International Journal of Industrial Engineering: Theory, Applications and Practice
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A FUZZY MATHEMATICAL MODEL FOR MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCHEDULING PROBLEM WITH NEW JOB INSERTION AND EARLINESS/TARDINESS PENALTY
No full textThe scheduling of flexible job shop systems is one of the important problems in various fields of production and has been currently considered in optimization problems. The main purpose of the present study is to design a flexible multi-objective job-shop scheduling model considering earliness/tardiness penalties along with fuzzy processing time and finally inserting a new job. In terms of research methodology, this study can be regarded as an applied-exploratory investigation. The present study includes the flexible job-shop scheduling problem (FJSSP) with multiple objectives to minimize maximum completion time (makespan), maximum machine workload, total machines workload, and earliness/tardiness penalty considering different constraints. The designed fuzzy linear programming was coded in CPLEX software and implemented on small, medium, and large-dimension examples. A mathematical model was designed using the Non-dominated Sorting Genetic Algorithm II (NSGA II) meta-heuristic algorithm, which was then coded in MATLAB software and implemented in the same examples. The results of the implementation of mathematical and meta-heuristic methods showed that in terms of implementation time and solution quality in problems with different dimensions, the proposed meta-heuristic method is efficient. Afterward, a two-step NSGA II algorithm was employed to insert the new job. Moreover, the mathematical solution method could be considered as the optimal method for the company under study. However, the two-stage NSGA II algorithm is preferred for the company under study if the need for rescheduling is due to the insertion of the new larger/medium job(s) during the current schedule
SCHEDULING OPTIMIZATION OF A WHEEL HUB PRODUCTION LINE BASED ON FLEXIBLE SCHEDULING
No full textFlexible production scheduling is a problem for the field of intelligent manufacturing. Generally, methods for solving flexible job-shop scheduling include tabu search, simulated annealing, and particle swarm optimization. However, most algorithms cannot be directly used to solve actual production scheduling problems because of their efficiency and quality problems. This paper proposes a particle swarm-immunization algorithm based on the bottleneck process. First, the particle swarm optimization is optimized by the time decomposition method and workpiece urgency parameter; it is then embedded in an artificial immune algorithm after the process and mathematical model structure decomposition. Next, a multiobjective optimization mathematical model is established as the concentration adjustment mechanism of the artificial immune algorithm, and the method of matching the highest efficiency process to the bottleneck process is used to improve the variation function. Finally, through empirical research on wheel hub production data analysis, our method can reduce production time and energy consumption
A COMBINED FUZZY MULTI-CRITERIA GROUP DECISION MAKING FRAMEWORK FOR MATERIAL SELECTION PROCEDURE: INTEGRATION OF FUZZY QFD WITH FUZZY TOPSIS
No full textToday, organizations give more priority to new product development (NPD) processes in order to gain competitive advantages and to satisfy customers’ requirements. Identification of a suitable material is a key issue in the design and development of new products. Material selection can be viewed as a complex multi-criteria decision making (MCDM) problem that needs to consider multiple conflicting criteria. The aim of this paper is to develop a fuzzy multi-criteria group decision making methodology, which combines 2-tuple fuzzy linguistic representation model, linguistic hierarchies, quality function deployment (QFD), and fuzzy TOPSIS method to specify the importance of selection attributes in material selection procedure and to determine the most appropriate material alternative that best fits the customers’ requirements. The developed approach is illustrated through a case study of washing liquid material selection procedure
DESIGN OF VEST PROTECTOR FOR SACRED PALANQUIN BEARER TO REDUCE FATIGUE LEVEL AND MEASUREMENT VIA BRAINWAVES
No full textMazu’s circumnavigation is a major celebration event in Taiwan. The palanquin is a means of transportation for the Gods, and it is also a symbol of a sacred object. Believers participate in the practice of passing under the palanquin or carrying a palanquin as a way to approach the divine space where the gods are located. This study took the palanquin as the theme and the people who carry a palanquin as the research subjects. In common, the fatigue level measurement is measured via questionnaire. Measurement using questionnaire survey is very subjective to the user and also hard to be correctly measured. We proposed an alternative using quantitative measurement via brainwaves. The fatigue level of palanquin bearer will be calculated by the system. In this paper, we compare the fatigue level among palanquin bearers. The first group is palanquin bearer without using our vest protector, and the second group is palanquin bearer using our vest protector. Based on the spirit of user-centered design, the design of the palanquin protector can protect the safety of the palanquin bearers, relieve pain and enhance comfort, so that the palanquin bearers can continue to maintain the manpower palanquin bearing tradition and express their respect for their gods. We also propose a deep learning model to do the classification of the fatigue level of the palanquin bearer. Our deep learning method can predict until 77% of the accuracy which is the best among state-of-the art models. From the experiments we proved that our vest protector design can reduce the fatigue level of the palanquin bearers. The vest protector help 3 persons of palanquin bearer reduce their fatigue level from moderate fatigue to normal, and help 4 persons of palanquin bearer from severe fatigue into moderate fatigue. We also compare our deep learning model with other models and get the best results from the experiments
HAZARDOUS MATERIALS TRANSPORTATION WITH FOCUSING ON INTERMODAL TRANSPORTATION: A STATE-OF-THE-ART REVIEW
No full textTransportation of hazardous materials (hazmat) is one of the most critical issues in transportation planning that involves multiple risks to the physical and social environments. Any improvements in it reduce not only environmental costs but also reduces external transport costs (e.g., reduces the risk of accidents which, in addition to environmental impact, also affects external costs). Besides, multi-modal transportation as a main part of transportation uses multiple modes (e.g., rail, ship, truck, air) to transport freight. If the containers carry hazmat, the government regulates their transportation due to the associated risks. Many researchers have studied the risk assessment of hazmat transportation to find ways for reducing hazardous material transportation risks. In this regard, the intermodal models and unimodal problems for hazmat transportation were studied by some researchers. In this study, after pointing out the importance of hazmat intermodal transportation and risks, the research related to hazmat intermodal transportation, including routing and scheduling, intermodal transportation, and location-routing problems. Then the reviewed literature is quantified and measured. Finally, the paper concludes by presenting some problems receiving less attention than the others and proposes several research opportunities in the field
PROCESS MINING IN THE MANUFACTURING CONTEXT: REVIEW AND RECOMMENDATIONS
No full textModern manufacturing systems generate large amounts of data regarding the automation of business processes, which can be analyzed with process mining techniques. However, there is not enough consolidation on process mining methodologies or guidelines on properly applying numerous techniques in particular manufacturing scenarios. This paper aims to synthesize data on process mining projects' goals, utilize information systems, analyze business processes, and apply process mining types, software tools, and algorithms applied in the manufacturing industry. The data on the key research elements are gathered through a systematic literature review and analyzed with descriptive statistics and crosstabs analysis in SPSS. The research results enable process mining practitioners, business analysts, and business managers to gain insight into the trends and benefits of process mining application in the manufacturing context, as well as a guideline on how process mining techniques can be applied depending on the analyzed business process and process mining projects' goal
DESIGNING REVERSE LOGISTICS NETWORK FOR END-OF-LIFE VEHICLES: A SUSTAINABILITY PERSPECTIVE IN A FRAGILE SUPPLY CHAIN
No full textEnvironmental guidelines in the automotive industry greatly emphasize the recycling, remanufacturing, and recovering of end-of-life vehicles (ELVs). Given the principle of extended producer responsibility, developing an effective reverse logistics network is the most significant digit ahead of the industry. However, initial attempts addressing the reverse logistics network design (RLND) problem were short-sighted, focusing on cost minimization. Undoubtedly, the whole concept of recycling was founded on the pillars of sustainability. Accordingly, reverse logistics network design must be motivated by long-term environmental and societal benefits. This fact has become even more prominent in the current pandemic environment as COVID-19 has added serious uncertainties and risks to the supply chain processes. This paper reiterates the essence of sustainability goals and proposes a multi-objective fuzzy mathematical model to RLND problem for ELVs under such a fragile and fuzzy environment. The coverage of the proposed model is to optimally determine the locations and numbers of the facilities and the flows among them concerning environmental, social, and economic aspects. Hence, the model aims to reach a robust compromise solution that leads to a resilient network design. A real case study on the ELV market in Istanbul/Turkey proves the merit of the developed model
OBTAINING MULTIPLE PROCESS PARAMETER COMBINATIONS USING A SUPERVISED CLUSTERING-OPTIMIZATION APPROACH
No full textThough much work has been done in input-output mapping and input parameter optimization of different manufacturing processes, very sparse work is available in obtaining multiple optimal process parameter combinations of these processes i.e. multimodal optimization. Multimodal optimization is necessary as a single optimal process parameter combination may theoretically satisfy the objective function, but it might not be applicable in real life and may lead to unstable experimentation conditions. In this paper, a new approach based on supervised clustering is presented for multimodal optimization. In the presented approach, k-means clustering algorithm is first used to cluster selected initial points. Next, the optimization is performed in a small radius surrounding these cluster centers. New cluster centers are then formed from select points of the previous iteration and the optimization results. The proposed technique aims to find multiple optimal solutions by taking advantage of the fact there is a high density of points with favorable objective value clustered around a peak or a valley. The presented approach is then applied for multimodal optimization of some benchmark problems as well as a case study of input parameters optimization of electrochemical micro-machining. The results from the benchmark problems and the case study validate the effectiveness and robustness of the proposed approach
AN IMPROVED APPROACH FOR SOLVING HIERARCHICALLY COUPLED CONSTRAINED OPTIMIZATION PROBLEM IN SIMULTANEOUS OPTIMIZATION OF NEURAL NETWORK STRUCTURE AND WEIGHTS
No full textNeural Networks (NN) structure is commonly determined before the training of its weights using a trial-and-error-based approach by an expert of the problem under consideration since there are no clear guidelines for selecting an optimal NN structure. The trial-and-error-based method can be very time-consuming and can also overlook possible optimal combinations due to the large amount of NN structure combinations available. To cope with these issues, the definitions developed by Zou et al. are used to identify the NN structure and weight optimization problem as a hierarchically coupled constrained optimization problem, and an improved version of the algorithm is developed and applied to find the optimal NN structure and weight values simultaneously. The proposed approach is then employed to build NN prediction models (referred to as NN-HCCOP) for a variety of case studies to test its validity. The results of the NN-HCCOP model are compared with five other prediction methods i.e., adaptive neuro-fuzzy inference system (ANFIS), ANFIS-firefly algorithm (ANFIS-FA), classical NN, regression analysis, and gaussian process regression analysis. The comparison results show that NN-HCCOP model can provide higher prediction accuracy in the majority of the testing scenarios compared to models built by other techniques
COMMENT ON “OPTIMAL SHELF-SPACE STOCKING POLICY USING STOCHASTIC DOMINANCE UNDER SUPPLY-DRIVEN DEMAND UNCERTAINTY”
Get PDFThe paper authored by Amit et al. [Amit, R. K., Mehta, P., & Tripathi, R. R. “Optimal shelf-space stocking policy using stochastic dominance under supply-driven demand uncertainty”, European Journal of Operational Research, 246(1), pp. 339-342 (2015)] proposes a newsvendor model in which demand depends on displayed inventory. Under stochastic dominance conditions, they claimed that the profit function is concave at its extremum points. In the present study, it is expressed that this statement is not generally precise