It has been broadly recognised that today’s manufacturing organisations face increasing pressures from continuous and unexpected changes in the business environment such as changes in product types, changes in demand pattern, changes in manufacturing technologies etc. To enable manufacturing organisations to rapidly and timely deal with these changes, operational decisions (e.g., process planning and production scheduling) have to be integrated with dynamic system restructure or reconfiguration so that manufacturing organisations do not only use the flexible resource utilisations to deal with these changes, but also can dynamically reconfigure their existing system structures in response these changes. A manufacturing system concept and implementation methodology is proposed by the Exeter Manufacturing Enterprise Centre (XMEC), which is called the Dynamically Integrated Manufacturing System (DIMS). The overall aim of DIMS is to provide a systematic modelling and control framework in which operational decisions can be integrated with the dynamic system restructuring decisions so as to help manufacturing systems to dynamically deal with changes in the business environment. This PhD research is a part of DIMS research, which focuses on the investigation on operational control in DIMS. Based on the established agent-based modelling architecture in DIMS, this research develops two agent bidding mechanisms for the hierarchical control of production planning and scheduling. These two mechanisms work together to assist manufacturing systems in making optimal and flexible operational decisions in response to changes in the business environment. The first mechanism is the iterative agent bidding mechanism based on a Genetic Algorithm (GA) which facilitates the determination of the optimal or near optimal allocation of a production job containing a set of sub-jobs to a pool of heterarchical resources. The second mechanism is the hierarchical agent bidding mechanism which enables product orders to be cost-efficiently and flexibly planned and scheduled to meet the orders’ due dates. The novelty of this mechanism is that it enables orders to be fulfilled within structural constraints of manufacturing systems as far as possible and however enables resources to be regrouped flexibly across system boundaries when orders cannot be fulfilled within structural constraints of manufacturing systems.

In this thesis we address capacity planning problems with different demand and service characteristics, motivated by healthcare applications. In the first application, we develop, implement, and assess the impact of analytical models, accompanied by a decision-support tool, for operating room (OR) staff planning decisions with different service lines. First, we propose a methodology to forecast the staff demand by service line. We use these results in a two-phase mathematical model that defines the staffing budget for each service line, and then decides how many staff to assign to each potential shift and day pair while considering staff overtime and pooling policies and other staff planning constraints. We also propose a heuristic to solve the model's second phase. We implement these models using historical data from a community hospital and analyze the effect of different model parameters and settings. Compared with the current practice, we reduce delays and staff pooling at no additional cost. We validate these conclusions through a simulation model. In the second application, we consider the problem of staff planning and scheduling when there is an accepted time window between each order's arrival and fulfillment, with the goal of obtaining a balanced schedule that focuses on on-time demand fulfillment but also considers staff characteristics and operational practices. Hence, solving this problem requires simultaneously scheduling the staff and the forecasted demand. We propose, implement, and analyze the results of a model for staff and demand scheduling under this setting, accompanied by a decision-support tool. We implement this model in a company that offers document processing and other back-office services to healthcare providers. We provide details on the model validation, implementation, and results, including a 25\% increase in the company's staff productivity. Finally, we provide insights on the effects of some of the model's parameters and settings, and assess the performance of a proposed heuristic to solve this problem. In the third application, we consider a non-consumable resource planning problem. Demand consists of a set of jobs, each job has a scheduled start time and duration, and belongs to a particular demand class that requires a subset of resources. Jobs can be `accepted' or `rejected,' and the service level is measured by the (weighted) percentage of accepted jobs. The goal is to find the capacity level that minimizes the total cost of the resources, subject to global and demand-class-based service level constraints. We first analyze the complexity of this problem and several of its special cases, and then we propose a model to find the optimal inventory for each type of resource. We show the convergence of the sample average approximation method to solve a stochastic extension of the model. This problem is motivated by the inventory planning decisions for surgical instruments for ORs. We study the effects of different model parameters and settings on the cost and service levels, based on surgical data from a community hospital.

Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 146-154). Also available in electronic version. Access restricted to campus users.

Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 56-58).

Thesis (Ph. D.)--University of Missouri-Columbia, 1997. / Typescript. Vita. Includes bibliographical references (leaves 77-85). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1997. / Typescript. Vita. Includes bibliographical references (leaves 77-85). Also available on the Internet.

Missions for planetary exploration are unique opportunities to provide very meaningful and high-valuable data about the analysed celestial bodies. These missions can characterize many aspects of them, thanks to the different remote sensing instruments included in their science payload. However, the observations in this context are influenced by complex constraints (e.g., limited resources, environment characteristics) and limitations, thus limiting the availability of acquisition opportunities. Hence, an accurate planning and scheduling of the acquisition operations by the science payload instruments of a Planetary Exploration mission is a crucial task in the mission design. This requires the development of automatic methodologies to aid this delicate phase, which are capable of considering all the different constraints, the science requirements and the characteristics of the instruments in order to produce feasible observation schedules that are optimized with respect to the acquisition quality. In this context, this thesis provides two main contributions related to: i) the analysis and the scheduling of the acquisitions by a single instrument and ii) the extension of the study to the simultaneous scheduling of the observations by multiple instruments. The first novel contribution presents a methodology for the automatic scheduling of the acquisition operations of a single instrument for planetary exploration missions. The presented methodology is based on 2 main phases and it uses a multi-objective optimization technique to produce an acquisition schedule, optimized with respect to the scientific requirements and the characteristics of the considered sensor and the mission constraints. The second contribution addresses the complexity of automatically generating and harmonizing observation schedules for multiple instruments simultaneously. The proposed method models the problem as a bilevel optimization task. At the lower level the acquisition schedule for each sensor is produced and evaluated, considering all the instrument-related requirements and limitations. At the upper level the harmonization of the individual sensor schedules is performed, considering all the mission- and resource-related constraints and maximizing the overall quality and science return. The proposed methods have been applied considering in detail the operations of radar sounder instruments. In particular, the first methodology has been tested on the observations by RIME, radar sounder of the JUICE mission and the second considered RIME and three other instruments of the same missions. The obtained results show the effectiveness of the proposed techniques, which aim at increasing the level of automation in the data acquisition planning and scheduling phase in Planetary Exploration missions.

Thesis (Ph.D.)--McMaster University, 2006. / Supervisor: George Steiner. Includes bibliographical references (leaves 123-126). Mode of access: World Wide Web.

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

This dissertation presents an integrated approach to planning and scheduling surgeries in operating-rooms (ORs) at strategic, tactical and operational levels. We deal with uncertainties of surgery demand and durations to reflect a reality in OR management. The strategic part of the dissertation studies capacity decisions that allocate surgical specialties to OR days with the objective of minimizing total expected costs due to penalties for any patients who are not accommodated and for under- (i.e., idleness) and over- (i.e., overtime) usage of OR capacity. It presents a prototypical non-linear, stochastic programming model to structure the problem and four adaptations, along with associated solution approaches, with the goal of facilitating solution by overcoming the computational disadvantages of the prototype. Each of these models offers advantages but is also attended by disadvantages. Computational tests compare the four models and solution approaches with respect to solution quality and run time. The tactical part of the dissertation prescribes an approach to optimize a master surgical schedule (MSS), which adheres to the block scheduling policy, using a new type of newsvendor-based model. Our newsvendor approach prescribes the optimal duration of each block and the best permutation, obtained by solving the sequential newsvendor problem, determines the optimal block sequence. We obtain closed-form solutions for the case in which surgery durations follow the normal distribution. Furthermore, we give a closed-form solution for optimal block duration with no-shows. We conduct numerical tests for surgery durations that follow normal, lognormal and gamma distributions. Results show that the closed-form solutions associated with the normal distribution gives close approximations to solutions associated with log-normal and gamma distributions. The operational part of the dissertation prescribes an optimal rule to sequence two or three surgeries in a block. The smallest-variance-first-rule (SV) is generally accepted as the optimal policy for sequencing two surgeries, although it has been proven formally only for several restricted cases. We extend prior work, studying three distributions as models of surgery duration (the lognormal, gamma, and normal) and including overtime in a total-cost objective function comprising surgeon-and-patient- waiting-, operating-room-idle-, and staff over-times. We specify expected waiting- and idle- time as functions of the parameters of surgery duration to identify the best rule to sequence two surgeries. We compare the relative values of expected waiting- and idle- times numerically with that of expected overtime. Results recommend that the SV rule be used to minimize total expected cost of waiting-, idle- and over-time. We find that gamma and normal distributions with the same mean and variance as the lognormal give nearly the same expected waiting- and idle- times, observing that the lognormal in combination with either the gamma or normal gives a similar result. Lastly, the dissertation investigates an appointment system with deterministic arrival times (D) and non-identical exponential service times (M). For two customers, we show that both the smallest-mean-first-rule and the SV minimize the sum of expected waiting- and idle-times. We prove that neither is optimal for three customers, but verifies that the first customer in the sequence should be the one with the smallest variance (mean).