Thesis (Ph. D.)--Ohio State University, 1984. / Includes bibliographical references (leaves 171-177). Available online via OhioLINK's ETD Center.

Our work focuses on an analysis of the processes for full and partial annealing of aluminium coils. Due to inefficient production management these processes show high inventory levels, long lead times and decreased delivery performance. We have also found inadequacies in the ways costs for these processes are distributed. We have established a new process mapping by initially investigating the strategic dimensions of the company and the processes for annealing, in order to later on establish performance measures congruent with the business objectives. Furthermore we have conducted extensive calculations and analyses to facilitate the successful implementation of a Just-in-time production philosophy, including necessary process improvements and redesigns to be made. Our proposed changes will lead to shorter lead times and low levels of WIP, which are important success factors of a JIT-based production philosophy. To do this we have developed a capacity analysis tool with which it is also possible to analyse other processing scenarios or the effect of load changes and/or product mix variations. This tool can also serve as a benchmark for capacity analysis of other processes. Finally, we have been able to establish more accurate costs per machine hour for full and partial annealing to be implemented in the managerial system. We believe that the processes for annealing are not the only ones suffering from poor cost control, why we would suggest that Sapa Heat Transfer investigates the cost distribution in more processes, and also develops and follows better guidelines for cost control.

In this thesis, we study a series of closely related multi-stage stochastic programming models in production planning, from both a modeling and an algorithmic point of view. We first consider a very simple multi-stage stochastic lot-sizing problem, involving a single item with no fixed charge and capacity constraint. Although a multi-stage stochastic integer program, this problem can be shown to have a totally unimodular constraint matrix. We develop primal and dual algorithms by exploiting the problem structure. Both algorithms are strongly polynomial, and therefore much more efficient than the Simplex method. Next, motivated by applications in semiconductor tool planning, we develop a general capacity planning problem under uncertainty. Using a scenario tree to model the evolution of the uncertainties, we present a multi-stage stochastic integer programming formulation for the problem. In contrast to earlier two-stage approaches, the multi-stage model allows for revision of the capacity expansion plan as more information regarding the uncertainties is revealed. We provide analytical bounds for the value of multi-stage stochastic programming over the two-stage approach. By exploiting the special simple stochastic lot-sizing substructure inherent in the problem, we design an efficient approximation scheme and show that the proposed scheme is asymptotically optimal. We conduct a computational study with respect to a semiconductor-tool-planning problem. Numerical results indicate that even an approximate solution to the multi-stage model is far superior to any optimal solution to the two-stage model. These results show that the value of multi-stage stochastic programming for this class of problem is extremely high. Next, we extend the simple stochastic lot-sizing model to an infinite horizon problem to study the planning horizon of this problem. We show that an optimal solution of the infinite horizon problem can be approximated by optimal solutions of a series of finite horizon problems, which implies the existence of a planning horizon. We also provide a useful upper bound for the planning horizon.

Trotz genauer Produktionsplanung und -steuerung mit modernen Algorithmen beobachtet man in Fabriksystemen immer wieder plötzliche und unvorhergesehene Zusammenbrüche der Materialströme. Erstaunlicherweise scheinen diese Kapazitätseinbrüche nur wenig durch die aktuelle Pufferauslastung beeinflusst zu sein. Um die entsprechenden Effekte zu unterbinden, wird in diesem Aufsatz eine neuartige Strategie zur Selbstorganisation individueller Güter in Transportund Puffersystemen vorgestellt. Durch ein kooperatives („soziales“) Verhalten dieser Güter lassen sich deren gegenseitige Behinderungen reduzieren, was zu einer deutlichen Harmonisierung des Materialflusses in der innerbetrieblichen Logistik führt. Das vorgeschlagene Konzept wird an einer beispielhaften Fabrik illustriert. Im Rahmen eines erfolgreichen Modellprojekts wird seine praktische Umsetzung derzeit bei einem englischen Verpackungshersteller erprobt. / Despite careful production planning and control using modern algorithms, one can repeatedly observe sudden and unpredicted breakdowns of the material flows in factory systems. Surprisingly, these capacity dips are only marginally affected by the current buffer storage level. To enable prevention of these effects, this paper describes a new strategy for the self-organisation of individual goods in material handling systems. Where the behaviour of the individual goods is cooperative (“social”), their mutual hindrances can be reduced, which in turn leads to a noticeable smoothing of the intra-plant material flows. The suggested concept will be illustrated with a factory example. Its practical applicability is currently being tested within the scope of a successful pilot project with a packaging manufacturer.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

No abstract available. / Thesis (M.T.R.P)-University Natal, Durban, 1999.

This research focuses on carbon emission-reduction issues in an area where the government imposes emission-reduction policies on local manufacturers. Policymaking problems for the government and production planning problems for the manufacturers are investigated with Operations Research/Management Science (OR/MS) approaches. Two types of emission-reduction policies, including emission-cap regulation policy and emission cap-and-trade scheme, are addressed. We first discuss manufacturers' long-term strategic decision problem under the government-imposed emission-cap regulation policy. With the objective of maximizing the manufacturers' profits, Stackelberg game model is formulated to optimize their decisions on carbon footprint, wholesale price and retailer selection. The problem is proven to be NP hard and a hybrid algorithm is developed to solve the model. We then investigate manufacturers' medium-term production planning to minimize the total production and inventory holding cost, by considering emission-reduction constraints through technology selection, some of the technologies being green. The problems are shown to be polynomially solvable. Based on these results, we study the government's policymaking problems to maximize the social welfare of the area. Stackelberg game models are formulated to optimize the emission-reduction policies by anticipating manufacturers' operational decisions in response to the governmental policies. Hybrid algorithms are developed to solve the problems. For each studied problem, numerical analyses are conducted to evaluate the algorithms. The computation results show that the algorithms developed in this research are effective. Some interesting and valuable managerial insights are drawn from computational results and sensitivity analyses.

JS McMillan Fisheries Ltd. (JSM) is a Vancouver-based company with operations in nearly all levels of the commercial fishing industry, from supply through distribution. The heart of the operation is the processing facilities where freshly caught Pacific salmon are prepared for sale to end consumers and institutional buyers. As the operations of JSM evolved, the decision making for allocating a catch of salmon with varying characteristics amongst a set of final products has become too complex and time consuming. The focus of this study is to determine an effective and efficient method for JSM to allocate daily a fresh salmon harvest between the various products they produce on a daily basis. The goal is short-term production planning, to allocate the catch among the products in such a manner that the profit potential of the catch is maximized, i.e. prepare a production schedule that maximizes the total profit over the planning horizon. Additional goals of this project include: automation of the decision making process for the catch allocation, "what if" planning, decreasing expert dependency, reducing decision making time, and building a practical and innovative decision support tool. In order to solve this problem efficiently and effectively, optimization models were developed for allocating the catch to the end products. A corresponding decision support tool was built for the end-users at JSM.

In this dissertation we examine several discrete optimization problems through the perspectives of modeling, complexity and algorithms. We first provide a probabilistic comparison of split and type 1 triangle cuts for mixed-integer programs with two rows and two integer variables in terms of cut coefficients and volume cutoff. Under a specific probabilistic model of the problem parameters, we show that for the above measure, the probability that a split cut is better than a type 1 triangle cut is higher than the probability that a type 1 triangle cut is better than a split cut. The analysis also suggests some guidelines on when type 1 triangle cuts are likely to be more effective than split cuts and vice versa. We next study a minimum concave cost network flow problem over a grid network. We give a polytime algorithm to solve this problem when the number of echelons is fixed. We show that the problem is NP-hard when the number of echelons is an input parameter. We also extend our result to grid networks with backward and upward arcs. Our result unifies the complexity results for several models in production planning and green recycling including the lot-sizing model, and gives the first polytime algorithm for some problems whose complexities were not known before. Finally, we examine how much complexity randomness will bring to a simple combinatorial optimization problem. We study a problem called the sell or hold problem (SHP). SHP is to sell k out of n indivisible assets over two stages, with known first-stage prices and random second-stage prices, to maximize the total expected revenue. Although the deterministic version of SHP is trivial to solve, we show that SHP is NP-hard when the second-stage prices are realized as a finite set of scenarios. We show that SHP is polynomially solvable when the number of scenarios in the second stage is constant. A max{1/2,k/n}-approximation algorithm is presented for the scenario-based SHP.

For the last two decades, demand for poultry meat has been soared, since it is healthier and less costly than its substitutes. In order to meet this increasing demand, integrated poultry organizations have been established all over the world. Usually, an integrated poultry organization has the divisions of breeder coops, incubation house, broiler coops, feed mill, slaughterhouse and marketing. This complex structure makes production planning activities more difficult for integrated poultry organizations. The aim of this study is to propose a production and financial planning model for &Ouml / nder Integrated Poultry Incorporation using mathematical modelling techniques and statistical methods.