Burçin Bozkaya

A tabu search heuristic and adaptive memory procedure for political districting

Abstract In political districting problems, the aim is to partition a territory into electoral constituencies, subject to some side constraints. The most common side constraints include contiguity, population equality, compactness, and socio-economic homogeneity. We propose a formulation in which the various constraints are integrated into a single multicriteria function. We solve the problem by means of a tabu search and adaptive memory heuristic. The procedure is illustrated on real data from the city of Edmonton.

Locating temporary shelter areas after an earthquake: A case for Turkey

In this study, we propose a mixed integer linear programming based methodology for selecting the location of temporary shelter sites. The mathematical model maximizes the minimum weight of open shelter areas while deciding on the location of shelter areas, the assigned population points to each open shelter area and controls the utilization of open shelter areas. We validate the mathematical model by generating a base case scenario using real data for Kartal, Istanbul, Turkey. Also, we perform a sensitivity analysis on the parameters of the mentioned mathematical model and discuss our findings. Lastly, we perform a case study using the data from the 2011 Van earthquake.

Designing New Electoral Districts for the City of Edmonton

Every few years, the city of Edmonton, Canada must review and evaluate changes to its electoral district boundaries. The review process that was completed in 2009 resulted in modifying the district plan from a six-ward system with two council members in each to a single-member 12-ward system. The authors of this paper designed the redistricting plan. This paper describes the algorithm we applied to solve the problem and the decision support system we used. The algorithm is based on a multicriteria mathematical model, which is solved by a tabu search heuristic embedded within a geographic information system (GIS)-based decision support system. The resulting district plan meets districting criteria, including population balance, contiguity, compactness, respect for natural boundaries, growth areas, and integrity of communities of interest. This plan was formally approved as a city bylaw and used in the municipal elections in 2010.

Money Walks: Implicit Mobility Behavior and Financial Well-Being.

Traditional financial decision systems (e.g. credit) had to rely on explicit individual traits like age, gender, job type, and marital status, while being oblivious to spatio-temporal mobility or the habits of the individual involved. Emerging trends in geo-aware and mobile payment systems, and the resulting “big data,” present an opportunity to study human consumption patterns across space and time. Taking inspiration from animal behavior studies that have reported significant interconnections between animal spatio-temporal “foraging” behavior and their life outcomes, we analyzed a corpus of hundreds of thousands of human economic transactions and found that financial outcomes for individuals are intricately linked with their spatio-temporal traits like exploration, engagement, and elasticity. Such features yield models that are 30% to 49% better at predicting future financial difficulties than the comparable demographic models.

Optimizing Fire Station Locations for the Istanbul Metropolitan Municipality

The Istanbul Metropolitan Municipality IMM seeks to determine locations for additional fire stations to build in Istanbul; its objective is to make residences and historic sites reachable by emergency vehicles within five minutes of a fire station’s receipt of a service request. In this paper, we discuss our development of a mathematical model to aid IMM in determining these locations by using data retrieved from its fire incident records. We use a geographic information system to implement the model on Istanbul’s road network, and solve two location models—set-covering and maximal-covering—as what-if scenarios. We discuss 10 scenarios, including the situation that existed when we initiated the project and the scenario that IMM implemented. The scenario implemented increases the city’s fire station coverage from 58.6 percent to 85.9 percent, based on a five-minute response time, with an implementation plan that spans three years.

A new VRPPD model and a hybrid heuristic solution approach for e-tailing

We analyze a business model for e-supermarkets to enable multi-product sourcing capacity through co-opetition (collaborative competition). The logistics aspect of our approach is to design and execute a network system where “premium” goods are acquired from vendors at multiple locations in the supply network and delivered to customers. Our specific goals are to: (i) investigate the role of premium product offerings in creating critical mass and profit; (ii) develop a model for the multiple-pickup single-delivery vehicle routing problem in the presence of multiple vendors; and (iii) propose a hybrid solution approach. To solve the problem introduced in this paper, we develop a hybrid metaheuristic approach that uses a Genetic Algorithm for vendor selection and allocation, and a modified savings algorithm for the capacitated VRP with multiple pickup, single delivery and time windows (CVRPMPDTW). The proposed Genetic Algorithm guides the search for optimal vendor pickup location decisions, and for each generated solution in the genetic population, a corresponding CVRPMPDTW is solved using the savings algorithm. We validate our solution approach against published VRPTW solutions and also test our algorithm with Solomon instances modified for CVRPMPDTW.

Trading-up on unmet expectations? Evaluating consumers’ expectations in online premium grocery shopping logistics

Geography and retail store locations are inherently bound together; this study links food retail changes to systemic logistics changes in an emerging market. The later include raising income and education, access to a wide range of technologies, traffic and transport difficulties, lagging retail provision, changing family structure and roles, as well as changing food culture and taste. The study incorporates demand for premium products defined by Kapferer and Bastien [2009b. The Luxury Strategy. London: Kogan Page] as comprising a broad variety of higher quality and unique or distinctive products and brands including in grocery organic ranges, healthy options, allergy free selections, and international and gourmet/specialty products through an online grocery model (n = 356) that integrates a novel view of home delivery in Istanbul. More importantly from a logistic perspective our model incorporates any products from any online vendors broadening the range beyond listed items found in any traditional online supermarkets. Data collected via phone survey and analysed via structural equation modelling suggest that the offer of online premium products significantly affects consumers’ delivery logistics expectations. We discuss logistics operations and business management implications, identifying the emerging geography of logistic models which respond to consumers’ unmet expectations using multiple sourcing and consolidation points.

A proposed risk model and a GIS framework for hazardous materials transportation

This paper presents a Geographical Information System (GIS) based risk assessment model for road transportation of hazardous materials (hazmat). Existing and proposed risk models are applied to truck shipments of hazmat through the road network of Istanbul. Our empirical analysis on the Istanbul road network points out that different risk models usually select different routes between a given origin-destination pair. In this study, we propose a new risk assessment model named as ldquotime-based risk modelrdquo for hazmat transportation. We speculate that the proposed model is the most suitable one for the city of Istanbul and alike.

Social Bridges in Urban Purchase Behavior

The understanding and modeling of human purchase behavior in city environment can have important implications in the study of urban economy and in the design and organization of cities. In this article, we study human purchase behavior at the community level and argue that people who live in different communities but work at close-by locations could act as “social bridges” between the respective communities and that they are correlated with similarity in community purchase behavior. We provide empirical evidence by studying millions of credit card transaction records for tens of thousands of individuals in a city environment during a period of three months. More specifically, we show that the number of social bridges between communities is a much stronger indicator of similarity in their purchase behavior than traditionally considered factors such as income and sociodemographic variables. Our findings also suggest that such an effect varies across different merchant categories, that the presence of female customers in social bridges is a stronger indicator compared to that of their male counterparts, and that there seems to be a geographical constraint for this effect, all of which may have implications in the studies of urban economy and data-driven urban planning.

Customer mobility signatures and financial indicators as predictors in product recommendation

The rapid growth of mobile payment and geo-aware systems as well as the resulting emergence of Big Data present opportunities to explore individual consuming patterns across space and time. Here we analyze a one-year transaction dataset of a leading commercial bank to understand to what extent customer mobility behavior and financial indicators can predict the use of a target product, namely the Individual Consumer Loan product. After data preprocessing, we generate 13 datasets covering different time intervals and feature groups, and test combinations of 3 feature selection methods and 10 classification algorithms to determine, for each dataset, the best feature selection method and the most influential features, and the best classification algorithm. We observe the importance of spatio-temporal mobility features and financial features, in addition to demography, in predicting the use of this exemplary product with high accuracy (AUC = 0.942). Finally, we analyze the classification results and report on most interesting customer characteristics and product usage implications. Our findings can be used to potentially increase the success rates of product recommendation systems.