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Explanation of a Concept: the difference between a good decision and a bad decision.
As a cognitive process, decision-making is intrinsically illogical or unreasonable (i.e., based on assumptions not supported by evidence). Decision-making is influenced by a person’s personality and past experiences. Thus, a person’s predispositions can impede or facilitate the decision-making process. According to research on human psychology, decisions are frequently influenced by a set of requirements and personal preferences. Need-based hierarchies are one of the most widely known and influential theories on the topic of motivation. According to Abraham Maslow’s theory, an individual must first satisfy his or her physiological and safety-related needs before he or she can be driven by high needs (e.g., love; self-actualization).
When it comes to making decisions, smart people know that resources like time, money, and reputation points are finite. People who make poor decisions drain the system of its resources, which impedes and, in certain cases, erodes development. They are wasteful. Good leaders accept responsibility for their actions when a choice goes wrong and begin working on a viable solution right away. Poor decision-makers blame others and make excuses, which leads to ruined relationships, diminished credibility, and a waste of time. Good decision-makers can focus on the work at hand because they have cleared their brains of the emotional baggage of previous outcomes. Fear, greed, and other emotions distort the judgment of bad decision-makers.
In every decision-making process, biases in how we think can be huge roadblocks. Biases create distortions and disruptions into the decision-making system. Sometimes we’re not even aware of our biases. The halo effect and many other cognitive biases are by far the most common. This bias occurs whenever decision-makers sought out facts that confirms their preconceived assumptions while rejecting or downplaying information that supports opposing views. The over-reliance on a single piece of knowledge or experience for future judgments is known as “anchoring.” 2. Judgments are made by changing away from the anchor once it has been established. This limits one’s capacity to correctly understand fresh, possibly relevant information.The “halo effect might influence people’s sentiments and opinions about something’s general character or qualities,” their overall image of an individual, company, product, or brand.
The tendency to overestimate one’s own judgments’ accuracy is known as the overconfidence biasIt’s possible that self-assurance in one’s talents, capacities to accomplish, degree of control, or chance of success falls under this category.
Simulation Modelling
When researching manufacturing processes or developing production systems, simulation is a must. An actual workshop with two job categories, Cat-1 and Cat-2, and five machines was the subject of this research. There were five jobs completed, each with a unique set of tasks. Using the virtual models of manufacturing processes and systems in all machines, we determined how the five jobs entered the system. We achieved realistic outcomes by taking them into account. Because of the model’s complexity, we used WITNESS to replicate all of the virtual processes and systems of the manufacturing industry. Comparing the outcomes of each category helped to find the optimal strategy. Instead of relying on a random selection procedure, the case job shop scenario would be able to better judge which work should be handled first.
The study objectives are to apply various virtual models of manufacturing processes and systems in our job shop scenario where Simulate all the digital model to determine the optimum technique for producing many low volume, high mix items. Then construct products with high mix and capacity. Correctly scheduling operations may lead to enhanced efficiency and productivity as well as reduced costs for a business ( Sridharan & Vinod , 2008). High-mix, low-volume environments are most often seen in workshops. CAT-1 and CAT-2 dynamic job shops with five machines focus on this research, which uses a simulation-based experimental examination of virtual models of manufacturing processes and scheduling systems.
One of the most pressing scheduling issues is the JSSP (Job-Shop Scheduling Problem) (Jayamohan and Rajendran, 2004). Several machines are gradually assigned to carry out tasks under certain restrictions to maximize a particular objective, such as reducing the make-span (Yang et al., 2010). Machines are used to complete work in a typical job shop scheduling challenge. Because of current technology’s predetermined and well-known limitations, every profession has a unique path to success. The time between the completion of one work and the start of the next job processing on a machine is the “setup period” (Vinod and Sridharan, 2008). A machine or workstation is set up when ready for the following operation. A Job shop buffer machine setup, for example, includes entering all of the orders, Remove any coolant that may have accumulated from the prior operation before starting the next one. Over the previous several decades of research into production processes and systems, no one category has indeed been found to perform well with all essential metrics such as average flow time, mean punctuality, and utilization. Which criterion is to be improved is a factor in determining the deadline (Holthaus and Rajendran, 1997).
Traditionally, researchers have tended to overlook while dealing with scheduling issues. The scheduling problem is simplified, yet the outcome may be unreasonable. Every virtual model of a manufacturing process or system is considered in this study. Machines 1 to 5 are the virtual models of manufacturing processes and systems selected for this research ( Holthaus & Rajendran, 1999). There are other ways to gauge how well each category is performing: looking at total completion time, averaging work – in – progress (WIP), etc.. This study’s five virtual models of manufacturing processes and systems employ WITNESS as its simulation engine. We may select the best category by comparing the results.
MODEL OF THE JOB SHOP
The workshop contains of five buffer machines m = 1 to 5. All of the devices are relatively similar in terms of functionality. In other words, each computer can do all operations for all workloads if it is configured in a specific way. The needs of its customers determine the shop’s operations and product output. Projects 1, 2, 3, 4, and 5 (I = 1, 2, 3, 4, and 5) all arrive simultaneously. we must follow step-by-step instructions in the order they are provided for each activity. The maximum number of j = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 tasks that a task can have is 5. All workloads on any computer are subject to stochastic processing delays. A machine can only perform one task at a time. We can accept jobs on any available computer. Therefore they will not necessarily begin with machine 1. As a result, all jobs passing through the five Job shop buffer machines have exceedingly complex paths.
Selection of Priority
Two tables in this study detail the terminology and virtual models of manufacturing processes and systems used in the study.
Table1: Terminologies
Table 2: Characteristics of Job Categories.
Simulation Modelling
In this five-machine workshop concept, measuring the performance of each due date standard is extremely challenging. We found the optimal strategy by simulating each category with WITNESS (Wong and Olugu, 2008; Mayer and Markt, 1997). The goodness-of-fit test was used to determine the probability distributions of the following data before we entered them into the simulation model.
Each job’s cycle time and each process’s cycle time.
Each job’s final inspection and packaging time.
Each project has a due date and a maximum number of units that can be ordered.
In addition, several assumptions were made, such as:
A single operation can be carried out on each given project by a single machine.
It is impossible to halt a machine operation once it has begun.
We can’t do a job’s subsequent operations until the previous ones are.
There isn’t any other way to get there. We must complete every step of the procedure sequentially.
We know how long it will take to process the operation and how many machines there are to do it.
There is a single queue for all machines, and it is endless.
Each machine is available for production at all times; no downtime is taken into account.
Neither scrap nor rework is considered while processing any of the components.
When the system starts, all jobs with specific lot sizes are readily available.
Experimentation
Because it runs for a certain time and then terminates, this simulation model be able to be termed a dismissing system. A warm-up period is not necessary for a terminating system. In this system, the initial state is fixed, and the end of the system is marked by a naturally occurring event (event E) (Banks et al., 2005). TE’s procedures have been done, which is a naturally occurring phenomenon. TE is the total conclusion time for all jobs for a given due date specification, and each TE number is distinct. We utilized random numbers in each of the three replications of the experiment. Rather, the system operated constantly until all of the components had been processed and distributed, regardless of how long the simulation should last. We selected a restart mode for the execution.
Validation and Proof
Experts examined WITNESS’s codes and input/output categories to ensure the model was correct. We tested the simulation model’s flow and actions. We used WITNESS’ run toolbar’s stop button to step through the model’s execution. We discovered that all components followed their predetermined paths, and the order in which they entered the system was correct.
We compared data as of the real production system by means of the simulation findings to verify the model. It took around five working days to accomplish all five jobs in the job shop manufacturing system (eight hours x 50 minutes x five days = 2880 minutes). With manual estimation, the total time for all the operations with given lot sizes totaled roughly 3000 minutes when calculating the cycle time, examination time, and packing time. According to WITNESS findings, the overall conclusion time of all jobs oscillated from 2835 to 2875 minutes. Due to its strong resemblance to the real production system data, the simulation model developed in this study may be legitimate.
Results and Discussions
we used the average work in development (WIP) and average overall completion time to evaluate the five virtual models of manufacturing processes and systems. The case job shop scenario placed particular emphasis on these metrics. Using the average of three replications, the following paragraphs summarize the results for each of these metrics.
Average Amount of Work Being Done (WIP)
The goal of every industry is to eliminate inventory waste by reducing the amount of work in progress (WIP). According to Figure 2, Machine 3 is greatest at reducing average WIP in a system. Put another way, a job with and is chosen first and stays in the system for a brief period. As a result, the average WIP is expected to be the lowest. MACHINE 2, MACHINE 1, MACHINE 5, and LV round out the top five in this category. MACHINE 3, MACHINE 2, and MACHINE 1 are at the top of the list regarding processing time, indicating that they are significant in reducing work in progress (WIP). In this regard, MACHINE 4 is the most problematic category since it simply considers the due date when making decisions.
Average Total Completion Time
The given simulation model was run up to when there was no planned event. We calculated the overall completion time based on the shipping dates for all components in the system. When it comes to decreasing overall completion time, MACHINE 3 appears to be the most efficient category. To shorten the production time, MACHINE 3 comes out on top. MACHINE 1, MACHINE 2, and MACHINE 4 round out the top five in this category. MACHINE 5 is the weakest category in this regard since it doesn’t take into account the cycle time and just examines the several operations it does.
Conclusions
This study aimed to provide the most appropriate due date specification for a job shop with two task categories, CAT-1 and CAT-2, and five machines. To simulate and acquire the results of the model, we utilized WITNESS. Because of the model’s complexity, this was the case. Comparing each individual categories average WIP and mean completion time may help us determine which category is most suited for each part of the project (ATC). In a production setting, MACHINE 3 is the best option for decreasing the average amount of average completion time and work in progress (WIP) . However, it is important to highlight that no one category can be used to maximize all kinds of performance assessment in all scenarios. To suit the requirements of a workshop scenario, multiple virtual models of manufacturing processes and systems will be required in different scenarios.
References
Banks, J., Carson, J. S., Nelson, B. L. & Nicol,
D. M. (2005). ‘Discrete-Event System Simulation,’ Pearson Prentice Hall, New Jersey.
Holthaus, O. & Rajendran, C. (1997). “Efficient Dispatching Category for Scheduling in a Job Shop,” International Journal of Production Economics, 48 (1). 87-105.
Jayamohan, M. S. & Rajendran, C. (2004).
“Development and Analysis of Cost-Based Dispatching Category for Job Shop Scheduling,” European Journal of Operational Research, 157 (2). 307-321.
Markt, P. L. & Mayer, M. H. (1997). “Witness Simulation Software: A Flexible Suite of Simulation Tools,” Proceedings of the 1997 Winter Simulation Conference, Atlanta, Georgia, 711-717.
Olugu, E.U. & Wong, K.Y. (2008). “Simulation Study on Lens Manufacturing Process Flow,” Proceedings of the Second Asia International Conference on Modelling and Simulation, Kuala Lumpur, Malaysia, 805-811.
Rajendran, C. & Holthaus, O. (1999). “A
Comparative Study of Dispatching Category in Dynamic Flowshops and Job shops,” European Journal of Operational Research, 115 (1). 155-170.
Vinod, V. & Sridharan, R. (2008). “Scheduling a Dynamic Job Shop
Production System with SequenceDependent Setups: An Experimental Study,” Robotics and Computer-Integrated Manufacturing, 24 (3). 435-449.
Yang, S., Wang, D., Chai, T. & Kendall, G.
(2010). “An Improved Constraint
Satisfaction Adaptive Neural Network for Job-Shop Scheduling,” Journal of Scheduling, 13 (1). 17-38.
