abstract: This research is motivated by a deterministic scheduling problem that is fairly common in manufacturing environments, where there are certain processes that call for a machine working on multiple jobs at the same time. An example of such an environment is wafer fabrication in the semiconductor industry where some stages can be modeled as batch processes. There has been significant work done in the past in the field of a single stage of parallel machines which process jobs in batches. The primary motivation behind this research is to extend the research done in this area to a two-stage flow-shop where jobs arrive with unequal ready times and belong to incompatible job families with the goal of minimizing total weighted tardiness. As a first step to propose solutions, a mixed integer mathematical model is developed which tackles the problem at hand. The problem is NP-hard and thus the developed mathematical program can only solve problem instances of smaller sizes in a reasonable amount of time. The next step is to build heuristics which can provide feasible solutions in polynomial time for larger problem instances. The basic nature of the heuristics proposed is time window decomposition, where jobs within a moving time frame are considered for batching each time a machine becomes available on either stage. The Apparent Tardiness Cost (ATC) rule is used to build batches, and is modified to calculate ATC indices on a batch as well as a job level. An improvisation to the above heuristic is proposed, where the heuristic is run iteratively, each time assigning start times of jobs on the second stage as due dates for the jobs on the first stage. The underlying logic behind the iterative approach is to improve the way due dates are estimated for the first stage based on assigned due dates for jobs in the second stage. An important study carried out as part of this research is to analyze the bottleneck stage in terms of its location and how it affects the performance measure. Extensive experimentation is carried out to test how the quality of the solution varies when input parameters are varied between high and low values. / Dissertation/Thesis / M.S. Industrial Engineering 2012

The motivation of this research investigation stems from a particular job shop production environment at a large international communications and information technology company in which electro-mechanical assemblies (EMAs) are produced. The production environment of the EMAs includes the continuous arrivals of the EMAs (generally called jobs), with distinct due dates, degrees of importance and routing sequences through the production workstations, to the job shop. Jobs are processed in batches at the workstations, and there are incompatible families of jobs, where jobs from different product families cannot be processed together in the same batch. In addition, there are sequence-dependent setups between batches at the workstations. Most importantly, it is imperative that all product deliveries arrive on time to their customers (internal and external) within their respective delivery time windows. Delivery is allowed outside a time window, but at the expense of a penalty. Completing a job and delivering the job before the start of its respective time window results in a penalty, i.e., inventory holding cost. Delivering a job after its respective time window also results in a penalty, i.e., delay cost or emergency shipping cost. This presents a unique scheduling problem where an earlinesstardiness composite objective is considered. This research approaches this scheduling problem by decomposing this complex job shop scheduling environment into bottleneck and non-bottleneck resources, with the primary focus on effectively scheduling the bottleneck resource. Specifically, the problem of scheduling jobs with unique due dates on a single workstation under the conditions of batching, sequence-dependent iii setups, incompatible job families in order to minimize weighted earliness and tardiness is formulated as an integer linear program. This scheduling problem, even in its simplest form, is NP-Hard, where no polynomial-time algorithm exists to solve this problem to optimality, especially as the number of jobs increases. As a result, the computational time to arrive at optimal solutions is not of practical use in industrial settings, where production scheduling decisions need to be made quickly. Therefore, this research explores and proposes new heuristic algorithms to solve this unique scheduling problem. The heuristics use order review and release strategies in combination with priority dispatching rules, which is a popular and more commonly-used class of scheduling algorithms in real-world industrial settings. A computational study is conducted to assess the quality of the solutions generated by the proposed heuristics. The computational results show that, in general, the proposed heuristics produce solutions that are competitive to the optimal solutions, yet in a fraction of the time. The results also show that the proposed heuristics are superior in quality to a set of benchmark algorithms within this same class of heuristics

Scheduling concerns the allocation of limited resources to tasks over time. In manufacturing systems, scheduling is nothing but assigning the jobs to the available processors over a period of time. Our research focuses on scheduling in systems of parallel processors which is challenging both from the theoretical and practical perspectives. The system of parallel processors is a common occurrence in different types of modern manufacturing systems such as job shop, batch shop and mass production. A variety of important and challenging problems with realistic settings in a system of parallel processors are considered. We consider two types of processors comprising discrete and batch processors. The processor which produces one job at a time is called a discrete processor. Batch processor is a processor that can produce several jobs simultaneously by keeping jobs in a batch form which is commonly seen in semiconductor manufacturing, heat treatment operations and also in chemical processing industries. Our aim is to develop efficient solution methodologies (heuristics/metaheuristics) for three different problems in the thesis. The first two problems consider the objective of minimizing total weighted tardiness in cases of discrete and batch processors where customer delivery time performance is critical. The third problem deals with the objective of minimizing the total weighted completion time in the case of batch processors to reduce work-in-process inventory. Specifically, the first problem deals with the scheduling of parallel identical discrete processors to minimize total weighted tardiness. We develop a metaheuristic based on Ant Colony Optimization(ACO) approach to solve the problem and compare it with the available best heuristics in the literature such as apparent tardiness cost and modified due date rules. An extensive experimentation is conducted to evaluate the performance of the ACO approach on different problem sizes with varied tardiness factors. Our experimentation shows that the proposed ant conony optimization algorithm yields promising results as compared to the best of the available heuristics. The second problem concerns with the scheduling of jobs to parallel identical batch processors for minimizing the total weighted tardiness. It is assumed that the jobs are incompatible in respect of job families indicating that jobs from different families cannot be processed together. We decompose the problem into two stages including batch formation and batch scheduling as in the literature. Ant colony optimization based heuristics are developed in which ACO is used to solve the batch scheduling problem. Our computational experimentation shows that the proposed five ACO based heuristics perform better than the available best traditional dispatching rule called ATC-BATC rule. The third scheduling problem is to minimize the total weighted completion time in a system of parallel identical batch processors. In the real world manufacturing system, jobs to be scheduled come in lots with different job volumes(i.e number of jobs) and priorities. The real settings of lots and high batch capacity are considered in this problem. This scheduling problem is formulated as a mixed integer non-linear program. We develop a solution framework based on the decomposition approach for this problem. Two heuristics are proposed based on the proposed decomposition approach and the performance of these heuristics is evaluated in the cases of two and three batch processors by comparing with the solution of LINGO solver.