Batch Sizing in Sustainable Production Systems with Imperfect Quality

In classic Economic Production Quantity (EPQ), an optimal batch size is determined to minimize total production cost including setup and inventory holding costs, and defective parts are not allowed. In this paper an imperfect EPQ system is studied to minimize the overall cost, where setup cost, scarp rate, batch size, and electrical power demand are determined by the model. In this imperfect production system, a percentage of the batch is defective in each cycle, which will be reworked at an extra charge. In addition, the model considers electrical power demand charge which accounts for a large percentage of industrial utility bills. This framework also determines the optimal level of investment on system design and flexibility which in turn, is a function of setup cost, electrical power requirement (power demand), and scrap rate. The proposed constrained cost minimization problem is formulated as a nonlinear mathematical model, and is solved using a posynomial Geometric Programming (GP) approach to present a closed form solution for the batch size, setup cost, allowable defective rate and power requirements. The model is illustrated through a numerical example and some sensitivity analysis is performed

Active learning in supply chain management course

Active Learning in the Supply Chain Management Course. This paper presents a new active learning approach implemented in the Supply ChainManagement (SCM) course in the Industrial and Manufacturing Engineering Department.Previously, in this course the fundamentals of supply chain and logistics, drivers of supply chainperformance and analytical tools necessary to develop solutions for a variety of SCM and designproblems were mainly covered through class lectures and case study discussions. In the past fewyears, due to the growth in the needs of the organizations to “Lean” principles, the course wasmodified to satisfy this requirement more efficiently. For this purpose a new hands on experienceworkshop was utilized where the students could physically simulate the implementation of leanprinciples in a supply chain network. Through this simulation, students learned the fundamentalconcepts of a supply chain such as demand management, inventory management, role ofinformation system and coordination, transportation, and finance and accounting. In addition thestudents had the opportunity to actively practice the lean concepts such as Kanban, pull andpush, just-in-time production systems, and product and process design by being physicallyinvolved in a teamwork educational game. This simulation game could enhance materialretention and foster critical thinking among the students. Moreover, several directedpresentations by speakers invited from diverse industries and ISM (Institute of Supply ChainManagement) were arranged to expose the students to some real case studies and then someassignments were defined to promote higher order of thinking. To assess the effectiveness of thecourse restructure and the applied pedagogical methods, a survey is conducted to measurestudents’ satisfaction and evaluate their perception of knowledge about the lean supply chainmanagement, and the results are analyzed

Developing Entrepreneurial Mindset in Industrial Engineering Classes: A Case Study

Instilling entrepreneurial mindset among engineering students is one of the challenges in engineering education. This paper presents the efforts to improve a core undergraduate industrial engineering course, Designing Value in Supply Chain, to infuse entrepreneurial thinking among students using an internally funded grant by Kern Entrepreneurial Engineering Network (KEEN). For this purpose, three new course modules are designed and their effectiveness on student learning is evaluated. This course is ideal for establishing entrepreneurially minded learning (EML) as a systematic approach is required for managing the chain of supply, especially since the impacts of the decisions are not isolated and will be spread out through the entire chain. In addition, creative multidisciplinary knowledge is required to address most of the supply chain challenges. The proposed modules are expected to promote students’ creative thinking, curiosity, collaboration and communication skills, and enable them to identify the opportunities where they can apply their technical skills to create value in the community based on customers’ expectations. These factors are key pillars of EML as proposed by KEEN. In the first course module, students propose a new product to be released to the market (idea generation). They complete this module as the product moves toward the end user in the supply chain following the concepts they learn during the term. This module enables the students to observe the domino impact of the decisions they make in the initial stages of supply chain and enhances structured learning experience by linking different concepts. In the second module, in order to expose the students to real life applications of the course content, wireless consumption data provided by students is used to practice different demand forecasting methods. Students also need to provide some economic analysis to choose the best solution alternative regarding their forecasted values. This module makes the learning process more meaningful as the learners observe a real life application of the subject. In the third module, students practice energy management in order to minimize energy waste as one of the most important types of waste in lean production systems. In this module, they are expected to determine several sources of energy waste on campus and propose action plans, and estimate the economic impact of their solution. As a result of this project, students learn how to create value and communicate an engineering solution in terms of economic benefits. Students provide a report for each module which is graded based on designed rubrics. All these modules are performed in teams which in turn improves students’ team work and collaboration skills. This paper elaborates the details of each module and learning outcomes, and presents the student evaluation results, and at the end discusses the lessons learned

Optimizing Production Schedule with Energy Consumption and Demand Charges in Parallel Machine Setting

Environmental sustainability concerns, along with the growing need for electricity and associated costs, make energy-cost reduction an inevitable decision-making criterion in production scheduling. In this research, we study the problem of production scheduling on nonidentical parallel machines with machine-dependent processing times and known job release dates to minimize total completion time and energy costs. The energy costs in this study include demand and consumption charges. We present a mixed-integer nonlinear model to formulate the problem. The model is then linearized and its performance is tested through numerical experiments

Comparing Three Instructional Modes for an Engineering Economy Course

This study compares three instructional modes in an “Engineering Economy” course: online, face-to-face (FtF), and flipped. Engineering Economy is a core course in this study and incorporates students with diverse backgrounds from different engineering majors. To discern the relation between student characteristics and teaching modality, an online questionnaire was designed for each mode and distributed over a two-year period. Data was collected and several statistical analyses were conducted to study the relationship between pedagogical delivery modes and various student-based factors such as gender, age, course load, living distance from campus, computer skills, work status, and first language. Students’ performance, persistence, and knowledge self-evaluation were also compared in different modes. The statistical analyses of data at 95% confidence level show that among all the factors, only the ratio of native English speakers, course load and work category differ significantly in different instructional modes. No statistically significant difference was observed between different modes for other factors

Fostering student engagement through a real-world, collaborative project across disciplines and institutions

Ample research has identified several features of a learning experience likely to enhance student learning, including collaboration, open-ended exploration, and problem-based learning in real-life scenarios. Missing is a model of how instructors might combine these elements into a single project that works flexibly across disciplines and institutions. This article fills this gap by offering such a model and reporting on its effectiveness in fostering student engagement. It describes a project that instructors at four colleges and universities in Flint, Michigan (USA) piloted during the height of the Flint water crisis. The project asked students to apply class content to the real-world problem unfolding around them, and offered students an opportunity to collaborate with peers. We collected qualitative and quantitative data on students’ reactions to the project, and found that the project succeeded in engaging students. We offer recommendations for how instructors can create similar projects in their own classrooms

Energy-aware Economic Production Quantity model with variable energy pricing

In this paper, an energy-aware Economic Production Quantity (EPQ) model is presented to determine optimum production run length and batch size with respect to variable energy cost. Here, variable unit production cost includes energy consumption charge which is a function of production time and time-of-use, and alternates between two prices during peak and off-peak hours. This paper addresses the above integration in order to minimize the overall cost of the system. In the first phase of this study, a new scenario-based framework is proposed to find the optimal value of production time. In the second phase, a general mixed integer nonlinear programming (MINLP) model is developed for the given framework. The energy cost defined by the framework and mathematical model depends on the number of peak periods during the production period and is calculated using floor functions. The MINLP is solved numerically and analytically, and a closed form solution is obtained for the production run length. The model is analyzed for different scenarios and the results are discussed

Mixed model multi-manned assembly line balancing problem: a mathematical model and a simulated annealing approach

Purpose – This paper aims to study a generalized type of mixed-model assembly line with multi-manned workstations where multiple workers simultaneously perform different tasks on the same product. This special kind of assembly line is usually utilized to assemble different models of large products, such as buses and trucks, on the same production line. Design/methodology/approach – To solve the mixed-model multi-manned assembly line balancing problem optimally, a new mixed-integer-programming (MIP) model is presented. The proposed MIP model is nondeterministic polynomial-time (NP)-hard, and as a result, a simulated annealing (SA) algorithm is developed to find the optimal or near-optimal solution in a small amount of computation time. Findings – The performance of the proposed algorithm is examined for several test problems in terms of solution quality and running time. The experimental results show that the proposed algorithm has a satisfactory performance from computational time efficiency and solution accuracy. Originality/value – This research is the very first study that minimizes the number of workers and workstations simultaneously, with a higher priority set for the number of workers, in a mixed-model multi-manned assembly line setting using a novel MIP model and an SA algorithm