Chart of the bill of timber for the railway bridge near Hurst’s, signed by S.D. Woodruff

Chart of the bill of timber for the railway bridge near Hurst’s, signed by S.D. Woodruff, Oct. 15, 1855.

Post card with a notice of freight arrival addressed to S.D. Woodruff from the Great Western Railway for castings, a keg and grates

Post card with a notice of freight arrival addressed to S.D. Woodruff from the Great Western Railway for castings, a keg and grates, Aug.11, [1876].

Printed blank for arrival of goods by Great Western Railway at St. Catharines

Printed blank for arrival of goods by Great Western Railway at St. Catharines, Aug. 24, 1876.

Printed blank for arrival of goods by Great Western Railway at St. Catharines

Printed blank for arrival of goods by Great Western Railway at St. Catharines, Nov. 18 [1876].

Printed blank from Grand Trunk Railway for shipping to St. Catharines from Buffalo for table

Printed blank from Grand Trunk Railway for shipping to St. Catharines from Buffalo for table, 1888.

Indenture of agreement between the Great Western Railway Company and the Erie and Ontario Railway Company

Indenture of agreement between the Great Western Railway Company and the Erie and Ontario Railway Company in order for the companies to unite, March 20, 1854.

Letter to Mr. Price from E.J. McIntryre regarding a cheque for the assessment on a railway

Letter to Mr. Price from E.J. McIntryre regarding a cheque for the assessment on a railway, Dec. 25, 1910.

Letter to Mr. W. D. Woodruff who is listed as the Treasurer of the B.N. and T. Railway Company from H.H. Collier

Letter to Mr. W. D. Woodruff who is listed as the Treasurer of the B.N. and T. Railway Company from H.H. Collier, Barrister regarding items that have been paid out of the account, Jan. 16, 1911.

Indenture (vellum) of mortgage between the Port Hope, Lindsay and Beaverton Railway Company and Joseph Augustus Woodruff of Niagara and Gilbert McMicken of the Village of Elgin in Welland

Indenture (vellum) of mortgage between the Port Hope, Lindsay and Beaverton Railway Company and Joseph Augustus Woodruff of Niagara and Gilbert McMicken of the Village of Elgin in Welland. This document was registered Jan.4, 1856 – instrument no. 586, Dec. 29, 1855.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.

Urban and peri-urban agricultural production along railway tracks: a case study from the Mumbai Metropolitan Region

Urban and peri-urban agriculture (UPA) contributes to food security, serves as an opportunity for income generation, and provides recreational services to urban citizens. With a population of 21 Million people, of which 60 % live in slums, UPA activities can play a crucial role in supporting people’s livelihoods in Mumbai Metropolitan Region (MMR). This study was conducted to characterize the railway gardens, determine their role in UPA production, and assess potential risks. It comprises a baseline survey among 38 railway gardeners across MMR characterized by different demographic, socio-economic, migratory, and labour characteristics. Soil, irrigation water, and plant samples were analyzed for nutrients, heavy metals, and microbial load. All the railway gardeners practiced agriculture as a primary source of income and cultivated seasonal vegetables such as lady’s finger (Abelmoschus esculentus L. Moench), spinach (Spinacia oleracea L.), red amaranth (Amaranthus cruentus L.), and white radish (Raphanus sativus var. longipinnatus) which were irrigated with waste water. This irrigation water was loaded with 7–28 mg N l^(−1), 0.3–7 mg P l^(−1), and 8–32 mg K l^(−1), but also contained heavy metals such as lead (0.02–0.06 mg Pb l^(−1)), cadmium (0.03–0.17 mg Cd l^(−1)), mercury (0.001–0.005 mg Hg l^(−1)), and pathogens such as Escherichia coli (1,100 most probable number per 100 ml). Levels of heavy metals exceeded the critical thresholds in surface soils (Cr, Ni, and Sr) and produce (Pb, Cd, and Sr). The railway garden production systems can substantially foster employment and reduce economic deprivation of urban poor particularly slum dwellers and migrant people. However this production system may also cause possible health risks to producers and consumers.

Thread Scheduling Mechanisms for Multiple-Context Parallel Processors

Scheduling tasks to efficiently use the available processor resources is crucial to minimizing the runtime of applications on shared-memory parallel processors. One factor that contributes to poor processor utilization is the idle time caused by long latency operations, such as remote memory references or processor synchronization operations. One way of tolerating this latency is to use a processor with multiple hardware contexts that can rapidly switch to executing another thread of computation whenever a long latency operation occurs, thus increasing processor utilization by overlapping computation with communication. Although multiple contexts are effective for tolerating latency, this effectiveness can be limited by memory and network bandwidth, by cache interference effects among the multiple contexts, and by critical tasks sharing processor resources with less critical tasks. This thesis presents techniques that increase the effectiveness of multiple contexts by intelligently scheduling threads to make more efficient use of processor pipeline, bandwidth, and cache resources. This thesis proposes thread prioritization as a fundamental mechanism for directing the thread schedule on a multiple-context processor. A priority is assigned to each thread either statically or dynamically and is used by the thread scheduler to decide which threads to load in the contexts, and to decide which context to switch to on a context switch. We develop a multiple-context model that integrates both cache and network effects, and shows how thread prioritization can both maintain high processor utilization, and limit increases in critical path runtime caused by multithreading. The model also shows that in order to be effective in bandwidth limited applications, thread prioritization must be extended to prioritize memory requests. We show how simple hardware can prioritize the running of threads in the multiple contexts, and the issuing of requests to both the local memory and the network. Simulation experiments show how thread prioritization is used in a variety of applications. Thread prioritization can improve the performance of synchronization primitives by minimizing the number of processor cycles wasted in spinning and devoting more cycles to critical threads. Thread prioritization can be used in combination with other techniques to improve cache performance and minimize cache interference between different working sets in the cache. For applications that are critical path limited, thread prioritization can improve performance by allowing processor resources to be devoted preferentially to critical threads. These experimental results show that thread prioritization is a mechanism that can be used to implement a wide range of scheduling policies.

Optimal Methodology for Synchronized Scheduling of Parallel Station Assembly with Air Transportation

We present an optimal methodology for synchronized scheduling of production assembly with air transportation to achieve accurate delivery with minimized cost in consumer electronics supply chain (CESC). This problem was motivated by a major PC manufacturer in consumer electronics industry, where it is required to schedule the delivery requirements to meet the customer needs in different parts of South East Asia. The overall problem is decomposed into two sub-problems which consist of an air transportation allocation problem and an assembly scheduling problem. The air transportation allocation problem is formulated as a Linear Programming Problem with earliness tardiness penalties for job orders. For the assembly scheduling problem, it is basically required to sequence the job orders on the assembly stations to minimize their waiting times before they are shipped by flights to their destinations. Hence the second sub-problem is modelled as a scheduling problem with earliness penalties. The earliness penalties are assumed to be independent of the job orders.

Planning and Scheduling Decisions in Supply Chains with Multiple Supply Modes: An Integrated Approach

We address the problem of jointly determining shipment planning and scheduling decisions with the presence of multiple shipment modes. We consider long lead time, less expensive sea shipment mode, and short lead time but expensive air shipment modes. Existing research on multiple shipment modes largely address the short term scheduling decisions only. Motivated by an industrial problem where planning decisions are independent of the scheduling decisions, we investigate the benefits of integrating the two sets of decisions. We develop sequence of mathematical models to address the planning and scheduling decisions. Preliminary computational results indicate improved performance of the integrated approach over some of the existing policies used in real-life situations.