The integration of production and distribution decisions presents a challenging problem for manufacturers trying to optimize their supply chain. At the planning level, the immediate goal is to coordinate production, inventory, and delivery to meet customer demand so that the corresponding costs are minimized. Achieving this goal provides the foundations for streamlining the logistics network and for integrating other operational and financial components of the system. In this paper, a model is presented that includes a single production facility, a set of customers with time varying demand, a finite planning horizon, and a fleet of vehicles for making the deliveries. Demand can be satisfied from either inventory held at the customer sites or from daily product distribution. A procedure centering on a reactive tabu search is developed for solving the full problem. After a solution is found, path relinking is applied to improve the results. A novel feature of the methodology is the use of an allocation model in the form of a mixed integer program to find good feasible solutions that serve as starting points for the tabu search. Lower bounds on the optimum are obtained by solving a modified version of the allocation model. Computational testing on a set of 90 benchmark instances with up to 200 customers and 20 time periods demonstrates the effectiveness of the approach. In all cases, improvements ranging from 10 - 20% were realized when compared to those obtained from an existing greedy randomized adaptive search procedure (GRASP). This often came at a three- to five-fold increase in runtime, however. A hybrid scheme that combines the features of reactive tabu search algorithm and branch-and-price algorithm is also developed. The combined approach takes advantage of the efficiency of the tabu search heuristic and the precision of the branch-and-price algorithm. Branching strategy that is suitable for the problem is proposed. Several advance techniques such as column generation heuristic and rounding heuristic are also implemented to improve the efficiency of the algorithm. Computational testing on standard data sets shows that a hybrid algorithm can practically solve instances with up to 50 customers and 8 time periods which is not possible by standard branch-and-price algorithm alone. / text

published_or_final_version / Mathematics / Master / Master of Philosophy

Thesis (Ph. D.)--Universität, Eichstätt-Ingolstadt, 2004. / Includes bibliographical references (p. [217]-227).

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This thesis aims to improve the inventory control system of a company´s RDC in Norrköping, Sweden. Inefficiencies and non-optimal processes have been identified in the current system, which could negatively affect operations. The study addresses three questions: understanding the current system, identifying improvements, and adjusting the improved system to fit inventory control constraints. A literature review and case study were conducted, revealing lower service levels and higher costs than expected. Experimental scenarios were simulated to examine different improvements, such as using economic order quantity, different safety stock equations, and investigating service levels. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

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Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-06-16T12:13:46Z No. of bitstreams: 2 Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) / Made available in DSpace on 2017-06-16T12:13:47Z (GMT). No. of bitstreams: 2 Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) Previous issue date: 2017-03-23 / CNPQ – Conselho Nacional de Desenvolvimento Científico e Tecnológico / A contribuição de metaheurísticas, em especial a dos algoritmos evolutivos, na área de otimização combinatória é de extrema relevância, pois auxiliam na busca de soluções próximas ao ótimo para problemas complexos da vida real cuja resolução em tempo aceitável é inviável devido a sua complexidade computacional, oferecendo uma flexibilidade importante na modelagem do problema. Este trabalho se propõe a apresentar e implementar um modelo computacional a ser utilizado na otimização do escalonamento de ordens de produção utilizando um Algoritmo Memético (AM), que permite a busca tanto da melhor sequência das ordens de produção quanto dos lotes de tamanho variável em que a quantidade de cada operação pode ser subdividida. A possibilidade de utilização de máquinas alternativas, de recursos secundários, de intervalos de indisponibilidade e de lotes de transferência, é apresentada no modelo, o que lhe proporciona grande robustez e aplicabilidade em ambientes de manufatura flexível, permitindo uma modelagem do Flexible Job Shop Scheduling Problem (FJSSP) que reflete com maior fidedignidade a realidade do ambiente fabril, gerando como resultado um escalonamento otimizado e aderente às necessidades da fábrica. Várias instâncias do FJSSP são utilizadas nos testes e os resultados obtidos comprovam que o algoritmo proposto consegue otimizar o escalonamento das ordens de produção de cada instância de maneira eficiente. / The contribution of meta-heuristics, especially evolutionary algorithms, in combinatorial optimization area is extremely important, as they help in finding near optimal solutions to complex real-life problems whose resolution is infeasible in acceptable time due to its computational complexity, offering an important flexibility in the modeling of problem. This study propose to present and implement a computational model to be used in optimizing the production scheduling of manufacturing orders using a Memetic Algorithm that allows to search both the best sequence of jobs as of variable size batches that the quantity of each operation can be subdivided. The possibility of using alternative resources, operations with secondary resources, unavailability intervals and batch transfer lots are features presented in the model, which lends it great robustness and applicability to flexible manufacturing environments, allowing the modeling of Flexible Job Shop Scheduling Problem (FJSSP) that reflects with higher accuracy the real manufacturing environment, generating optimized scheduling results that are adhering to the plant needs. Multiple instances of FJSSP are used in the tests and the results show that the proposed algorithm succeeds in optimizing the scheduling of production orders for each instance so efficient.

Timing of ordering of inventory items is of very great importance in Materials Requirement Planning. Uncertainties in timing can have an adverse effect on the system performance. Most often the lead time variation contribute to timing uncertainties; and their effects are reflected in added costs. Lead time error effects are investigated in this thesis. The study attempts to estimate the effects through some relevant costs, and their variations across the lot sizing rules. The hypotheses for this study are 1) Between any two lot sizing rules, there will be a significant difference in error coats due to combined effect of purchased lead time error and manufacturing lead time errors; 2) Relative cost performance of lot sizing rules in MRP is influenced by the lead time errors; 3) There will be a difference in error cost between lot for l o t rule and least total cost rule even with single source of lead time variation. To carry out the study a MRP programme was developed, in FORTRAN 77 with provisions to include the lot sizing rules while exploding the structure. The lot sizing rules used in the study are Lot for Lot, Silver and Meal heuristics, Wagner-Whitin algorithm, Least total cost, Least unit cost and Part Period balancing. A simulation model is developed using GPSS/PC, to test the hypotheses. An hypothetical production situation with three end items, each with a different product structure is designed. In addition, a master production schedule and a job shop are also structured. Appropriate distributions are assumed for both manufacturing lead times and purchase lead times. These provide the stochastic variables in the simulation experiments. A series of experiments were carried out with the model to investigate into the variations of costs amongst lot sizing rules. Results of the simulation experiments prove that there are costs associated with lead time errors in MRP. These error costs vary significantly with different lot sizing rules. It is also found that the resultant error costs vary significantly even with a single source of lead time variation. Least unit cost rule gives the beat performance having least error costs. Lot for Lot rule has shown the worst performance amongst the lot sizing rules considered. Other interesting results have emerged out of the study.

Long cycle times in semiconductor manufacturing represent an increasing challenge for the industry and lead to a growing need of break-through approaches to reduce it. Small lot sizes and the conversion of batch processes to mini-batch or single-wafer processes are widely regarded as a promising means for a step-wise cycle time reduction. Our analysis with discrete-event simulation and queueing theory shows that small lot size and the replacement of batch tools with mini-batch or single wafer tools are beneficial but lot size reduction lacks persuasive effectiveness if reduced by more than half. Because the results are not completely convincing, we develop a new semiconductor tool type that further reduces cycle time by lot streaming leveraging the lot size reduction efforts. We show that this combined approach can lead to a cycle time reduction of more than 80%.

The supply function has an important role to support the business to create a customer value. Two important parts of this process is to have the warehouses and production sites in the right location and to have the right items stocked at the right level.   This thesis is concerned with those two parts of the supply chain management. Three different areas of inventory control are dealt with. In the first part we consider the classical dynamic lot size problem without backlogging. The second part deals with estimation of holding and shortage costs in two-level distribution inventory systems. In the third part of the thesis we consider the localisation problem in a multi-level supply network system where items are consolidated at a warehouse and distributed to customers on routes.   Within the area of inventory control we have evaluated a method earlier suggested by Axsäter (1988), the method is evaluated using a set of test problems and compared other heuristic methods, including the well-known Silver-Meal’s method (Silver and Meal, 1973).  The result shows that the method suggested by Axsäter does perform better than the other methods. In the latest contribution we point to the important differences between Least Period Cost and Silver-Meal when several periods have zero demand. In the area of inventory control we have also studied a two-echelon inventory system where we present methods for estimating the shortage- and stockholding costs in such inventory systems.   The second part subject of the thesis concerns supply network optimization. We present a MIP formulation of the problem and evaluate in detail the approximation of the distribution cost when customers are delivered on multi-stop routes. An improved method for estimating the distribution is presented.   Besides this introductory overview five research papers are included in the thesis. The first and the last paper consider evaluation of dynamic lot sizing heuristics. The second and third paper deals with cost evaluation of a stochastic two-echelon inventory system and the forth paper with evaluation of methods for estimating distribution costs in a supply network.