International Journal of Industrial Engineering: Theory, Applications and Practice
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VERIFICATION METHOD FOR DISCRETE-EVENT SIMULATION BASED ON DISCRETE-EVENT SYSTEM FORMALISM
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As simulation models become more complex, their requirement specifications also become more complicated and difficult to verify. This study proposes requirement modeling and verification methods using discrete event systems based on a discrete event system formalism. This process involves describing the requirements, translating them into a requirement model, and verifying whether the target system satisfies them. The benefits include automated verification and simplified event monitoring using a discrete event system formalism. The main contributions of this study are the proposed requirement discrete event system model, the logic and translation method, and the verification process. In addition, this study introduces the requirements for engagement simulation with multiple agents and its discrete event system model as a case study
PLAN-DO-CHECK-ACTION (PDCA) BASED APPROACH TO REDUCE MACHINE DOWNTIME BY PROPOSING DESIGN OF MACHINE COMPONENT: THE CASE OF A BOTTLED WATER COMPANY
No full textThe purpose of this paper is to report on the application of the PDCA cycle in reducing machine downtime of bottled water production in an Indonesian manufacturing company. The PDCA cycle was used as a methodology in this study to solve machine downtime problems. During brainstorming sessions with the company’s representative, tools such as 5W+1H and the fishbone diagram were used. The collected data was then used to design a machine component prototype in order to reduce downtime. The main result of this study was the design of a machine component to help reduce machine downtime by adding it to the cap-sealing machine. Based on observations over three months, the assembled component reduced downtime from 31.37% to 8.63%, exceeding the company’s standard of a maximum loss of 10%. Furthermore, the company will benefit from this research because the designed machine component will reduce machine downtime and successfully increase the company’s KPI value
A MULTI-OBJECTIVE APPROACH TO HOME HEALTH CARE ROUTING PROBLEM WITH TEAM FORMATION
No full textHome health care (HHC) services provide the elderly, disabled, and those with chronic diseases health services in their homes. The demand for HHC services has increased due to the growth of the elderly population, the increase in hospital occupancy rates during the pandemic, and developments in medical device technologies. In this study, team formation is taken into consideration in a multi-period and multi-objective HHC assignment, scheduling and routing problem. The team and the assigned patients to this team must be compatible with skill levels and operational requirements. The objective functions are minimization of total completion time and maximum working time overall caregivers. To evaluate our approach, an HHC service unit in a state hospital is adopted as the operational scenario, and we propose a multi-objective mathematical model to solve the problem. In this model, HHC teams are formed, the patients are assigned to the teams, and the route of each team is determined in an integrated manner. The variable neighborhood search algorithm is modified to solve the multi-objective optimization model and is improved with a local search algorithm. The proposed algorithm has been compared with the state-of-the-art multi-objective algorithms in the literature over test problems. Current results show that the model to be an effective means of estimating and predicting system behavior in this complex environment
DISPATCHING RULES FOR SCHEDULING PRODUCT PORTFOLIOS
No full textDuring the production and marketing coordination stage, scheduling product portfolios allows for evaluating the utility performance of production lines and assists in determining the priorities of product portfolios that need to be assigned. Feasible or optimal scheduling of work orders for product portfolios is subject to a sequence of combinations of work orders. Thus, it involves many scheduling models. It is beyond the scope of a single scheduling model to address this issue. Many mathematical scheduling models and hybrid dispatching rules are too complex to be included in a packaged ERP solution, and they may not even be necessary at the enterprise-wide decision-making level. Therefore, we developed a static scheduling program for job shop problems and conducted experiments in which we tested the ten most common dispatching rules in various scenarios. According to the testing results, seven dispatching rules are suitable for most scenarios and recommended for ERP system implementation
PERFORMANCE IMPACT OF DISPATCHING AND ROUTING IN AN AUTOMATED GUIDED VEHICLE SYSTEM
Get PDFDispatching and routing are fundamental operational decisions in automated material-handling systems. Numerous studies have been conducted on these two operational decisions, with more focus being recently made on intelligent routing decisions. However, comparative studies between the effects of dispatching and routing methods have not been reported so far. In this study, we have investigated three dispatching and three routing algorithms and measured their impacts using a simulation model for an automated guided vehicle (AGV) system designed for a real-world production line, in which a grid-type material flow layout is used, and the AGVs need to stop before changing their direction of movement. Two routing algorithms are developed in this study. Simulation experiments revealed that both dispatching and routing algorithms affect the performance of the AGV system, although dispatching methods showed a more significant impact. Good dispatching and routing algorithms are mandatory to improve the overall performance of AGV systems
ACO-GCN: A FAULT DETECTION FUSION ALGORITHM FOR WIRELESS SENSOR NETWORK NODES
Get PDFWireless Sensor Network (WSN) has become a solution for real-time monitoring environments and is widely used in various fields. A substantial number of sensors in WSNs are prone to succumb to failures due to faulty attributes, complex working environments, and their hardware, resulting in transmission error data. To resolve the existing problem of fault detection in WSN, this paper presents a WSN node fault detection method based on ant colony optimization-graph convolutional network (ACO-GCN) models, which consists of an input layer, a space-time processing layer, and an output layer. First, the users apply the random search algorithm and the search strategy of the ant colony algorithm (ACO) to find the optimal path and locate the WSN node failures to grasp the overall situation. Then, the WSN fault node information obtained by the GCN model is learned. During the data training process, where the WSN fault node is used for error prediction, the weights and thresholds of the network are further adjusted to increase the accuracy of fault diagnosis. To evaluate the performance of the ACO-GCN model, the results show that the ACO-GCN model significantly improves the fault detection rate and reduces the false alarm rate compared with the benchmark algorithms. Moreover, the proposed ACO-GCN fusion algorithm can identify fault sensors more effectively, improve the service quality of WSN and enhance the stability of the system
AN INTEGRATED METHODOLOGY FOR SUSTAINABLE SUPPLIER SELECTION AND ORDER ALLOCATION TO TACKLE GLOBAL SUPPLY CHAIN UNCERTAINTIES
No full textThe existence of pandemic and natural catastrophe situations have created unprecedented challenges for global supply chains, resulting in uncertainty about the specific demand, cost, and supply of the commodities. This study proposes an integrated framework considering global supply chain uncertainties during sustainable supplier selection and order allocation. The suggested technique is divided into three phases. The best-worst method (BWM) is used to compute the criteria weights and suppliers’ performance scores in the first phase. A scenario-based multi-objective linear programming (SMOBLP) model is developed in the second phase. Later, the ɛ-constraint and LP-metrics methods are utilized to determine the Pareto front of the developed SMOBLP model. The technique for order preference by similarity to ideal solution (TOPSIS) is applied in phase three to get an ultimate optimal solution from the non-dominated solutions. Finally, a real-life case study of the garment manufacturing organization is offered to demonstrate the practicability of the proposed methodology
MULTI-OBJECTIVE ROBUST PRODUCTION PLANNING CONSIDERING WORKFORCE EFFICIENCY WITH A METAHEURISTIC SOLUTION APPROACH
Get PDFTimely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significantly affects production delivery time, which can be achieved through the maximization of workforce efficiency. This issue becomes more complicated when the parameters of the production system are under uncertainty. This paper presents a bi-objective scenario-based robust production planning model considering maximizing workforce efficiency and minimizing costs where the backorder, demand, and costs are uncertain. Also, backorder, raw materials purchasing, inventory control, and manufacturing time capacity are considered. A case study in a faucet manufacturing plant is considered to solve the model. Furthermore, the ε-constraint method, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and the Pareto Envelope-based Selection Algorithm II (PESA-II) are employed to solve the model. Also, the Taguchi method is used to tune the parameters of these algorithms. To compare these algorithms, five indicators are defined. The results show that the SPEA2 is the most time-consuming algorithm and the NSGA-II is the fastest, while their objective function values are nearly the same
NETWORK DESIGN FOR THE TEMPORAL AND SPATIAL COLLABORATION WITH SERVICE CLASS IN DELIVERY SERVICES
Get PDFThe COVID-19 pandemic has significantly impacted e-commerce and the delivery service sector. As lockdowns and social distancing measures were put in place to slow the spread of the virus, many brick-and-mortar stores were forced to close, leading to an increase in online shopping. This situation led to a surge in demand for delivery services as more people turned to the internet to purchase goods. However, this increase in demand also created several challenges for delivery companies. They experienced delays in delivering packages due to increased volume, limited staff, and disruptions to supply chains. It led to more competition and increased pressure on delivery companies to improve their services and delivery times. To overcome such competition, collaboration among small and medium-sized delivery companies can be a good way to compete with larger delivery companies. By working together, small and medium-sized companies can combine their resources and expertise to offer more extensive coverage and competitive prices than they could individually. This can help them to gain market share and expand their customer base. This study proposes a network design model for collaboration with service class in delivery services considering multi-time horizon. The problem to be considered is deciding which company is dedicated to delivering certain types of items, such as regular or refrigerated items, in designated regions in each time horizon. During the agreed-upon timeframe, the companies operate, using each other's infrastructure (such as vehicles and facilities) and sharing delivery centers for the coalition's benefit to improve efficiency and reduce costs. We also propose a multi-objective, nonlinear programming model that maximizes the incremental profit of participating companies and a linearization methodology to solve it. The max-sum criterion and Shapley value allocation methods are applied to find the best solution and ensure a fair distribution of profits among the collaborating group. The efficiency of the suggested model is shown through a numerical illustration
A NOVEL TYPE OF FLEXIBLE SOFT ANALYTIC NETWORK PROCESS TO SOLVE THE MULTIPLE-ATTRIBUTE DECISION-MAKING PROBLEM
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Research and development of scientific and technological products have been changing with each passing day in this new millennium. Decisions related to the production of technical products are the key to affecting the sustainable development and market share of enterprises. However, the decision-making related to the production of technology products contains many different evaluation criteria as well as qualitative and quantitative evaluation attributes. Moreover, the correlation between criteria must be considered so it can be treated as a complex multiple-attribute decision-making (MADM) problem. Moreover, performing a multi-attribute decision evaluation often encounters incomplete or missing information provided by experts, which will lead to difficulties in the solution process. In view of the incomplete or missing information of the assessment data, the traditional analytic network process (ANP) method and decision-making trial and evaluation laboratory ANP (DANP) method will delete the incomplete information during the process of assessment and decision-making, and this will bring about non-objective assessment results. In order to solve the above problems, this study proposes a novel type of flexible soft ANP (SANP) method to solve the MADM problems and uses a practical example of smartphone text entry to prove the effectiveness and suitability of the proposed SANP method