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This paper introduces a deterministic model to plan the physician requirements for outpatient clinics to achieve service targets for the appointment lead-times of patients. The Ministry of Health of Singapore has established targets for the median, 95th percentile, and 100th percentile of appointment lead-times for patients, since long appointment postponements are regarded as being unacceptable for health care services. The study is to match the capacity of the healthcare providers to the patient demand for a re-entry system, subject to restrictions on the appointment lead-times for patients. We propose a mixed-integer programming model for planning capacity with the minimization of the maximum required capacity as its objective. In the model we assume a finite planning horizon, deterministic arrivals, multiple types of patients, identical physicians, and dependent demand between types of patients. We solve this model with a Branch and Cut algorithm. We test the model with numerical experiments using real data from the chosen specialty at the outpatient clinic of the studied hospital. The results show the value of the proposed model via a systematic push-pull mechanism in scheduling patients' requests to minimize the objective. The clinic should use one of the appointment lead-time targets to determine the patients' appointment dates. Finally, from the sensitivity analyses we demonstrate that the objective is negatively correlated with first-visit patients' appointment lead-time targets, the discharge rates, and the re-visit patients' mean appointment lead-time; we find a positive correlation between the first-visit patients' mean appointment lead-time and the appointment lead-time targets.

This paper deals with a flow-shop scheduling problem with limited intermediate buffer. Jobs are grouped in incompatible job families. Each job has to be processed by a batch processor followed by a discrete processor in the same order. The batch processor can process several jobs simultaneously so that all jobs of the same batch start and complete together. We assume that the capacity of batch processor is bounded. The batch processing time is identical for batches of the same family. A batch which has completed processing on the batch processor may block the processor until there is a free unit in the buffer. The objective is to determine a batching and scheduling for all jobs so as to minimise mean completion time. A lower bound and two heuristics algorithm are developed. Moreover, a two-stage method embedded with a Differential Evolution (DE) algorithm is also developed. DE is one of the latest evolutionary computation algorithms, which implements mutation, crossover, and selection operators to improve the candidate solutions iteratively. Three variants of DE are first compared with a continuous Genetic Algorithm employing the random key representation. Then, one variant of the DE with the best convergence speed is selected. Numerical experiments are conducted to evaluate the performances of the selected two-stage meta-heuristic and two heuristics.

We analyse the problem of minimising the mean cycle time of a batch processing stage containing K > 1 batch processors in parallel with incompatible job families and future job arrivals. We provide an integer linear programming formulation and a dynamic program formulation for small problem instances. For larger problem instances, we propose an online heuristic policy MPC_REPEAT. At each instance a decision has to be made, MPC_REPEAT decomposes the problem of simultaneously assigning multiple batches to multiple processors into sequentially assigning multiple batches to multiple processors. When job families are uncorrelated, we show via simulation experiments that MPC_REPEAT has significantly lower mean cycle time than a previously proposed look-ahead method except when: (MPC_REPEAT ignores some job families AND the traffic intensity is high.) Our experiments also reveal that increasing the job family correlation of consecutive job arrivals results, with a few exceptions, in a mean cycle-time reduction, for both policies evaluated. This reduction in cycle time generally increases with: increasing number of job families, decreasing number of processors, and increasing time between job arrivals. Our findings imply that controlling the upstream processors, such that job families of consecutive job arrivals are correlated, can reduce the cycle time at the batch processing stage. Furthermore, the expected mean cycle time reduction due to this strategy can be substantially larger than that expected from switching to a more complex batch processing stage policy, under less stringent conditions.

Many companies are enhancing their competitiveness by offering Just-in-Time (JIT) delivery. Costs or penalties are incurred by delivering an order either earlier or later than the customer’s due-dates. Besides, maintaining short response time from order acceptance to final delivery is one of the key competitive advantages. Thus, many companies deliver products to customers directly after production without holding finished product inventory. This is particularly true for the industries with short product life cycle, such as consumer electronics manufacturing, ready-mix concrete supplying and food catering industry (Garcia et al. 2004; Chen and Vairaktarakis, 2005; Li et al., 2005). In order to improve customer service and reduce production and transportation costs, scheduling of assembly manufacturing and transportation should be synchronized. Nowadays, due to the professional services provided by third party logistics (3PL) provider, it is more efficient to outsource the transportation or distribution to 3PL. There are two types of operations. If 3PL only serves one customer, the schedule of 3PL’s vehicle is determined by the order completion time in manufacturing. This type of operation is particularly true for 3PLs that providing road transportation services. On the hand, if 3PL provides services to more than one manufacturer, the departure and arrival time of the vehicle is determined by 3PL rather than by the manufacturer. In addition, the unit transportation cost of each vehicle varies. The manufacturer can book capacity on the available vehicles accordingly. Then, decision on allocation of orders to the vehicles is made to utilize the booked capacity efficiently. The typical case is the air-cargo transportation service provided by cargo airlines. Motivated by above application, this chapter studies the problem of synchronized scheduling of assembly manufacturing and transportation in the make-to-order (MTO) consumer electronics supply chain (CESC). In this context, materials or components are kept in inventory before assembly. Upon reception of customer orders, the materials are transferred to manufacturing job shop. Through several processes such as assembly, testing, packing, the assembly manufacturing is completed. Then, the order is transported to customers directly by the vehicle of 3PL. Chen and Vairaktarakis (2005) addressed the

Cluster tools have gained a lot of importance in today's semiconductor manufacturing. Cluster tools are machines that combine several processing steps in one machine. A cluster tools basically consists of one or more loadlocks where wafers enter and exit the cluster tool, two or more processing chambers where the processes are performed, and one or more handlers which transport the wafers between loadlocks and processing chambers. They can be regarded as small factories inside a factory. This paper focuses on understanding the behavior of cluster tool system by simulation modeling, experimenting on the model and finding out the factors that influence the tool overall cycle time. The number of deposition chambers, number of robot grippers, clean cycle, MTTF and MTTR are identified as the most important factors for cycle time reduction at cluster tool in semiconductor wafer fabrication.

This paper addresses the problem of minimizing makespan for a given set of n jobs to be processed on each of m machines in a static jobshop, subject to the minimum completion time variance (CTV). A lower bound on CTV is developed for the static jobshop problem. A backward scheduling approach is proposed using the observations on the development of lower bound for hierarchical minimization of CTV and makespan. A lower bound on makespan subject to minimum CTV is also presented for this problem. Finally, we present two simulated annealing heuristic approaches using the concepts of forward and backward scheduling. Their performances are compared against each other through the use of the lower bounds established in this work. The simulated annealing heuristic based on backward scheduling is shown to perform well by evaluating the developed heuristics on 82 jobshop problems taken from literature. � 2005 Elsevier Ltd. All rights reserved.

This paper presents and evaluates a new job release methodology, WIPLOAD Control, in semiconductor wafer fabrication environment. The performance of the proposed methodology is evaluated using simulation experiments under different system configurations, in comparison with two widely discussed existing job release methodologies, CONWIP and Workload Regulating. A case study is also conducted by simulating a real-life wafer fabrication facility. Based on the experimental results, it appears that WIPLOAD Control is a reliable job release methodology, which can efficiently reduce average cycle time and standard deviation of cycle time for a given throughput level

In semiconductor manufacturing, hot lots are often introduced into the system out of marketing and business considerations for extra flexibility regarding delivery lead times. The objective of this paper is to study the scheduling policies to minimize the cycle time of hot lots in batch processes. We present three scheduling policies based on look ahead batching (LAB) and study their effect on the performance of cycle time of hot lots and other priority lots in the system. In LAB, the scheduling decisions are based on the arrival epochs of incoming lots, which are easily predictable in a computer integrated manufacturing environment. The steady state simulation results show that exploiting the knowledge of future arrivals leads to 10% reduction in the cycle time of hot lots without affecting their standard deviation and also without affecting the cycle time performance of other priority lots in batch processes at lower values of utilization level.

This work introduces the concept of Pareto-controllability of objectives in dynamic scheduling of single machine systems in a production supply chain. A convex combination approach based on compromise programming is presented in this paper to achieve the desired level of control in dynamic scheduling considering two contradicting objectives, viz., the mean cycle time and the maximum tardiness. At each decision instance during simulation, a Pareto-job is selected using the convex combination approach and loaded on the machine for processing. This approach uses the combination of the concepts of simulation and scheduling together, called as conjunctive simulated scheduling (CSS), for dynamic scheduling of jobs in production environment. We demonstrate using simulation experiments the concept of Pareto-controllability, and show how a single machine system can be controlled at specified operating conditions (scheduling objectives) dictated by the customer demands, which vary with time based on dynamic market forces.

Bottleneck-based scheduling is a popular approach in production scheduling, and it has achieved promising results in industry. To incorporate this approach in discrete event simulation tools is difficult since the approach requires multiple passes, forward and backward, to reach a good solution for the scheduling problem. We propose a two-pass scheduling approach using discrete-event simulation that takes bottlenecks into consideration. In the first pass, a simulation run is performed and bottlenecks are determined. If significant bottlenecks are identified, a second-pass simulation is performed to reduce the loading on bottlenecks through specific scheduling strategies.

A simulation-based real-time scheduling mechanism for dynamic discrete manufacturing is presented. Modified mean flow time performance for different scheduling approaches is compared through off-line simulation experiments, under dynamic manufacturing environments that are subject to disturbances such as machine breakdowns. These experimental results are used as reference indices for the real-time scheduling mechanism to select the better scheduling approaches for further evaluation based on the actual manufacturing conditions. Discrete-event simulation is used online to evaluate the selected approaches and the corresponding schedules to determine the best solution. The selected schedule is used until the deviation of actual performance from the estimated one exceeds a given limit, or when a major event occurs. A new simulation is then performed with the remaining operations to select a new schedule.

In semiconductor manufacturing, it requires more than one objective such as cycle time, machine utilization and due date accuracy to be kept in focus simultaneously, while developing an effective scheduling. In this paper, a near optimal solution, which is not inferior to any other feasible solutions in terms of all objectives, is generated with a combination of the analytically optimal and simulation based scheduling approach. First, the job shop scheduling problem is modeled using the discrete event simulation approach and the problem is divided in to simulation clock based lot selection sub-problems. Then, at each decision instant in simulated time, a Pareto optimal lot is selected using the various techniques to deal with multiobjective optimization such as weighted aggregation approach, global criterion method, minimum deviation method, and compromise programming. An illustration shows how these techniques work effectively in solving the multiobjective scheduling problem using discrete event simulation.

This paper involves the study of a simulation based proactive decision support module for the shop-floor scheduling of the plastic processing section (PPS) at Bharti Telecom Limited, Gurgaon, India. The flow of material and information in this shop is highly complex as it involves multiple product parts, sequence dependent setup, molding machine specifications, mould restrictions etc. with a variety of scheduling and operational choices. The shop floor planning and scheduling decisions are being exercised manually and there is enough scope of using this simulation based tool to improve the shop-performance. In this work, efforts have been made to simulate the scheduling environment of this section. The performance of the shop depends on various parameters such as initial conditions of the machines, the load on the system, sequencing rule etc. Also the user requirements are so varied and situation dependent that it requires the use of simulation techniques to get a better schedule. Further, there are some findings based on the simulation experiments, which are in the process of implementation in the shop.

Batch processors can concurrently process more than one job, and are commonly used in complex manufacturing systems such as wafer fabrication facilities (wafer fabs). Previous work has shown that there can be a significant reduction in cycle time when the control of the upstream serial processor is dependent on the batch processor. We wish to determine whether this is also true when the upstream processor is also a batch processor, with smaller capacity. We model a two-stage system with an upstream batch processor and a downstream batch processor under differing simple control policies as a continuous time-discrete state Markov chain. In doing so, we show that the concept of constraining the production of the upstream processor in accordance to the anticipated needs of the downstream batch processor can similarly reduce mean system cycle time when the upstream processor is a smaller batch processor. We also perform simulation experiments, using parameters close to those found in wafer fabs, and confirm that the strategy of prioritising the formation of larger batches for the downstream batch processor typically results in substantial cycle time reductions for the two-stage system.

Routing traffic across a capacitated hub-and-spoke network is very common in freight transportation and telecommunications. Most of the hub-and-spoke network models available in the literature assume a fully inter-connected hub network and allow traffic to pass through a maximum of two hubs. In the absence of a fully inter-connected hub network, the traffic may have to be routed through more than two hubs. In this paper, we develop a mathematical model and a simulated annealing (SA)-based solution methodology for routing freight through not more than three hubs in a given capacitated network. We generate test problem instances to evaluate the efficacy of the mathematical model and the meta-heuristic procedure. The effect of each network parameter on the performance of the SA procedure is analysed thoroughly with extensive computational experiments and relevant statistical tests. The results show that near-optimal solutions for large problem instances can be derived in little computational time.

Batch processors can concurrently process more than one job. In wafer fabrication, the processing time of a batch is independent of batch size, and only jobs from the same job family can be batched together. We consider a two-stage subsystem of a wafer fabrication facility (wafer fab), comprised of the diffusion furnace (a batch processor) and its upstream serial processor, with random job arrivals. We hypothesise that allowing the serial processor to anticipate the job family preference of the batch processor will reduce the overall cycle time of jobs passing through this system. To evaluate this hypothesis, we model the performance of the two-stage system under different system parameters and processor control policies as discrete state continuous time Markov chains. We characterise the system performance and show that the concept of constraining the production of the upstream processor according to the anticipated needs of the batch processor can reduce the mean cycle time of jobs being processed. We also perform simulation experiments to show that a simple heuristic based on this insight can translate to substantial cycle time reductions for systems with assumptions closer to those found in wafer fabs.

A supply chain is a network of different entities, such as suppliers, manufacturers, distributors and retailers. Each supply chain member adopts a myopic policy for replenishment and production-related decisions to maximise its revenue or minimise its costs. It results in local optimisation and hence reduced overall supply chain performance. This work investigates the benefits of coordinating the production and distribution decisions in a scenario comprising single supplier and a cluster of multiple buyers. The problem is modelled as a mixed-integer linear programme. Factor analysis is conducted to identify dominant factors that favour the benefits of coordination. A heuristic procedure is developed to solve large problem instances in very less computational time. Encouraging results are obtained.

We consider the problem of scheduling semiconductor burn-in operations, where burn-in ovens are modelled as batch processing machines. Most of the studies assume that ready times and due dates of jobs are agreeable (i.e., ri < rj implies di ≤ dj). In many real world applications, the agreeable property assumption does not hold. Therefore, in this paper, scheduling of a single burn-in oven with non-agreeable release times and due dates along with non-identical job sizes as well as non-identical processing of time problem is formulated as a Non-Linear (0-1) Integer Programming optimisation problem. The objective measure of the problem is minimising the maximum completion time (makespan) of all jobs. Due to computational intractability, we have proposed four variants of a two-phase greedy heuristic algorithm. Computational experiments indicate that two out of four proposed algorithms have excellent average performance and also capable of solving any large-scale real life problems with a relatively low computational effort on a Pentium IV computer.

In this paper, we consider the problem of scheduling semiconductor burn-in operation, where the burn-in oven is modelled as a batch processing machine. In this study, both oven capacity and the number of boards available are taken as constraints. The objective measure of the problem is minimising the total completion time of all jobs. The computational difficulty in getting optimal solution is shown empirically. Since the problem under study is NP-hard, two variants of Tabu Search method are proposed. A series of computational experiments show that the proposed variants of Tabu Search method outperforms the existing heuristic for most system configurations in very meagre CPU times on P4 machine.

Motivated by a major PC manufacturer in the consumer electronics industry, we study the problem of synchronisation of assembly manufacturing and final deliveries using air transportation to achieve accurate delivery with minimised delivery cost in consumer electronics supply chain (CESC). The customer orders are shipped to the customers by cargo flights. Orders are allocated to the available flight capacities based on customer delivery dates. Synchronisation is considered to match the assembly completion of each order with the departure time of its predetermined allocated flights. We break the synchronisation problem down into two sub-problems consisting of an air transportation allocation problem and an assembly scheduling problem. The first sub-problem is formulated as an Integer Linear Programming (ILP) model. Two solution methodologies are proposed for the second sub-problem. Computational results indicate that the proposed methodologies can achieve considerable cost reduction compared to the existing industry practice.

Batch processors can concurrently process more than one batch and are regularly used in the semiconductor manufacturing industry, particularly in the wafer fabrication stage. The reentrant nature of the wafer fab and the incompatibility of jobs from different job families, combined with the typically long processing times of the batch processor, make the control of batch processors an interesting problem with important implications. Current literature has predominantly assumed that the batch processor exists in isolation, and treats the job arrival pattern it receives as a problem constraint. We propose to control the job arrival pattern of the batch processors by controlling the processor immediately upstream of the batch processor, such that the performance of the over-all system is improved. DOCTOR OF PHILOSOPHY (IMST)

In this dissertation, we focus on deriving globally optimal algorithms for three specialized fractional programming problems arising in the context of the independent set problem and management applications in wireless communications. Most of the research efforts thus far involving fractional programs have focused on solving various classes of the single-ratio fractional programs, or multi-ratio cases involving linear fractional functions. However, fractional programming problems become substantially more difficult as the number of ratios in the objective function increases, and this complexity is further amplified when the involved terms are higher-order polynomials. While some classes of multiple-ratio problems can be solved to optimality by recasting them as equivalent 0-1 mixed-integer programming problems, and embedding them within a tailored branch-and-bound algorithm, there is yet significant scope to devise specialized algorithms for solving the generic class of sum of ratios fractional programs, particularly for the case where the involved functions are higher-degree polynomials. We begin by investigating continuous optimization approaches, notably fractional programming methods, to determine the stability number (or independence number) of a graph. Traditionally, this problem has been solved using integer programming methods, but these methods have been known to suffer from a number of shortcomings, in addition to the complexity of dealing with discrete variables. In this context, a new class of vertex sets is defined, and the structure of these vertex sets is utilized to derive explicit characterizations of the number of alternate optima present in both discrete and continuous formulations. Moreover, these vertex sets also enable a simple, yet powerful, construction procedure to efficiently determine maximal independent sets. We also develop a global optimization algorithm to solve the FP formulation, and we demonstrate that this continuous approach stays on par with the 0-1 discrete formulation with respect to various performance metrics. As seen in our numerical experiments, we showed that the computational time required per optimal solution is comparable and in some instances lower for Problem FP as compared to Problem MIS (as the number of alternate optima for Problem FP is significantly greater when compared to Problem MIS) Having established the importance of fractional programming formulations as a viable source for solving hard optimization problems, in the next phase of this research, we focus on solving real-world applications arising in the context of cellular network design. We present a set of (exact and approximate) mathematical models and algorithms for determining the set of (globally) optimal distributed antenna deployments and the supported user demand in cellular code division multiple access (CDMA) systems. We focus on the uplink (user-to-base) formulation and assume that the base station combines all the received signals at each of the antennas using path-gain based weights. In CDMA systems, as all users occupy the system bandwidth at the same time thereby interfering with each other, this results in complicated mixed-integer 0-1 multi-linear programming problem, where the objective function maximizes the total system capacity, while ensuring that the minimum signal-to-interference plus noise ratio (SINR) constraints and maximum transit power constraints for each user are satisfied. This highly nonlinear, nonconvex problem is reformulated to yield a tight mixed-integer 0-1 linear programming representation via the addition of several auxiliary variables and constraints, and a specialized algorithm is designed to determine globally optimal solutions. Finally, we extend the above developed methodology for optimally locating DAS antennas in CDMA cellular networks in the presence of femtocells. Although a femtocell is a simple plug-and-play device, its location requires pre-approval by the service provider. The introduction of femtocells into a cellular network can be very beneficial to both service providers as well as end users. Once again, our focus is on developing an analytical framework that provides a way of computing optimal DAS deployments so that the total capacity is maximized in the CDMA cellular network. Such a framework would be critical for service providers who are interested in adopting the use of femtocells. In conclusion, this thesis provides an algorithmic framework for deriving globally optimal solutions for the class of fractional programming problems, and this approach is validated by solving various practical problems arising in engineering and management applications. Doctor of Philosophy (MAE)

In the make-to-order (MTO) business model, manufacturing and outbound transportation are intimately linked due to little or no finished product inventory. Collaborative planning of the production and delivery is desired. This research seeks to explore a new collaborative scheduling method for production and distribution in supply chain for discrete manufacturing and distribution environment. A coordinated production and delivery scheduling problem, in which jobs are processed on a single batch processing machine (BPM) and then delivered to geographically dispersed customers, is addressed. The coordinated production and distribution model is applicable in many industries such as the integrated circuit (IC) burn-in process and delivery in the subcontract semiconductor assembly and test environment. Burn-in operation is operated in burn-in processor, which is considered as a type of batch processing machine in the semiconductor manufacturing. IC products after the burn-in operation are shipped to customers in different locations for the final test. In this thesis, two delivery methods, namely ‘Individual Delivery’ and ‘Batch Delivery’, are studied. For the ‘Individual Delivery’ method, a job after completion will be directly delivered to a customer. For the ‘Batch Delivery’ method, jobs completed on BPM will be delivered to multiple customers in batches. Especially, vehicle routing problem is considered in the batch delivery problem. Specifically, this thesis studies three subproblems: 1) coordinated production and individual and immediate delivery scheduling problem, 2) coordinated production and individual delivery scheduling problem with limited buffers, and 3) coordinated batch production and batch delivery problem with discrete split and vehicle routing. The objective of this research is to coordinate job schedule both on the production and distribution stages so as to minimize the time and cost related criteria. The objective functions studied in this research includes the makespan, delivery time and transportation cost. This research starts from the analysis of computational complexity of each problem. For those problems which are solvable, algorithms are developed in this research to obtain an optimal job schedule for the coordinated production and distribution problems. For those problems which are proved to be NP-hard, this research characterizes properties of optimal schedule. These optimal properties are beneficial to develop heuristic algorithms. Then both effective meta-heuristics and simple heuristics are designed to obtain promising job sequences. In particular, different efficient Differential Evolution algorithms combined with a dynamic programming algorithm, local search procedure or simple heuristics are developed in this thesis. Numerical experiments are conducted to evaluate the performance of proposed algorithms. Doctor of Philosophy (MAE)

Aircraft Engine Overhaul Divisions overhaul aero engines that have encountered unexpected failure during flight operation. The process of overhauling damaged parts sometimes doesn't involve only refurbishing damaged parts or component replacement, but also investigating into the root cause behind failure. It is mandated by aviation authorities to find out reasons behind an engine failure as flight safety has to be accounted for. In an Engine Overhaul Division (EOD) every engine needs to be overhauled within a certain fixed number of days or overhauling Tum Around Time (TAT). But when engines need to be repaired for certain failure and investigated into failure, the process can consume extra days from the fixed Engine Tum Around Time (TAT) without keeping up with the target. This delay can have an impact on engine availability for airline customers as airlines would have planned flight operations based on availability of engines. Delay or unavailability of engine can entail costs in terms of fleet management and also human factors, if a failure repeats itself leading to catastrophe. This also reflects on the productivity and competitiveness of the EOD. This study focuses on predicting the delay encountered by engines which have a specific repair and investigation requirement. The repair engines overhauled in an Engine Overhaul Division are classified into six groups based on similar failure symptoms. Based on past engine data, the delays in TAT of engines are calculated and suitable forecasting methods are identified to predict future delays. Two forecasting models, namely the Method of Moving Averages and Non Linear Grey Bernoulli method are used to generate suitable forecasts. Accuracy of the forecast is evaluated using error diagnostic Mean Absolute Percentage Error (MAPE). The results are compared between the actual recorded values and the predicted values generated by the forecasting methods. On comparing the results it was found that both forecasting methods have performed well with reasonable forecasting power. Also, one group of investigation demonstrated high MAPE possibly suggesting improper regrouping. Master of Science (Aerospace Engineering)

Customers have varying requirements on price and delivery time in make-to-order manufacturing environment. Due to limited availability of the production capacity, the firm can increase revenue if it allocates priority to time-sensitive customers, charging them a higher price for the same product. The remaining capacity can then be allocated amongst the price-sensitive customers with a relatively lower price. This strategy is aimed at maximizing the overall revenue for the firm. Moreover, the quoted price and delivery time have effects on the demand in the market. In literature, the demand function is considered as a linear decreasing function with respect to price and delivery time, respectively. The challenge is to coordinate the price quotation and the delivery time quotation for customers so that it results in maximizing the net revenue for a manufacturer. There are two types of capacity settings to differentiate products by price and delivery time. These are dedicated capacity and shared capacity. In the dedicated capacity, customers are categorized into several groups. Different customer groups are served in different production facilities. Customers from one customer group share a common price quotation and a common delivery time quotation. The common price and delivery time quotation problem is formulated for a single customer group. The optimality of this problem is studied and applied to develop an algorithm to find the optimal solution in polynomial time. Numerical examples show that the common price and delivery time quotation strategy is applicable when customers are more price-sensitive rather than time-sensitive. In the situation of shared capacity, all customers are served using the same production facility. The production priority is given to the customers who are more time-sensitive. Orders from the customers who are more price-sensitive are scheduled to the rear of the production sequence. Each customer will have a unique quotation of price and a unique quotation of delivery time. The problem is formulated and proven to be NP-complete even when prices are pre-determined parameters. A branch-and-bound algorithm is developed to obtain optimal solutions for the moderate-sized problem. Moreover, a heuristic algorithm is proposed to obtain the near-optimal solution in a short time. Numerical experiments are conducted to test the performance of these two methods. DOCTOR OF PHILOSOPHY (MAE)

64 p. The supply chain risk is the outcome of uncertainty which is always presented during the courses of action to match supply with demand. Evolutional risk, one of the risks in supply chain, is inherited from continuous operation of a business. The evolutional risk could be reduced by increasing the certainty of performance outcome. A sound performance management is the key factor to mitigate the evolutional risk in the supply chain. Master of Science (Logistics)

In this thesis, the performance of various scheduling heuristics of a single serial processing machine with multiple job types in the semiconductor assembly test environment is analyzed. Two types of heuristics are developed in this research. The heuristic with next arrival information is compared with the heuristic without future information to study how the performance is influenced by forecast. Considering the dynamic manufacturing characteristics of the semiconductor industry, the impact of different level of forecast error is analyzed under different traffic intensities. Two sets of heuristics are tested in simulations to demonstrate that the performance of a model with future information highly depends on how the forecast information is utilized in the heuristics. ​Master of Science (IMST)

Although microfluidic technology is at its infancy stage, it offers many advantages for disease screening. In this thesis, our focus is to develop the supply chain strategies for serving the elusive pandemic demand in Singapore. To make the supply chain more robust, a few opportunities for delayed differentiation are identified. Next, demand-capacity evaluations for pandemic disease screening device were performed which suggests that the pandemic demand in Singapore is too low to warrant the establishment of a dedicated production facility. If a production facility is to be built, a possible business model could be to produce normal blood testing kits and reserved some capacity to satisfy the pandemic demand. Several production options are then discussed and that the production facility is advised to choose the option which can bring the highest expected profit. ​Master of Science (IMST)

An effective production strategy would lead to efficient production line performance as well as increased profit. However, there is no fixed or generalized solution. In this project, the author treat the n-job, two-stage hybrid flow line problem with 2 different machines in the first station and 7 identical machines in the second station. The objective for this project is to reducing the total cost. The author researched the ways to improve a company’s performance by reducing the total cost from four different aspects (Production batches, transfer batches, dedicated line as well as predetermining a shared product type). After this project, the total estimated saving for company will be S$18988.6 per 42 weeks, which is over 10% of total cost. The research is meaningful and can provide a guide for manufacturing industry. ​Master of Science (IMST)

Very little research have considered the combined aspects of scheduling involving (i) a dynamic and stochastic shop floor, (ii) Sequence-Dependent Setup (SDS), (iii) transportation, and (iv) the consideration of user preferences to achieve the best/preferred trade-offs amongst the multiple contradicting objectives. Given these complexities, it is indeed challenging to determine when and how the next task (i.e. transportation, setup, processing of a lot) should be selected and executed on each machine such that the best/preferred trade-offs amongst the multiple conflicting objectives can be achieved based on user preferences in an online, near-real-time manner. In this research, the NEXt Task Scheduling (NEXTS) methodology, made up of a NEXTS time policy and a NEXTS decision policy, is proposed. The NEXTS time policy specifies when to schedule. In essence, the time policy stipulates the appropriate timing (i.e. before and as close as possible to the time when the machine is available) for the selection and transportation of the next lot. The NEXTS decision policy specifies how a scheduling decision is to be made. The decision policy is made up of a multiobjective rule (i.e. weighted aggregation of dispatching criteria/rule) that is applied for the selection of the next lot and a Look Ahead Simulation-based Genetic Algorithm with Desirability function (LASGAD) that optimizes the weights used in the multiobjective rule in accordance with user preferences. The multiple performance measures of interest in this research are average cycle time, standard deviation of cycle time, average tardiness, and standard deviation of tardiness. The NEXTS time policy is shown to perform better than the existing Periodic Scheduling/Rescheduling and dispatching time policies when there are transportation and decision making times involved. Simulation results also show that on-line LASGAD is better at finding the optimal set of weights than the off-line response surface methodology with desirability function method. The novel NEXTS methodology contributes to the advancement of knowledge by making use of real-time data for effective on-line scheduling of a dynamic and stochastic shop floor with sequence-dependent setups, transportation times, and user specified preferences to obtain Pareto optimal performance in the face of multiple conflicting objectives. DOCTOR OF PHILOSOPHY (MAE)

A transportation network for employees at a local company faced problems with unbalanced utilization and passenger complains caused by occasional overcrowding in vehicles. The objective of this work was to develop a method for planning transportation routes and establish its reliability in consistently delivering a near or optimal solution. Two models were built each with a different Integer programming formulation- Formulation I and II. Formulation I consists of vehicle cost and penalty cost components in its objective function so that the optimal solution would balance actual cost and passenger service level. The model was implemented in the EXCELbased Risk Solver Platform but could not find a feasible solution for even a small set of 4-node problem. Formulation II, built upon experience from the first model, had an improved cost function that excluded the penalty cost component. An implementation in XPRESS-MP and subsequent verification with enumeration solutions proved its ability to solve both the 3-node and 4-node problems to optimality. A profile of objective value over various vehicle capacity values served as a guide to setting passenger service level. The XPRESS-MP model was implemented in Stage II of this work on a 13-node problem based on actual data. The solution was compared with manually generated routes and was found to outperform the latter by at least 9%. Even with passenger service level imposed on the model, a 5% cost saving was attained. Proactive efforts in data collection and maintenance are encouraged to ensure successful full scale implementation of this model in the future. ​Master of Science (IMST)

This project investigates a production line that faces fluctuating demand with seasonal pattern. The cumulative capacity exceeds the cumulative demand in a one year period; however, its expected weekly capacity is not able to meet the weekly demand during the peak season. In addition, examining its past production outputs, there existed a large gap below the expected capacity. Therefore, it has become crucial to study the significant factors influencing the line performance. ​Master of Science (IMST)

This project aims to study the effects of open-loop and closed loop job release policies and dispatching rules in an electrical appliance company in Singapore. While maintaining the throughput at the required level with high utilization rate, the project aims to reduce the work-in-process inventory level, as well as to reduce average cycle time, cycle time standard deviation and thus system variability. The developed models and solution methods are validated using actual production data and the results are compared among each other and against the current performance. ​Master of Science (IMST)

In PCB Assembly manufacturing environment, there are usually three objectives to achieve – minimizing setup cost, minimizing average cycle time, and minimizing job lateness. With the re-entrant trait of PCB Assembly system, scheduling job release is a complicated but crucial problem which plays an important role in improving the whole factory performance, maximizing factory production capability, minimizing waste and maximizing revenue. How to schedule jobs with dynamic queue to achieve the three objectives is where we focus on. ​Master of Science (IMST)

92 p. In recent years, the electronic product industry is developing rapidly around the world. Electronics manufacturers are facing rigorous challenges compelled by this fast advancement. To survive and profit, these companies are required to provide products in shorter innovation cycles and deliver them on time with lower costs. Master of Science (Logistics)

As an emerging industry, microfluidic device manufacturing has little large scale production experience. Many areas, like base materials selection, mass production processes and equipment design, have not been well established. The uncertainties bring both challenges and opportunities. If we are able to anticipate the potential production system related problems proactively, it will greatly help the research and development better realize the concept of design-for-manufacturing and prepare us to accelerate fast once the large scale production takes place. In this thesis project, we began with characterization of polymer based microfluidic manufacturing processes. A probable production flow was constructed for relatively simple devices and extended to represent more complicated and generic systems. The potential challenges in the production planning were discussed and corresponding recommendations were made on process development and equipment designs. ​Master of Science (IMST)

123 p. This dissertation studies the Enterprise Resource Planning (ERP) software selection and implementation methodologies and issues in ERP implementation for organizations. The importance of ERP applications for enterprises is studied in detail. Various functional issues in ERP implementation are discussed which are needed to be addressed by the ERP software implementation. Important of ERP implementation and related issues such as ERP and e-commerce, reasons to undertake ERP for organizations, Return On Investment aspects is emphasized for successful ERP implementation. Other issues such as suitability of ERP software to business processes and its benefit, functional requirements of ERP software, Requirements Engineering aspects are discussed in detail. Master of Science (Computer Integrated Manufacturing)

The objective of this research is to characterize the control curves for the multi-objective dynamic scheduling (MODS) of serial and batch manufacturing processes. The pairs of conflicting objectives considered in this thesis are (1) mean cycle time and maximum tardiness, (2) mean cycle time and cycle time variance, and (3) earliness and tardiness related objectives. The motivation for using these conflicting objectives is to simultaneously incorporate the manufacturer's as well as the customer's concerns in scheduling. The interest of the decision maker lies not in just achieving a single optimal solution, but in defining a trade-off curve so that the decision maker can operate the scheduling along the trade-off curve according to the dynamic needs of the manufacturing shop. DOCTOR OF PHILOSOPHY (MPE)

Many businesses have been focusing on two priorities in their manufacturing systems in recent years - cost and quality. In order to achieve these key priorities, organizations are adopting various business improvement methodologies such as six sigma, lean manufacturing, cost reduction, business process re-engineering, total quality management, etc. ​Master of Science (IMST)

75 p. This report discusses about the manufacturing outsourcing in Singapore, which the author believe that when executed successfully, manufacturing outsourcing can help businesses reduce costs and working capital while maintaining and even improving product quality and design. It can even speed up supply chain velocity, by facilitating closer proximity and technical integration with supply chain partners. As the IT hardware and consumer electronics markets have become increasingly price aggressive, Original Equipment Manufacturers (OEMs) like HP, Lexmark, Sony and Apple find profit margins shrinking to less than adequate levels. Risking substantial loss in market share by increasing price, OEMs are forced to look to margin increases by more efficiently managing back end costs. As a result, they have increasingly turned to less labor-intensive regions of the world to fulfill manufacturing needs, outsourcing up to all of the product manufacturing/development to contract manufacturers. Master of Science (Computer Integrated Manufacturing)

To stay competitive in the booming rig-building industry and era, world leading rigdesigner and builder like Keppel Offshore and Marine endeavors to manage the construction resource in a scientific way to cut cost and improve operation efficiency. Although a leader in operation and manufacturing excellence, it is eager to exploit room for management improvement in the areas of painting operation resource, scaffolding resource and manpower resource. ​Master of Science (IMST)

93 p. The recent year's development in the industry has led to procurement activities increasing in their importance. With companies increasing their activities in global sourcing and the intensity of cost pressure, companies have no choice but to respond to this changing landscape. In order to cope with this development, companies are developing new sourcing strategies as a way to achieve greater competitiveness and enhance the overall supply chain performance. It also means there need to be a paradigm shift in the areas of supplier relationship management. The traditional arms length approach will no longer be sufficient to build a successful working relationship together. Instead, companies need to move into mutual collaboration with suppliers in order to align their suppliers strategically within the supply chain network. Master of Science (Logistics)