The industrial gas industry represents a multi-billion dollar global market and provides essential product to manufacturing and service organizations that drive the global economy. In this dissertation, we focus on improving distribution efficiency in the industrial gas industry by addressing the strategic level problem of bulk tank allocation (BTA) while considering the effects of important operational issues. The BTA problem determines the preferred size of bulk tanks to assign to customer sites to minimize recurring gas distribution costs and initial tank installation costs. The BTA problem has a unique structure which includes a resource allocation problem and an underlying vehicle routing problem with split deliveries. In this dissertation, we provide an exact solution approach that solves the BTA problem to optimality and recommends tank allocations, provides a set of delivery routes, and determines delivery amounts to customers on each delivery route within reasonable computational time. The exact solution approach is based on a branch-and-price algorithm that solves problem instances with up to 40 customers in reasonable computational time. Due to the complexity of the problem and the size of industry representative problems, the solution approaches published in the literature rely on heuristics that require a set of potential routes as input. In this research, we investigate and compare three alternative route generation algorithms using data sets from an industry partner. When comparing the routes generation algorithms, a sweep-based heuristic was the preferred heuristic for the data sets evaluated. The existing BTA solution approaches in the literature also assume a single bulk tank can be allocated at each customer site. While this assumption is valid for some customers due to space limitations, other customer sites may have the capability to accommodate multiple tanks. We propose two alternative mathematical models to explore the possibility and potential benefits of allocating multiple tanks at designated customer site that have the capacity to accommodate more than one tank. In a case study with 20 customers, allowing multiple tank allocation yield 13% reduction in total costs. In practice, industrial gas customer demands frequently vary by time period. Thus, it is important to allocate tanks to effectively accommodate time varying demand. Therefore, we develop a bulk tank allocation model for time varying demand (BTATVD) which captures changing demands by period for each customer. Adding this time dimension increases complexity. Therefore, we present three decomposition-based solution approaches. In the first two approaches, the problem is decomposed and a restricted master problem is solved. For the third approach, a two phase periodically restricting heuristic approach is developed. We evaluate the solution approaches using data sets provided by an industrial partner and solve the problem instances with up to 200 customers. The results yield approximately 10% in total savings and 20% in distribution cost savings over a 7 year time horizon. The results of this research provide effective approaches to address a variety of distribution issues faced by the industrial gas industry. The case study results demonstrate the potential improvements for distribution efficiency. / Ph. D.

This paper focuses on how to improve strategic asset allocation in practice. Strategic asset allocation is perhaps the most fundamental issue in portfolio management and it has been thoroughly discussed in previous research. We take our starting point in the traditional work of Markowitz within portfolio optimization. We provide a new solution of how to perform portfolio optimization in practice, or more specifically how to estimate the covariance matrix, which is needed to perform conventional portfolio optimization. Many researchers within this field have noted that the return distribution of financial assets seems to vary over time, so called regime switching, which makes it dicult to estimate the covariance matrix. We solve this problem by using a Bayesian approach for developing a Markov chain Monte Carlo algorithm that detects break points in the return distribution of financial assets, thus enabling us to improve the estimation of the covariance matrix. We find that there are two break points during the time period studied and that the main difference between the periods are that the volatility was substantially higher for all assets during the period that corresponds to the financial crisis, whereas correlations were less affected. By evaluating the performance of the algorithm we find that the algorithm can increase the Sharpe ratio of a portfolio, thus that our algorithm can improve strategic asset allocation over time. / Detta examensarbete fokuserar på hur man kan förbättra tillämpningen av strategisk tillgångsslagsallokering i praktiken. Hur man allokerar kapital mellan tillgångsslag är kanske de mest fundamentala beslutet inom kapitalförvaltning och ämnet har diskuterats grundligt i litteraturen. Vårt arbete utgår från Markowitz traditionella teorier inom portföljoptimering och utifrån dessa tar vi fram ett nytt angreppssätt för att genomföra portföljoptimering i praktiken. Mer specifikt utvecklar vi ett nytt sätt att uppskatta kovar-iansmatrisen för avkastningsfördelningen för finansiella tillgångar, något som är essentiellt för att kunna beräkna de optimala portföljvikterna enligt Markowitz. Det påstås ofta att avkastningens fördelning förändras över tid; att det sker så kallade regimskiften, vilket försvårar uppskattningen av kovariansmatrisen. Vi löser detta problem genom att använda ett Bayesiansk angreppssätt där vi utvecklar en Markov chain Monte Carlo-algoritm som upptäcker brytpunkter i avkastningsfördelningen, vilket gör att uppskattningen av kovar-iansmatrisen kan förbättras. Vi finner två brytpunkter i fördelningen under den studerade tidsperioden och den huvudsakliga skillnaden mellan de olika tidsperioderna är att volatiliten var betydligt högre för samtliga tillgångar under den tidsperiod som motsvaras av finanskrisen, medan korrelationerna mellan tillgångsslagen inte påverkades lika mycket. Genom att utvärdera hur algoritmen presterar finner vi att den ökar en portföljs Sharpe ratio och således att den kan förbättra den strategiska allokeringen mellan tillgångsslagen över tid.

本研究著重於分析發行大量長年期利率敏感型契約、高財務槓桿比例的人壽保險業中公司經理人之投資決策，發現台灣壽險業亦存在Canner et al.(1997)提出之資產配置迷思，亦即風險性資產中債券與股票之比率於不同壽險公司間有差異，與共同基金分離理論中陳述之風險態度不同之投資人所持有之債券與股票比率應相同不相符。本文嘗試以Sorensen(1999)提出之擬似動態規劃法(Quasi- dynamic Programming)最適化到期之效用函數，試算經理人於股票及不同到期固定收益債券之最適持有比例。且詳細探討不同風險偏好及投資期限對於壽險公司投資組合之影響。將業主權益之最適投資策略加上負債之複製投資組合成為策略性資產配置結果，並將其與目前台灣壽險公司之資產配置做比較。研究結果顯示： 1.以擬似動態規畫法求得之最適投資組合於不同風險態度下皆為長期債券以及股票。當經理人之風險趨避程度增加時，投資於股票之比例會減少、投資於債券之比例會增加。 2.比較台灣壽險公司之債券與股票配置比例與本研究之結果發現，本資公司之風險態度較外資公司積極，本資公司應提高其債券之持有比例。 本研究最後以Bajeux-Besnainou et al. (2001)提出之資產配置迷思解釋說明本資公司與外資公司持有之債券與股票比率之所以不同非因資產配置迷思之存在，本資公司與外資公司於風險性資產中持有之債券與股票比率是相同的，但因風險態度較為趨避之公司，投資於風險性資產比率下降、提高避險部位之配置，導致整體之股票與債券比率增加。 關鍵字：資產負債管理、策略性資產配置、擬似動態規劃法。

The Nuclear Waste Fund constitutes a financial system that finances future costs of the management of spent nuclear fuel as well as decommissioning of nuclear power plants. The fund invests its capital under strict rules which are stipulated in the investment policy established by the board. The policy stipulates that the fund can only invest according to certain allocation limits, and restricts it to invest solely in nominal and inflation-linked bonds issued by the Swedish state as well as treasury securities. A norm portfolio is built to compare the performance of the NWF’s investments. On average, the NWF has outperformed the norm portfolio on recent years, but it may not always have been optimal. Recent studies suggest that allocation limits should be revised over time as the return and risk parameters may change over time. This study focused on simulating three different portfolios where the allocation limits and investment options were extended to see if these extensions would outperform the norm portfolio while maintaining a set risk limit. Portfolio A consisted of OMRX REAL and OMRX TBOND indexes, Portfolio B consisted of OMRX REAL, OMRX TBOND and S&P Sweden 1+ Year Investment Grade Corporate Bond Indexes, and Portfolio C consisted of OMXR REAL, OMRX TBOND and OMXSPI indexes. The return of each portfolio for different weight distributions of the assets were simulated in MATLAB, and polynomial regression models were built in order to optimize the return as a function of the assets’ weights using a Lagrange Multiplier approach for each portfolio. The results depicted that the maximal returns of Portfolios A, B and C were 4.00%, 4.13% and 7.93% respectively, outperforming the norm portfolio’s average return of 3.69% over the time period 2009-2016.