An Efficient Constructive Heuristic to Balance Trade-Offs Between Makespan and Flowtime in Permutation Flow Shop Scheduling

Balancing trade-offs between production cost and holding cost is critical for production and operations management. Utilization of a production line affects production cost, which relates to makespan, and work-in-process (WIP) inventories in a production line affect holding cost, which relate to flowtime. There are trade-offs between two objectives, to minimize makespan and to minimize flowtime. Without addressing trade-off balancing issues in flow shop scheduling, WIP inventories are still high in manufacturing, generating unnecessary holding cost. However, utilization is coupled with WIP inventories. Low WIP inventory levels might lower utilization and generate high production cost. Most existing constructive heuristics focus only on single-objective optimization. In the current literature, the NEH heuristic proposed by Nawaz, Enscore, and Ham (1983) is the best constructive heuristic to minimize makespan, and the LR heuristic proposed by Liu and Reeves (2001) is the best to minimize flowtime. In this paper, we propose a current and future deviation (CFD) heuristic to balance trade-offs between makespan and flowtime minimizations. Based on 5400 randomly generated instances, 120 instances in Taillard’s benchmarks, and one-year historical records of operating room scheduling from University of Kentucky HealthCare (UKHC), our CFD heuristic outperforms the NEH and LR heuristics on trade-off balancing, and achieves the most stable performances from the perspective of statistical process control (SPC)

TRADE-OFF BALANCING FOR STABLE AND SUSTAINABLE OPERATING ROOM SCHEDULING

The implementation of the mandatory alternative payment model (APM) guarantees savings for Medicare regardless of participant hospitals ability for reducing spending that shifts the cost minimization burden from insurers onto the hospital administrators. Surgical interventions account for more than 30% and 40% of hospitals total cost and total revenue, respectively, with a cost structure consisting of nearly 56% direct cost, thus, large cost reduction is possible through efficient operation management. However, optimizing operating rooms (ORs) schedules is extraordinarily challenging due to the complexities involved in the process. We present new algorithms and managerial guidelines to address the problem of OR planning and scheduling with disturbances in demand and case times, and inconsistencies among the performance measures. We also present an extension of these algorithms that addresses production scheduling for sustainability. We demonstrate the effectiveness and efficiency of these algorithms via simulation and statistical analyses

Queueing theory and operations management.

Management; Theory;

Production planning under dynamic product environment: a multi-objective goal programming approach

Production planning is a complicated task that requires cooperation among multiple functional units in any organization. In order to design an efficient production planning system, a good understanding of the environment in terms of customers, products and manufacturing processes is a must. Although such planning exists in the company, it is often incorrectly structured due to the presence of multiple conflicting objectives. The primary difficulty in modern decision analysis is the treatment of multiple conflicting objectives. A formal decision analysis that is capable of handling multiple conflicting goals through the use of priorities may be a new frontier of management science. The objective of this study is to develop a multi objective goal programming (MOGP) model to a real-life manufacturing situation to show the trade-off between different some times conflicting goals concerning customer, product and manufacturing of production planning environment. For illustration, two independent goal priority structures have been considered. The insights gained from the experimentation with the two goal priority structures will guide and assist the decision maker for achieving the organizational goals for optimum utilization of resources in improving companies competitiveness. The MOGP results of the study are of very useful to various functional areas of the selected case organization for routine planning and scheduling. Some of the specific decision making situations in this context are: (i). the expected quality costs and production costs under identified product scenarios, (ii).under and over utilization of crucial machine at different combinations of production volumes, and (iii). the achievement of sales revenue goal at different production volume combinations. The ease of use and interpretation make the proposed MOGP model a powerful communication tool between top and bottom level managers while converting the strategic level objectives into concrete tactical and operational level plans.

Stochastic BI-Level Optimization Models for Efficient Operating Room Planning

Within a hospital, the operating room (OR) department has the largest cost and revenue. Because of the aging population, the demand for surgical services has been increasing sharply in recent years. At the other hand, the rate of OR capacity expansion is lower than the rate of increasing demand. As a result, OR managers must leverage their resources by efficient OR planning. OR planning is challenging because of multiple competing\conflicting objectives such cost minimization and throughput maximization. Inherent uncertainty in the surgical procedures and patients arrivals complicate the decision making process. This increases the risk of non-realization of the system objectives. In this paper, stochastic bi-level optimization models were formulated to optimize total cost and throughput of ORs under the presence of uncertainties in patient arrivals and case times. Newsvendor model and chance-constrained optimization method were used to optimize multiple objectives under the presence of uncertainties. Using historical data, a simulation model was established to validate the results of optimization models. Using statistical process control (SPC) stability of each model was investigated. Using bi-level optimization, we addressed managerial preferences over total cost and throughput. Optimizing one objective may lead to compromise on the optimality of the other objective, which generates trade-offs. Using a trade-off balancing model, we found solutions that minimize the sum of deviations from the best solutions for both total cost and throughput. Trade-off balancing optimization models may lead to better solutions, compared to traditional multi-objective optimization models. The results of this paper are applicable to manufacturing systems, where managers face multiple objectives and uncertainties in the system

Control Systems for Logistics Performance

This text is concerned with identifying and outlining the various aspects of logistics control and performance measurement process. The objective is to identify, based on available literature, methods and techniques which can be used to measure the performance of logistics. Logistics management is essentially a task of balancing between minimizing cost and ensuring availability objectives. Availability can be seen as the output of logistics system. On the input side, management is concerned with minimization of costs caused by holding inventories, warehousing, transportation, production and administration of logistics activities. Logistics control is normally directed toward two subjects: the control of logistics output or service level and the input of logistics system. Key objects of logistics control are: service level, inventory turnover, warehousing costs, transportation costs and administrative costs. The logistics control systems involves setting goals and standards for performance, measuring performance, and taking corrective actions. Various methods such as productivity ratios, flexible budgets, standards, control charts, and audits, can be used to measure the performance of logistics activities. The methods used to measure logistics activities usually provide a comparison to the past, not to the future. The nature of the problem in logistics is change. The focus must be to respond to change by projecting possible future system states. Control systems should assume possible future conditions in order to plan for response and to guide future decisions

A forecast-driven tactical planning model for a serial manufacturing system

We examine tactical planning for a serial manufacturing system that produces a product family with many process steps and low volumes. The system is subject to uncertainty in demand, in the supply of raw materials, and in yield at specific process steps. A multi-period forecast gets updated each period, and demand uncertainty is realised in terms of forecast errors. The objective of the system is to satisfy demand at a high service level with minimal operating costs. The primary means for handling the system uncertainty are inventory and production flexibility: each process step can work overtime. We model the trade-offs associated with these tactics, by building a dynamic programming model that allows us to optimise the placement of decoupling buffers across the line, as well as to determine the optimal policies for production smoothing and inventory replenishment. We test the model using both data from a real factory as well as hypothetical data. We find that the model results confirm our intuition as to how these tactics address the trade-offs; based on these tests, we develop a set of managerial insights on the application of these operating tactics. Moreover, we validate the model by comparing its outputs to that from a detailed factory simulation

Getting quality the old-fashioned way : self confirming attributions in the dynamics of process improvement

Cover title.Includes bibliographical references (p. 53-56).Supported by the NSF. SBR-9422228Nelson P. Repenning and John D. Sterman

The impact of high-mix, low volume products in semiconductor manufacturing

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2003.Includes bibliographical references (p. 75).by Vida A. Killian.S.M.M.B.A

Developing a framework to evaluate the existence of a complexity threshold

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2006.Includes bibliographical references (leaves 49-51).An automotive manufacturer facing decreasing average product volumes as a result of market fragmentation while simultaneously reducing its manufacturing plant footprint must adapt to the difficult challenge of increased product mix within its manufacturing system. The increase in complexity resulting from greater product mix is considered to be a significant driver in increasing plant investment cost and reducing plant operating effectiveness. Thus, the ability to fully understand and more effectively balance the complexity trade-offs associated with different product-to-manufacturing plant allocation scenarios is critically important, as the manufacturer formulates its strategy and analyzes the associated costs and benefits. The ultimate question to be addressed is whether there exists a "complexity threshold" in terms of the maximum number of differentiated body styles (unique vehicle models) to be produced inside a single assembly plant. This thesis analyzes the challenge of manufacturing system and plant complexity by first developing a competitive benchmark study of body-style complexity at the major North American OEMs' plants. Then, manufacturing and operations data is analyzed for evidence of a "complexity threshold" in one manufacturer's operations.(cont.) Finally, a linear-program based optimization model is developed to enable a Manufacturing Planning group to better understand the company's tolerance for plant complexity by quantifying manufacturing costs associated with various product-to-manufacturing plant allocation scenarios. This tool enables the planner to simultaneously consider thousands of different possible combinations of which products to produce in which plants, by analyzing manufacturing investment and per-vehicle operating cost estimates for each combination. The ability to impose constraints on the maximum number of body styles produced at any one plant yields insight on the value of pursuing a higher-mix (in terms of body styles) manufacturing strategy in particular plants, or across the entire plant footprint.by Matthew J. Hasik.M.B.A.S.M