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PurposeExpert evaluation is the backbone of the multi-criteria decision-making (MCDM) techniques. The experts make pairwise comparisons between criteria or alternatives in this evaluation. The mainstream research focus on the ambiguity in this process and use fuzzy logic. On the other hand, cognitive biases are the other but scarcely studied challenges to make accurate decisions. The purpose of this paper is to propose pilot filters – as a debiasing strategy – embedded in the MCDM techniques to reduce the effects of framing effect, loss aversion and status quo-type cognitive biases. The applicability of the proposed methodology is shown with analytic hierarchy process-based Technique for Order-Preference by Similarity to Ideal Solution method through a sustainable supplier selection problem.Design/methodology/approachThe first filter's aim is to reduce framing bias with restructuring the questions. To manipulate the weights of criteria according to the degree of expected status quo and loss aversion biases is the second filter's aim. The second filter is implemented to a sustainable supplier selection problem.FindingsThe comparison of the results of biased and debiased ranking indicates that the best and worst suppliers did not change, but the ranking of suppliers changed. As a result, it is shown that, to obtain more accurate results, employing debiasing strategies is beneficial.Originality/valueTo the best of the author's knowledge, this approach is a novel way to cope with the cognitive biases. Applying this methodology easily to other MCDM techniques will help the decision makers to take more accurate decisions.

Patients are getting in trouble with care processes due to waste and diversity in health care services. Although patients feel uncomfortable with these problems, service providers do not deal with their complaints promptly due to excessive workload and organizational disorder. The aim of this study is to implement lean techniques to solve the operational problems of a public hospital physical therapy and rehabilitation service in Eskisehir, Turkey. The process is analyzed both from the patient and service provider perspectives simultaneously in lean consumption context. Genchi gembutsu, value stream mapping (VSM), integrated consumption and provision map, A3 and heijunka were the lean techniques used. Mapping of the system gives the opportunity to relax the organizational complexity. Consequently, cognitive load of nurses is decreased with daily and weekly assignment algorithms designed as part of heijunka implementation. Also 26.84% of patient flow time and 14.28% of process step reduction are recorded as a result of realized improvements. Hastalar sağlık hizmetlerindeki israf ve değişkenlik nedeniyle bakım süreçlerinde sorunlar yaşamaktadır. Hastaların bu problemler nedeniyle yaşadıkları rahatsızlığa rağmen, hizmet sağlayıcılar aşırı işyükü ve örgütsel kargaşa nedeniyle şikayetler ile ivedilikle ilgilenememektedir. Bu çalışmanın amacı Türkiye'de Eskişehir ilinde hizmet veren bir kamu hastanesinin Fizik Tedavi ve Rehabilitasyon servisinde yaşanan operasyonel problemlerin çözümünde yalın tekniklerin uygulanmasıdır. Süreç, yalın tüketim bağlamında hem hasta hem de hizmet sağlayıcı bakış açısından eş zamanlı olarak analiz edilmiştir. Genchi gembutsu, değer akış haritalama (DAH), bütünleşik tüketim ve tedarik haritası, A3 ve heijunka bu çalışma kapsamında kullanılan yalın tekniklerdir. Sistemin haritalandırılması örgütsel karmaşıklığın çözülmesi için fırsat sunmaktadır. Sonuç olarak, heijunka uygulaması kapsamında tasarlanan günlük ve haftalık atama algoritmaları ile hemşirelerin bilişsel yükü azaltılmıştır. Ayrıca gerçekleştirilen iyileştirmeler sonucunda hasta akış süresinde %26.84, süreç adımlarında ise %14.28 azalma kaydedilmiştir.

PurposeAlthough disassembly balancing lines has been studied for over two decades, there is a gap in the robotic disassembly. Moreover, combination of problem with heterogeneous employee assignment is also lacking. The hazard related with the tasks performed on disassembly lines on workers can be reduced by the use of robots or collaborative robots (cobots) instead of workers. This situation causes an increase in costs. The purpose of the study is to propose a novel version of the problem and to solve this bi-objective (minimizing cost and minimizing hazard simultaneously) problem.Design/methodology/approachThe epsilon constraint method was used to solve the bi-objective model. Entropy-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization methods for Enrichment Evaluation (PROMETHEE) methods were used to support the decision-maker. In addition, a new criterion called automation rate was proposed. The effects of factors were investigated with full factor experiment design.FindingsThe effects of all factors were found statistically significant on the solution time. The combined effect of the number of tasks and number of workers was also found to be statistically significant.Originality/valueIn this study, for the first time in the literature, a disassembly line balancing and employee assignment model was proposed in the presence of heterogeneous workers, robots and cobots to simultaneously minimize the hazard to the worker and cost.

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Disassembly is a crucial subject in view of the fact that it has a pivotal role in reverse supply chains. It will be possible to make effective disassembly operations with disassembly line balancing. In this study, ELECTRE is used for the first time in the literature to determine the assignment priorities of the tasks to work stations. Two algorithms are also proposed to improve the initial assignments. The first improvement algorithm aims to minimize the workstation number if it is possible. On the other hand, the aim of the second improvement is to make a better balance with interchanging the tasks between workstations. The result is compared with the results in the literature. A novel comparison criteria is also proposed called Deviation Mean (DM) in this study.

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