Journal of Quality Engineering and Production Optimization
Volume:7 Issue: 2, Summer-Autumn 2022

Modeling and measuring the demand variance propagation from the initial hours of the supply chain (SC) to the final hours is one of the most important challenges facing the logestics experts. Factors such as time delay increase demand fluctuations over the time in SC networks. This problem often referred to the Bullwhip Effect (BWE) in production systems. In this paper, a flight scheduling network as a supply chain are designed using Inverse Data Envelopment Analysis (IDEA). The effects of the arrival time of the aircraft (landing time) to the airport, as well as the delayed flights (depart time with delay) to the next destination on the demand variance were examined. The results show that demand fluctuations increased significantly by delaying flights in the closing hours. Also, the bullwhip effect due to landing time, at the beginning of the day are more than the final flight hours. This means that due to the higher demand during office hours (beginning of the day), there are many fluctuations in the variance of orders. Comparing the results with the control engineering approach, we found that the proposed method shows the hidden points (effect of flight shifts on involuntary oscillations) of the size of the bullwhip effect. While these hidden points have not been identified in previous methods.

Emerging technologies, such as vehicle-to-road infrastructure connectivity via wireless telecommunications systems, in addition to reducing the role of humans in driving activities, can meaningfully improve road performance compared to traditional traffic control systems. Today, automated vehicles (AVs), as an emerging player in modern urban transportation, would significantly influence customer satisfaction. For AVs, the optimal routes must be found by a decision support system. This problem becomes more challenging when a suitable route is concurrently chosen by the majority of vehicles and network congestion occurs. In this research, a mathematical model for seeking the optimal route and scheduling of AVs by considering road traffic is presented. GAMS software is used for solving and analyzing the mathematical model. The results for a sample example show that the optimal routes are successfully obtained for the AVs. Sensitivity analysis reveals that as traffic time increases, so do the cost and service time. This model calls for government agencies to allocate portions of road networks to AVs to regulate vehicle movement and thus increase the output and performance of the network.

In this paper, we offer an analysis and model of a manufacturing line that uses a priority mechanism to process various types of parts in faulty machinery. The manufacturing line comprises machines separated in a set order by storage rooms where components are fluxed. When it is possible, a machine works on the most important part first and only switches to less important parts if it is unable to produce the most important ones. Only one sort of function is required for each section. Because it is expected that the processing line machinery can handle a range of part types, switching from one kind of component to another will not result in any setup penalty. Only when unable to process higher priority parts owing to obstruction or hunger can the machines work on the lower priority parts. The machines function according to a fixed priority rule. The purpose of this study is to develop mathematical formulations and procedures for each kind of component in a flexible production line. In a variety of supply and demand scenarios, the multipart line's qualitative behavior is described.. To better understand the line, we devise decomposition equations and a solution technique to put them to use. With suitable line parameters, the method converges consistently. The findings of the decomposition were verified using simulations. The line's fascinating behavior may be seen in the system's study of many parameters.

This paper addresses optimal locating healthcare facilities problem regarding the essential role of these systems on expense and equity at the strategic level to decision-makers. As a result, a multi-objective model with a hierarchical structure and congestion consideration is proposed for the location issue, which is the main contribution of this study. A mixed-integer non-linear programming (MINLP) model is developed to reduce overall system expenses, such as setup, operating, travel costs, and total waiting time at facility levels, while concurrently maximizing the number of covered patients. Furthermore, two M/M/1/K and M/M/C/K queue systems are utilized at facility levels. Then, two LP-metric and Augmented epsilon-constraint methods are implied. Several examples are conducted and evaluated using statistical tests and the TOPSIS approach to assess the performance of the solution strategies. After that, a sensitivity analysis is carried out. The findings indicate that the proposed model may be used as a tool to assist decision-makers in the design of multi-level healthcare facilities.

In this study, we combine an interval type-2 fuzzy best-worst method (IT2FBWM) with the interval VIKOR method for the first time to evaluate and prioritize sustainable suppliers in circular supply chains. To weigh the criteria, an interval type-2 best worst approach is employed, and the interval VIKOR methodology is utilized to assess the suppliers in the presence of uncertainty. Risk is presented in all supply chain activities, and its occurrence affects all dimensions of the supply chain and can cause damage to them and, therefore, must be appropriately managed. A new mixed-integer linear programming model is then formulated to identify each risk's optimal strategy or response. The multi-objective model minimizes total costs and response time and maximizes risk responses to secondary and primary risks. An improved version of augmented ε-constraint method (AUGMECON2) is also employed to produce separate Pareto-optimal solutions. Finally, the suggested strategy is applied to four main suppliers in the food company. The findings of the proposed integrated approach demonstrate the applicability and efficiency in the food industry.

Over the past few years, understanding sustainability issues such as cost savings and pollution reduction in the industry has led to the design of closed-loop logistics networks with hybrid facilities Also, the occurrence of sudden disturbances and the damages caused by them has developed the use of reliability approaches The present study has applied the strategy of reliable support facilities in the multi-product forward-reverse logistics network and has used stochastic programming to model the disorder To face the decision-maker ambiguity in the confidence levels, the constraints, and objectives of the problem, and in continuation, to ensure the optimality of the above classes, flexible-robust combination programming has been employed, presented in the form of a mixed-integer linear mathematical programming model Then Benders decomposition algorithm is proposed to solve the model, which with a subset of optimization cuts and appropriate convergence rate, improved optimal solutions are produced for optimal planning path.

We aim to rank the factors influencing a customer's selection of Islamic banks. Islamic banks have an Islamic interest-free basis. Reviewing papers in the literature review shows a gap in the customer satisfaction area in the Islamic banking systems. Our contribution tries to fill this gap using three stages of KANO's analysis: 1) Identifying customers' preferences, classifying them based on their effect on satisfaction; 2) Computing a Satisfaction Increasing Index (SII) to find what happens if some expectations were met; 3) Computing a Dissatisfaction Decreasing Index (DII) to find what happens if these same expectations weren't met. Taking on a case study in Iran, studied 19 Factors. The finding shows that Islamic banking attributes are, by nature, required in Islamic banks. However, this does not make them significant in increasing satisfaction or decreasing dissatisfaction. Contributuion of this study is identifying and analyzing the priorities of bank customers' requirements. The study findings can be of use to decision-makers in the Islamic banking industries by helping adjust the planning and marketing strategies, improving the allocation of resources, and attracting new customers.

In many practical cases, product or process quality is defined by frequency table of two or more qualitative variables. This frequency table is called contingency table. Monitoring the contingency tables is an area in statistical process control with many applications in industrial and service units.
On the other hand, reducing quality costs is the most fundamental issue preoccupying the minds of managers. It is clear that a quicker diagnosis of the assignable causes can reduce the quality costs. Estimating change point by limiting the probable interval of change, reduces the cost and time of detecting assignable causes. In this research, using maximum likelihood approach, the step and linear drift change points estimators are proposed for multivariate multi-nominal contingency tables. After the change point, parameters are estimated with making the average in the proposed step estimator, and using the linear regression in the proposed linear drift estimator. Results of the simulations demonstrated that the proposed step change point estimator carries out very well in all shift types and shift magnitudes from small to large. Furthermore, the proposed estimator of the linear drift change point has relatively good performance in moderate changes. Finally, the proposed estimators’ performance is assessed by a numerical example.

This paper considers the integrated order picking (joint order batching and picker routing) and delivery problem in a manual picker-to-parts and multi-block 3D warehouse with considering overbooking and delivery-delay allowed strategies. Received orders by the customers are grouped into the batches, assigned to the pickers with horizontal and vertical velocities to compute the travel time, picked up from the shelves of the warehouse, and delivered to customers’ community. The warehouse’s policy is to accept orders for a certain number of unavailable products in addition to the available products. Thus, the concept of the overbooking strategy for supplying unavailable products and the delivery postponed strategy for delayed delivery is applied. Hence, this study introduces a novel mathematical model to deal with such a system, where the objective aims to minimize the cost of the completion time of all batches, the purchasing of the unavailable products and the return time of all vehicles to the depot. To solve this model, four new heuristic algorithms are devised, a broad range of numerical experiments is investigated to illustrate the validity and applicability of the proposed model and solution approaches.

There is a great need to improve the classical inventory models so that they can address real-world problems more properly. The presence of multiple products and a variety of inventory items have complicated the inventory control process, so companies need to classify inventory items to reduce costs. On the other hand, the supplier selection problem is important, as there may be several suppliers with different options in the market. Also, several factors impact the demand for products and cause uncertainty for this parameter. This research develops a multi-product EPQ model that simultaneously classifies products, selects the best possible supplier for each group, and determines the replenishment policy under uncertainty in demand. To solve the proposed model, we present a simulation-optimization approach. This approach uses genetic and simulated annealing metaheuristic algorithms to solve the problem. Also, there is a simulation module that helps the algorithm to evaluate the fitness function. The parameters of algorithms are tuned by employing the Taguchi method. The results are analyzed for three categories of examples. Finally, the sensitivity of the objective function to the input parameters is also analyzed. We found that the system's total cost is highly sensitive to products unit holding cost.

The Markov chain is widely used in state-dependent inventory control of spare parts because of its ability to model the gradual degradation process of components and predict their condition. Also, according to previous studies, considering system information causes a significant reduction in costs. Therefore, the present study tries to extract the system information using a machine learning algorithm and provide it as a transition matrix to the Markov decision process (MDP) to determine the future states of the critical spare parts inventory system. In the presented method, the machine learning algorithm, here Adaptive Neuro-Fuzzy Inference System (ANFIS), is in charge of the training data. The Markov chain uses the trained data to predict the future states of the inventory system. For this purpose, four states have been considered, each representing a level of tension and demand in the inventory system. Applying the model to the data collected for a critical component showed that the model has good accuracy in predicting the following states of the system. Also, the presented model offers a lower error rate, RMSE, and MAPE, compared to the ARIMA model for predicting the next state of the inventory system

In this study a scenario-based multi-objective fuzzy model was provided in the SCLSC , which in addition to three aspects of sustainability including, social impact such as the creation of job opportunities, customer satisfaction, and so on, environmental impact such as reducing air pollution, and so on, economic impact such as reducing cost, increasing the reliability of the SC and product routing have been modeled. Two algorithms, including MOPSO and NSGA-II Algorithms, were applied to solve the proposed model. After tuning their parameters by the Taguchi method, their performance in problems with different dimensions were tested followed by evaluating them by powerful criteria. The proposed model was implemented on Chipboard Pooya Company in Iran in two scenarios of economic recession and prosperity aimed at evaluating its accuracy. A sensitivity analysis was eventually performed on the proposed model followed by making some suggestions to develop the model.