Amaç: Bu yazıda mevcut verilere dayalı olarak Endüstri Mühendisliği (EM)’nin Türkiye’deki tarihsel gelişimi, bugünkü durumu ve geleceğiyle ilgili bir değerlendirme yapmak, gelecekte yapılacak farklı değerlendirme çalışmaları için bir temel oluşturmak amaçlanmıştır. Yöntem: Literatür taraması yapılmış, mevcut istatistiklerin değerlendirilerek yazarların alanla ilgili deneyim ve bilgi birikimine dayalı öneriler geliştirilmiştir. Bulgular: Bu yazıda "EM'nin Türkiye’de en çok tercih edilen ve geniş bir yelpazede uygulama alanı bulan meslekler" arasında olduğu istatistiklere dayalı olarak ortaya konmuştur. Günümüzde yaşanan hızlı değişime uygun olarak EM'nin bu konumunun sürdürülebilirliğinin nasıl sağlanacağı incelenmiştir. İyileştirmeye açık alanlara, örneğin; Üniversite-Sanayi işbirliği, EM'nin dijitalleşme alanında yerini alması, Z-kuşağı'nın dikkate alınarak eğitim ve öğretim programlarının yenilenmesi, hizmet sektörüne doğru kayan eğilimin imalat sektörüne döndürülmesi gibi konulara dikkat çekilmiştir. Bunlara ek olarak küresel problemlerin örneğin açlık, sağlık, eğitim, girişimcilik-yenilikçilik (inovasyon), sürdürülebilirlik, barış ve demokrasi, ekonomik kalkınma vb. ayırdında olan Endüstri mühendislerinin nasıl yetiştirilebileceği tartışılmıştır. Özgünlük: Türkiye’de EM’nin tarihsel gelişimi başlangıçtan günümüze dek bu boyutta ilk kez ele alınmış ve bu değerlendirme çalışması hazırlsanmıştır.

Interest in multiobjective permutation flow shop scheduling (PFSS) has increased in the last decade to ensure effective resource utilization. This study presents a modified self-adaptive local search (MSALS) algorithm for the biobjective permutation flow shop scheduling problem where both makespan and total flow time objectives are minimized. Compared to existing sophisticated heuristic algorithms, MSALS is quite simple to apply to different biobjective PFSS instances without requiring effort or time for parameter tuning. Computational experiments showed that MSALS is either superior to current heuristics for Pareto sets or is incomparable due to other performance indicators of multiobjective problems.

Hierarchical organizations, especially in government agencies, are known by their pyramidal structures and continuous training needs resulting from promotions and/or assignments. Using scientific and rational methods in the job analysis/description, recruitment/selection, assignment, performance appraisal and career planning functions of human resource management (HRM) process decreases training costs. In this study, we develop a new chain of methodologies (the cost-effective course planning model (CECPM)) to decrease training costs and increase the level of specialization. This methodology is implemented in the following steps of the HRM process: (1) the job analysis/description step, where our Mission Description Matrix defines in measurable units the amount of training needed for an employee assigned to a position, (2) the career matrix step, where the minimum training costs for an employee's career path are determined using our network-flow model and (3) the assignment step, where we propose a decision support system composed of an analytical hierarchy process, linear programming and Pareto optimality analysis. The results indicate that our proposed system ensures minimum training needs while satisfying person-to-position compatibility and personnel's preferences. (C) 2014 Elsevier B.V. All rights reserved.

This paper examines a manufacturer’s integrated planning problem for the production and the delivery of a set of orders. The manufacturer in this setting can use two vehicle types for outbound shipments. The first type of vehicle is available in unlimited numbers, but expensive. The second type, which is relatively low in its price, has limited and time-varying availability. We analyze the manufacturer’s planning problem under different delivery policies characterized by each of the following: whether orders can be split or not, whether they can be consolidated or not, and whether their sizes are restricted to be in integer multiples of vehicle capacities or not.

Prescription fraud is a main problem that causes substantial monetary loss in health care systems. We aimed to develop a model for detecting cases of prescription fraud and test it on real world data from a large multi-center medical prescription database. Conventionally, prescription fraud detection is conducted on random samples by human experts. However, the samples might be misleading and manual detection is costly. We propose a novel distance based on data-mining approach for assessing the fraudulent risk of prescriptions regarding cross-features. Final tests have been conducted on adult cardiac surgery database. The results obtained from experiments reveal that the proposed model works considerably well with a true positive rate of 77.4% and a false positive rate of 6% for the fraudulent medical prescriptions. The proposed model has the potential advantages including on-line risk prediction for prescription fraud, off-line analysis of high-risk prescriptions by human experts, and self-learning ability by regular updates of the integrative data sets. We conclude that incorporating such a system in health authorities, social security agencies and insurance companies would improve efficiency of internal review to ensure compliance with the law, and radically decrease human-expert auditing costs.

Modelling and analysis of complex and co-ordinated supply chains is a crucial task due to its inherent complexity and uncertainty. Therefore, the current research direction is to devise an efficient modelling technique that maps the dynamics of a real life supply chain and assists industrial practitioners in evaluating and comparing their network with other competing networks. Here an effective modelling technique, the hybrid Petri-net, is proposed to efficiently handle the dynamic behaviour of the supply chain. This modelling methodology embeds two enticing features, i.e. cost and batch sizes, in deterministic and stochastic Petri-net for the modelling and performance evaluation of supply chain networks. The model is subsequently used for risk management to investigate the issues of supply chain vulnerability and risk that has become a major research subject in recent years. In the test bed, a simple productive supply chain and an industrial supply chain are modelled with fundamental inventory replenishment policy. Subsequently, its performance is evaluated along with the identification and assessment of risk factors using analytical and simulation techniques respectively. Thus, this paper presents a complete package for industrial practitioners to model, evaluate performance and manage risky events in a supply chain.

It is very important to use combat simulation in personnel training and preparing them for different war scenarios. Simulation modeling and analysis methodologies gives an opportunity to staff officers and commanders to measure the effectiveness of their plans and take necessary precautions. In a simulated environment, different combat scenarios can be tried without actually deploying the units to the combat area and getting 'losts, costs, and risks'. As one of the most complicated and decisive operations on the road to victory, 'air assault operations' are high-risk, high-payoff operations that, when properly planned and vigorously executed, allow commanders to take the initiative in combat areas. In this study, we develop a simulation system called the Air Assault Operations Simulation Model (AAOSM) that allows planners to: (1) analyze air assault operations early in the decision process and refine those models as their decision process evolves, (2) perform 'bottleneck analysis' of the preplanned operations, and (3) perform 'risk management' of the operation before conducting the real operation. AAOSM is developed by using the ARENA simulation programming language. The outputs of the model are analyzed using statistical methods. The factors that have significant effect on air assault operations are identified. The possible scenarios are also evaluated for different weather and terrain conditions and for various refueling and maintenance configurations.

We consider the bicriteria scheduling problem of minimizing the number of tardy jobs and average flowtime on a single machine. This problem, which is known to be NP-hard, is important in practice, as the former criterion conveys the customer’s position, and the latter reflects the manufacturer’s perspective in the supply chain. We propose four new heuristics to solve this multiobjective scheduling problem. Two of these heuristics are constructive algorithms based on beam search methodology. The other two are metaheuristic approaches using a genetic algorithm and tabu-search. Our computational experiments indicate that the proposed beam search heuristics find efficient schedules optimally in most cases and perform better than the existing heuristics in the literature.

A new scheduling system for selecting dispatching rules in real time is developed by combining the techniques of simulation, data mining, and statistical process control charts. The proposed scheduling system extracts knowledge from data coming from the manufacturing environment by constructing a decision tree, and selects a dispatching rule from the tree for each scheduling period. In addition, the system utilises the process control charts to monitor the performance of the decision tree and dynamically updates this decision tree whenever the manufacturing conditions change. This gives the proposed system the ability to adapt itself to changes in the manufacturing environment and improve the quality of its decisions. We implement the proposed system on a job shop problem, with the objective of minimising average tardiness, to evaluate its performance. Simulation results indicate that the performance of the proposed system is considerably better than other simulation-based single-pass and multi-pass scheduling algorithms available in the literature. We also illustrate knowledge extraction by presenting a sample decision tree from our experiments.

Distributed scheduling (DS) is an approach that enables local decision makers to create schedules that consider local objectives and constraints within the boundaries of the overall system objectives. Local decisions from different parts of the system are then integrated through coordination and communication mechanisms. Distributed scheduling attracts the interest of many researchers from a variety of disciplines, such as computer science, economics, manufacturing, and service operations management. One reason is that the problems faced in this area include issues ranging from information architectures, to negotiation mechanisms, to the design of scheduling algorithms. In this paper, we provide a survey and a critical analysis of the literature on distributed scheduling. While we propose a comprehensive taxonomy that accounts for many factors related to distributed scheduling, we also analyse the body of research in which the scheduling aspect is rigorously discussed. The focus of this paper is to review the studies that concern scheduling algorithms in a distributed architecture, not, for example, protocol languages or database architectures. The contribution of this paper is twofold: to unify the literature within our scope under a common terminology and to determine the critical design factors unique to distributed scheduling and in relation to centralised scheduling.

In practice, scheduling systems are subject to considerable uncertainty in highly dynamic operating environments. The ability to cope with uncertainty in the scheduling process is becoming an increasingly important issue. This paper takes a proactive scheduling approach to study scheduling problems with two sources of uncertainty: processing time variability and machine breakdowns. Two robustness (expected total flow time and expected total tardiness) and three stability (the sum of the squared and absolute differences of the job completion times and the sum of the variances of the realized completion times) measures are defined. Special cases for which the measures can be easily optimized are identified. A dominance rule and two lower bounds for one of the robustness measures are developed and subseqently used in a branch-and-bound algorithm to solve the problem exactly. A beam search heuristic is also proposed to solve large problems for all five measures. The computational results show that the beam search heuristic is capable of generating robust schedules with little average deviation from the optimal objective function value (obtained via the branch-and-bound algorithm) and it performs significantly better than a number of heuristics available in the literature for all five measures.

Scheduling is a decision-making process that is concerned with the allocation of limited resources to competing tasks (operations of jobs) over a time period with the goal of optimising one or more objectives. In theory, the objective is usually to optimise some classical system performance measures such as makespan, tardiness/earliness and flowtime under deterministic and static assumptions. In practice, however, scheduling systems operate in dynamic and stochastic environments. Hence, there is a need to incorporate both uncertainty and dynamic elements into the scheduling process. In this paper, the major issues involved in scheduling decisions are discussed and the basic approaches to tackle these problems in manufacturing environments are analysed. Proactive scheduling is then focused on and several robustness and stability measures are presented. Previous research on scheduling robustness and stability is also reviewed and further research directions are suggested.

An assembly line is a production line in which units move continuously through a sequence of stations. The assembly line balancing problem is defined as the allocation of tasks to an ordered sequence of stations subject to precedence constraints with the objective of optimizing a performance measure. In this paper, we propose ant colony algorithms to solve the single-model U-type assembly line balancing problem. We conduct an extensive experimental study in which the performance of the proposed algorithm is compared against best known algorithms reported in the literature. The results indicate that the proposed algorithms display very competitive performance against them.

In this paper, a new local search metaheuristic is proposed for the permutation flow-shop scheduling problem. In general, metaheuristics are widely used to solve this problem due to its NP-completeness. Although these heuristics are quite effective to solve the problem, they suffer from the need to optimize parameters. The proposed heuristic, named STLS, has a single self-tuning parameter which is calculated and updated dynamically based on both the response surface information of the problem field and the performance measure of the method throughout the search process. Especially, application simplicity of the algorithm is attractive for the users. Results of the experimental study show that STLS generates high quality solutions and outperforms the basic tabu search, simulated annealing, and record-to-record travel algorithms which are well-known local search based metaheuristics.

This paper addresses the issue of finding robust and stable schedules with respect to random disruptions. Specifically, two surrogate measures for robustness and stability are developed. The proposed surrogate measures, which consider both busy and repair time distributions, are embedded in a tabu-search-based scheduling algorithm, which generates schedules in a single-machine environment subject to machine breakdowns. The performance of the proposed scheduling algorithm and the surrogate measures are tested under a wide range of experimental conditions. The results indicate that one of the proposed surrogate measures performs better than existing methods for the total tardiness and total flowtime criteria in a periodic scheduling environment. A comprehensive bibliography is also presented.