Journal Of Industrial Engineering International
Volume:14 Issue: 4, Autumn 2018

This study considers instant decision-making needs of the automobile manufactures for resequencing vehicles before final assembly (FA). We propose a rule-based two-stage stochastic model to determine the number of spare vehicles that should be kept in the pre-assembly buffer to restore the altered sequence due to paint defects and upstream department constraints. First stage of the model decides the spare vehicle quantities, where the second stage model recovers the scrambled sequence respect to pre-defined rules. The problem is solved by sample average approximation (SAA) algorithm. We conduct a numerical study to compare the solutions of heuristic model with optimal ones and provide following insights: (i) as the mismatch between paint entrance and scheduled sequence decreases, the rule-based heuristic model recovers the scrambled sequence as good as the optimal resequencing model, (ii) the rule-based model is more sensitive to the mismatch between the paint entrance and scheduled sequences for recovering the scrambled sequence, (iii) as the defect rate increases, the difference in recovery effectiveness between rule-based heuristic and optimal solutions increases, (iv) as buffer capacity increases, the recovery effectiveness of the optimization model outperforms heuristic model, (v) as expected the rule-based model holds more inventory than the optimization model.

This paper addresses a drayage problem, which is motivated by the case study of a real carrier. Its trucks carry one or two containers from a port to importers and from exporters to the port. Since up to four customers can be served in each route, we propose a set-covering formulation for this problem where all possible routes are enumerated. This model can be efficiently solved to optimality by a commercial solver, significantly outperforming a previously proposed node-arc formulation. Moreover, the model can be effectively used to evaluate a new distribution policy, which results in an enlarged set of feasible routes and can increase savings w.r.t. the policy currently employed by the carrier.

This research is collaborative investigation with the general-purpose motor manufacturer. To review construction method in production process, we applied the parameter design method of quality engineering and tried to approach the optimization of construction method. Conventionally, press-fitting method has been adopted in process of fitting rotor core and shaft which is main component of motor, but quality defects such as core shaft deflection occurred at the time of press fitting. In this research, as a result of optimization design of “shrink fitting method by high-frequency induction heating” devised as a new construction method, its construction method was feasible, and it was possible to extract the optimum processing condition.

The advance in the global environment, rapidly changing markets, and information technology has created a new stage for design. In such an environment, one strategy for success is the Collaborative Product Development (CPD). Organizing people effectively is the goal of Collaborative Product Development, and it solves the problem with certain foreseeability. The development group activities are influenced not only by the methods and decisions available, but also by correlation among personnel. Grouping the personnel according to their correlation intensity is defined as collaboration space division (CSD). Upon establishment of a correlation matrix (CM) of personnel and an analysis of the collaboration space, the genetic algorithm (GA) and minimum description length (MDL) principle may be used as tools in optimizing collaboration space. The MDL principle is used in setting up an object function, and the GA is used as a methodology. The algorithm encodes spatial information as a chromosome in binary. After repetitious crossover, mutation, selection and multiplication, a robust chromosome is found, which can be decoded into an optimal collaboration space. This new method can calculate the members in sub-spaces and individual groupings within the staff. Furthermore, the intersection of sub-spaces and public persons belonging to all sub-spaces can be determined simultaneously.

This paper adopts a modified approach of data envelopment analysis (DEA) to measure the academic efficiency of university departments. In real-world case studies, conventional DEA models often identify too many decision-making units (DMUs) as efficient. This occurs when the number of DMUs under evaluation is not large enough compared to the total number of decision variables. To overcome this limitation and reduce the number of decision variables, multi-objective data envelopment analysis (MODEA) approach previously presented in the literature is applied. The MODEA approach applies Shapley value as a cooperative game to determine the appropriate weights and efficiency score of each category of inputs. To illustrate the performance of the adopted approach, a case study is conducted in a university in the Philippines. The input variables are academic staff, non-academic staff, classrooms, laboratories, research grants, and department expenditures, while the output variables are the number of graduates and publications. The results of the case study revealed that all DMUs are inefficient. DMUs with efficiency scores close to the ideal efficiency score may be emulated by other DMUs with least efficiency scores.

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg–Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg–Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.

Ceramic is one of the highly competitive products in Thailand. Many Thai ceramic companies are attempting to know the customer needs and perceptions for making favorite products. To know customer needs is the target of designers and to develop a product that must satisfy customers. This research is applied Kansei Engineering (KE) and Data Mining (DM) into the customer driven product design process. KE can translate customer emotions into the product attributes. This method determines the relationships between customer feelings or Kansei words and the design attributes. Decision tree J48 and Class association rule which implemented through Waikato Environment for Knowledge Analysis (WEKA) software are used to generate a predictive model and to find the appropriate rules. In this experiment, the emotion scores were rated by 37 participants for training data and 16 participants for test data. 6 Kansei words were selected, namely, attractive, ease of drinking, ease of handing, quality, modern and durable. 10 mugs were selected as product samples. The results of this study indicate that the proposed models and rules can interpret the design product elements affecting the customer emotions. Finally, this study provides useful understanding for the application DM in KE and can be applied to a variety of design cases.

In today’s global environment, technology is constantly evolving. Being able to stay up-to-date with the very latest technological advances can be extremely hard to accomplish. As a result of these changes and developments in technology, which often come unexpectedly, consumers are frequently tempted to update their devices to the very latest model. The result is that the life cycle of a product is becoming shorter and shorter than before. Manufacturers attempt to respond to consumers’ concerns involving environmental issues as well as the more governmentally stringent environmental legislations by establishing facilities which include the minimization of the totality of waste relocated to landfills by recovering materials and components from returned, or End-Of-Life products and reuse them to build a remanufactured product, and/or novel components. With the rapid growth of interest in remanufactured products’ market, offering warranty for remanufactured products and components is becoming a necessity for remanufacturer in order to meet customers’ requirement and as a marketing mechanism. During that process, maintenance policies are of great importance in order to reduce the warranty cost on the remanufacturer. In this paper, an optimization simulation model for remanufactured items sold with one-dimensional non-renewing money-back guarantee (MBG) warranty policy is proposed from the view of remanufacturer, in which, an End-Of-Life product is subjected to upgrade action at the end of its past life and during the warranty period, preventive maintenance actions are carried out when the remaining life of the product reaches a pre-specified value so that the remanufacturer’s expected profit can be maximized. Finally, a numerical example and design of experiment analysis are provided to demonstrate the proposed approach.

The use of the simulation-based technique in facility layout has been a choice in the industry due to its convenience and efficient generation of results. Nevertheless, the solutions generated are not capable of addressing delays due to worker’s health and safety which significantly impact overall operational efficiency. It is, therefore, critical to incorporate ergonomics in facility design. In this study, workstation analysis was incorporated into Promodel simulation to improve the facility layout of a garment manufacturing. To test the effectiveness of the method, existing and improved facility designs were measured using comprehensive risk level, efficiency, and productivity. Results indicated that the improved facility layout generated a decrease in comprehensive risk level and rapid upper limb assessment score; an increase of 78% in efficiency and 194% increase in productivity compared to existing design and thus proved that the approach is effective in attaining overall facility design improvement.

In the past few years, there has been an increasing interest in developing project control systems. The primary purpose of such systems is to indicate whether the actual performance is consistent with the baseline and to produce a signal in the case of non-compliance. Recently, researchers have shown an increased interest in monitoring project’s performance indicators, by plotting them on the Shewhart-type control charts over time. However, these control charts are fundamentally designed for processes and ignore project-specific dynamics, which can lead to weak results and misleading interpretations. By paying close attention to the project baseline schedule and using statistical foundations, this paper proposes a new ex ante control chart which discriminates between acceptable (as-planned) and non-acceptable (not-as-planned) variations of the project’s schedule performance. Such control chart enables project managers to set more realistic thresholds leading to a better decision making for taking corrective and/or preventive actions. For the sake of clarity, an illustrative example has been presented to show how the ex ante control chart is constructed in practice. Furthermore, an experimental investigation has been set up to analyze the performance of the proposed control chart. As expected, the results confirm that, when a project starts to deflect significantly from the project’s baseline schedule, the ex ante control chart shows a respectable ability to detect and report right signals while avoiding false alarms.

Automated storage and retrieval systems (AS/RSs) are material handling systems that are frequently used in manufacturing and distribution centers. The modelling of the retrieval–travel time of an AS/RS (expected product delivery time) is practically important, because it allows us to evaluate and improve the system throughput. The free-fall-flow-rack AS/RS has emerged as a new technology for drug distribution. This system is a new variation of flow-rack AS/RS that uses an operator or a single machine for storage operations, and uses a combination between the free-fall movement and a transport conveyor for retrieval operations. The main contribution of this paper is to develop an analytical model of the expected retrieval–travel time for the free-fall flow-rack under a dedicated storage assignment policy. The proposed model, which is based on a continuous approach, is compared for accuracy, via simulation, with discrete model. The obtained results show that the maximum deviation between the continuous model and the simulation is less than 5%, which shows the accuracy of our model to estimate the retrieval time. The analytical model is useful to optimise the dimensions of the rack, assess the system throughput, and evaluate different storage policies.

Wire electric discharge machining (WEDM) is one of the advanced machining processes. Response surface methodology coupled with Grey relation analysis method has been proposed and used to optimise the machining parameters of WEDM. A face centred cubic design is used for conducting experiments on high speed steel (HSS) M2 grade workpiece material. The regression model of significant factors such as pulse-on time, pulse-off time, peak current, and wire feed is considered for optimising the responses variables material removal rate (MRR), surface roughness and Kerf width. The optimal condition of the machining parameter was obtained using the Grey relation grade. ANOVA is applied to determine significance of the input parameters for optimising the Grey relation grade.

This study models a joint pricing, inventory, and preservation decision-making problem for deteriorating items subject to stochastic demand and promotional effort. The generalized price-dependent stochastic demand, time proportional deterioration, and partial backlogging rates are used to model the inventory system. The objective is to find the optimal pricing, replenishment, and preservation technology investment strategies while maximizing the total profit per unit time. Based on the partial backlogging and lost sale cases, we first deduce the criterion for optimal replenishment schedules for any given price and technology investment cost. Second, we show that, respectively, total profit per time unit is concave function of price and preservation technology cost. At the end, some numerical examples and the results of a sensitivity analysis are used to illustrate the features of the proposed model.

Automated Guided Vehicle System (AGVS) provides the flexibility and automation demanded by Flexible Manufacturing System (FMS). However, with the growing concern on responsible management of resource use, it is crucial to manage these vehicles in an efficient way in order reduces travel time and controls conflicts and congestions. This paper presents the development process of a new Memetic Algorithm (MA) for optimizing partitioning problem of tandem AGVS. MAs employ a Genetic Algorithm (GA), as a global search, and apply a local search to bring the solutions to a local optimum point. A new Tabu Search (TS) has been developed and combined with a GA to refine the newly generated individuals by GA. The aim of the proposed algorithm is to minimize the maximum workload of the system. After all, the performance of the proposed algorithm is evaluated using Matlab. This study also compared the objective function of the proposed MA with GA. The results showed that the TS, as a local search, significantly improves the objective function of the GA for different system sizes with large and small numbers of zone by 1.26 in average.