In this master thesis study, new inventory control mechanisms are developed for the repairables in Nedtrain. There is a multi-item, multi echelon system with a continuous review and one for one replenishment policy and there are different demand supply options in each control mechanism. There is an aggregate mean waiting time constraint in each local warehouse and the objective is to minimize the total system cost. The base stock levels in each warehouse are determined with an approximation method. Then different demand supply options are compared with each other.

ABSTRACT A HEURISTIC APPROACH FOR THE SINGLE MACHINE SCHEDULING TARDINESS PROBLEMS &Ouml / zbakir, Saffet Ilker M.Sc., Department of Industrial Engineering Supervisor : Prof. Dr. &Ouml / mer Kirca September 2011, 102 pages In this thesis, we study the single machine scheduling problem. Our general aim is to schedule a set of jobs to the machine with a goal to minimize tardiness value. The problem is studied for two objectives: minimizing total tardiness value and minimizing total weighted tardiness value. Solving optimally this problem is difficult, because both of the total tardiness problem and total weighted tardiness problem are NP-hard problems. Therefore, we construct a heuristic procedure for this problem. Our heuristic procedure is divided to two parts: construction part and improvement part. The construction heuristic is based on grouping the jobs, solving these groups and then fixing some particular number of jobs. Moreover, we used three type improvement heuristics. These are sliding forward method, sliding backward method and pairwise interchange method. Computational results are reported for problem size = 20, 40, 50 and 100 at total tardiness problem and for problem size = 20 and 40 at total weighted tardiness problem. Experiments are designed in order to investigate the effect of three factors which are problem size, tardiness factor and relative range of due dates on computational difficulties of the problems. Computational results show that the heuristic proposed in this thesis is robust to changes at these factors.

In this study, we consider a capacity allocation problem in flexible manufacturing systems. We assume time and tool magazine capacities on the Numerical Controlled (NC) machines and limited number of available tools. Our problem is to allocate the available capacity of the NC machines to the required demand of the operations, so as to maximize the total weight of operation assignments. We formulate the problem as a Mixed Integer Linear Program and show that it is NP-hard in the strong sense. We solve the moderate-sized problems optimally by the available Integer Programming software. We also develop Lagrangean relaxation based upper bounds and several heuristic procedures. Our computational results have revealed that the Lagrangean upper bounds are very close to optimal solutions and the heuristic procedures produce near optimal solutions in very small solution times even when the problem sizes are large.

In this study, a single machine batch-scheduling problem with incompatible jobs and dynamic arrivals is examined. The objective function is the minimization of the total flow time of the jobs. For solving problems a case specific branch and bound algorithm with a heuristic upper bound scheme and two alternative lower bound procedures is used. An extensive computational experiment is conducted to investigate the effects of certain parameters on the computation time. For the most difficult parameter combination branch and bound algorithm can solve the problems about 25 jobs with 4 different job types in a 10 minutes time on average. For the problem types with higher number of jobs and the most difficult parameter combination proposed upper bound heuristic can be used to obtain near optimal solutions.

In this study we address a periodic review capacitated inventory system with supply uncertainty where advance demand information is available. A stochastic dynamic programming formulation is applied with the objective of minimizing the expected inventory related costs over a finite horizon. Three different supply processes are assumed. Under the all-or-nothing type supply process and partially available supply process, the structure of optimal policy is proved to be a base stock policy and numerical examples are given to demonstrate the effects of system parameters. Under Binomially distributed supply process it is shown that a simple base stock policy is not optimal.

In today&rsquo / s conditions, systems that surround individuals have evolved in structure such that, nature of variable interactions are much more complex and changing continuously. Logistics systems, which constitute an example for such systems, have also necessitated fast management and decision-making in a fast paced environment, under limited sources with the additional effect of increasing customer requirements and competition. These conditions require a dynamic analysis. In this study, system dynamics approach was shown to be a competent alternative to analyze complex and dynamic systems like logistics systems and its cost structure studied. A theoretical logistics model with a user interface is presented. Then the developed model implemented in an organization operating in aluminum industry by making the required modifications. A scenario analysis regarding a new investment decision studied. All analysis and modeling studies were carried out in Stella 6.0.1.

In today&#039 / s conditions, systems that surround individuals have evolved in structure such that, nature of variable interactions are much more complex and changing continuously. Logistics systems, which constitute an example for such systems, have also necessitated fast management and decision-making in a fast paced environment, under limited sources with the additional effect of increasing customer requirements and competition. In this study, system dynamics approach was shown to be a competent alternative to analyze complex and dynamic systems like logistics systems and its cost structure is studied. A theoretical logistics model with a user interface is presented. Then the developed model implemented in an organization operating in aluminum industry by making the required modifications. A scenario analysis regarding a new investment decision studied. All analysis and modeling studies were carried out in Stella 6.0.1.

This study considers bicriteria approaches to the minimum cost network flow problem and a rescheduling problem where those approaches find their applications. For the bicriteria integer minimum cost network flow problem, we generate all efficient solutions in two phases. The first phase generates the extreme supported efficient points that are the extreme points of the objective space of the continuous bicriteria network flow problem. In the second phase, we generate the nonextreme supported and unsupported efficient points by Integer Programming Based approaches. Our rescheduling problem considers parallel unrelated machine environments. The criteria are the total flow time as an efficiency measure and the total reassignment cost as a stability measure. We show that the problems that address linear functions of the two criteria can be represented by bicriteria network flow models. To generate all efficient solutions, we use a Classical Approach that is based on the optimal solutions of the singly constrained network flow problem and provide a Branch and Bound approach that starts with extreme supported efficient set and uses powerful bounds. To find an optimal solution to any nonlinear function of the two criteria, we provide a Branch and Bound approach and an Integer Programming Based approach that eliminates some portions of the efficient set that cannot provide improved solutions. We contribute both to the network flow and scheduling literature by proposing algorithms to the bicriteria network flow models and applying them to a rescheduling problem that is bicriteria in nature. The results of our extensive computations with up to 100 jobs and 12 machines have revealed that, the Branch and Bound algorithm finds the efficient set in less computational effort compared to the classical approach. In minimizing a nonlinear function of the two criteria both IP Based approach and Branch and Bound algorithm perform quite satisfactory.

In this study a time based bidding framework is developed which is used for dispatching jobs to manufacturing resources in a virtual shop-floor environment. Agent-based shop-floor control approach is implemented with machine and part agents. The Contract-net communication protocol is utilized as the negotiation scheme between these agents. Single step product reservation (SSPR) technique is adopted throughout the study. Primary objective is determined as meeting the due dates and if the lateness is inevitable, avoiding the parts of high priority from being late. A balanced machine utilization rate is set as the secondary objective. During bid construction step, the SSPR technique is augmented with W(SPT+CR) sequencing rule in order to obtain weighted tardiness results. Bids containing Earliest Finishing Time (EFT) and machine loading values of the corresponding machine are evaluated with considering the priority of the part. An elimination algorithm which discards the highly deviated bids having obvious differences is implemented at the initial stage of the bid evaluation step. A basic algorithm to control the maximum tardiness value is applied, as well. A simulation test bed is developed in order to implement the time concept into the presented bidding framework. The test bed is mainly based on the Computer Integrated Manufacturing Laboratory (CIMLAB) located in Middle East Technical University, Department of Mechanical Engineering. The developed bidding algorithm is tested under several cases. Results revealed that the proposed bidding framework was quite successful in meeting the objectives. The study is concluded with some specific future work, outlined in the light of the results obtained.

Production lines with unreliable machines have received a great amount of attention in the literature. Especially, two-station systems have mostly been studied because such systems are easier to handle when compared to the longer lines. In literature, longer lines are usually evaluated by a decomposition algorithm, whereby the long line is partitioned into chunks of two-station lines. Decomposition algorithms require intermediate buffer storages of capacity at least two or three. The trends in modern manufacturing practices, on the other hand, such as the Toyota Production System, dictate that intermediate storages be eliminated. Our work studies multi-station lines with no intermediate storage. We develop software to automate the generation of transition probability matrices to allow the analysis of system behavior. The algorithm allows the use of software packages to handle computations and to solve for exact solutions. Long-run behavior is obtained via the algorithm developed in the computational environment MATLAB. The purpose is to analyze the system performance measures such as starvation and blockage times of stations, production rate and work-in-process. In addition, the production rate and the work-in-process measures over failure and repair probabilities are curve-fit to establish simple and useful empirical formulas for lines consisting three, four and five identical stations. Numerical analyses show that the proposed algorithm is effective for exact solutions and the suggested formulas are valid for approximate solutions.