Master of Science By Research

Permanent URI for this collectionhttp://192.248.9.226/handle/123/13948

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Now showing 1 - 11 of 11

item: Thesis-Full-text
Investigating the impact of parking violations on the performance of a curbside with-flow bus priority lanes : a simulation-based approach
(2023) Samarakoon, GS; Sivakumar, T
A Bus Priority Lane (BPL) is a lane where priority is given to buses over other traffic. Travel time reduction is one of the main benefits expected from implementing a BPL. Literature reveals that the success of a BPL in achieving its expected benefits depends entirely on the adherence by motorists and the enforcement by the enforcement authority of the BPL Rules. Literature classifies BPL violations into two types, namely parking and driving violations (Agrawal, Goldman, & Hannaford, 2012; Mundy, Trompet, Cohen, & Graham, 2017). The impact of Parking Violations (PV) is explored in this study. A PV is when an unauthorised vehicle enters the BPL and parks in the BPL for a period of time. A driving violation is when an unauthorized vehicle drives in the BPL without stopping. Curbside with-flow BPLs, such as in the case of Colombo, Sri Lanka, are most at risk for parking violations due to high number of curbside activities. Experimenting PVs on the ground is not only costly but also risky. Microsimulation is a valuable traffic engineering tool that offers many benefits in evaluating traffic scenarios before implementation. However, as it is an approximation of the real world, the models should closely reflect the real world for the outputs to be credible. This credibility is achieved by calibrating parameters influencing the vehicle trajectory decisions in the simulations. Many studies have used microsimulation to analyse bus priority strategies by calibrating several parameters. These include vehicle-specific parameters such as dimensions, speed, lateral and longitudinal gap acceptance, etc., and simulation model-specific parameters such as car-following and lane-changing models. “SUMO TraCI” has been used successfully to simulate the stopping of a vehicle in a lane for a predetermined time period, which was used to simulate the PV s in this study. The output showed that the average travel time of buses increases exponentially with increasing frequency of PVs, duration of PVs and the flow of buses (headways). While both increase of duration of PVs and flow of buses causes the average bus travel times to increase at an increasing rate, further analysis showed that the duration of PVs increases the travel time at an increased rate higher than that of the flow of buses increases (shorter headways). The investigation of the impact of PVs on the travel time of buses in BPLs is identified as a research gap. Anwar, Fujiwara, & Zhang (2011) modelled the impact of illegal on-street occupancy on the vi impact of link travel time as a reduction in the link capacity using the Bureau of Public Roads (BPR) function developed in the USA. The parameters of BPR function have been recalibrated considering the effect of illegal on-street occupancy on travel time prediction. The microsimulation model developed in this study has been calibrated for Sri Lankan Traffic conditions. The model comply with Greenshield’s traffic flow model. Microsimulation is used to obtain the average travel time of buses in the BPL for varying flow of buses, frequency of PVs and the duration of parking. The output is then used to calibrate the BPR function to model the impact of PVs on the travel time of buses in a BPL. The α and β values of the calibrated BPR function are 3.403 and 2.493, which is similar to the parameters used by the authorities who estimate travel time on national roads in Sri Lanka. Thus, the overall travel time in BPLs increases more than three times (340%) compared to that in free-flow conditions due to PVs, while the increase is random as of occurrence of PV. Since the study is focused on modelling the travel time of buses on BPLs, the impact of violator vehicle movements on the travel times of the general traffic is not investigated. Another limitation of this study is that it considers only one vehicle type to perform PVs. Therefore, expanding this study to include different types of vehicles as violators and investigating the impact of PVs in BPL on the general traffic on other lanes in the same direction is recommended as future research avenues.
item: Thesis-Abstract
An Optimization model for multi-objective vehicle routing problem for perishable goods distribution
(2022) Fernando M; Thibbotuwawa A; Perera HN
Vehicle Routing Problem (VRP) is a well-studied area of operations research that has resulted in significant cost savings in global transportation. The primary goal of the VRP is to find the best route plan that minimizes the total distance traveled. The current study used VRP to solve the problem of fresh Agri products distribution in retail chains. With the advancement of computation power, researchers pay more attention to incorporating real-world characteristics when developing VRP, making it more practical for use in real-world applications. Existing literature identifies a research gap in richer problems that use real-world characteristics concurrently. This study created an integrated bi-objective VRP model that focused on resource optimization, order scheduling, and route optimization all at the same time. Two objectives aim to minimize distribution costs while ensuring product deliveries to retail outlets on time. To improve real-world applicability, the model incorporated multiple real-world characteristics simultaneously. All the algorithms were developed using an open-source optimization library called OR-tools. This research compared several heuristics and metaheuristic methods respectively, to obtain the IBFS (Initial Basic Feasible Solutions) and iterative improvements. Thereafter, best performing heuristic method (savings algorithms) and metaheuristic method (guided local search) were hybridized to develop the proposed two-phase solution method. All the solution algorithms and the developed VRP model were tested using the data obtained from one of the largest retail chains in Sri Lanka. Numerical experiments show the efficiency of the proposed solution algorithm in solving a real-world VRP problem. Further, numerical experiments show that the proposed VRP model has achieved a 16% saving in daily distribution cost while ensuring on-time deliveries to 95% of the retail outlets. Further, on-time deliveries of fresh Agri products ensure the freshness conditions. The developed VRP model is efficient to use as an operational planning tool for planning distribution operations in retail chains.
item: Thesis-Abstract
Optimization applications in seaport container terminal operations and fuzzy logic-based inter terminal trucking
(2022) Weerasinghe BCA; Perera HN
Operations research techniques have helped optimize container terminal operations over the past decades and have been a regular feature of maritime logistics and maritime supply chain literature in addition to being in practice at container terminals across the globe. The first phase of the project, a systematic review systematically collates through Scopus and analyzes 1631 papers published in the domain to find the main research clusters and understand future research directions. Studies based on both quayside and landside planning are encapsulated for this research. Five research clusters that discuss simulation, scheduling and automation, quayside operations, integrated operations, and container transportation are identified based on author keywords of the systematically derived paper pool. In addition to that, the evolution of applying optimization techniques in container terminal operations planning is discussed in this study alongside the suggested trajectory of the research agenda under each cluster. The analysis finds that genetic algorithms, integer linear programming and heuristics are the most widely used operations research techniques in container terminal optimization. The review proposes the application of methods such as neural network, fuzzy logic and deep learning models related to artificial intelligence to widen our understanding of container terminal operations. The second phase of the project is conducted to optimize ITT truck flows in container terminals by following the research direction that is derived through the cluster, container transportation in the systematic review. The proposed model is developed using fuzzy logic in MATLAB software. The model can allocate ITT trucks based on the demand from terminal yards and current truck arrival rates at gatehouses. More industry and academia as well as inter-terminal collaborations are needed in future studies for enhancing ITT operations and the overall operations in container terminals.
item: Thesis-Abstract
Retail sales forecasting in the presence of promotions : comparison of statistical and machine learning forecasting methods
(2022) Chamara HHHR; Perera HN
Retail sales forecasting is the process of estimating the number of future sales for a specific product or products. However, producing reliable and accurate sales forecasts at a product level is a very challenging task in the retail context. Many factors can influence observed sales data at the product level, such as sales promotions, weather, holidays, and special events, all of which causes demand irregularities. Sales promotions are one of the salient drivers in generating irregular sales patterns. Sales promotions confound retail operations, causing sudden demand changes not just during the promotion period, but also throughout the demand series. As a result, three types of periods are relevant for sales promotions: normal, promotional, and a post-promotional. However, previous research has mostly focused on promotional and normal (i.e., non-promotional) periods, often neglecting the post-promotional period. To address this gap, we explore the performance of comprehensive methods, namely gradient-boosted regression trees, random forests, and deep learning in all periods. Moreover, we compare proposed approaches with conventional forecasting approaches in a retail setting. Our results demonstrate that machine learning methods can deal with demand fluctuations generated by retail promotions while enhancing forecast performance throughout all time periods. The base-lift model outperformed machine learning methods, although with more effort necessary to cleanse sales data. Our findings indicate that machine learning methods can automate the forecasting process and provide significant performance even with the standard approach. Hence, our research demonstrates the way retailers can successfully apply machine learning methods in forecasting sales.
item: Thesis-Abstract
Effects of multiple adjustments in supply chain forecasting on forecast accuracy
(2021) Aruchunarasa B; Perera HN
Behavioral supply chain management is a subdiscipline within behavioral operations management that is growing rapidly. Judgmental adjustments of forecasts are considered part of this domain given the salience of forecasts to the smooth functioning of a supply chain. System-generated forecasts are frequently modified in the industry by forecasting professionals for numerous purposes. Accurate forecasts are significant to supply chain management and efficient organizational planning. Multiple adjustments occur when forecasts are subjected to more than one adjusted in its life cycle. Multiple adjustments are one of the key forecasting issues which impact forecast accuracy. Despite this, multiple adjustments to forecasts remain a not well-addressed research gap in academia. There are very few preliminary studies that investigate multiple adjustments to forecasts. Thus, to investigate the effect of multiple adjustments to forecasts to enhance forecast accuracy in the SC, the researcher employed a laboratory experiment with four different treatments to measure the forecasters’ behavior specifically on multiple adjustments to forecasts. 194 undergraduate and MBA students were recruited as participants for the experiment. In the Control Group, forecasts with first adjustments were observed while other treatments investigate how the participants would perform when they do subsequent adjustments with different levels of information availability. The authors found that multiple adjustments to forecasts significantly improve forecast accuracy. This expands the knowledge of multiple adjustments to forecasts to industry and academic professionals. Moreover, the provision of relevant information related to the previous adjustment allows the forecasters to perform better. The authors suggest the industries to increase information visibility among supply chain partners to have accurate forecasts and subsequent results in supply chain optimization. The results emphasize the importance of industry exposure and understanding the practical situations for a forecaster to improve his/her decision-making regarding judgmental adjustments. This study stresses the supply chain management-related degree programs to provide industry exposure to students to understand the practical implications of forecasting and other supply chain issues. Further works in this avenue, such as developing a forecasting model by integrating multiple adjustments and investigating the impact of the black-box effect in multiple adjustments are encouraged.
item: Thesis-Full-text
A model to estimate CO₂ emissions from air traffic movement in airports
(2020) Dissanayaka DMMS; Adikariwattage V V; Pasindu HR
The importance of airport emission inventory is more specific in the local context as it directly affects the local air quality. The assessment of emission from different phases of flight separately has not received sufficient attention. The specific gap addressed by this research is evaluating the CO2 emission from different phases of aircraft within the Landing Take-off (LTO) cycle and the CO2 emission from flight delays since they allow initiating more precise emission reduction strategies. Using currently available methodologies for assessing the emission from the LTO cycle in the Sri Lankan context has significant limitations. Industry-wide standards have been found to overestimate actual volumes specific to local conditions. Reviewing current CO2 emission calculation methods related to aeronautical activities within the LTO cycle, developing a model incorporating data specific to local conditions to estimate CO2 emission and estimating additional CO2 emission due to delay and validating the model are the main objectives of this study. The results of the suggested methodology for calculating CO2 emission were compared with the industry standards and actual operational values. The CO2 emission of different phases of flight and the CO2 emission due to delays within the LTO was assessed using the suggested methodology. The suggested methodology shows the unnecessary fuel burn and emissions according to current practices. The outcomes encourage stakeholders to initiate emission reduction methods. This study can be used as a reference when implementing those reduction methods. The suggested methodology can be applied in any airport which has data and technological constraints. The CO2 emission from delays at the taxiing phase has a significant influence on local air quality. The taxiing out phase which is the highest contributor to delays within the LTO should be given the most priority when initiating emission reduction methods.
item: Thesis-Full-text
Effect of economic and social aspects in sustainable supply chain management in apparel industry
Sudusinghe, JI; Jayaratne, P; Kumarage, AS
Apparel industry being a labor sensitive arena in the global market, the social sustainability conscious global consumers are demanding for transparency along the global apparel supply chain. Meanwhile, the outsourced apparel manufacturing in developing countries such as Sri Lanka, Bangladesh along with other local manufacturers are seeking for economic improvements despite the attention towards sustainability practices. However, the social sustainability practices resulting in improved economic performance is an under investigated arena both in the operational and research level. Hence this research fills the gap of exploring the effect of social sustainability on the economic sustainability of the apparel supply chain. The inputs from industry experts were gathered and analyzed while initiating to develop a common framework for social sustainability and economic sustainability dimensions under the Global Goals (UN SDGs). Further, a survey was conducted in order to understand the socially and economically sustainable practices in the Sri Lankan apparel supply chain. Finally, the relationship between social and economic sustainability dimension was explored using Partial Least Square Structural Equation Modelling (PLS SEM) technique. The best social sustainability performance was well reflected by the actions of exporting apparel manufacturers compared to the local apparel manufacturers. However, it was revealed that the social sustainability practices are resulting in the economic performance improvements of the apparel manufacturers in the Sri Lankan context.
item: Thesis-Full-text
Understanding travel to work attributes using mobile network big data : (study area : Western Province)
Jeewanthi, NKB; Kumarage, AS
As people become more mobile, urban traffic patterns become more complex, creating a need for more continuous transportation planning processes. Currently, manual and online surveys are the primary source for such analysis. However, such data collection while being expensive, takes time and is often outdated by the time it is made available for analysis. Mobile Network Big Data (MNBD) which concerns large data sets has the potential to supplement such traditional data sampling programs. Call Detail Records (CDR) which is a subset of MNBD is readily available as most of the telecommunication service providers maintain such data. Thus, analyzing CDR leads to an efficient identification of human behavior and location. This research uses the CDRs of nearly 10,000 mobile phone users in the Western Province (WP) of Sri Lanka for a period of three months for the analysis of their caller locations in order to determine their mobility patterns. In analyzing the CDRs, the frequency of making calls from a specific location is identified, classified them into potential home and non-home locations based on the regularity and time of day and week these calls were generated from each such location. Users are thereby categorized hierarchical levels based on the regularity of presence within the study area, identification of province of residence and by typical employment categories across the sample of users. An estimate of home-based work trips made within the Western Province was identified using the CDR and validated by comparing with the origin-destination matrix for work trips calculated from an extensive survey of 35,000 households under the CoMTrans Study (JICA, 2014) obtaining a fit of 76%. The successful validation and the identification of sources of errors in CDR data provides direction for further research in using CDRs for travel estimation and the identification of the appropriate comprehensive data mining techniques.
item: Thesis-Full-text
Contribution of port logistics developments for the maritime connectivity of a port
(2019) Rupasinhghe RAS; Sigera I; Cahoon S
Maritime connectivity of a port explains how well a port is connected to international maritime networks. When a container line selects a port of call, it takes into consideration maritime connectivity of the particular port. Therefore, port authorities strive to enhance the quality of services offered by ports with the assistance of suitable port logistics facilities. This nature motivated to study the contribution of port logistics developments for the maritime connectivity of ports. The methodology adopted for the current study is comprised of two stages. First stage online mail survey based on the perception of senior managers attached to global container line agencies and local offices registered in Sri Lanka try to identify, which port logistics developments affect maritime connectivity of ports. The second stage quantitative data analysis was conducted using Pearson correlation method to validate the results of the mail survey. And simple linear regression analysis was performed to assess how significant is each port logistics development on the maritime connectivity of a ports. Accordingly, port annual handling capacity, number of quay cranes available, number of reefer plugging facilities available, number of berths available, quay length and number of terminals are identified as significant port logistics developments to the maritime connectivity of a port. Due to the limitations in collecting required data the second stage analysis is limited only to the superstructure and infrastructure related port logistics developments. This current study envisions new area on which port logistics developments affect maritime connectivity of a port. Therefore, this is beneficial for both port terminal operators and ship operators. Terminal operators will be benefited in identifying optimal development options to enhance port connectivity while container line network planners will be benefited in identifying which factors they should consider in identifying most connected hub ports for their container linear services.
item: Thesis-Abstract
Inventory allocation behavior of the distributor during demand spikes and supplier disruptions
(2024) Lakshan KKMD; Perera HN
Inventory allocation stands as a fundamental operation within any supply chain. Typically, the responsibility of allocating inventory to retailers falls upon the distributor at a distribution center. The literature outlines three primary allocation methods; proportional allocation, linear allocation, and uniform allocation that distributors can employ when distributing inventory to retailers. However, distributors often grapple with determining the optimal allocation method, particularly when there is a mismatch between available supply and requested orders. While existing studies often explore order allocation for suppliers by retailers, there is a scarcity of research on distributor driven order allocation for retailers. This behavioral aspect of inventory allocation decisions remains largely unexplored under different practical scenarios. Specifically, the allocation decisions of a distributor during demand spikes and supplier disruptions have not attracted attention in the extant literature. In our study, we investigate the behavior of distributors across three distinct scenarios: (i) instances of demand spikes, (ii) occurrences of supply disruptions, and (iii) situations where both scenarios coincide. To comprehensively analyze this, we executed a computerized laboratory experiment, recruiting undergraduate students. These participants assumed the role of distributors and were tasked with allocating inventory using one of the three inventory allocation mechanisms. Our findings reveal intriguing insights. We observed that the proportional allocation mechanism emerged as the most effective strategy in scenarios involving demand spikes. Conversely, when faced with supply disruptions, the linear allocation mechanism demonstrated superior performance. Moreover, in scenarios where both demand spikes and supply disruptions intersect, our study suggests that employing the linear allocation mechanism might be the optimal course of action for distributors. Further, our research sheds light on practical recommendations for distributors to navigate and optimize their allocation strategies amidst demand fluctuations and supply disruptions. Keywords: Behavioral Supply Chain Management; Behavioral Operations; Inventory Allocation Decisions; Laboratory Experiments
item: Thesis-Abstract
Key drivers to the growth of air cargo demand for Combi-Carriers; based on air network of Sri Lanka
(2022) Karunathilake BLAN; Fernando A
Today air transportation is vital for implementing best international business practices, including just-in-time inventory management and build-to-order production. Regardless of the geographical location, air cargo transportation enables regions and nations to connect distant markets and sustain the global supply chain efficiently and faster. According to the IATA reports, nearly 40 percent of the value of global trade is carried by air, where more than 50 percent of the air cargo is carried on passenger flights (Moving Air Cargo Globally, 2016) (IATA, 2020). Therefore, it is essential to identify the optimum route network coupling in both passenger and cargo demand-driven networks. Cargo demand-driven networks are dynamic upon the factors that affect the cargo demand in respective destinations. This study mainly focuses on identifying the key influencing factors and industries that could affect the growth of air cargo demand in the cargo and passenger demand-driven network. Unfortunately, most airlines focus only on passenger demand, whereas cargo demand at the destination is often neglected. However, more than 98% of air cargo trades in Sri Lanka are happening through Combi carriers. As the first step to address this issue, it is necessary to understand air cargo generation and attraction at a destination. Sri Lanka will be a best-case study to analyze the Combi carriers. Hence, this study introduces preliminary criteria on how air cargo is generated and assigned to a particular demand. Five years of export air cargo data, including more than five hundred thousand shipment details, have been used for the cluster analysis to identify similar commodity groups based on air cargo weight and value. Before the analysis, all the export air cargo was classified into 97 commodity groups (According to HS code classification) to get clear output from the analysis. Nine distinct clusters were identified below, showing apparent differences in air cargo behavior Identified clusters were classified into four quadrants according to high-high, high-low, low-high and low-low value to weight ratio. In addition, the social and economic impact on cargo generation, the final destinations of the cargo, sub-commodity types, and characteristics are discussed. The findings will benefit airlines in strengthening their air route network and increasing global market access & traffic growth. Likewise, a country could implement the basement to enhance the overall level of productivity. It will help to boost exports and the competition in the home market. Many research studies have been conducted using different factors and models to forecast air cargo demand, and those did not consider demand from Combi and All-cargo carriers together. More than 30 factors were identified through literature reviews and interviews with industry experts. The independent variables for the analysis were selected, covering different areas that would affect air cargo demand growth at a destination, like an airport and airline capabilities, economic, market, environmental, and human factors. The population of a country, Population Growth, GDP, GDP Growth, Total Passenger demand, Total Cargo Demand, Hub Connectivity, Employment rate, and CO2 emission due to the aviation industry are the selected factors under-considered areas. Regression analysis was conducted for the analysis, and the Connectivity index and the air cargo demand at the destination were identified as the key influencing factors for the growth of air cargo demand at a destination for Combi carriers. These derived factors can assist in assigning flight schedules, route development, and facility improvements of airports and airlines. Hence the outcomes of this research would benefit the airlines, airports, and freight forwarders in their strategic decision-making.

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