Taeho Park

Determination of an optimal set of design requirements using house of quality

Abstract Quality Function Deployment (QFD) has been used to translate customer needs and wants into technical design requirements in order to increase customer satisfaction. QFD utilizes the house of quality (HOQ), which is a matrix providing a conceptual map for the design process, as a construct for understanding Customer Requirements (CRs) and establishing priorities of Design Requirements (DRs) to satisfy them. Some methodological issues occurring in the conventional HOQ are discussed, and then a new integrative decision model for selecting an optimal set of DRs is presented using a modified HOQ model. The modified HOQ prioritization procedure employs a multi-attribute decision method for assigning relationship ratings between CRs and DRs instead of a conventional relationship rating scale, such as 1–3–9. The proposed decision model has been applied to an indoor air quality improvement problem as an illustrative example.

A two-phase heuristic algorithm for determining buffer sizes of production lines

In-process inventories are often used in many manufacturing systems to smooth and balance work flow. The buffer design problem of determining the best size of storage space between operations (or workstations) is one that has been approached in past and current research issues. Although past research attempted to apply traditional optimization methods to the buffer design problems, the applications revealed that some fundamental drawbacks were occurring due to the unique characteristics of the buffer design in the production lines. This paper addresses the unique characteristics involved in the buffer design, discusses drawbacks of traditional optimization methods applied, and finally presents an efficient two-phase heuristic method using a dimension reduction strategy and a buffer utilization-based beam search method, fn addition, numerical results show sensitivity analyses for the performance of the heuristic method with respect to parameters involved in the second phase of the method.

FMS design model with multiple objectives using compromise programming

There has been a great change in manufacturing systems toward computer-controlled flexible manufacturing systems (FMS). The design and operation of the FMS involve intricate and interconnected decisions that result in the maximum performance of the system. However, the design and operational decisions have been made separately in consideration of a single-system performance measure. This paper presents a method for simultaneously determining design and control parameters of an FMS with the multiple performance objectives via full-factorial design of experiments, regression analysis and compromise programming. For a numerical example, the SIMAN simulator models a hypothetical FMS with six workstations. Eight design and control parameters are simultaneously determined by compromising four performance measures that are formulated using regression analysis.

Allocating data and workload among multiple servers in a local area network

Abstract Local area networks (LANs) have become indispensable in todays highly competitive business environment. A LAN connects multiple file servers for many concurrent information retrievals. The system administrator has to resolve two allocation problems, workload and data. The allocation decisions are interrelated; a transaction is routed to the server that contains the data items requested. Affinity-based workload allocation is employed, and the database is thus fragmented into data files that are allocated across multiple servers. The papers two primary objectives are (i) to provide a rationale for workload and data allocation and (ii) to present an analytical model which will attain effective allocation policies. The integrated problem is presented in the form of a nonlinear zero-one integer program. A special structure of the problem is employed to demonstrate the complexity of the problem, and to propose an algorithm.

A MODEL FOR DETERMINING JOB THROUGHPUT TIMES FOR MANUFACTURING FLOW LINE WORKCELLS WITH FINITE BUFFERS

A model is presented for calculating the starting and finishing times of jobs processed in a group technology (GT)-structured flow line workcell in light of transient and steady state conditions. The GT-based workcell is becoming an important component of a just-in-time (JIT) strategy to gain increased productivity and proper response to customers in small-to-medium lot size manufacturing operations. Considering the four factors of machines in series, finite buffers between machines, piece-by-piece continuous flow processing, and setup times treated separately from processing times, the model provides critical information in implementing JIT such as job starting and finishing times as well as the makespan for processing a set of jobs. The recursive procedure of the model is presented in a step-by-step fashion, and illustrated through a numerical example. Furthermore, the performance of the model is evaluated through a set of industrial data by comparing results obtained from a deterministic simulation model.

Admission and inventory control of a single-component make-to-order production system with replenishment setup cost and lead time

We consider the admission control and inventory management problems of a single-component make-to-order production system. Components are purchased from suppliers in batches of fixed size subject to stochastic lead times and setup costs. A control policy specifies when a batch of components is purchased, and whether the demand for each MTO production is accepted upon arrival. We formulate the problem as a Markov decision process (MDP) model, and characterize the structure of optimal admission control and inventory replenishment policies. We show that a state dependent base-stock policy is optimal for the inventory replenishment, although the MDP value function is not necessarily convex. We also show that the optimal admission control can be identified as a lattice dependent policy. A sensitivity analysis is conducted to show how the optimal policy changes as a function of the system parameters. To effectively coordinate admission and inventory control decisions, we propose simple, implementable, and yet effective heuristic policies. Our extensive numerical results suggest that the proposed heuristics can greatly help firms to effectively coordinate their admission and inventory control activities.

Design of a manufacturing cell in consideration of multiple objective performance measures

Publisher Summary Group technology (GT)-based cellular manufacturing (CM) has emerged as a manufacturing strategy that changes manufacturing practices from functional production to small- to medium-sized batch production. Manufacturing cells composing a cellular manufacturing system (CMS) are clusters of machines or processes, located in close proximity, which are associated with the manufacture of a part family (or part families). The cell formation that is the first step in the design of a CMS is to group parts (machines) into part families (work cells) and to assign part families to the cells. The physical equipment layout of a cell also affects its performance: efficiency of the flow of materials within a cell, of the direct labor, and of the flow of product to any off-line process required. The chapter describes a method for designing a manufacturing cell (MC) with multiple performance objectives via a simulation-based experimental design method and compromise programming. In the chapter, Taguchi method is employed to establish an efficient design of experiments those can reduce the computational efforts while still obtaining enough statistical information for designing the manufacturing cell (MC).

The quest for carbon-neutral industrial operations: renewable power purchase versus distributed generation

Integrating renewable energy into the manufacturing facility is the ultimate key to realising carbon-neutral operations. Although many firms have taken various initiatives to reduce the carbon footprint of their facilities, there are few quantitative studies focused on cost analysis and supply reliability of integrating intermittent wind and solar power. This paper aims to fill this gap by addressing the following question: shall we adopt power purchase agreement (PPA) or onsite renewable generation to realise the eco-economic benefits? We tackle this complex decision-making problem by considering two regulatory options: government carbon incentives and utility pricing policy. A stochastic programming model is formulated to search for the optimal mix of onsite and offsite renewable power supply. The model is tested extensively in different regions under various climatic conditions. Three findings are obtained. First, in a long term onsite generation and PPA can avoid the price volatility in the spot or wholesale electricity market. Second, at locations where the wind speed is below 6 m/s, PPA at

Multicriteria hub decision making for rural area telecommunication networks

70/MWh is preferred over onsite wind generation. Third, compared to PPA and wind generation, solar generation is not economically competitive unless the capacity cost is down below

A Sustainable Electricity Supply Chain Based on Distributed Renewable Energy

1.5 M/MW.