A Study of Time Series Model for Predicting Jute Yarn Demand: Case Study

In today’s competitive environment, predicting sales for upcoming periods at right quantity is very crucial for ensuring product availability as well as improving customer satisfaction. This paper develops a model to identify the most appropriate method for prediction based on the least values of forecasting errors. Necessary sales data of jute yarn were collected from a jute product manufacturer industry in Bangladesh, namely, Akij Jute Mills, Akij Group Ltd., in Noapara, Jessore. Time series plot of demand data indicates that demand fluctuates over the period of time. In this paper, eight different forecasting techniques including simple moving average, single exponential smoothing, trend analysis, Winters method, and Holt’s method were performed by statistical technique using Minitab 17 software. Performance of all methods was evaluated on the basis of forecasting accuracy and the analysis shows that Winters additive model gives the best performance in terms of lowest error determinants. This work can be a guide for Bangladeshi manufacturers as well as other researchers to identify the most suitable forecasting technique for their industry.

Effectiveness of Physical Barriers Installation for Prevention of Incidents in Mexico City’s Subway System

Nowadays, suicides inside the installations of subway platforms are considered a public health problem in Mexico City. One solution to prevent them is the installation of physical barriers, but their high cost is unattractive for governmental authorities. Traditional approaches of research on the effectiveness of physical barriers for preventing suicides have been limited to analyzing statistically the effects of installing platform screen doors and blue lights on subway platforms. Although considerable progress has been made in this field, many important issues remain unexplored. This study investigates the effectiveness of physical barriers installation for prevention of incidents in Mexico City subway system by means of an agent-based simulation model. Firstly, the design of physical barriers for prevention of incidents in Mexico City subway system is described. Secondly, a conceptual model of the Zócalo station subway platform is presented. Thirdly, an agent-based simulation model of Zócalo station subway platform is implemented using AnyLogic™ software considering normal operations of the subway station. This study shows that physical barriers installation on the Zócalo subway platform can effectively prevent 76% of passenger’s suicides.

Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy Minimizing Vehicle Routing Problem

During the last decade, tremendous focus has been given to sustainable logistics practices to overcome environmental concerns of business practices. Since transportation is a prominent area of logistics, a new area of literature known as Green Transportation and Green Vehicle Routing has emerged. Vehicle Routing Problem (VRP) has been a very active area of the literature with contribution from many researchers over the last three decades. With the computational constraints of solving VRP which is NP-hard, metaheuristics have been applied successfully to solve VRPs in the recent past. This is a threefold study. First, it critically reviews the current literature on EMVRP and the use of metaheuristics as a solution approach. Second, the study implements a genetic algorithm (GA) to solve the EMVRP formulation using the benchmark instances listed on the repository of CVRPLib. Finally, the GA developed in Phase 2 was enhanced through machine learning techniques to tune its parameters. The study reveals that, by identifying the underlying characteristics of data, a particular GA can be tuned significantly to outperform any generic GA with competitive computational times. The scrutiny identifies several knowledge gaps where new methodologies can be developed to solve the EMVRPs and develops propositions for future research.

Inventory Control Systems Model for Strategic Capacity Acquisition

Variation of installed industrial capacity has been found to follow a cyclic pattern. This paper discusses the application of control theory to the problem of the timely acquisition of extra production capacity. The control system based model presented here is compared with a System Dynamics model proposed by Sterman. Key differences are the method of implementing rational decisions about deployment of extra capacity and the use of a nonlinear APVIOBPCS inventory model. Benefits of this new model are a more measurable process and the ability to select parameter values to optimise capacity deployment. Simulation of the model indicates that the results found by Sterman underestimate the production backlog and time taken to reach equilibrium. The use of a Proportional, Integral, and Derivative (PID) controller in the capacity control loop model illustrates that it is possible not only to alter the backlog levels but at the same time to reduce the sales force and improve the revenue. The model also shows clearly that the impact of not increasing capacity promptly results in catastrophic failure of sales as a structural, rather than a business, problem. This model is simple enough to be implemented as a spreadsheet for use as a guide by managers.

A Framework to Measure the Service Quality of Distributor with Fuzzy Graph Theoretic Approach

A combination of fuzzy logic and graph theoretic approach has been used to find the service quality of distributor in a manufacturing supply chain management. This combination is termed as the fuzzy graph theoretic (FGT) approach. Initially the identified factors were grouped by SPSS (statistical package for social science) software and then the digraph approach was applied. The interaction and inheritance values were calculated by fuzzy graph theory approach in terms of permanent function. Then a single numerical index was calculated by using permanent function which indicates the distributor service quality. This method can be used to compare the service quality of different distributors.

Development of a Pneumatically Driven Cell for Low Cost Automation

The present work addresses the development of a pneumatically driven manufacturing cell for low cost automation applications. This cell can be used in innovative applications as a low cost alternative to increase production and quality in industry. The state of the art shows that technological advances in computing have made possible a drop in equipment prices, making them more accessible. The aim of this work is to develop automation through a classic methodology for a manufacturing cell to minimize errors and facilitate the sequential logic conception. This experimental prototype has been developed at the UNIJUI with financial support by public organizations and companies. Pneumatic actuator used in bench driven has the following advantages: its maintenance is easy and simple, is of relatively low cost, self-cooling properties, and good power density (power/dimension rate), and is fast acting with high acceleration and installation flexibility. However, there are difficulties of control logic due to the complex systems. The sequential controller strategy design considers the pneumatic system, experimental results, and performance of the proposed control strategy.

Development of a Layout Model Suitable for the Food Processing Industry

The food processing industry is a subset of the manufacturing sector with unique challenges. Among these, ensuring food hygiene and preventing contamination are two issues of prime importance. Hence, designers have to overcome such challenges when designing facilities suitable for food processing. The paper formulates a model that simplifies the layout planning process for the food processing facilities (FPF) in order to help the designers. A generalised framework that helps to visualise the Facilities Layout Problem (FLP) was initially developed. A layout model for FPF was then developed considering the unique features that need to be present in the layout. The model classifies the areas of FPF into five: primary, secondary, utilities, warehouse, and administration based on the activities and the level of risk present in food processing. It further proposes specific locations for the five sections in the factory layout. In order to test the performance of the framework and the model, a case study was conducted in a malted milk powder processing facility in Sri Lanka by changing its layout according to the proposed model. The framework and the model showed promise in its implementation. However, the approach and the layout model need to be evaluated in further cases in order to ascertain their usability and performance.

Application of Systems Simulation for Predicting and Optimising Energy Requirements for HDF Production

Even though simulation has been used so far in manufacturing facilities for modelling supply chain management, production management, and business processes, its applications in managing the energy consumption within manufacturing companies represent a new and innovative research domain. This prompted the research undertaken for the work present in this paper. The main focus of this research is to analyse production management in a manufacturing facility and correlate it with the energy consumption. The research initially concentrates on different simulation methodologies and their application in the current manufacturing domain. Literature relating to the correlation of energy consumption with production management has also been reviewed. This review identified very few previous instances where simulation tools were used to predict the energy consumption in a manufacturing facility. This research brings a novel approach to investigating the adaptability of industrial simulation processes and tools for modelling the energy consumption with respect to a variable production output. The end result of this investigation consists of a better understanding of the production system and the energy losses that were captured by the simulation model.

A Quality Function Deployment (QFD) Approach in Determining the Employer’s Selection Criteria

The industrial training program is part of the academic curriculum at the tertiary level. It plays an important role in providing students with the exposure to a real working environment in the industry. Through this program, the higher education institutions (HEI) could identify the gaps in the curriculum based on the requirements from the industry. In addition, the feedback from the industry will help the HEI to equip students with relevant skills according to the demands from the industry. Indirectly, it can help to address the issue of unemployed graduates of which the number seems to increase from time to time. Therefore, this study aims to obtain the feedback from the employers out there in order to determine the most preferred criteria in selecting students for the industrial training placement by using Quality Function Deployment (QFD) approach. The findings, through the development of house of quality based on the QFD approach, show that communication skills and students’ participation in sports and cocurricular activities at their university are the most preferred selection criteria. Therefore, the QFD approach can be used to translate the employers’ feedback in improving the marketability of the students in the industry.

Developing an Inspection Optimization Model Based on the Delay-Time Concept

Infrastructures are considered as important facilities required for every country and society to be able to work properly. Aging and deterioration of such structures during their lifetime are a major concern both for maintenance researchers in the academic world and for the practitioners. This concern is mainly because the deterioration increases the maintenance costs dramatically and lowers the reliability, availability, and safety of the structural system. Preventive maintenance and inspection activities are the most usual means for keeping the structure in a good condition. This paper utilizes the concept of delay-time for developing the optimal inspection policy for deteriorating structures. In the proposed stochastic model, discrete times of inspection activities are taken as the decision variables of an optimization problem, in a way that the obtained aperiodic (nonuniform) inspection schedule minimizes the total downtime ratio of the structure. To illustrate the model capabilities, various numerical examples are solved and results are compared with the traditional periodic (uniform) inspection policies. The results indicate the substantial reduction in system downtime due to the wisely planned inspection schedule and the appropriate utilization of delay-time concept, which is indeed a powerful framework for inspection optimization problems.