Major League Baseball (MLB) is a 30-team North American professional baseball league and Minor League Baseball (MiLB) is the hierarchy of developmental professional baseball teams for MLB. Most MLB players first develop their skills in MiLB, and MLB teams employ scouts, experts who evaluate the strengths, weaknesses, and overall potential of these players. In this dissertation, we study the problem of designing a scouting network for a Major League Baseball (MLB) team. We introduce the problem to the operations research literature to help teams make strategic and operational level decisions when managing their scouting resources. The thesis consists of three chapters that aim to address decisions such as how the scouts should be assigned to the available MiLB teams, how the scouts should be routed around the country, how many scouts are needed to perform the major scouting tasks, are there any trade-off s between the scouting objectives, and if there are any, what are the outcomes and insights. In the first chapter, we study the problem of assigning and scheduling minor league scouts for Major League Baseball (MLB) teams. There are multiple objectives in this problem. We formulate the problem as an integer program, use decomposition and both column-generation-based and problem-specific heuristics to solve it, and evaluate policies on multiple objective dimensions based on 100 bootstrapped season schedules. Our approach can allow teams to improve operationally by finding better scout schedules, to understand quantitatively the strategic trade-offs inherent in scout assignment policies, and to select the assignment policy whose strategic and operational performance best meets their needs. In the second chapter, we study the problem under uncertainty. In reality we observe that there are always disruptions to the schedules: players are injured, scouts become unavailable, games are delayed due to bad weather, etc. We presented a minor league baseball season simulator that generates random disruptions to the scout's schedules and uses optimization based heuristic models to recover the disrupted schedules. We evaluated the strategic benefits of different policies for team-to-scout assignment using the simulator. Our results demonstrate that the deterministic approach is insufficient for evaluating the benefits and costs of each policy, and that a simulation approach is also much more effective at determining the value of adding an additional scout to the network. The real scouting network design instances we solved in the first two chapters have several detailed complexities that can make them hard to study, such as idle day constraints, varying season lengths, off days for teams in the schedule, days where some teams play and others do not, etc. In the third chapter, we analyzed a simplified version of the Single Scout Problem (SSP), stripping away much of the real-world complexities that complicate SSP instances. Even for this stylized, archetypal version of SSP, we find that even small instances can be computationally difficult. We showed by reduction from Minimum Cost Hamiltonian Path Problem that archetypal version of SSP is NP-complete, even without all of the additional complexity introduced by real scheduling and scouting operations.

This thesis aims to review and analyze the scheduling problem in general and Job Shop Scheduling Problem (JSSP) in particular and the solution techniques applied to these problems. The JSSP is the most general and popular hard combinational optimization problem in manufacturing systems. For the past sixty years, an enormous amount of research has been carried out to solve these problems. The literature review showed the inherent shortcomings of solutions to scheduling problems. This has directed researchers to develop hybrid approaches, as no single technique for scheduling has yet been successful in providing optimal solutions to these difficult problems, with much potential for improvements in the existing techniques. The hybrid approach complements and compensates for the limitations of each individual solution technique for better performance and improves results in solving both static and dynamic production scheduling environments. Over the past years, hybrid approaches have generally outperformed simple Genetic Algorithms (GAs). Therefore, two novel priority heuristic rules are developed: Index Based Heuristic and Hybrid Heuristic. These rules are applied to benchmark JSSP and compared with popular traditional rules. The results show that these new heuristic rules have outperformed the traditional heuristic rules over a wide range of benchmark JSSPs. Furthermore, a hybrid GA is developed as an alternate scheduling approach. The hybrid GA uses the novel heuristic rules in its key steps. The hybrid GA is applied to benchmark JSSPs. The hybrid GA is also tested on benchmark flow shop scheduling problems and industrial case studies. The hybrid GA successfully found solutions to JSSPs and is not problem dependent. The hybrid GA performance across the case studies has proved that the developed scheduling model can be applied to any real-world scheduling problem for achieving optimal or near-optimal solutions. This shows the effectiveness of the hybrid GA in real-world scheduling problems. In conclusion, all the research objectives are achieved. Finaly, the future work for the developed heuristic rules and the hybrid GA are discussed and recommendations are made on the basis of the results.

The group scheduling problem commonly arises in cellular manufacturing systems, where parts are grouped into part families. It is characterized by a sequencing task on two levels: on the one hand, a sequence of jobs within each part family has to be identified while, on the other hand, a family sequence has to be determined. In order to solve this NP-hard problem usually heuristic solution approaches are used. In this thesis different aspects of group scheduling are discussed and problem specific heuristics are developed to solve group scheduling problems efficiently. Thereby, particularly characteristic properties of flowshop group scheduling problems, such as the structure of a group schedule or missing operations, are identified and exploited. In a simulation study for job shop manufacturing cells several novel dispatching rules are analyzed. Furthermore, a comprehensive review of the existing group scheduling literature is presented, identifying fruitful directions for future research.

Thesis (PhD (Logistics))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: Packing problems are common in industry and there is a large body of literature on the subject. Two packing problems are considered in this dissertation: the strip packing problem and the bin packing problem. The aim in both problems is to pack a speci ed set of small items, the dimensions of which are all known prior to packing (hence giving rise to an o ine problem), into larger objects, called bins. The strip packing problem requires packing these items into a single bin, one dimension of which is unbounded (the bin is therefore referred to as a strip). In two dimensions the width of the strip is typically speci ed and the aim is to pack all the items into the strip, without overlapping, so that the resulting packing height is a minimum. The bin packing problem, on the other hand, is the problem of packing the items into a speci ed set of bins (all of whose dimensions are bounded) so that the wasted space remaining in the bins (which contain items) is a minimum. The bins may all have the same dimensions (in which case the problem is known as the single bin size bin packing problem), or may have di erent dimensions, in which case the problem is called the multiple bin size bin packing problem (MBSBPP). In two dimensions the wasted space is the sum total of areas of the bins (containing items) not covered by items. Many solution methodologies have been developed for above-mentioned problems, but the scope of the solution methodologies considered in this dissertation is restricted to heuristics. Packing heuristics follow a xed set of rules to pack items in such a manner as to nd good, feasible (but not necessarily optimal) solutions to the strip and bin packing problems within as short a time span as possible. Three types of heuristics are considered in this dissertation: (i) those that pack items into levels (the heights of which are determined by the heights of the tallest items in these levels) in such a manner that all items are packed along the bottom of the level, (ii) those that pack items into levels in such a manner that items may be packed anywhere between the horizontal boundaries that de ne the levels, and (iii) those heuristics that do not restrict the packing of items to levels. These three classes of heuristics are known as level algorithms, pseudolevel algorithms and plane algorithms, respectively. A computational approach is adopted in this dissertation in order to evaluate the performances of 218 new heuristics for the strip packing problem in relation to 34 known heuristics from the literature with respect to a set of 1 170 benchmark problem instances. It is found that the new level-packing heuristics do not yield signi cantly better solutions than the known heuristics, but several of the newly proposed pseudolevel heuristics do yield signi cantly better results than the best of the known pseudolevel heuristics in terms of both packing densities achieved and computation times expended. During the evaluation of the plane algorithms two classes of heuristics were identi ed for packing problems, namely sorting-dependent and sortingindependent algorithms. Two new sorting techniques are proposed for the sorting-independent algorithms and one of them yields the best-performing heuristic overall. A new heuristic approach for the MBSBPP is also proposed, which may be combined with level and pseudolevel algorithms for the strip packing problem in order to nd solutions to the problem very rapidly. The best-performing plane-packing heuristic is modi ed to pack items into the largest bins rst, followed by an attempted repacking of the items in those bins into smaller bins with the aim of further minimising wasted space. It is found that the resulting plane-packing algorithm yields the best results in terms of time and packing density, but that the solution di erences between pseudolevel algorithms are not as marked for the MBSBPP as for the strip packing problem. / AFRIKAANSE OPSOMMING: Inpakkingsprobleme kom algemeen in die industrie voor en daar is 'n aansienlike volume literatuur oor hierdie onderwerp. Twee inpakkingsprobleme word in hierdie proefskrif oorweeg, naamlik die strook-inpakkingsprobleem en die houer-inpakkingsprobleem. In beide probleme is die doel om 'n gespesi seerde versameling klein voorwerpe, waarvan die dimensies almal voordat inpakking plaasvind, bekend is (en die probleem dus 'n sogenaamde a yn-probleem is), in een of meer groter houers te pak. In die strook-inpakkingsprobleem word hierdie voorwerpe in een houer, waarvan een dimensie onbegrens is, ingepak (hierdie houer word dus 'n strook genoem). In twee dimensies word die wydte van die strook gewoonlik gespesi seer en is die doel om al die voorwerpe sonder oorvleueling op s o 'n manier in die strook te pak dat die totale inpakkingshoogte geminineer word. In die houer-inpakkingsprobleem, daarenteen, is die doel om die voorwerpe op s o 'n manier in 'n gespesi seerde aantal houers (waarvan al die dimensies begrens is) te pak dat die vermorste of oorblywende ruimte in die houers (wat wel voorwerpe bevat) 'n minimum is. Die houers mag almal dieselfde dimensies h^e (in welke geval die probleem as die enkelgrootte houer-inpakkingsprobleem bekend staan), of mag verskillende dimensies h^e (in welke geval die probleem as die veelvuldige-grootte houer-inpakkingsprobleem bekend staan, afgekort as VGHIP). In twee dimensies word die vermorste ruimte geneem as die somtotaal van daardie deelareas van die houers (wat wel voorwerpe bevat) waar daar geen voorwerpe geplaas word nie. Verskeie oplossingsmetodologie e is al vir die bogenoemde twee inpakkingsprobleme ontwikkel, maar die bestek van die metodologie e wat in hierdie proefskrif oorweeg word, word beperk tot heuristieke. 'n Inpakkingsheuristiek volg 'n vaste stel re els waarvolgens voorwerpe in houers gepak word om so spoedig moontlik goeie, toelaatbare (maar nie noodwendig optimale) oplossings tot die strook-inpakkingsprobleem en die houer-inpakkingsprobleem te vind. Drie tipes inpakkingsheuristieke word in hierdie proefskrif oorweeg, naamlik (i) heuristieke wat voorwerpe langs die onderste randte van horisontale vlakke in die houers pak (die hoogtes van hierdie vlakke word bepaal deur die hoogtes van die hoogste item in elke vlak), (ii) heuristieke wat voorwerpe op enige plek binne horisontale stroke in die houers pak, en (iii) heuristieke waar inpakking nie volgens horisontale vlakke of stroke beperk word nie. Hierdie drie klasse heuristieke staan onderskeidelik as vlakalgoritmes, pseudo-vlakalgoritmes en platvlakalgoritmes bekend. 'n Berekeningsbenadering word in hierdie proefskrif gevolg deur die werkverrigting van die 218 nuwe heuristieke vir die strook-inpakkingsprobleem met die werkverrigting van 34 bekende heuristieke uit die literatuur te vergelyk, deur al die heuristieke op 1 170 toetsprobleme toe te pas. Daar word bevind dat die nuwe vlakalgoritmes nie 'n noemenswaardige verbetering in oplossingskwaliteit in vergeleke met soortgelyke bestaande algoritmes in die literatuur lewer nie, maar dat verskeie nuwe pseudo-vlakalgoritmes wel noemenswaardige verbeteringe in terme van beide inpakkingsdigthede en oplossingstye in vergeleke met die beste bestaande algoritmes in die literatuur lewer. Assessering van die platvlakalgoritmes het gelei tot die identi kasie van twee deelklasse van algoritmes, naamlik sorteringsafhanklike- en sorteringsonafhanklike algoritmes. Twee nuwe sorteringstegnieke word ook vir die deelklas van sorteringsonafhanklike algoritmes voorgestel, en een van hulle lewer die algeheel beste inpakkingsheursitiek. 'n Nuwe heuristiese benadering word ook vir die VGHIP ontwikkel. Hierdie benadering kan met vlak- of pseudo-vlakalgoritmes vir die strook-inpakkingsprobleem gekombineer word om baie vinnig oplossings vir die VGHIP te vind. Die beste platvlakheuristiek vir die strookinpakkingsprobleem word ook aangepas om voorwerpe eers in die grootste houers te pak, en daarna in kleiner houers te herpak met die doel om vermorste ruimte verder te minimeer. Daar word bevind dat die resulterende platvlakalgoritme die beste resultate in terme van beide inpakkingsdigtheid en oplossingstyd lewer, maar dat oplossingsverskille tussen die pseudovlakalgoritmes nie so opmerklik vir die VGHIP is as wat die geval met die strookinpakkingsprobleem was nie.

Scheduling large real world problems is a complex process and finding high quality solutions is not a trivial task. In cooperation with Trimble MRM Ltd., who provide scheduling solutions for many large companies, a problem is identified and modelled. It is a general model which encapsulates several important scheduling, routing and resource allocation problems in literature. Many of the state-of-the-art heuristics for solve scheduling problems and indeed other problems require specialised heuristics tailored for the problem they are to solve. While these provide good solutions a lot of expert time is needed to study the problem, and implement solutions. This research investigates methods to enhance existing search based methods. We study hyperheuristic techniques as a general search based heuristic. Hyperheuristics raise the generality of the solution method by using a set of tools (low level heuristics) to work on the solution. These tools are problem specific and usually make small changes to the problem. It is the task of the hyperheuristic to determine which tool to use and when. Low level heuristics using exact/heuristic hybrid method are used in this thesis along with a new Tabu based hyperheuristic which decreases the amount of CPU time required to produce good quality solutions. We also develop and investigate the Variable Fitness Function approach, which provides a new way of enhancing most search-based heuristics in terms of solution quality. If a fitness function is pushing hard in a certain direction, a heuristic may ultimately fail because it cannot escape local minima. The Variable Fitness Function allows the fitness function to change over the search and use objective measures not used in the fitness calculation. The Variable Fitness Function and its ability to generalise are extensively tested in this thesis. The two aims of the thesis are achieved and the methods are analysed in depth. General conclusions and areas of future work are also identified.

A just-in-time supply pickup and delivery system (JSS) manages the logistic operations between a manufacturing plant and its suppliers by controlling the sequence, timing, and frequency of container pickups and parts deliveries, thereby coordinating internal conveyance, external conveyance, and the operation of cross-docking facilities. The system is important to just-in-time production lines that maintain small inventories. This research studies the logistics, supply chain, and production control of JSS. First, a new meta-heuristics approach (taboo search) is developed to solve a general frequency routing (GFR) problem that has been formulated in this dissertation with five types of constraints: flow, space, load, time, and heijunka. Also, a formulation for cross-dock routing (CDR) has been created and solved. Second, seven issues concerning the structure of JSS systems that employ the previously studied common frequency routing (CFR) problem (Chuah and Yingling, in press) are explored to understand their impacts on operational costs of the system. Finally, a discreteevent simulation model is developed to study JSS by looking at different types of variations in demand and studying their impacts on the stability of inventory levels in the system. The results show that GFR routes at high frequencies do not have common frequencies in the solution. There are some common frequencies at medium frequencies and none at low frequency, where effectively the problem is simply a vehicle routing problem (VRP) with time windows. CDR is an extension of VRP-type problems that can be solved quickly with meta-heuristic approaches. GFR, CDR, and CFR are practical routing strategies for JSS with taboo search or other types of meta-heuristics as solvers. By comparing GFR and CFR solutions to the same problems, it is shown that the impacts of CFR restrictions on cost are minimal and in many cases so small as to make simplier CFR routes desirable. The studies of JSS structural features on the operating costs of JSS systems under the assumption of CFR routes yielded interesting results. First, when suppliers are clustered, the routes become more efficient at mid-level, but not high or low, frequencies. Second, the cost increases with the number of suppliers. Third, negotiating broad time windows with suppliers is important for cost control in JSS systems. Fourth, an increase or decrease in production volumes uniformly shifts the solutions cost versus frequency curve. Fifth, increased vehicle capacity is important in reducing costs at low and medium frequencies but far less important at high frequencies. Lastly, load distributions among the suppliers are not important determinants of transportation costs as long as the average loads remain the same. Finally, a one-supplier, one-part-source simulation model shows that the systems inventory level tends to be sticky to the reordering level. JSS is very stable, but it requires reliable transportation to perform well. The impact to changes in kanban levels (e.g., as might occur between route planning intervals when production rates are adjusted) is relatively long term with dynamic after-effects on inventory levels that take a long time to dissapate. A gradual change in kanban levels may be introduced, prior to the changeover, to counter this effect.

Some oil and gas companies are drilling and developing fields in the Arctic Ocean, which has an environment with sea ice called ice floes. These companies must protect their platforms from ice floe collisions. One proposal is to use a system that consists of autonomous underwater vehicles (AUVs) and docking stations. The AUVs measure the under-water topography of the ice floes, while the docking stations launch the AUVs and recharge their batteries. Given resource constraints, we optimize quantities and locations for the docking stations and the AUVs, as well as the AUV scheduling policies, in order to provide the maximum protection level for the platform. We first use an queueing approach to model the problem as a queueing system with abandonments, with the objective to minimize the abandonment probability. Both M/M/k+M and M/G/k+G queueing approximations are applied and we also develop a detailed simulation model based on the queueing approximation. In a complementary approach, we model the system using a multi-stage stochastic facility location problem in order to optimize the docking station locations, the AUV allocations, and the scheduling policies of the AUVs. A two-stage stochastic facility location problem and several efficient online scheduling heuristics are developed to provide lower bounds and upper bounds for the multi-stage model, and also to solve large-scale instances of the optimization model. Even though the model is motivated by an oil industry project, most of the modeling and optimization methods apply more broadly to any radial detection problems with queueing dynamics. / text

<p>After rigorous criticism on trait approach and with the emergence of behavioral approach in entrepreneurship during 1980s, the researchers started to introduce learning and cognitive theories in entrepreneurship to describe and explain the dynamic nature of entrepreneurship. Many researchers have described venture creation as a core and the single most important element of entrepreneurship. This thesis will discuss and present the role of entrepreneurial learning and heuristics in venture creation. Hence, the purpose of this research thesis is to study and analyze the role of entrepreneurial learning and heuristics in venture creation.</p><p>To fulfill the purpose of this thesis, we followed qualitative research and conducted semi structured interviews with open ended questionnaires to collect empirical data. For this study, we have included only four interviews which were conducted on four different businesses based in Jönköping, Sweden, following convenience sampling. In the analysis, we used data analysis model of Walker, Cooke and McAllister (2008) and inductively generated three propositions, depicting the role and importance of entrepreneurial learning and heuristics in venture creation.</p><p>Individuals adopt entrepreneurship in their careers with necessary skills, abilities, and knowledge, which are learned or gained through experiential learning and/or vicarious learning (i.e., learning by observing or modeling the actions of others). Learning by doing is considered the most important factor by entrepreneurs which helped them to overcome different business start up hurdles, to make various entrepreneurial decisions and to perform many entrepreneurial activities during venture creation. Similarly, individuals within their own situation use, learning by observing or modeling other people’s behaviour, actions and consequences of the actions. Entrepreneurs use learning by modeling the behaviour and actions of others as benchmarking strategy during venture creation. Entrepreneurs believe that without any learning they will not be able to start their own businesses. Heuristics as decisions making mechanism, particularly during venture creation, is used by entrepreneurs as simplifying strategy when sufficient information related to a specific market, certain industry and products are scarce. Additionally, entrepreneurs are passionate to grab profitable business opportunity, and due to time pressure and brief window of opportunity, they can’t go for gathering each and every information of the potential business or product. Hence, heuristics as decisions making mechanism is considered the best suitable approach to make many entrepreneurial decisions during venture creation.</p><p> </p><p> </p><p> </p><p> </p><p> </p>

The overall aim of the thesis has been to introduce Personal Mobile Grids (PMGrids) as a novel paradigm in grid computing that scales grid infrastructures to mobile devices and extends grid entities to individual personal users. In this thesis, architectural designs as well as simulation models for PM-Grids are developed. The core of any grid system is its resource scheduler. However, virtually all current conventional grid schedulers do not address the non-clairvoyant scheduling problem, where job information is not available before the end of execution. Therefore, this thesis proposes a honeybee inspired resource scheduling heuristic for PM-Grids (HoPe) incorporating a radical approach to grid resource scheduling to tackle this problem. A detailed design and implementation of HoPe with a decentralised self-management and adaptive policy are initiated. Among the other main contributions are a comprehensive taxonomy of grid systems as well as a detailed analysis of the honeybee colony and its nectar acquisition process (NAP), from the resource scheduling perspective, which have not been presented in any previous work, to the best of our knowledge. PM-Grid designs and HoPe implementation were evaluated thoroughly through a strictly controlled empirical evaluation framework with a well-established heuristic in high throughput computing, the opportunistic scheduling heuristic (OSH), as a benchmark algorithm. Comparisons with optimal values and worst bounds are conducted to gain a clear insight into HoPe behaviour, in terms of stability, throughput, turnaround time and speedup, under different running conditions of number of jobs and grid scales. Experimental results demonstrate the superiority of HoPe performance where it has successfully maintained optimum stability and throughput in more than 95% of the experiments, with HoPe achieving three times better than the OSH under extremely heavy loads. Regarding the turnaround time and speedup, HoPe has effectively achieved less than 50% of the turnaround time incurred by the OSH, while doubling its speedup in more than 60% of the experiments. These results indicate the potential of both PM-Grids and HoPe in realising futuristic grid visions. Therefore considering the deployment of PM-Grids in real life scenarios and the utilisation of HoPe in other parallel processing and high throughput computing systems are recommended.

In this thesis, the gate matrix layout problem in VLSI design is considered where the goal is to minimize the number of tracks required to layout a given circuit and a taxonomy of approaches to its solution is presented. An efficient hybrid heuristic is also proposed for this combinatorial optimization problem, which is based on the combination of probabilistic hill-climbing technique and greedy method. This heuristic is tested experimentally with respect to four existing algorithms. As test cases, five benchmark problems from the literature as well as randomly generated problem instances are considered. The experimental results show that the proposed hybrid algorithm, on the average, performs better than other heuristics in terms of the required computation time and/or the quality of solution. Due to the computation-intensive nature of the problem, an exact solution within reasonable time limits is impossible. So, it is difficult to judge the effectiveness of any heuristic in terms of the quality of solution (number of tracks required). A probabilistic model of the gate matrix layout problem that computes the expected number of tracks from the given input parameters, is useful to this respect. Such a probabilistic model is proposed in this thesis, and its performance is experimentally evaluated.