Industrial Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10679/45

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Now showing 1 - 20 of 36

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Allocating cost of service to customers in inventory routing
(Informs, 2013) Özener, Okan Örsan; Ergun, Ö.; Savelsbergh, M.; Industrial Engineering; ÖZENER, Okan Örsan
Vendor-managed inventory VMI replenishment is a collaboration between a supplier and its customers, where the supplier is responsible for managing the customers' inventory levels. In the VMI setting we consider, the supplier exploits synergies between customers, e.g., their locations, usage rates, and storage capacities, to reduce distribution costs. Due to the intricate interactions between customers, calculating a fair cost-to-serve for each customer is a daunting task. However, cost-to-serve information is useful when marketing to new customers or when revisiting routing and delivery quantity decisions. We design mechanisms for this cost allocation problem and determine their characteristics both analytically and computationally.
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An application of unrelated parallel machine scheduling with sequence-dependent setups at Vestel Electronics
(Elsevier, 2019-11) Ekici, Ali; Elyasi, Milad; Özener, Okan Örsan; Sarıkaya, M. B.; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan; Elyasi, Milad
In this paper, we analyze a variant of the unrelated parallel machine scheduling problem with the objective of minimizing the total tardiness and earliness in the presence of sequence-dependent setups, unequal release times, machine-job compatibility restrictions and workload balance requirements. This study is motivated by the production scheduling operations at a television manufacturer, Vestel Electronics. Vestel produces LCD/LED TVs and has a significant market share in the consumer electronics sector in Europe. TV manufacturing is planned based on a make-to-order strategy, and Vestel uses 15 assembly lines to produce 110 different product groups and 3817 different models. Once the orders are received, production scheduling is performed at the beginning of each month, and the goal is to satisfy the demand on time as much as possible. The decision maker has to consider several factors including job-assembly line compatibility, the release and due dates of the jobs and a workload balance among different assembly lines when forming the production schedule. To address this problem, we propose a wide range of heuristics including (i) a sequential algorithm, (ii) a tabu search algorithm, (iii) a random set partitioning approach, and (iv) a novel matheuristic approach utilizing the local intensification and global diversification powers of a tabu search algorithm. Through a computational study, we observe that all the proposed approaches not only significantly outperform the current practice but also provide solutions with around 5% less optimality gap compared to a benchmark algorithm in the literature.
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Blood supply chain management and future research opportunities
(Springer, 2018) Ekici, Ali; Özener, Okan Örsan; Göktürk, Elvin Çoban; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan; GÖKTÜRK, Elvin Çoban
In this chapter, we discuss the challenges and research opportunities in the blood collection operations and explore the benefits of recent advances in the blood donation process. According to the regulations, donated blood has to be processed in a processing facility within 6 h of donation. This forces blood donation organizations to schedule continuous pickups from donation sites. The underlying mathematical problem is a variant of well-known Vehicle Routing Problem (VRP). The main differences are the perishability of the product to be collected, and the continuity of donations. We discuss the implications of such differences on collection routes from donation centers. Recent advances such as multicomponent apheresis (MCA) allow the donation of more than one component and/or more than one transfusable unit of each blood product. MCA provides several opportunities including (1) increasing the donor utilization, (2) tailoring the donations based on demand, and (3) reducing the infection risks in the transfusion. We also discuss MCA, its potential benefits and how to best use MCA in order to improve blood products availability and manage donation/disposal costs.
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A column generation-based approach for the adaptive stochastic blood donation tailoring problem
(Taylor & Francis, 2023) Elyasi, Milad; Özener, Okan Örsan; Yanıkoğlu, İhsan; Ekici, Ali; Dolgui, A.; Industrial Engineering; ÖZENER, Okan Örsan; YANIKOĞLU, Ihsan; EKİCİ, Ali; Elyasi, Milad
Managing blood donations is a challenging problem due to the perishability of blood, limited donor pool, deferral time restrictions, and demand uncertainty. The problem addressed here combines two important aspects of blood supply chain management: the inventory control of blood products and the donation schedule. We propose a stochastic scenario-based reformulation of the blood donation management problem that adopts multicomponent apheresis and utilises donor pool segmentation into here-and-now and wait-and-see donors. We propose a flexible donation scheme that is resilient against demand uncertainty. This scheme enables more flexible donation schedules because wait-and-see donors may adjust their donation schedules according to the realised values of demand over time. We propose a column generation-based approach to solve the associated multi-stage stochastic donation tailoring problem. The numerical results show the effectiveness of the proposed optimisation model, which provides solutions with less than a 7% optimality gap on average with respect to a lower bound. It also improves the operational cost of the standard donation scheme that does not use wait-and-see donors by more than 18% on average. Utilising multicomponent apheresis and flexible wait-and-see donations are suggested for donation organisations because they yield significant cost reductions and resilient donation schedules.
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Coordinating collection and appointment scheduling operations at the blood donation sites
(Elsevier, 2015-09) Mobasher, A.; Ekici, Ali; Özener, Okan Örsan; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan
According to the regulations imposed by the U.S. Food and Drug Administration and the American Association of Blood Banks, in order to extract platelets, donated blood units have to be processed at a processing center within six hours of donation time. In this paper, considering this processing time requirement of donated blood units for platelet production we study collection and appointment scheduling operations at the blood donation sites. Specifically, given the blood donation network of a blood collection organization, we try to coordinate pickup and appointment schedules at the blood donation sites to maximize platelet production. We call the problem under consideration Integrated Collection and Appointment Scheduling Problem. We first provide a mixed integer linear programming model for the problem. Then, we propose a heuristic algorithm called Integer Programming Based Algorithm. We perform a computational study to test the performance of the proposed model and algorithm in terms of solution quality and computational efficiency on the instances from Gulf Coast Regional Blood Center located in Houston, TX.
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Cost allocation mechanisms in a peer‐to‐peer network
(Wiley, 2019-01) Özener, Başak Altan; Özener, Okan Örsan; Economics; Industrial Engineering; ÖZENER, Başak Altan; ÖZENER, Okan Örsan
This study analyzes a cooperative game between a service provider and a set of users. We consider a P2P network where the service provider broadcasts the content across the network and the users collaborate to seed the content to a subset of users in the network. The objective of the service provider is to determine the minimum cost network solution and to allocate this joint-cost fairly among the users. The minimum cost network solution can be determined by solving a minimum cost Steiner tree problem. We propose four cost allocation mechanisms: a dual linear programming based mechanism, an approximation mechanism to the Shapley value, a partition-based mechanism, and an approximation mechanism to the nucleolus. We conduct an extensive computational study to assess the performance of the proposed mechanisms on randomly generated instances. We conclude that our partition-based mechanism and the nucleolus-approximation outperform the other allocation mechanisms, including the benchmark mechanism.
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Cyclic delivery schedules for an inventory routing problem
(Informs, 2015-11) Ekici, Ali; Özener, Okan Örsan; Kuyzu, G.; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan
We consider an inventory routing problem where a common vendor is responsible for replenishing the inventories of several customers over a perpetual time horizon. The objective of the vendor is to minimize the total cost of transportation of a single product from a single depot to a set of customers with deterministic and stationary consumption rates over a planning horizon while avoiding stock-outs at the customer locations. We focus on constructing a repeatable (cyclic) delivery schedule for the product delivery. We propose a novel algorithm, called the Iterative Clustering-Based Constructive Heuristic Algorithm, to solve the problem in two stages: (i) clustering, and (ii) delivery schedule generation. To test the performance of the proposed algorithm in terms of solution quality and computational efficiency, we perform a computational study on both randomly generated instances and real-life instances provided by an industrial gases manufacturer. We also compare the performance of the proposed algorithm against an algorithm developed for general routing problems.
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Cyclic ordering policies from capacitated suppliers under limited cycle time
(Elsevier, 2019-02) Ekici, Ali; Özener, Okan Örsan; Duran, S.; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan
In this paper, we study the ordering policy of a manufacturer/retailer which procures a single item from multiple capacitated suppliers and satisfies an exogenous deterministic and constant demand. Manufacturer's objective is to minimize total periodic ordering cost which has three components: (i) fixed ordering cost, (ii) variable purchasing cost, and (iii) inventory holding cost. We are interested in developing a cyclic ordering policy for the manufacturer where the orders are repeated with a certain frequency and the cycle length/time is limited by the manufacturer. We analyze two cases: (i) each supplier receives at most one order from the manufacturer in a cycle, and (ii) suppliers may receive multiple orders from the manufacturer in a cycle. We propose a novel iterative-natured heuristic framework to develop cyclic ordering policies. Computational experiments on randomly generated instances show that the proposed heuristic framework provides better results compared to other methods in the literature, especially in the presence of a restrictive cycle time limitation.
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A decomposition-based heuristic for a waste cooking oil collection problem
(Springer, 2020-01-01) Gültekin, Ceren; Ölmez, Ömer Berk; Koyuncu, Burcu Balçık; Ekici, Ali; Özener, Okan Örsan; Industrial Engineering; KOYUNCU, Burcu Balçık; EKİCİ, Ali; ÖZENER, Okan Örsan; Gültekin, Ceren; Ölmez, Ömer Berk
Every year, a tremendous amount of waste cooking oil (WCO) is produced by households and commercial organizations, which poses a serious threat to the environment if disposed improperly. While businesses such as hotels and restaurants usually need to have a contract for their WCO being collected and used as a raw material for biodiesel production, such an obligation may not exist for households. In this study, we focus on designing a WCO collection network, which involves a biodiesel facility, a set of collection centers (CCs), and source points (SPs) each of whom represents a group of households. The proposed locationrouting problem (LRP) determines: (i) the CCs to be opened, (ii) the number of bins to place at each CC, (iii) the assignment of each SP to one of the accessible CCs, and (iv) the vehicle routes to collect the accumulated oil from the CCs. We formulate the problem as a mixed-integer mathematical model and solve it by using commercial solvers by setting a 1-h time limit. We also propose a decompositionbased heuristic and conduct a computational study. Our decomposition algorithm obtains the same or better solutions in 95% of all the test instances compared to the proposed mathematical model.
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Developing a collaborative planning framework for sustainable transportation
(Hindawi Publishing, 2014) Özener, Okan Örsan; Industrial Engineering; ÖZENER, Okan Örsan
Currently, as being the highest petroleum consuming sector in the world, transportation significantly contributes to the total greenhouse gas emissions in the world. Road transportation not only is responsible for approximately 20% of the total emissions of carbon dioxide in the EU and in the US but also has a steadily increasing trend in contributing to global warming. Initiatives undertaken by authorities, such as Emission cap and trade in the EU, limit the emissions resulted from the actions of the companies and also give economic incentives to companies to reduce their emissions. However, in logistics systems with multiple entities, it is difficult to assess the responsibilities of the companies both in terms of costs and emissions. In this study, we consider a delivery network with multiple customers served by a single carrier, which executes a delivery plan with the minimum transportation cost, and allocate the resulting costs and the emissions among the customers in a fair manner. We develop allocation mechanisms for both costs and emissions. In order to develop a mechanism that provides further reduction of the emissions, we study a setting where the carrier takes the responsibility of the emissions and reflects the resulting inefficiencies while charging the customers.
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An effective formulation of the multi-criteria test suite minimization problem
(Elsevier, 2020-10) Özener, Okan Örsan; Sözer, Hasan; Industrial Engineering; Computer Science; ÖZENER, Okan Örsan; SÖZER, Hasan
Test suite minimization problem has been mainly addressed by employing heuristic techniques or integer linear programming focusing on a specific criterion or bi-criteria. These approaches fall short to compute optimal solutions especially when there exists overlap among test cases in terms of various criteria such as code coverage and the set of detected faults. Nonlinear formulations have also been proposed recently to address such cases. However, these formulations require significantly more computational resources compared to linear ones. Moreover, they are also subject to shortcomings that might still lead to sub-optimal solutions. In this paper, we identify such shortcomings and we propose an alternative formulation of the problem. We have empirically evaluated the effectiveness of our approach based on a publicly available dataset and compared it with respect to the state-of-the-art based on the same objective function and the same set of criteria including statement coverage, fault-revealing capability, and test execution time. Results show that our formulation leads to either better results or the same results, when the previously obtained results were already the optimal ones. In addition, our formulation is a linear formulation, which can be solved much more efficiently compared to non-linear formulations.
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A game theoretical approach for improving the operational efficiencies of less-than-truckload carriers through load exchanges
(Springer, 2021-09) Özener, Başak Altan; Özener, Okan Örsan; Economics; Industrial Engineering; ÖZENER, Başak Altan; ÖZENER, Okan Örsan
Less-than-truckload (LTL) transportation offers fast, flexible and relatively low-cost transportation services to shippers. In order to cope with the effects of economic recessions, the LTL industry implemented ideas such as reducing excess capacity and increasing revenues through better yield management. In this paper, we extend these initiatives beyond the reach of individual carriers and propose a collaborative framework that facilitates load exchanges to reduce the operational costs. Even though collective solutions are proven to provide benefits to the participants by reducing the inefficiencies using a system-wide perspective, such solutions are often not attainable in real-life as the negotiating parties are seeking to maximize their individual profits rather than the overall profit and also they are unwilling to share confidential information. Therefore, a mechanism that enables collaboration among the carriers should account for the rationality of the individual participants and should require minimal information transfer between participants. Having this in mind, we propose a mechanism that facilities collaboration through a series of load exchange iterations and identifies an equilibrium among selfish carriers with limited information transfer among the participants. Our time-efficient mechanism can handle large instances with thousands of loads as well as provide significant benefits over the non-collaborative management of LTL networks.
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Genetic algorithms and heuristics hybridized for software architecture recovery
(Springer, 2023-06-26) Elyasi, Milad; Simitcioğlu, Muhammed Esad; Saydemir, Abdullah; Ekici, Ali; Özener, Okan Örsan; Sözer, Hasan; Industrial Engineering; Computer Science; EKİCİ, Ali; ÖZENER, Okan Örsan; SÖZER, Hasan; Simitcioğlu, Muhammed Esad; Saydemir, Abdullah; Elyasi, Milad
Large scale software systems must be decomposed into modular units to reduce maintenance efforts. Software Architecture Recovery (SAR) approaches have been introduced to analyze dependencies among software modules and automatically cluster them to achieve high modularity. These approaches employ various types of algorithms for clustering software modules. In this paper, we discuss design decisions and variations in existing genetic algorithms devised for SAR. We present a novel hybrid genetic algorithm that introduces three major differences with respect to these algorithms. First, it employs a greedy heuristic algorithm to automatically determine the number of clusters and enrich the initial population that is generated randomly. Second, it uses a different solution representation that facilitates an arithmetic crossover operator. Third, it is hybridized with a heuristic that improves solutions in each iteration. We present an empirical evaluation with seven real systems as experimental objects. We compare the effectiveness of our algorithm with respect to a baseline and state-of-the-art hybrid genetic algorithms. Our algorithm outperforms others in maximizing the modularity of the obtained clusters.
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Humanitarian relief distribution problem: an adjustable robust optimization approach
(Informs, 2023-07) Avishan, Farzad; Elyasi, Milad; Yanıkoğlu, İhsan; Ekici, Ali; Özener, Okan Örsan; Industrial Engineering; AVISHAN, Farzad; YANIKOĞLU, Ihsan; EKİCİ, Ali; ÖZENER, Okan Örsan; Elyasi, Milad
Management of humanitarian logistics operations is one of the most critical planning problems to be addressed immediately after a disaster. The response phase covers the first 12 hours after the disaster and is prone to uncertainties because of debris and gridlock traffic influencing the dispatching operations of relief logistics teams in the areas affected. Moreover, the teams have limited time and resources, and they must provide equitable distribution of supplies to affected people. This paper proposes an adjustable robust optimization approach for the associated humanitarian logistics problem. The approach creates routes for relief logistics teams and decides the service times of the visited sites to distribute relief supplies by taking the uncertainty in travel times into account. The associated model allows relief logistics teams to adjust their service decisions according to the revealed information during the process. Hence, our solutions are robust for the worst-case realization of travel times, but still more flexible and less conservative than those of static robust optimization. We propose novel reformulation techniques to model these adjustable decisions. The resulting models are computationally challenging optimization problems to be solved by exact methods, and, hence, we propose heuristic algorithms. The state-of-the-art heuristic, which is based on clustering and a dedicated decision-rule algorithm, yields near-optimal results for medium-sized instances and is scalable even for large-sized instances. We have also shown the effectiveness of our approach in a case study using a data set obtained from an earthquake that hit the Van province of Turkey in 2011.
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Humanitarian relief supplies distribution: an application of inventory routing problem
(Springer, 2019-12) Çankaya, Emre; Ekici, Ali; Özener, Okan Örsan; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan; Çankaya, Emre
In this paper, we study the distribution of humanitarian relief supplies. In humanitarian relief, supplies including food, water and medication are received in batches/waves from the suppliers and the donors. Then, these supplies are distributed to local dispensing sites located in the affected areas. Fast and fair distribution of these relief supplies is the key to the success of humanitarian relief operations. Motivated by the practices in humanitarian relief chain, we study an application of Inventory Routing Problem where the goal is equitable distribution of these supplies to the affected areas over a planning horizon. We measure the fairness of the distribution plan by the safety stock level at a demand location, and our goal is to maximize the minimum safety stock level at any location. Such a difference in the objective requires a solution approach that is significantly different than the ones proposed in the literature for classical cost-minimization routing problems. In order to address this distribution problem, we propose a three-phase (clustering, routing and improvement) solution approach. Due to nature of the problem, routing and allocation decisions significantly affect each other. The proposed approach (i) considers the interaction between routing and resource allocation decisions in a novel way to produce equitable relief supplies distribution plans, (ii) outperforms the existing algorithms by finding solutions with around 1.4% lower optimality gap on average, (iii) provides solutions with 2.6% optimality gap on average when compared to an upper bound, and (iv) finds a solution in < 5 min.
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Improving blood products supply through donation tailoring
(Elsevier, 2019-02) Özener, Okan Örsan; Ekici, Ali; Göktürk, Elvin Çoban; Industrial Engineering; ÖZENER, Okan Örsan; EKİCİ, Ali; GÖKTÜRK, Elvin Çoban
Recent technological advances, called Multicomponent Apheresis, allow tailoring the blood donations based on the demand and current inventory levels of blood products. Different from the most common type of blood donation (known as Whole Blood Donation), Multicomponent Apheresis allows the donation of one or more transfusable units of one or more blood products. Considering the changing demand for blood products during a planning horizon, deferral times, perishability of blood products, and limited donor pool, Multicomponent Apheresis provides an opportunity for increased donor utilization and hence a better managed blood supply chain. However, except some general guidelines proposed by blood donation organizations, the literature lacks analytical tools which can be used to fully explore the potential advantages of Multicomponent Apheresis, including the reduction in donation related costs and better utilization of the donor pool. In this paper, we develop models and solution approaches for tailoring the donations in order to quantify the potential benefits of Multicomponent Apheresis. More specifically, we define the Blood Donation Tailoring Problem where the objective is to minimize the total donation, inventory and disposal costs of blood products while satisfying the demand for blood products during a planning horizon by determining the donation schedule of a given donor pool. We develop a mathematical model and a column generation approach to tailor the donations. We also propose a more practical rule-of-thumb which can be easily implemented by the blood donation organizations. We compare the performances of the proposed approaches against a lower bound and the current practice at an apheresis facility. Finally, we also show that the proposed column generation approach can easily be modified to handle realistic aspects of the problem including stock-out and donor eligibility/preferences.
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Integrated blood collection and appointment scheduling
(Institute of Industrial Engineers, 2012) Mobasher, A.; Ekici, Ali; Özener, Okan Örsan; Industrial Engineering; ÖZENER, Okan Örsan; EKİCİ, Ali
Considering the processing requirements of donated blood, we study an integrated blood collection and appointment scheduling problem. We develop a mixed integer programming model to maximize the amount of donated blood that can be delivered to the processing center before spoilage as well as determine the schedule of donation appointments. Since the problem complexity and the computational time of MIP formulation is exponentially increasing by including more donation sites, we propose an insertion/saving heuristic algorithm to find a good feasible solution and develop a large neighborhood search method to improve the solution further.
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Inventory routing for the last mile delivery of humanitarian relief supplies
(Springer Nature, 2020-09) Ekici, Ali; Özener, Okan Örsan; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan
Fast and equitable distribution of the humanitarian relief supplies is key to the success of relief operations. Delayed and inequitable deliveries can result in suffering of affected people and loss of lives. In this study, we analyze the routing operations for the delivery of relief supplies from a distribution center to the dispensing sites. We assume that the relief supplies to be distributed arrive at the distribution center in batches and are consumed at the dispensing sites with a certain daily rate. When forming delivery schedules, we use the ratio of the inventory to the daily consumption rate at the dispensing sites as our decision criterion. This ratio is called theslackand can be considered as the safety stock (when positive) in case of a delay in the deliveries. Negative value for theslackmeans the dispensing site has stock-outs. Our objective is to maximize the minimum value of thisslackamong all dispensing sites. This is equivalent to maximizing the minimum safety stock or minimizing the maximum duration of the stock-outs. Due to multi-period structure of the problem, it is modeled as a variant of theInventory Routing Problem. To address the problem, we propose a general framework which includes clustering, routing and improvement steps. The proposed framework considers the interdependence between all three types of decisions (clustering, routing and resource allocation) and makes the decisions in an integrated manner. We test the proposed framework on randomly generated instances and compare its performance against the benchmark algorithms in the literature. The proposed framework not only outperforms the benchmark algorithms by at least 1% less optimality gap but also provides high-quality solutions with around 2-3% optimality gaps.
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Lane-exchange mechanisms for truckload carrier collaboration
(Informs, 2011-02) Özener, Okan Örsan; Ergun, Ö.; Savelsbergh, M.; Industrial Engineering; ÖZENER, Okan Örsan
Because of historically high fuel prices, the trucking industry's operating expenses are higher than ever and thus profit margins are lower than ever. To cut costs, the trucking industry is searching for and exploring new ideas. We investigate the potential of collaborative opportunities in truckload transportation. When carriers serve transportation requests from many shippers, they may be able to reduce their repositioning costs by exchanging one or more of them. We develop optimization models to determine the maximum benefit that can be derived from collaborating. We also develop various exchange mechanisms which differ in terms of information sharing requirements and side payment options that allow carriers to realize some or all of the costs savings opportunities.
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League scheduling and game bundling in sports industry
(Elsevier, 2014-08) Duran, S.; Özener, Okan Örsan; Yakıcı, E.; Industrial Engineering; ÖZENER, Okan Örsan
Most sport clubs offer season tickets first and they allow purchasing single tickets at a later date. There are several decision problems within this context; the determination of the optimal time at which the switch from bundled tickets to single tickets should occur, the decision of which event tickets to include into the bundle depending on the schedule of the team and the creation of a league schedule enabling revenue enhancements from game bundling. In this paper we have focused on the last decision problem. We analyze league scheduling and game bundling decisions together for a double round robin tournament in order to maximize the total revenue generated by all of the participating teams in the league. A heuristic method is offered which utilizes the approximate expected revenue values obtained by revenue increase and decrease patterns of bundled tickets. We test the offered heuristic’s performance and observe significant benefits numerically.

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