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.

Closed-loop supply chain (CLSC) management provides opportunity for cost savings through the integration of product recovery activities into traditional supply chains. Product recovery activities, such as remanufacturing, reclaim a portion of the previously added value in addition to the physical material. Our problem setting is motivated by the practice of an Original Equipment Manufacturer (OEM) in the automotive service parts industry, who operates a well established forward network. The OEM faces customer demand due to warranty and beyond warranty vehicle repairs. The warranty based demand induces part returns. We consider a case where the OEM has not yet established a product recovery network, but has a strategic commitment to implement remanufacturing strategy. In accomplishing this commitment, complications arise in the network design due to activities and material movement in both the forward and reverse networks, which are attributed to remanufacturing. Consequently, in implementing the remanufacturing strategy, the OEM should simultaneously consider both the forward and reverse flows for an optimal network design, instead of an independent and sequential modeling approach. In keeping with these motivations, and with the goal of implementing the remanufacturing strategy and transforming independent forward and reverse supply chains to CLSCs, we propose to investigate the following research questions: 1. How do the following transformation strategies leverage the CLSC?s overall cost performance? ? Extending the already existing forward channel to incorporate reverse channel activities. ? Designing an entire CLSC network. 2. How do the following network flow integration strategies influence the CLSC?s overall cost performance? ? Using distinct forward and reverse channel facilities to manage the corresponding flows. ? Using hybrid facilities to coordinate the flows. In researching the above questions, we address significant practical concerns in CLSC network design and provide cost measures for the above mentioned strategies. We also contribute to the current literature by investigating the optimal CLSC network design. More specifically, we propose three models and develop mathematical formulations and novel solution approaches that are based on decomposition techniques, heuristics, and meta-heuristic approaches to seek a solution that characterizes the configuration of the CLSC network, along with the coordinated forward and reverse flows.

In this thesis the capacitated vehicle routing problem with time windows (VRPTW) is studied, where the objective is to serve a set of geographically dispersed customers with known demands and predefined time windows at the minimum cost. It is hard to find an optimal solution for the VRPTW even if the problem size is small. Therefore, many heuristic methods are developed to obtain near optimal solutions. In this study a local search algorithm is proposed for solving the VRPTW, which consist of route construction and route improvement phases. Computational experiments are conducted with Solomon (1987)&rsquo / s and Homberger and Gehring (1999)&rsquo / s problem sets in order to test the performance of the proposed algorithm. From the computational results encouraging results are obtained in terms of solution quality.

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.

Nowadays, assembly robots are increasingly used in the manufacturing industry to replace or collaborate with human labors. This is the goal of the dual arm assembly robot developed by ABB. With the rapid upgrading in consumer electronics products, the lifetime of an assembly line could be only a few months. However, even for experienced programmers, to manually construct a good enough assembly sequence is time consuming, and the quality of the generated assembly sequence is not guaranteed. Moreover, a good robot assembly sequence is important to the throughput of an assembly line. For dual arm robots, it is also important to obtain a balance between the two arms, as well as handling scheduling conflicts and avoiding collisions in a crowded environment. In this master thesis, a program is produced to automatically generate the optimal assembly sequence for a class of real-world assembly cases. The solution also takes the layout of the assembly cell into account, thus constructing the best combination of cell layout, workload balancing, task sequence and task scheduling. The program is implemented using Google OR-Tools – an open-source support library for combinatorial optimization. A customized search strategy is proposed and a comparison between this strategy and the built-in search strategy of Google OR-Tools is done. The result shows that the used approach is effective for the problem study case. It takes about 4 minutes to find the optimal solution and 32 minutes to prove its optimality. In addition, the result also shows that the customized search strategy works consistently with good performance for different problem cases. Moreover, the customized strategy is more efficient than built-in search strategy in many cases. / Numera används monteringsrobotar alltmer inom tillverkningsindustrin för att ersätta eller samarbeta med människor. Detta är måluppgiften för den tvåarmiga monteringsroboten, YuMi, som utvecklats av ABB. Med den korta produktlivslängden för hemelektronikprodukter kan livslängden för en monteringslinje vara ett fåtal månader. Även för erfarna robotprogrammerare är det svårt och tidsödande att manuellt konstruera en tillräckligt bra monteringsordning, och dessutom kan resultatets kvalitet inte garanteras. En bra monteringsordning är nödvändig för genomströmningen i en monteringslinje. För tvåarmiga robotar, är det också viktigt att få en balans mellan de två armarna, samt hantering av schemakrockar och undvika kollisioner i en trång miljö. I detta examensarbete har ett program skrivits, som automatiskt genererar optimala lösningar för en klass av verkliga monteringsfall. Lösningen tar hänsyn till utformningen av monteringscellen och arrangerar cellen på bästa sätt, balanserar arbetsbelastningen, ordnar och tidsbestämmer uppgifter. Programmet använder sig av Google OR-Tools – ett öppet kodbibliotek för kombinatorisk optimering. Dessutom föreslås en skräddarsydd sökstrategi, som jämförs med Google OR-Tools inbyggda sökstrategi. Resultatet visar att den använda metoden är effektiv för problemtypen. Det tar ungefär 4 minuter att hitta den optimala lösningen och 32 minuter för att bevisa optimalitet. Dessutom visar resultatet att den anpassade sökstrategin konsekvent har en bra prestanda för olika problemfall. Dessutom är den anpassade strategin effektivare än den inbyggda sökstrategin i många fall.