Management Sciences
http://hdl.handle.net/10012/9910
Sat, 19 Oct 2019 05:32:36 GMT
20191019T05:32:36Z

Optimization Models for the Perishable Inventory Routing Problem
http://hdl.handle.net/10012/14987
Optimization Models for the Perishable Inventory Routing Problem
Singla, Ajitesh Rajiv
In this thesis, three models for the Perishable Inventory Routing Problem (PIRP) are explored. The first model of the inventory routing problem considers one perishable product and known (deterministic) demands in the context of consignment inventory. The objective of the problem maximizes the profit of the supplier who owns all the inventory until it is sold. The supplier gives a fixed percentage discount on the selling price of the product as it deteriorates. The shelf life and the number of vehicles used in this problem is also fixed. Computational results, comparing the PIRP with the standard Inventory Routing Problem (IRP), are presented. Based on the calculations using the branchandcut algorithm, we conclude that adding perishability to the IRP leads to better inventory management as a result of which fresher products are delivered to the customers. The PIRP model is also solved using the Benders Decomposition algorithm. The results indicate that in the above stated problem, the branchandcut algorithm yields better results as compared to the Benders Decomposition algorithm.
The second model extends the PIRP to consider uncertain (stochastic) demands (SPIRP). This is done to make the model comparable to a reallife problem. Several demand scenarios are created within a fixed range to capture the uncertainty with respect to the perishable product. Since estimating the actual probability is difficult, a large number of equally probable demand scenarios are assumed. The model was run on the branchandcut and the Benders Decomposition algorithm. The results generated were then compared. It was inferred that even when demand scenarios enable easy decomposition of the problem, the Benders algorithm performs worse than the branchandcut algorithm.
In the third model, the robust formulation of the SPIRP is proposed to resolve the abovementioned limitations. Robustness was added to the SPIRP to enable the use of a small number of scenarios to obtain solutions that are competitive with modeling a large number of scenarios in the case of SPIRP. An innovative way of formulating the robust counterpart of the SPIRP was developed while keeping the probability of each demand scenario uncertain. An algorithm was devised to compare the effectiveness of the robust model to the deterministic and stochastic models.
Computational results compare the average profit values generated by the three models. It is concluded that while the deterministic model captures no uncertainty, the stochastic model with many scenarios accounts for the most demand uncertainty; the robust model, through the use of far fewer scenarios, can account for a significant uncertainty in demand. Another interpretation of the results is that an increased number of robust scenarios has a significant effect on the average profit values of that model.
Thu, 29 Aug 2019 00:00:00 GMT
http://hdl.handle.net/10012/14987
20190829T00:00:00Z

A Systems Approach to Examining Cooperative Education: A Case Study
http://hdl.handle.net/10012/14966
A Systems Approach to Examining Cooperative Education: A Case Study
Pretti, Tracey Judene
Cooperative education (coop) is a model of learning where students alternate between academic and work terms. Coop offers potential benefits for three key stakeholder groups: students, employers and academic institutions. While the literature reveals a number of outcomes of coop for each of the stakeholder groups, it has not examined how outcomes for multiple stakeholders are achieved simultaneously. That is, can students’ and employers’ goals for participation be balanced in such a way that they both benefit, and if so, how?
This research was based primarily on two theoretical contributions: Ashby’s Law of Requisite Variety (1957) and Katz and Kahn’s roletaking model (1978). The goal of this research was to examine the two key phases of coop, the recruitment phase and the work term phase, to understand how variety is managed by students, employers and academic institutions. A mixed methods case study approach was used to examine a coop system in depth using both qualitative and quantitative analysis. Data was captured from multiple sources and analyzed using thematic analysis as well as statistical techniques to understand how the objectives and actions of each of the stakeholder groups affected the others.
The findings revealed numerous ways that variety is managed in the coop system. In the recruitment phase, variety was reduced for students and employers by providing an opportunity to assess one another for ‘fit’ and to set expectations for the role and the organization. In the work term phase, variety was balanced through the 1) assignment of tasks whose difficulty level matched the students’ capabilities and 2) the provision of support to students aligned with the difficulty and importance of the task. The difficulty level of the task and the support provided to the student were found to be positively associated with students’ reports of learning.
The use of a systems approach in examining cooperative education revealed a task classification model which can be leveraged by employers and academic institutions in balancing task assignment with intended outcomes. Through this research, a systems model for cooperative education was developed which captures the key processes within the coop system and the association between those processes outcomes for both students and employers.
Wed, 28 Aug 2019 00:00:00 GMT
http://hdl.handle.net/10012/14966
20190828T00:00:00Z

Benders Decomposition for Profit Maximizing Hub Location Problems with Capacity Allocation
http://hdl.handle.net/10012/14758
Benders Decomposition for Profit Maximizing Hub Location Problems with Capacity Allocation
Taherkhani, Gita; Alumur, Sibel A.; Hosseini, Seyed Mojtaba
This paper models capacity allocation decisions within profit maximizing hub location problems to satisfy demand of commodities from different market segments. A strong deterministic formulation of the problem is presented and two exact algorithms based on a Benders reformulation are described to solve largesize instances of the problem. A new methodology is developed to strengthen the Benders optimality cuts by decomposing the subproblem in a twophase fashion. The algorithms are enhanced by the integration of improved variable fixing techniques. The deterministic model is further extended by considering uncertainty associated with the demand to develop a twostage stochastic program. To solve the stochastic version, a MonteCarlo simulationbased algorithm is developed that integrates a sample average approximation scheme with the proposed Benders decomposition algorithms. Novel acceleration techniques are presented to improve the convergence of the algorithms proposed for the stochastic version. The efficiency and robustness of the algorithms are evaluated through extensive computational experiments. Computational results show that largescale instances with up to 500 nodes and three demand segments can be solved to optimality, and that the proposed algorithms generate cuts that provide significant speedups compared to using Paretooptimal cuts. The proposed twophase methodology for solving the Benders subproblem as well as the variable fixing and acceleration techniques can be used to solve other discrete location and network design problems.
Sat, 01 Jun 2019 00:00:00 GMT
http://hdl.handle.net/10012/14758
20190601T00:00:00Z

Profit Maximizing Hub Location Problems
http://hdl.handle.net/10012/14661
Profit Maximizing Hub Location Problems
Taherkhani, Gita; Alumur, Sibel A.
In this paper, we study profit maximizing hub location problems. We formulate mathematical models determining the location of hubs, designing the hub networks, and routing the demand in order to maximize profit. The profit is calculated by summing the total revenue minus total cost. Total cost includes the total transportation cost, the installation cost of hubs, and the cost of operating hub links. We consider all possible allocation strategies: multiple allocation, single allocation, and rallocation. As an extension, for each allocation strategy, we also model the cases in which direct connections between nonhub nodes are allowed. To test and evaluate the performances of the proposed models, we use two wellknown data sets from the literature. We analyze the resulting hub networks under various different parameter settings.
The final publication is available at Elsevier via https://doi.org/10.1016/j.omega.2018.05.016 © 2018. This manuscript version is made available under the CCBYNCND 4.0 license http://creativecommons.org/licenses/byncnd/4.0/
Mon, 01 Jul 2019 00:00:00 GMT
http://hdl.handle.net/10012/14661
20190701T00:00:00Z