International Journal of Industrial Engineering: Theory, Applications and Practice
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MULTI-OBJECTIVE INVENTORY AND ROUTING MODEL FOR A MULTI-PRODUCT AND MULTI-PERIOD PROBLEM OF VETERINARY DRUGS
No full textIn this study, a multi-objective, multi-product, and multi-period inventory management and routing model was designed for perishable products with a focus on veterinary drugs. The proposed model is a four-objective model to achieve a balance between the costs of drug distribution. The first and second objective functions are related to inventory management, covering inventory costs and demand forecast error, respectively. The third and fourth objective functions involve routing to minimize the transportation costs and greenhouse gas emissions, respectively. Data on three types of drugs were collected for 25 periods (weeks) from one drug distribution center and nine pharmacies in Iran. Non-dominated Sorting Genetic Algorithm-II was employed to find a satisfactory solution. The meta-heuristic method was combined with a Multi-layer Perceptron artificial neural network algorithm to forecast the demand. The model was implemented in MATLAB software. The results show the ability of the proposed model and algorithm to find high-quality solutions
GAME-THEORETIC MODELS FOR WARRANTY AND POST-WARRANTY MAINTENANCE WITH RISK-AVERSE SERVICE PROVIDERS
No full textIn this paper, a warranty-maintenance service contract is designed between a manufacturer and third-party agent who provide warranty and maintenance services respectively and may suffer from financial risks due to the demand uncertainty from consumers. We model the utility functions for the firms considering uncertain demand, risk attitude, and different options of warranty and maintenance service strategies. By using game theory, optimal sale price and warranty period for the manufacturer, whereas the optimal repair price or maintenance price for the third-party agent is explicitly derived by maximizing their expected utilities. Analytical results show that a more risk-averse manufacturer (or third-party agent) sets a lower price and gets lower utility as compared to a risk-neutral manufacturer (or third-party agent), which consequently leads to increases in product demand. The lower price decision of a more risk-averse player benefits a less risk-averse counterpart competitor in the market to set a higher price and get maximum utility. A numerical example is presented to illustrate the results
ASSESSING LEAN AND GREEN COEXISTENCE, AN APPROACH BASED ON TRIZ AND DISCRETE EVENT SIMULATION TECHNIQUE
No full textNowadays, Lean supply chain managers are seeking Green implementation, which is no longer a choice but an obligation; to comply with government regulations about the environmental footprint, to preserve the image, and to satisfy customers. Although several research works have been conducted in the framework of Lean and Green, the relationship between both paradigms remains ambiguous at a conceptual level. The purpose of this research work is to provide managers and decision-makers with a formal, relevant, and generalizable approach that explicit how to accurately assess Lean and Green coexistence. We propose the approach based on the Theory of Inventive Problem Solving (TRIZ) and Discrete Event Simulation technique (DES). Integrating this technique amounts to its ability to describe complex systems and their performance. Besides, DES is time-saving, cost-saving, and labor-saving. An academic case study is presented in order to demonstrate the applicability of the proposed approach. The deployment of the approach revealed that Lean and Green aspects could be the antagonist. Indeed, thinking Green is not necessarily Lean and vice versa, which supports that Lean Green supply chain challenges may surpass classic optimization techniques and methods; hence, its management requires innovative approaches. In this setting, our findings highlight the importance of integrating TRIZ. The developed framework in this research work stands out for its simplicity, flexibility, and generalizability
MULTI-OBJECTIVE OPTIMIZATION STRATEGY BASED ON ENTROPY WEIGHT, GREY CORRELATION THEORY, AND RESPONSE SURFACE METHOD IN TURNING
No full textMachining with low energy consumption, high efficiency, and processing quality has become the preferred processing strategy for manufacturing enterprises. An effective multi-objective optimization method can help to formulate a good machining strategy. Gray correlation analysis is a powerful tool for formulating a multi-objective optimization strategy, and the parameters combination can be evaluated by the grey correlation degree. In multi-objective optimization problems, the weights assignment techniques are a key factor for making a decision. However, the conventional grey correlation analysis method ignores the weight of each optimization objective. Besides, the grey correlation analysis can only be carried out in the range of training samples, which will lead to inaccurate optimization results. Therefore, this paper develops a multi-objective optimization strategy based on entropy weight, grey correlation theory, and response surface method to improve the original defects and formulate the best machining strategy in turn. Firstly, in the orthogonal experiments of turning AISI 1045 steel with three factors and five levels, the machine tool-specific energy, surface roughness, and processing efficiency are taken as optimization objectives. Then the weight of optimization objectives is determined by entropy theory. Finally, the response surface method is introduced to establish the grey correlation degree prediction model according to the entropy results. Based on the particle swarm optimization algorithm, the optimum processing strategy is obtained without the limitation of training samples. The results illustrate that cutting depth is the most significant factor affecting grey correlation degree, and the grey correlation degree increases by 14.01% on the initial basis. The research establishes a mathematical model with grey correlation degree as the objective function, which provides a new idea for formulating a multi-objective optimization strategy
Time and Motion Study For Affordable Traffic Data Collection System For The State of Florida
Get PDFThe Florida Department of Transportation initiated and funded development of electronic crash and citation reporting for the State of Florida using the TraCS (Traffic and Criminal Software) platform. Seven Florida law enforcement agencies were selected as a test-bed for electronic crash and citation reporting. The agencies were provided with required equipment and training to use the TraCS software for the data collection process. Ride-alongs were conducted with law enforcement personnel in two of those agencies and a time and motion study was used to compare the time to complete crash and citation forms both with and without the TraCS software. The time and motion study showed improvement in efficiency and accuracy of Florida traffic records using the electronic data collection system (TraCS). The efficiency of officer’s using TraCS differed based on the learning curve, equipment provided, and mindset of officers
VARIABLE SAMPLING INTERVAL SYNTHETIC CONTROL CHARTS FOR JOINTLY MONITORING PROCESS MEAN AND STANDARD DEVIATION
No full textThis paper proposes a synthetic control chart for jointly monitoring the shifts in the mean and/or standard deviation of a normally distributed process. The synthetic chart is a combination of the Max chart and the conforming run length (eRL) chart. The Max chart can be treated as a special case of the synthetic chart. The operation, design, and performance of this control chart are described. Comparisons of the average run length (ARL) performance of the synthetic chart, the Max chart and the traditional joint X and S charts indicate that the synthetic chart outperforms the other two charts. The variable sampling interval (VSI) schemes as an enha.ncement to the synthetic chart are discussed to further improve the chart performance. A numerical example is given to illustrate the application of synthetic chart and its VSI scheme
Artificial Neural Networks for Finite Capacity Scheduling: A Comparative Study
No full textIn this study artificial neural networks are applied for finite capacity scheduling. Utilisation of artificial neural networks on solving finite scheduling problems is examined. Also a comparative model is proposed by using multi layer perceptron (MLP) neural networks and branch-and-bound algorithm, and carried out to solve a real world problem in a job shop scheduling system
AN APPROACH FOR MODELING THE RISK TRANSFORMATION PROCESSES
Get PDFOne of the most critical issues that every decision maker needs to face is the risk in association with the decisions to be finalized and the actions to be taken. It is not only due to the future uncertainties involved, but also the decision maker’s inability of adapting himself to the changing environments. Formulating better plan and finding best personnel to execute the plan may potentially reduce the possibility of risk occurrence. However, it is still not possible for the decision maker to eliminate all risks that will potentially affect the outcome of the decision, because an exhaustive list of risk events is difficult to obtain. Previous researches seldom focused on the risk transformation phenomenon, and therefore cannot provide a complete and overall exploration of risk management. The objective of this study is to develop a model that can predict the transformation of the risk that may be exhibited during the execution of a decision and an operation.
MAKING PRODUCT CUSTOMIZATION PROFITABLE
Get PDFThe main result presented in this paper is the Framework for Product Family Master Plan. This framework supports the identification of a product architecture for companies that customize products and services. The framework has five coherent aspects, the market, product assortment, supply-production, organization and work processes. One of the unique results is that these aspects are linked, which make it possible to make explicit recommendations for an architecture (the way a product family should be structured with clear interfaces), architecture elements and consequences. By means of a case study it is shown that the potential EBIT (Earning Before Interests and Taxes) improvement of the case company is 10%
REPRESENTATION AND PERFORMANCE ANALYSIS OF MANUFACTURING CELL BASED ON GENERALIZED STOCHASTIC PETRI NET
No full textTo achieve high flexibility and agility for rapidly changing customer's demand, Petri net has been adopted as a modeling and performance analysis tool. The Purpose of this paper is to propose modeling and performance analysis schemes of flexible manufacturing line using a generalized stochastic Petri net. The manufacturing line can be represented using workflows which are composed of bill of material and processes. Bill of process shows the precedence of processes and the relationship among the manufacturing and assembly operations. An algorithm generating a Petri net from bill of material database is proposed. The scheme of generalized stochastic Petri net utilizing both immediate and exponential distributed transitions are adopted to model a manufacturing cell with flexible machines, assembly lines and buffers. Performance analyses are performed based on qualitative and quantitative properties. For the qualitative analysis, behavioral and structural analyses are applied. Quantitative analyses are conducted using a Petri net simulator