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The disassembly line balancing (DLB) problem assigns the set of tasks to each workstation for each product to be disassembled and aims at attaining several objectives, such as minimising the number of workstations, ensuring similar idle time at each workstation and removing hazardous parts/highly demanded components at the earliest moments possible. Over the past two decades, the DLB problem and its variants have grown ever more popular for researchers and practitioners of environmentally conscious manufacturing. Yet, the problem characteristics and assumptions vary widely and there is no literature review to classify the existing articles accordingly. Hence, a comprehensive literature review of recent and state-of-the-art papers is vital to draw a framework of the past, and to shed light on future directions. To do so, 116 studies published in proceedings and journals since 1999 are selected and reviewed. The papers are then analysed and categorised to construct a useful foundation of past research. Finally, trends and gaps in the literature are identified to clarify and to suggest future research opportunities. [ABSTRACT FROM AUTHOR]

The collection of used products is the driving force of remanufacturing systems and enterprises can gain significant economic, technical and social benefits from recycling. All products are disassembled up to some level in remanufacturing systems. The best way to disassemble returned products is valid by a well-balanced disassembly line. In this paper, a mixed integer programming (MIP) model is proposed for a mixed model disassembly line balancing (MMDLB) problem with multiple conflicting objectives: (1) minimising the cycle time, (2) minimising the number of disassembly workstations and (3) providing balanced workload per workstation. In most real world MMDLB problems, the targeted goals of decision makers are frequently imprecise or fuzzy because some information may be incomplete and/or unavailable over the planning horizon. This study is the first in the literature to offer the binary fuzzy goal programming (BFGP) and the fuzzy multi-objective programming (FMOP) approaches for the MMDLB problem in order to take into account the vague aspirations of decision makers. An illustrative example based on two industrial products is presented to demonstrate the validity of the proposed models and to compare the performances of the BFGP and the FMOP approaches. [ABSTRACT FROM PUBLISHER]

Responsible and sustainable manufacturing deals with the development of strategies, methodologies and management of competitive, agile and responsive technologies to address legislative mandates, ethical standards and customer needs, while maintaining profitability and ecofriendly ideals throughout the lifecycles of products and services. Optimal incentives for varying product qualities are then computed by utilising this cost in the trade-in policy model. Antecedents and Outcomes of Sustainable Manufacturing Practices', Gouda and Saranga ([1]), empirically test the antecedents and consequences of sustainable manufacturing practices across emerging and developed countries. [Extracted from the article]

This study proposes a genetic algorithm (GA) based model to generate examination schedules such that they focus on students' success in addition to satisfying the hard constraints required for feasibility. The model is based on the idea that the student success is positively related to the adequate preparation and resting time among exams. Therefore, the main objective of this study is to maximize time length among exams (i.e., paper spread) considering the difficulties of exams. Two different genetic algorithm models were developed to optimize paper spread. In the first genetic algorithm model, a high penalty approach was used to eliminate infeasible solutions throughout generations. The second genetic algorithm model controls whether or not each chromosome joining the population satisfies the hard constraints. To evaluate the models, a set of experiments have been designed and studied using the data collected from the College of Engineering in Pamukkale University. [ABSTRACT FROM AUTHOR]