Endüstri Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/279
Browse
7 results
Search Results
Article Citation - Scopus: 4Scheduling With Lot Streaming in a Two-Machine Re-Entrant Flow Shop(Regional Association for Security and crisis management, 2021) Duman, M.; Çetinkaya, F.C.Lot streaming is splitting a job-lot of identical items into several sublots (portions of a lot) that can be moved to the next machines upon completion so that operations on successive machines can be overlapped; hence, the overall performance of a multi-stage manufacturing environment can be improved. In this study, we consider a scheduling problem with lot streaming in a two-machine re-entrant flow shop in which each job-lot is processed first on Machine 1, then goes to Machine 2 for its second operation before it returns to the primary machine (either Machine 1 or Machine 2) for the third operation. For the two cases of the primary machine, both single-job and multi-job cases are studied independently. Optimal and near-optimal solution procedures are developed. Our objective is to minimize the makespan, which is the maximum completion time of the sublots and job lots in the single-job and multi-job cases, respectively. We prove that the single-job problem is optimally solved in polynomial-time regardless of whether the third operation is performed on Machine 1 or Machine 2. The multi-job problem is also optimally solvable in polynomial time when the third operation is performed on Machine 2. However, we prove that the multi-job problem is NP-hard when the third operation is performed on Machine 1. A global lower bound on the makespan and a simple heuristic algorithm are developed. Our computational experiment results reveal that our proposed heuristic algorithm provides optimal or near-optimal solutions in a very short time. © 2021 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Article Citation - WoS: 8Citation - Scopus: 9Intermodal Humanitarian Logistics Using Unit Load Devices(Springer Heidelberg, 2022) Kavlak, Hasan; Ertem, Mustafa Alp; Satir, BenhurIntermodal freight transportation facilitates today's global trade. The benefits of intermodal freight transportation have been studied and are more observable in commercial logistics; however, the potential benefits of humanitarian logistics have not been thoroughly investigated. This research aims to present a resilient transportation framework by modeling intermodal transportation utilizing interoperable loading devices during disaster responses. We developed an integer programming model based on a time-space network by considering route and vehicle availabilities that are allowed to change with time. We consider vehicles with varying capacities in three transportation modes (i.e., ground, maritime, and air). The contribution of this study is threefold: (1) Two compatible unit load devices are proposed for humanitarian logistics; (2) a mathematical model that includes integer variable representation for vehicle fleets in different transportation modes is developed; and (3) intermodal transportation is compared with single-mode transportation using a real-life dataset. Our main results are as follows: In terms of cost, intermodal transportation is effective when demand occurs in consecutive periods and response time is short. Inventory is held more in intermodal transportation when it is cost-effective to use transportation modes with large capacities. Thus, the benefits of the responsiveness of intermodal transportation outweigh the costs of mode interchange and inventory holding for sudden-onset disasters where quick responses are needed within a short time.Article Citation - WoS: 24Citation - Scopus: 27Bi-Objective Adaptive Large Neighborhood Search Algorithm for the Healthcare Waste Periodic Location Inventory Routing Problem(Springer Heidelberg, 2022) Aydemir-Karadag, AyyuceThere has been an unexpected increase in the amount of healthcare waste during the COVID-19 pandemic. Managing healthcare waste is vital, as improper practices in the waste system can lead to the further spread of the virus. To develop effective and sustainable waste management systems, decisions in all processes from the source of the waste to its disposal should be evaluated together. Strategic decisions involve locating waste processing centers, while operational decisions deal with waste collection. Although the periodic collection of waste is used in practice, it has not been studied in the relevant literature. This paper integrates the periodic inventory routing problem with location decisions for designing healthcare waste management systems and presents a bi-objective mixed-integer nonlinear programming model that minimizes operating costs and risk simultaneously. Due to the complexity of the problem, a two-step approach is proposed. The first stage provides a mixed-integer linear model that generates visiting schedules to source nodes. The second stage offers a Bi-Objective Adaptive Large Neighborhood Search Algorithm (BOALNS) that processes the remaining decisions considered in the problem. The performance of the algorithm is tested on several hypothetical problem instances. Computational analyses are conducted by comparing BOALNS with its other two versions, Adaptive Large Neighborhood Search Algorithm and Bi-Objective Large Neighborhood Search Algorithm (BOLNS). The computational experiments demonstrate that our proposed algorithm is superior to these algorithms in several performance evaluation metrics. Also, it is observed that the adaptive search engine increases the capability of BOALNS to achieve high-quality Pareto-optimal solutions.Article Citation - WoS: 1Citation - Scopus: 1Cycle Cost Considerations in a Continuous Review Inventory Control Model(Taylor & Francis Ltd, 2021) Yildirim, Gonca; Konur, DincerIn this study, the continuous review order-quantity-re-order point (Q, R) model is analysed with cycle cost considerations. First, we formulate the maximum cycle cost of a given (Q, R) policy using a distribution-free approach. Then, two approaches are introduced to minimize the maximum cycle cost: (i) adjusting R of a given (Q, R) policy and (ii) designing a new (Q, R) policy. Optimum inventory control decisions are characterized for each approach. A set of numerical studies is presented to compare the outcomes of both approaches to three long-term cost minimization approaches, namely the cost minimizing (Q, R) policy, the distribution-free minmax (Q, R) policy, and the distribution-free (Q, R) policy based on the maximum entropy principle. Our numerical results demonstrate the viability of the two approaches introduced and discuss implications of penalty costs and lead time demand's coefficient of variation. Later, we formulate a bi-objective model with the objectives of expected cost and maximum cycle cost minimizations and propose a bi-directional method to approximate the set of Pareto efficient solutions. Numerical examples are presented to illustrate the algorithm and demonstrate the Pareto front.Article Citation - WoS: 20Citation - Scopus: 22Shipment Consolidation With Two Demand Classes: Rationing the Dispatch Capacity(Elsevier Science Bv, 2018) Erenay, Fatih Safa; Bookbinder, James H.; Satir, BenhurWe analyze the problem faced by a logistics provider that dispatches shipment orders (parcels or larger packages) of two order classes, viz. expedited and regular. Shipment orders arrive according to a compound Poisson process for each class. Upon an arrival, the logistics provider may continue consolidating arriving orders by paying a holding cost. Alternatively, the provider may dispatch, at a fixed cost, a vehicle containing (a portion of) the load consolidated so far. In addition, the provider must specify the composition of each dispatch by allocating (rationing) the volume of the vehicle between expedited and regular shipment orders. We model this problem as a continuous-time Markov Decision Process and minimize the expected discounted total cost. We prove the existence of quantity-based optimal threshold policies under particular conditions. We also structurally analyze the thresholds of these optimal policies. Based on these structural properties, we develop an efficient solution approach for large problem instances which are difficult to solve using the conventional policy-iteration method. For two real-life applications, we show that the quantity-based threshold policies derived using the proposed approach outperform the time policies used in practice. (C) 2018 Elsevier B.V. All rights reserved.Article Citation - WoS: 31Citation - Scopus: 35Review of Intermodal Freight Transportation in Humanitarian Logistics(Springeropen, 2017) Arslan, Aysenur Sahin; Ertem, Mustafa Alp; Isbilir, Melike; Şahin Arslan, AyşenurPurpose Using intermodal transportation is vital for the delivery of relief supplies when single mode alternative becomes unusable or infeasible. The objective of this paper is to investigate the use of intermodal freight transportation in humanitarian logistics. Methods This paper first identifies the differences between multimodal and intermodal transportation. Then, we examine the use of each transportation mode for specific disaster types and phases. When combinations of transportation modes (i.e. air, road, rail and sea) for intermodal transportation are considered together with different disaster types (e.g. earthquake, flood and famine), the feasible decision space becomes rather large. To explore this decision space, we have reviewed the academic and practitioner studies as well as several non-governmental organizations (NGO)' disaster archives. Results From this exploration, we developed a transportation mode/disaster-type combination matrix and a transportation mode/disaster-phase combination matrix. We then discuss examples of real life usage of intermodal transportation in humanitarian logistics and share our findings and analyses. Of 369 academic humanitarian logistics articles, only 20 have mentioned transportation mode changes. In practitioner studies, we found a decreasing percentage of the usage of slower modes (e.g. sea and rail) in the disaster response phase over time. We were not able to find a significant relationship between a specific transportation mode and a specific disaster-type or - phase. Road transportation seems to cover most of the disaster operations regardless of the disaster-type or - phase. Conclusions We can conclude that intermodality and the transportation unit concept is not being studied extensively in humanitarian logistics. Most of the relief organizations do not share transported freight amounts in their reports and those that do share transported freight amounts in their reports do not explicitly mention mode changes. We discuss the enablers of and obstacles to the effective use of intermodal transportation in humanitarian logistics and propose future research directions. We anticipate that intermodal transportation in humanitarian logistics will garner greater research attention and increased utilization in coming years.Article Citation - WoS: 8Citation - Scopus: 12Estimating Parameters of a Multiple Autoregressive Model by the Modified Maximum Likelihood Method(Elsevier, 2010) Bayrak, Oezlem Tuerker; Akkaya, Aysen D.We consider a multiple autoregressive model with non-normal error distributions, the latter being more prevalent in practice than the usually assumed normal distribution. Since the maximum likelihood equations have convergence problems (Puthenpura and Sinha, 1986) [11], we work Out modified maximum likelihood equations by expressing the maximum likelihood equations in terms of ordered residuals and linearizing intractable nonlinear functions (Tiku and Suresh, 1992) [8]. The solutions, called modified maximum estimators, are explicit functions of sample observations and therefore easy to compute. They are under some very general regularity conditions asymptotically unbiased and efficient (Vaughan and Tiku, 2000) [4]. We show that for small sample sizes, they have negligible bias and are considerably more efficient than the traditional least Squares estimators. We show that Our estimators are robust to plausible deviations from an assumed distribution and are therefore enormously advantageous as compared to the least squares estimation. We give a real life example. (C) 2009 Elsevier B.V. All rights reserved.