Industrial Engineering

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

Browse

Now showing 1 - 20 of 27

Metadata only
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.
Metadata only
Bin packing problem with conflicts and item fragmentation
(Elsevier, 2021-02) Ekici, Ali; Industrial Engineering; EKİCİ, Ali
In this paper, we study the Bin Packing Problem with Conflicts and Item Fragmentation (BPPC-IF) which has applications in the delivery and storage of items that cannot be packed together. Given a set of items each with a certain size, the goal in BPPC-IF is to pack these items into a minimum number of fixed-capacity bins while not packing fragments of conflicting items into the same bin. We assume a size-preserving fragmentation, i.e., the total size of fragments of an item packed into the bins has to be equal to the item's original size. We first prove that BPPC-IF is still NP-hard even though items can be fragmented. Unlike the Bin Packing Problem with Item Fragmentation (BPPIF), we show that BPPC-IF does not necessarily admit optimal solutions with a special structure. Moreover, we show that preprocessing an instance with oversized items (items with size greater than bin capacity) by packing a fragment of such items with size equal to bin capacity to a single bin does not necessarily yield an optimal solution. Using this observation, we develop a lower bounding procedure. Finally, we propose a heuristic algorithm which sequentially packs items into the bins using the observation about the oversized items. Through an extensive computational study, we demonstrate the superior performance of the proposed solution approach over the existing algorithms in the literature.
Metadata only
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.
Metadata only
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.
Metadata only
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.
Metadata only
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.
Metadata only
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.
Metadata only
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.
Metadata only
Dynamic facility location with supplier selection under quantity discount
(Elsevier, 2019-08) Emirhüseyinoğlu, Görkem; Ekici, Ali; Industrial Engineering; EKİCİ, Ali; Emirhüseyinoğlu, Görkem
Retailers have to consider several factors when making facility location decisions including suppliers' and customers' locations, customer demand and price offered by suppliers. In this study, we analyze the multi-period facility location decisions of a retailer which procures the products from multiple suppliers under an incremental quantity discount scheme and in turn satisfies an exogenous demand. The retailer decides (i) where and when to open a facility, (ii) how much to order from each supplier in each time period, and (iii) from which facility locations to satisfy the demand. We formulate the problem as a mixed-integer mathematical model. To handle large instances, we develop a decomposition-based solution approach which considers the decisions in the first echelon (suppliers and facilities) and the second echelon (facilities and customers) in an iterative manner until convergence. We propose two implementations of the proposed solution approach. The first one limits the search space by considering only a subset of the facility locations for each customer. In the second implementation of the proposed solution approach, we develop a novel two-phase strategy where we first eliminate some of the facility locations entirely from the problem using a simplified version of the first approach and then implement the first approach to the reduced set of facility locations. We demonstrate the effectiveness of the heuristic approaches through an extensive computational study. The proposed heuristics provide significantly better results compared to a simple heuristic inspired by a related study. Moreover, we observe that for small instances both heuristics provide solutions with quite low optimality gaps and for larger instances they find better solutions in less amount of time when compared against CPLEX results obtained within a 12-h time limit.
Metadata only
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.
Metadata only
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.
Metadata only
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.
Metadata only
HYGAR: a hybrid genetic algorithm for software architecture recovery
(ACM, 2022) Elyasi, Milad; Simitcioğlu, Muhammed Esad; Saydemir, Abdullah; Ekici, Ali; Sözer, Hasan; Industrial Engineering; Computer Science; EKİCİ, Ali; SÖZER, Hasan; Elyasi, Milad; Simitcioğlu, Muhammed Esad; Saydemir, Abdullah
Genetic algorithms have been used for clustering modules of a software system in line with the modularity principle. The goal of these algorithms is to recover an architectural view in the form of a modular structural decomposition of the system. We discuss design decisions and variations in existing genetic algorithms devised for this purpose. We introduce HYGAR, a novel hybrid variant of existing algorithms. We apply HYGAR for software architecture recovery of 5 real systems and compare its effectiveness with respect to a baseline and a state-of-the-art hybrid algorithm. Results show that HYGAR outperforms these algorithms in maximizing the modularity of the obtained clustering.
Metadata only
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.
Metadata only
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.
Metadata only
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.
Metadata only
A large neighborhood search algorithm and lower bounds for the variable-sized bin packing problem with conflicts
(Elsevier, 2023-08-01) Ekici, Ali; Industrial Engineering; EKİCİ, Ali
In this paper, we study the Variable-Sized Bin Packing Problem with Conflicts (VSBPPC). In VSBPPC, a set of items each with a certain size has to be packed into bins of various types. Bin types differ in terms of their capacity and cost, and certain pairs of items cannot be packed into the same bin due to conflicts. The goal is to pack the items into the bins such that the total cost of the used bins is minimized. VSBPPC generalizes both the Variable-Sized Bin Packing Problem (VSBPP) and Bin Packing Problem with Conflicts (BPPC). We propose new lower bounds and develop a large neighborhood search algorithm for the problem. In the proposed solution approach, we destroy the solution by unpacking some of the bins and then repair the solution by a greedy method considering the unit cost of packing each item followed by a local search procedure. In the local search phase, we improve the repaired solution by (i) transferring items from its current bin to another bin, and (ii) swapping the items between bins. We evaluate the performance of the proposed solution approach not only against a lower bound but also against the benchmark algorithms from the literature. The proposed solution approach outperforms the benchmark algorithms with at least a margin of 4.39% on average. Moreover, the solutions obtained by the proposed approach have an average optimality gap of 2.77% with respect to the lower bound.
Metadata only
Managing platelet supply through improved routing of blood collection vehicles
(Elsevier, 2018-10) Özener, Okan Örsan; Ekici, Ali; Industrial Engineering; ÖZENER, Okan Örsan; EKİCİ, Ali
In this paper, we study the routing of blood collection vehicles for improving the platelet supply in the blood supply chain. In order to extract platelets, donated blood has to be processed at a central processing facility within six hours of donation time. Blood collection organizations have to dispatch collection vehicles and schedule pickups from the donation sites so that the donated units can be used in platelet production. Because of the accumulating behavior of donations and the six-hour processing time limit, routing of blood collection vehicles is a time-sensitive routing problem. We analyze the routing decisions in such a setting and propose an integrated clustering and routing framework to collect and process the maximum number of donations for platelet production. In our analysis, motivated by the practices in real-life, we cluster the donation sites so that only a single vehicle serves the donation sites in each cluster. In the proposed framework, we make the clustering and routing decisions in an integrated manner so that we can foresee the impact of adding a donation site to a cluster on the routing decisions. For the routing step, we propose several heuristic algorithms, two of which have a greedy nature and the others are based on a priori tour generation and selection scheme. To evaluate the performances of the proposed heuristics, we develop an upper bound by relaxing the number of vehicles so that one vehicle is available for each donation site. Using the proposed heuristic algorithms, we obtain solutions with around 15% optimality gaps with respect to the upper bound.
Metadata only
Modeling influenza pandemic and planning food distribution
(Informs, 2014) Ekici, Ali; Keskinocak, P.; Swann, J. L.; Industrial Engineering; EKİCİ, Ali
Based on the recent incidents of H5N1, H1N1, and influenza pandemics in history (1918, 1957, and 1968) experts believe that a future influenza pandemic is inevitable and likely imminent. Although the severity of influenza pandemics vary, evidence suggests that an efficient and rapid response is crucial for mitigating morbidity, mortality, and costs to society. Hence, preparing for a potential influenza pandemic is a high priority of governments at all levels (local, state, federal), nongovernmental organizations (NGOs), and companies. In a severe pandemic, when a large number of people are ill, infected persons and their families may have difficulty purchasing and preparing meals. Various government agencies and NGOs plan to provide meals to these households. In this paper, in collaboration with the American Red Cross, we study food distribution planning during an influenza pandemic. We develop a disease spread model to estimate the spread pattern of the disease geographically and over time, combine it with a facility location and resource allocation network model for food distribution, and develop heuristics to find near-optimal solutions for large instances. We run our combined disease spread and facility location model for the state of Georgia and present the estimated number of infections and the number of meals needed in each census tract for a one-year period along with a design of the supply chain network. Moreover, we investigate the impact of voluntary quarantine on the food demand and the food distribution network and show that its effects on food distribution can be significant. Our results could help decision makers prepare for a pandemic, including how to allocate limited resources and respond dynamically.
Metadata only
A non-clustered approach to platelet collection routing problem
(Elsevier, 2023-12) Talebi Khameneh, R.; Elyasi, Milad; Özener, Okan Örsan; Ekici, Ali; Industrial Engineering; ÖZENER, Okan Örsan; EKİCİ, Ali; Elyasi, Milad
One of the blood components that can be extracted from whole blood is the platelet, which has a wide range of uses in medical fields. Due to the perishable nature of platelets, it is recommended that the separation occurs within six hours after the donation. Moreover, platelets constitute less than one percent of the whole blood volume, yet they are highly demanded. Given the importance of platelets in healthcare, their perishability, and their limited supply, an effective platelet supply chain leans on well-managed whole blood collection operations. In this study, we consider a blood collection problem (BCP) focusing on the collection of whole blood donations from the blood donation sites (BDSs). Different from the basic form of BCP, we consider the processing time limit (PTL) of blood and arbitrary donation patterns of donors as well as relaxing the assumption of assigning each blood collection vehicle (BCV) to a set of BDSs. Therefore, we define the non-clustered maximum blood collection problem (NCMBCP) as a variant of BCP. In this study, we examine routing decisions for platelet collections while relaxing the clustering requirement from the BDSs, which results in a significant increase in the complexity of the problem. In order to solve the problem, we propose a hybrid genetic algorithm (HGA) and an invasive weed optimization (IWO) algorithm that provide considerable improvements over the best solution in the literature for the clustered variant of the problem and outperform it (on average) by 8.68% and 8.16%, respectively.

Browse

Browsing by Institution Author "EKİCİ, Ali"