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Performance benchmarking of electricity distribution markets is essential for improving industry performance parameters. In a benchmarking study, the most important problem is that regulators often do not have accurate, specific, and sufficient information to determine current input use to achieve expected amount of output. The study combines statistical symmetric error structure with stochastic chance constrained DEA models and compares deterministic data envelopment analysis (DEA) models with stochastic chance-constrained DEA models within random input and output variables. The proposed models were applied on Turkey's electricity distribution units for assessment of energy efficiency. Study revealed that the results obtained with random data softened efficiency frontier. This study contains symmetric error structure and random inputs and outputs for performance benchmarking of electricity distribution markets by stochastic data envelopment analysis within a symmetric error structure in Turkey.

Bu çalışmada, Türkiye’de bulunan 21 elektrik dağıtım şirketinin performans ölçümü ve etkinlik çerçevesinde kıyaslanması amaçlamıştır. Dağıtım şirketlerinin rasgele girdi ve çıktı değişkenleri, stokastik girdiye ve çıktıya yönelimli şans kısıtlı Banker, Charnes ve Cooper (BCC) Veri Zarflama Analizi (VZA) modellerine simetrik hata yapısı ile dâhil edilmiştir. Deterministik girdiye ve çıktıya yönelimli BCC VZA modelleriyle stokastik modeller karşılaştırılmış, stokastik ölçüm değerleri, deterministik ölçüm değerleriyle doğrulanmıştır. Çalışmada, belirsiz verilerle çalışan stokastik VZA için şans kısıtlı problemler oluşturulmuş, stokastik modeller deterministik eşdeğerlerine dönüştürülmüş ve doğrusallaştırılmıştır. VZA modellerinde Türkiye’nin 21 elektrik dağıtım şirketi karar verme birimleri (KVB) olarak kabul edilmiştir. Elde edilen sonuçlarla, deterministik ve stokastik modellerin etkinlik sonuçlarına göre, etkin ve etkin olmayan elektrik dağıtım şirketleri belirlenmiştir. Amaç, analiz edilen verilerde istatistiksel gürültüyü VZA modellerine ekleyip, performans ölçümünü bu doğrultuda gerçekleştirmektir. Modellere gürültü unsuru tek faktörlü simetrik hata yapısı kapsamında dâhil edilmiş, modeller arasındaki farklı sonuçları incelemek amacıyla her hata seviyesi için modeller ayrı ayrı ele alınmıştır. Çalışma, Türkiye'de elektrik dağıtım şirketlerinin değerlendirilmesinde, simetrik hata yapısı ile stokastik verileri VZA’ya dâhil eden ilk yaklaşımdır.

WOS: 000496662600010 In this study, a satisfaction index estimation model is proposed integrating structural equation modeling and mathematical programming methods with fuzzy customer data. Firstly, a deep literature survey is conducted in this field of study. Then, a new model is proposed by taking into consideration gaps in the literature. The estimation model is composed of five stages and first stage is building conceptual model in which measurement and latent variables are introduced. At the second stage, a fuzzy evaluation method is developed for decreasing subjectivity in customer data. At the third stage, for measurement variables that are directly observed, a measurement model is developed with Linear Structural Relations. In the solution of the measurement model maximum likelihood algorithm is used. In the solution of structural model that is composed of latent variables that are not directly observed, a mathematical estimation model is developed in this study at the fourth stage. Mathematical model is coded in ILOG Cplex Optimization Studio. In the mathematical model that minimizes estimation errors, structural relations and measurement variable weights (precedence coefficients) are defined as constraints. At the fifth and last stage, index scores are calculated with mathematical model outputs. Application of the model is carried out in public sector at a local government service point. In the application model, service quality, innovation, communication, satisfaction and cost perception dimensions are used. Application results are discussed for both measurement and latent variables in detail. The results of model we developed are also compared with an alternative model outcomes and we show that we achieve optimum estimation capability with minimum estimation errors. 1001 scienctific project programme of The Scientific and Technological Research Council of Turkey-TUBITAK; TUBITAK 1001 programTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK); TUBITAK 2211 programTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) This study was partially supported by 1001 scienctific project programme of The Scientific and Technological Research Council of Turkey-TUBITAK. Our special thanks go to TUBITAK 1001 and 2211 programs for financial support, to expert team for their scientific efforts, to the students of Kirikkale University Industrial Engineering Department for their support in data collection and finally to anonymous reviewers for their constructive comments.

In a production flow line with stochastic environment, variability affects the system performance. These stochastic nature of real-world processes have been classified in three types: arrival, service and departure process variability. So far, only service process – or task time – variation has been considered in assembly line (AL) balancing studies. In this study, both service and flow process variations are modelled along with AL balancing problem. The best task assignment to stations is sought to achieve the maximal production. A novel approach which consists of queueing networks and constraint programming (CP) has been developed. Initially, the theoretical base for the usage of queueing models in the evaluation of AL performance has been established. In this context, a diffusion approximation is utilised to evaluate the performance of the line and to model the variability relations between the work stations. Subsequently, CP approach is employed to obtain the optimal task assignments to the stations. To assess the effectiveness of the proposed procedure, the results are compared to simulation. Results show that, the procedure is an effective solution method to measure the performance of stochastic ALs and achieve the optimal balance. © 2016 Informa UK Limited, trading as Taylor & Francis Group.

This paper presents a new constraint programming model for mixed-model assembly line balancing problem. The proposed model minimizes the cycle time for a given number of stations. The proposed model is tested with literature problems and its performance is evaluated by comparing to mathematical model. Best obtained solution and elapsed CPU time are used as performance criteria. The experimental results show that the proposed constraint programming model performs well and can be used as an alternative modeling technique to solve the problem., Bu çalışmada karma modelli montaj hattı dengeleme problemleri için yeni bir kısıt programlama modeli sunulmuştur. Önerilen model verilen bir istasyon sayısı ile çevrim zamanını en küçüklemektedir. Önerilen model literatürdeki örnek problemler ile test edilmiştir ve modelin performansı karma modelli montaj hattı dengeleme problemlerinin matematiksel modeli ile karşılaştırmalı olarak değerlendirilmiştir. Performans kriterleri olarak ulaşılan en iyi çözüm değeri ve CPU süresi kullanılmıştır. Deneysel sonuçlar önerilen kısıt programlama modelinin problemin çözümünde iyi performans gösteren bir alternatif modelleme tekniği olduğunu göstermiştir.

Two-sided assembly lines are often designed to produce large-sized products, such as automobiles, trucks and buses. In this type of production line, both left-side and right-side of the line are used in parallel. In all studies on two-sided assembly lines, sequence-dependent setup times have not yet been considered. However, in real life applications, setups may exist between tasks. Performing a task directly before another task may influence the latter task inside the same station, because a setup for performing the latter task may be required. Furthermore, if a task is assigned to a station as the last one, then it may cause a setup for performing the first task assigned to that station since the tasks are performed cyclically. In this paper, the problem of balancing two-sided assembly lines with setups (TALBPS) is considered. A mixed integer program (MIP) is proposed to model and solve the problem. The proposed MIP minimises the number of mated-stations (i.e., the line length) as the primary objective and it minimises the number of stations (i.e., the number of operators) as a secondary objective for a given cycle time. A heuristic approach (2-COMSOAL/S) for especially solving large-size problems based on COMSOAL (computer method of sequencing operations for assembly lines) method is also presented. An illustrative example problem is solved using 2-COMSOAL/S. To assess the effectiveness of MIP and 2-COMSOAL/S, a set of test problems are solved. The computational results show that 2-COMSOAL/S is very effective for the problem.

In this paper, a simulated annealing approach is developed for the parallel mixed-model assembly line balancing and model sequencing (PMMAL/BS) problem which is an extension of the parallel assembly line balancing (PALB) problem introduced by Gokcen et al. (2006). In PALB, the aim is to balance more than one assembly line together. Balancing of the lines simultaneously with a common resource is very important in terms of resource minimisation. The proposed approach maximises the line efficiency and distributes the workloads smoothly across stations. The proposed approach is illustrated with two numerical examples and its performance is tested on a set of test problems. The computational results show that the proposed approach is very effective for PMMAL/BS.

Two-sided assembly lines are often used at the assembly of large-sized high-volume products, such as cars, trucks, and buses. In this type of a production line, the left side and the right side of the line are used in parallel. This paper presents a tabu search algorithm for two-sided assembly line balancing. The line efficiency and the smoothness index are considered as the performance criteria. The proposed algorithm is illustrated with two numerical example problems, and its performance is tested on a set of test problems taken from literature. The performance of the proposed algorithm is compared to the existing approaches. The computational results show that the proposed algorithm performs well.

WOS: 000255320900019 In this study, the well-known one machine problem with the performance criterion of minimizing total weighted tardiness is considered. This problem is known to be NP-hard, and consists of one machine and n independent jobs. Each of these jobs has a distinct integer processing time, a distinct integer weighting factor, and a distinct integer due date. The purpose of this problem is to find a sequence of these jobs minimizing the sum of the weighted tardiness. Using benchmarking problems, this study compares performances of eleven genetic crossover operators which have been widely used to solve other types of hard scheduling problems. (c) 2007 Elsevier Inc. All rights reserved.

The model proposed bridges the gap in post analysis of customer satisfaction (CS) and customer loyalty (CL) research.An integrated approach is presented using data mining and structural models together.We contribute to customer classification literature by adapting decision tree results to CS-CL matrix.CS-CL matrix is created by integrating group and criteria evaluation results using the advantage of structural models.A customer strategy development tool is offered according to matrix-based model outcomes. Businesses can maintain their effectiveness as long as they have satisfied and loyal customers. Customer relationship management provides significant advantages for companies especially in gaining competitiveness. In order to reach these objectives primarily companies need to identify and analyze their customers. In this respect, effective communication and commitment to customers and changing market conditions is of great importance to increase the level of satisfaction and loyalty. To evaluate this situation, level of customer satisfaction and loyalty should be measured correctly with a comprehensive approach. In this study, customers are investigated in 4 main groups according to their level of satisfaction and loyalty with a criteria and group based analysis with a new method. We use classification algorithms in WEKA programming software and Structural Equation Modeling (SEM) with LISREL tools together to analyze the effect of each satisfaction and loyalty criteria in a satisfaction-loyalty matrix and extend the customer satisfaction and loyalty post-analysis research bridging the gap in this field of research. To convert developed conceptual thought to experimental study, white goods industry is exemplified. 15 criteria are used for evaluation in 4 customer groups and a satisfaction-loyalty survey developed by experts is applied to 200 customers with face-to-face interviews. As a result of the study, a customer and criteria grouping method is created with high performance classification methods and good fit structural models. In addition, results are evaluated for developing a customer strategy improvement tool considering method outcomes.

In this study, variation of the task times, and precedence relation effects are investigated to evaluate the line performance. A new solution procedure based on queueing networks and constraint programming is proposed to model and solve the Assembly Line Balancing Problem (ALBP). Station utilization, total average number of jobs and smoothness index are used as performance measures. Bowl effect, inverted bowl effect and variability imbalance which are seen in balanced lines are examined by using proposed procedure. Also effects of the variability on the line performance are reviewed. Literature data sets are utilized to assess the effectiveness of the procedure.

Assembly lines of big-size products such as buses, trucks and helicopters are very different from the lines studied in the literature. These products' manufacturing processes have a lot of tasks most of which have long task times. Since traditional assembly line models including only one worker in each station (i.e. simple assembly lines) or at most two workers (two-sided assembly lines) may not be suitable for manufacturing these type of products, they need much larger shop floor for a number of stations and long product flow times. In this study, an assembly line balancing problem (ALBP) with parallel multi-manned stations is considered. Following the problem definition, a mixed integer programming formulation is developed. A detailed study of priority rules for simple ALBPs is also presented, and a new efficient constructive heuristic algorithm based on priority rules is proposed. In order to improve solutions found by the constructive heuristic, a genetic algorithm-based solution procedure is also presented. Benchmark instances in the literature are solved by using the proposed mathematical programming formulation. It has been seen that only some of the small-size instances can be solved optimally by this way. So the efficiency of the proposed heuristic method is verified in small-size instances whose optimal solutions are found. For medium- and big-size instances, heuristics' results and CPU times are demonstrated. A comparative evaluation with a branch and bound algorithm that can be found in the literature is also carried out, and results are presented.

When a task or work is done continuously, there will be an experience so following times needs of required resources (manpower, materials, etc.) will be reduced. This learning curve described first by Wright. Wright determined how workmanship costs decreased while proceed plain increasing. This investigations correctness found consistent by plain producers. Learning effect is an effect that, works can be done in shorter time in the rate of repeat of work with repeating same or similar works in production process. Nowadays classical production systems adapted more acceptable systems with new approaches. Just in time production system (JIT) philosophy is one of the most important production system philosophies. JIT which is known production without stock stands on using all product resources optimum. Minimization problem of Earliness/Tardiness finishing penalty, which we can describe Just in time scheduling, appeared by inspired from JIT philosophy. In this study, there is literature survey which directed to earliness/tardiness performance criteria and learning effect processing in scheduling and as a result of this it is obtained some establishing for literature., Bir görev veya iş sürekli yapıldığı takdirde belirli bir alışkanlık ve öğrenme olur ve ilerleyen zamanlarda bu işi tamamlamak için gerekli kaynaklara olan (işgücü, malzeme, vb.) ihtiyaç azalır. Bu "Öğrenme Eğrisi" ile ilk kez Wright tarafından tanımlanmıştır. Wright uçakların üretiminde üretilen uçak sayısı artarken direk işçilik maliyetlerinde nasıl bir azalma olduğunu tespit etmiştir. Bu gözlemin doğruluğu uçak üreticileri tarafından da tutarlı bulunmuştur. "Öğrenme Etkisi" ise; aynı veya benzer işlerin tekrarlanmasıyla üretim sürecinde işlerin tekrar sayısı nispetinde daha kısa sürede yapılmasını ifade eden etkidir. Günümüzde klasik üretim sistemlerine yeni yaklaşımlarla çağın gereklerine daha uygun sistemler uyarlanmıştır. Tam Zamanında Üretim Sistemi (TZÜ) felsefesi de en önemli modern üretim felsefelerinden biridir. Stoksuz üretim veya "0" envanter gibi isimlerle de bilinen TZÜ, tüm üretim kaynaklarının optimum kullanımı ilkesine dayanır. Tam zamanında çizelgeleme olarak ta nitelendirebileceğimiz Erken(Earliness)/Geç(Tardiness) tamamlanma cezalarının minimizasyonu problemi TZÜ felsefesinden esinlenerek ortaya çıkmıştır. Bu çalışmada çizelgelemede erken/geç tamamlanma performans kriteri ve öğrenme etkili işleme özelliğinin dikkate alındığı yayınlara yönelik literatür taraması ve sonucunda literatüre yönelik bazı tespitler yapılmıştır.

In the event that big-sized complex products (containing a large number of assembly tasks most of which have long task times) are produced in simple or two-sided assembly lines, hundreds of stations are essentially required. Long product flow time, a large area for establishment of the line, a high budget for the investment of equipment, and tools in stations and several work-in-process are also required for these kinds of products. In order to avoid these disadvantages, assembly lines with parallel multi-manned workstations can be utilized. In this paper, these lines and one of their balancing problems are addressed, and a branch and bound algorithm is proposed. The algorithm is composed of a branching scheme, some efficient dominance and feasibility criteria based on a problem-specific knowledge. A heuristic-based guidance for enumeration process is included as an efficient component of the algorithm as well. VWSolver algorithm proposed for a special version of the problem in the literature has been modified and compared with the proposed algorithm. Results show that proposed algorithm outperforms VWSolver in terms of both CPU times and quality of feasible solutions found. Highlights? We considered assembly line balancing problems with parallel multi-manned workstations which can be used for modeling lines producing big-sized complex products. ? A branch and bound based exact solution algorithm including some efficient components which are developed based on problem-specific knowledge is proposed. ? Through an analysis of comparison results, it has been seen that the proposed algorithm has better performance than the other one in terms of CPU times of optimal solutions, and quality of feasible solutions.

WOS: 000307285400025 In the literature, earliness/tardiness (E/T) problem was known as weighted absolute deviation problem, and both tardiness and earliness is very important performance criteria for scheduling problem. While total tardiness criteria provides adaptation for due date (ignoring results of earliness done jobs), it deals with only cost of tardiness. However this phenomenon has been started to change with just-in-time (JIT) production concept. On JIT production, earliness is as important as tardiness. The phenomenon of the learning effect has been extensively studied in many different areas of operational research. However, there have been a few studies in the general context of production scheduling such as flow-shop scheduling. This paper addresses the minimization of the total earliness/tardiness penalties under learning effects in a two-machine flow-shop scheduling problem. Jobs have a common due date. We present mathematical model to obtain an optimal schedule for a given job sequence. We also present heuristics that use genetic algorithm and tabu search, based on proposed properties. Furthermore, random search was used for showing the significance of the study by comparison purpose. A new set of benchmark problems is presented with the purpose of evaluating the heuristics. The experimental results show that the performance of proposed approach is quite well, especially for the instances of large size.

WOS: 000275524100013 In this research, a detailed study of the permutation flowshop scheduling problem with the objective of minimizing total tardiness is presented and a steady-state genetic algorithm solution procedure is developed for such problems. Also, using problem-specific knowledge, a very efficient elite guided solution improvement scheme and an appropriate crossover operator have been developed and integrated into the proposed method. Using benchmark problems, the algorithm has been compared with heuristic algorithms having the best performance in the literature. The performance of the developed algorithm is shown to be superior using a simulation study. (C) 2009 Elsevier Ltd. All rights reserved.

Two-sided assembly lines are usually designed to produce large-sized products such as automobiles, trucks and buses. In this type of production line, both left-side and right-side of the line are used. In parallel assembly lines, one or more product types are produced on two or more assembly lines located in parallel to each other. Both production lines have several serious practical advantages. For this purpose, in this paper, two or more two-sided assembly lines located in parallel to each other are considered and a tabu search algorithm which combines the advantages of both types of production lines is developed. To assess the effectiveness of the proposed algorithm, a set of test problems are solved. The proposed algorithm is illustrated with two examples, and some computational properties of the algorithm are given.

This paper presents a new mathematical model and a simulated annealing algorithm for the mixed-model two-sided assembly line balancing problem. The proposed mathematical model minimizes the number of mated-stations (i.e., the line length) as the primary objective and minimizes the number of stations (i.e., the number of operators) as a secondary objective for a given cycle time. In the proposed simulated annealing algorithm, two performance criteria are considered simultaneously: maximizing the weighted line efficiency and minimizing the weighted smoothness index. The proposed approach is illustrated with an example problem, and its performance is tested on a set of test problems. The experimental results show that the proposed approach performs well. (c) 2008 Elsevier Ltd. All rights reserved.

Two-sided assembly lines are especially used at the assembly of large-sized products, such as trucks and buses. In this type of a production line, both sides of the line are used in parallel. In practice, it may be necessary to optimize more than one conflicting objectives simultaneously to obtain effective and realistic solutions. This paper presents a mathematical model, a pre-emptive goal programming model for precise goals and a fuzzy goal programming model for imprecise goals for two-sided assembly line balancing. The mathematical model minimizes the number of mated-stations as the primary objective and it minimizes the number of stations as a secondary objective for a given cycle time. The zoning constraints are also considered in this model, and a set of test problems taken from literature is solved. The proposed goal programming models are the first multiple-criteria decision-making approaches for two-sided assembly line balancing problem with multiple objectives. The number of mated-stations, cycle time and the number of tasks assigned per station are considered as goals. An example problem is solved and a computational study is conducted to illustrate the flexibility and the efficiency of the proposed goal programming models. Based on the decision maker's preferences, the proposed models are capable of improving the value of goals. (C) 2008 Elsevier Ltd. All rights reserved.

This paper presents a new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems which is based on the idea of adaptive learning approach and simulated annealing. The proposed approach uses a weight parameter to perturb task priorities of a solution to obtain improved solutions. The weight parameters are then modified using a learning strategy. The maximization of line efficiency (i.e., the minimization of the number of stations) and the equalization of workloads among stations (i.e., the minimization of the smoothness index or the minimization of the variation of workloads) are considered as the performance criteria. In order to clarify the proposed solution methodology, a well known problem taken from literature is solved. A computational study is conducted by solving a large number of benchmark problems available in the literature to compare the performance of the proposed approach to the existing methods such as simulated annealing and genetic algorithms. Some test instances taken from literature are also solved by the proposed approach. The results of the computational study show that the proposed approach performs quite effectively. It also yields optimal solutions for all test problems within a short computational time.

Logical relations occur frequently in integer programming problems and are modelled by introducing binary variables in association with linear expressions. Applications requiring constraints involving precedence, exclusion, implication and other conditions give rise to the logical relations OR and IMPLIES in the models. These relations will be considered in this paper from a modelling point of view and formulations investigated for situations where the logical variables link sets of integer variables. Valid inequalities (cuts) that can be added to a model will be developed for a number of the formulations and the computational benefits of these cuts will be considered from an experimental point of view by considering the performance of sets of problem instances. New formulations and combinations of older established formulations will be considered. It will be contended that tight formulations may not always be the most successful.

A fuzzy goal programming model for the simple U-line balancing (SULB) problem with multiple objectives is presented. In real life applications of the SULB problem with multiple objectives, it is difficult for the decision-maker(s) to determine the goal value of each objective precisely as the goal values are imprecise, vague, or uncertain. Therefore a fuzzy goal programming model is developed for this purpose. The proposed model is the first fuzzy multi-objective decision-making approach to the SULB problem with multiple objectives which aims at simultaneously optimizing several conflicting goals. The proposed model is illustrated using an example. A computational study is conducted by solving a large number of test problems to investigate the relationship between the fuzzy goals and to compare them with the goal programming model proposed by Gken and Apak (Gken, H. and Apak, K., European Journal of Operational Research, 171, 577-585, 2006). The results of the computational study show that the proposed model is more realistic than the existing models for the SULB problem with multiple objectives and also provides increased flexibility for the decision-maker(s) to determine different alternatives.

Many scheduling problems are NP-hard problems. For such NP-hard combinatorial optimization problems, heuristics play a major role in searching for near-optimal solutions. In this paper we develop a genetic algorithm-based heuristic for the flow shop scheduling problem with makespan as the criterion. The performance of the algorithm is compared with the established NEH algorithm. Computational experience indicates that genetic algorithms can be good techniques for flowshop scheduling problems. Journal of the Operational Research Society (2004).

Much research into multi-level lot-sizing problems has concentrated on fixed horizon problems ignoring more realistic conditions such as appending new demand(s) in the planning periods. The aim of this paper is to illustrate that a heuristic, which was developed earlier by the authors, is applicable to the multilevel lot-sizing problem with a bottleneck under a rolling schedule environment. The results illustrate that the Silver-Meal (SM) approach performed better than the Economic Order Quantity (EOQ) approach in terms of cost measures, when incorporated within the heuristic procedure. The results also show that, although the total cost of the problem is increased in a rolling schedule environment, the heuristic continues to perform favourably in producing low cost solutions.