduction is identified as one of the key facilitating factors for just-in-time manufacturing. In light of this development, there is an emerging stream of literature on setup reduction. This research stream generally follows the tradition of the lot-sizing literature; the optimal amount of setup reduction is determined given a total cost model and a setup cost/reduction investment function. However, a limitation of these models is that they focus on a single item and typically include only setup and inventory costs. A key insight from the literature is the counter-intuitive notion that setup reduction yields increasing marginal returns. Thus, we consider the issues faced by a manager trying to prioritize setup reductions for multiple items. As with the SMED philosophy, where the target is to reduce all setups to less than ten minutes, we do not seek to optimize the amount of setup reduction. Instead we are concerned with establishing an economical sequence for reductions. In general, we assume setup reductions are cost-justified by direct or indirect benefits. This paper is organized into six sections. The first two sections introduce the problem and review the relevant literature. The third section determines for a single item operating under the EOQ assumptions, what fraction setup must be reduced to achieve a target order quantity. The target may be zero-inventory, which is alternately defined as an EOQ of one or lot-for-lot production. A simple graph for use by managers is provided to show the relationship of target order quantity and fraction setup reduction required. In the fourth section, the implications of setup reduction are extended to multiple items. The principal management issue in this situation is that given two items, which item should be scheduled for setup reduction first. We provide the manager with a simple guideline in the form of the savings ratio (SR), which contrasts the potential savings of the two setups. We show that the decision between two items is dependent on the dollar volume of the items adjusted for how far each item is from a period order quantity of one. Section five develops a formula for determining the maximum allowable setup cost to achieve lot-for-lot (zero-inventory) production for the case of time-varying demand. Using a marginal cost approach, we determine that a manager need only target setup reduction to justify not batching the lowest expected single period demand over the planning horizon. We show that a setup should be reduced more when a longer planning horizon is considered because there is a greater chance for a low single period demand. In the special case where there are periods of zero demand we find that the maximum setup cost may be higher because the setup cost is compared against multiple periods of carrying charges. The final section considers the application of the savings ratio concept to multiple items with time-varying demand. SR is defined only for the general case, because the unique interactions of item parameters and the calculation of total costs with the Wagner-Whitin algorithm preclude a closed-form solution. However, we did find items with greater variability and. therefore, lower expected single period demands will tend to have higher priority for setup reduction. Thus. a management objective parallel to setup reduction may be to manage the variability of demand. This research is unique in that it suggests guidelines for managers initiating setup-reduction agendas with multiple items. Our research shows that the potential savings and preferred sequence of reductions is dependent on the interactions of several item parameters (i.e., current setup cost, unit costs, total demand, demand variability, and target lot-size). To account for these interactions we introduce the savings ratio as a simple procedure to prioritize items for setup reduction. This paper concentrates on setup defined as setup time and assumes a “relevant range” of capacity in which the opportunity costs are constant per unit time. Thus. we assume that regardless of how much additional time is generated by setup-time reduction, each time unit will yield the same benefit.

Efficient Solutions to a Linear Programming Model for Production Scheduling With Capacity Constraints and No Initial Stock

Abstract In this paper we present a decomposition approach to solve large scale linear programming models for production scheduling when there are multiple capacity-constrained facilities. The formulation assumes that there are no initial inventories, and hence is most useful in a planning environment where the current shop status is not the primary concern. The approach can be implemented as an exact procedure or with heuristic stopping rules. We determine problem characteristics for which the decomposition approach is faster than LP, so that very large problems could be solved. Problem difficulty is found to be related to size and tightness of the capacity constraints. Quality-of-solution versus CPU time tradeoffs are given for various stopping rules. Finally, we discuss the potential importance of this formulation and approach in manufacturing problems.

An Optimization Based Heuristic for Scheduling Parallel Project Networks with Constrained Renewable Resources

Abstract In this paper we address the scheduling problem for the simultaneous management of multiple resource constrained project networks in parallel. This situation is common in the construction of a housing development where the building contractor is concerned with the simultaneous completion of a number of individual, identical construction projects. If only one project were managed, a resource constrained critical path method could be utilized. In this paper we present a model for the simultaneous planning of many individual project networks. We formulate the problem as an integer program that is similar to models found in the inventory/production scheduling literature. A Dantzig-Wolfe decomposition is used to obtain solutions to the linear programming relaxation of the problem. The algorithm selects a subset of these solutions to form a final schedule. Computational results are presented. Extensions of the algorithm, including non-identical projects, alternative objective functions and implementati...

Fly now or fly later? The delayed consumption problem

Abstract The Analytic Hierarchy Process (AHP) is a decision analysis technique that uses judgments from a group of relevant decision makers along with hierarchical decomposition to derive a set of ratio-scaled measures for decision alternatives. This paper addresses implementation issues for the AHP when the problem is one of assessing delayed vs. immediate consumption. The tradeoff that must be evaluated is the immediate benefit of using ones limited resources for the purchase or acquisition of a good or service against the delayed benefit of waiting for future purchase or acquisition. We also allow for the possibility of an increased (or decreased) amount of available resources over time. We develop a technique that combines a dynamic programming algorithm with the AHP approach. Although we present a sample implementation of this technique for the decision of cashing in accumulated frequent flyer points for airline premiums, the technique presented in this paper can be applied to many multiple-criteria, time-dependent decision problems such as production-inventory scheduling, investment in new technologies and aggregate planning. The procedure involves prioritizing criteria of the possible alternatives, scoring the alternatives and then comparing the score of an alternative within a time period with the gains that can be accrued through delayed consumption.

Lean Tools for Service Business Model Innovation in Healthcare

In this chapter, we describe lessons learned from a number of lean process improvement projects we have implemented at the University of Virginia Health System (UVAHS). Working at multiple levels, facilities, and geographic locations has enabled us to become familiar with the organization’s vast range of goals, initiatives, and needs. Lean project locations have included a remote clinic, the departments of medicine and radiology, and the advanced microscopy core facility. Other efforts have included the clinical research process; instructional support, billing and payroll processes; hiring, credentialing and onboarding processes; and high-level budgeting and planning. The chapter describes common themes and principles for successful lean transformations in health care operations. Most of these begin with a value stream analysis of a process whose value has never been evaluated across organizational or departmental (silo) boundaries or for which embracing continuous improvement as a way of life has never been seriously attempted. The emphasis is on implementation challenges.

Structural Supply Chain Collaboration Among Grocery Manufacturers

Both academicians and practitioners have extensively explored the important topic of supply chain collaboration. Most research, however, has focused on unilateral coordination via pricing signals or tactical opportunities such as Vendor Managed Inventory or Collaborative Planning, Forecasting and Replenishment (CPFR). Our research fills a gap in the current literature by quantifying the opportunity for strategic, multi-lateral collaboration through a shared distribution network. Specifically, we examine the opportunity for manufacturers to consolidate distribution networks to serve a set of common retailer customers. Through cost modeling of a factorial combination of scenarios, we show that:

Analysis of investments in autonomous maintenance activities

Abstract In this paper, we model the situation where operator maintenance activities improve the failure process of equipment. We analyze the business decision to reduce both the mean and variance of the production cycle time and the overall inventory level through an investment in planned autonomous maintenance. We answer: (i) when do optimal autonomous maintenance decisions most improve inventory levels?; and (ii) how do capacity restrictions, equipment characteristics the maintenance response function, and product characteristics impact the autonomous maintenance investment decision? Extensive numerical analyses are performed to develop an approximation to the optimal response for both inventory and autonomous maintenance investments over a wide range of problem parameters. Our solutions provide guidelines on how much time should be invested in autonomous maintenance activities and describe when companies can most benefit from autonomous maintenance programs that increase equipment reliability. We determine the investment in autonomous maintenance activities as a function of available capacity, equipment reliability and demand characteristics.

ITT Defense: Electro Optical Products Division

This case describes an award-winning quality-management program. The dilemma facing Marshall Bowden, manager of Operations, concerns the selection of a process for the manufacture of night-vision goggles. One choice would require planned rework, which would cost less than the choice requiring the least rework or the most costly option. The low-cost option runs exactly counter to the philosophy that won the division its achievement awards. Videotape #8223, “Tim Reid,” is designed for use with this case.