Kristin A. Thoney

Adapting lean manufacturing principles to the textile industry

A research project was conducted to determine which lean principles are appropriate for implementation in the textile industry. Lean manufacturing involves a variety of principles and techniques, all of which have the same ultimate goal: to eliminate waste and non-value-added activities at every production or service process in order to give the most satisfaction to the customer. To stay competitive, many US textile manufacturers have sought to improve their manufacturing processes so that they can more readily compete with overseas manufacturers. This study identifies the different tools and principles of lean. The use of lean manufacturing in the textile industry was examined in this research through interviews, plant tours and case studies. A model for implementing lean tools and principles in a textile environment was developed.

Satisfying due-dates in large multi-factory supply chains

A procedure is developed for the simultaneous scheduling of multi-factory supply chains, including inter-factory transportation. A job-shop scheduling procedure, known to provide near-optimal solutions to industrial-sized problems, is enhanced to include transportation elements in the fundamental model. In order to demonstrate the quality of the solutions, a lower bound calculation is compared to the procedures solutions on a number of large-scale test problems. The lower bound is an enhancement of the classic lower bound calculation for the N-job, M-machine job shop. The computational effort in scheduling is linear in the size of the problem, and high quality solutions to large-scale problems can be obtained in seconds.

On satisfying due-dates in large job shops: idle time insertion

We consider the problem of minimizing maximum lateness in a job shop. A conceptually simple simulation based procedure described in a recent paper by Hodgson et al. [1] is modified to provide improved schedules. Computational experimentation is provided to identify the conditions under which the approach is most viable, and to report the procedures performance on known test problems.

Rolling horizon scheduling in large job shops

The Virtual Factory is a job shop scheduling tool that was developed at NC State. It has been shown to provide near-optimal solutions to industrial-sized problems in seconds through comparison to a computed lower bound. It is an iterative simulation-based procedure, whose objective is minimizing maximum lateness. Like many other job shop scheduling tools, the Virtual Factory has been evaluated primarily in a transient setting, even though a rolling horizon setting is more indicative of the situation in which scheduling algorithms are used in industry. Consequently, a rolling horizon procedure has been developed with which the Virtual Factory was tested. Experimental results indicate that the Virtual Factory also performs well under these circumstances.

Incorporating economies of scale into facility location problems in carpet recycling

The Carpet America Recovery Effort (CARE) set a goal to divert 40% of used carpet from landfills in the United States by 2012, but only achieved a 10% diversion rate. To achieve the 40% diversion rate, approximately 1.4 billion lbs would need to have been diverted. Diverting this significant quantity may require the design of a larger, more effective reverse logistics network to process the used materials. A new facility location heuristic originally developed for the forward distribution of products is applied to the reverse logistics system for carpet recycling. The objective is to locate an unknown number of carpet recycling facilities to minimize the total cost. The model includes transportation costs, as well as fixed facility and processing costs at the recycling plant, the latter exhibiting economies of scale (EOS) as the facility size increases. We evaluate the model using data from the CARE collection network in the continental United States and compare these findings to models that assume a significant increase in collection locations and rates to meet specific carpet diversion targets. We show the impact of EOS of the recycling facilities on the solution structure, as well as the impact that collection volumes have on the solution.

A practical method for evaluating worker allocations in large-scale dual resource constrained job shops

In two recent articles, Lobo et al. present algorithms for allocating workers to machine groups in a Dual Resource Constrained (DRC) job shop so as to minimize Lmax , the maximum job lateness. Procedure LBSA delivers an effective lower bound on Lmax , while the heuristic delivers an allocation whose associated schedule has a (usually) near-optimal Lmax  value. To evaluate an HSP-based allocation’s quality in a given DRC job shop, the authors first compute the gap between HSP’s associated Lmax  value and ’s lower bound. Next they refer this gap to the distribution of a “quasi-optimality” gap that is generated as follows: (i) independent simulation replications of the given job shop are obtained by randomly sampling each job’s characteristics; and (ii) for each replication, the associated quasi-optimality gap is computed by enumerating all feasible allocations. Because step (ii) is computationally intractable in large-scale problems, this follow-up article formulates a revised step (ii) wherein each simulation invokes , an improved version of , to yield an approximation to the quasi-optimality gap. Based on comprehensive experimentation, it is concluded that the -based distribution did not differ significantly from its enumeration-based counterpart; and the revised evaluation method was computationally tractable in practice. Two examples illustrate the use of the revised method.

Minimizing L max for large-scale, job-shop scheduling problems

The academic literature in 2000 presented a procedure for solving the job-shop-scheduling problem of minimizing L max. The iterative-adaptive simulation-based procedure is shown here to perform well on large-scale problems. However, there is potential for improvement in closing the gap between best-known solutions and the lower bound. In the present paper, a simulated annealing post-processing procedure is presented and evaluated on large-scale problems. A new neighbourhood structure for local searches in the job-shop scheduling problem is developed. The procedure is also evaluated using benchmark problems and new upper bounds are established.

Production scheduling in a knitted fabric dyeing and finishing process

Abstract Developing detailed production schedules for dyeing and finishing operations is a very difficult task that has received relatively little attention in the literature. In this paper, a scheduling procedure is presented for a knitted fabric dyeing and finishing plant that is essentially a flexible job shop with sequence-dependent setups. An existing job shop scheduling algorithm is modified to take into account the complexities of the case plant. The resulting approach based on family scheduling is tested on problems generated with case plant characteristics.

Reverse Logistics of US Carpet Recycling

A high volume of post-consumer carpet (PCC) is discarded each year in the USA, placing significant pressure on landfills and leading to the loss of valuable materials contained in carpets. To explain factors that influence landfill diversion rates for different types of products, an overview of the reverse logistics framework in the literature is provided. The framework is used to analyze the current state of carpet recycling in the USA, and PCC recycling is shown to be a typical material recovery network. Therefore, because PCC recycling requires a high volume of carpet to be collected and transportation costs to be minimized for it to be economical, a well-organized reverse logistics network is critical. In this respect, a review of reverse network design studies for different products is provided and research conducted to design PCC collection and recycling networks is discussed in detail.

Optimal data use in staple yarn manufacturing

The contemporary cotton spinning mill is home to modern machinery capable of generating a plethora of data. This data comes in the form of online data, which is real-time data created by the processing machinery, and offline data, which is created via laboratory testing of samples. This paper describes a study which applied statistical techniques to the two data sets. One came from an actual open-end spinning plant. The other was created in a laboratory environment. This analysis served to discover trends within this data sample and to determine the optimal data use for the cotton spinning industry. In addition, the paper presents a perspective into the current state of data management in the cotton spinning industry obtained by visiting an assortment of active spinning mills.