Optimization of No-Wait Flowshop Scheduling Problem in Bakery Production with Modified PSO, NEH and SA

In bakery production, to perform a processing task there might be multiple alternative machines that have the same functionalities. Finding an efficient production schedule is challenging due to the significant nondeterministic polynomial time (NP)-hardness of the problem when the number of products, processing tasks, and alternative machines are higher. In addition, many tasks are performed manually as small and medium-size bakeries are not fully automated. Therefore, along with machines, the integration of employees in production planning is essential. This paper presents a hybrid no-wait flowshop scheduling model (NWFSSM) comprising the constraints of common practice in bakeries. The schedule of an existing production line is simulated to examine the model and is optimized by performing particle swarm optimization (PSO), modified particle swarm optimization (MPSO), simulated annealing (SA), and Nawaz-Enscore-Ham (NEH) algorithms. The computational results reveal that the performance of PSO is significantly influenced by the weight distribution of exploration and exploitation in a run time. Due to the modification to the acceleration parameter, MPSO outperforms PSO, SA, and NEH in respect to effectively finding an optimized schedule. The best solution to the real case problem obtained by MPSO shows a reduction of the total idle time (TIDT) of the machines by 12% and makespan by 30%. The result of the optimized schedule indicates that for small- and medium-sized bakery industries, the application of the hybrid NWFSSM along with nature-inspired optimization algorithms can be a powerful tool to make the production system efficient.

IOT AND IT’S SMART APPLICATIONS

We have a tendency to enter an incredibly new age in computer technology. In the cloud, IoT can be a type of "universal world neural network" connecting various objects. IoT can be a display of intelligent connected devices and networks consisting of sensitive machines communicating with alternative machines, environments, artifacts, infrastructures, and human activity. Radio Frequency Identification (RFID) and detector network technologies may emerge to meet this new challenge. Therefore, a huge amount of data is produced, stored, and information is transformed into helpful acts that will make our lives much easier and safer. Internet connectivity is, however, provided to citizens on networks and their mobile devices in most nations, meaning that the transmission of data across the network is also much simpler and less costly.

Hybrid Evolutionary Algorithm with an Optimal Sample Allocation Strategy for Multifidelity Simulation Optimization Problems

Simulation has been applied to evaluate system performance even when the target system does not exist in practice. Dealing with model fidelity is required to apply simulation to practice. A high-fidelity (HF) simulation model is generally more accurate and requires more computational resources than a low-fidelity (LF) one. A low-fidelity model may have less accuracy than a HF one, but it can rapidly evaluate a design alternative. Consequently, the performance accuracy of the constructed simulation model and its computational cost involves a tradeoff. In this research, the simulation optimization problem under a large design space, where a LF model may not be able to evaluate all design alternatives in the limited computational resource, is studied. We extended multifidelity (MF) optimization with ordinal transformation and optimal sampling (MO2TOS), which enables the use of LF models to search for a HF one efficiently, and proposed a combination of the genetic algorithm and MO2TOS. A novel optimal sample allocation strategy called MO2TOSAS was proposed to improve search efficiency. We applied the proposed methods to two experiments on MF function optimization and a simultaneous scheduling problem of machine and vehicles (SSPMV) in flexible manufacturing systems. In SSPMV, we developed three fidelity simulation models that capture important characteristics, including the preventive deadlock situation of vehicles and alternative machines. Simulation results show that the combination of more than one fidelity level of simulation models can improve search efficiency and reduce computational costs.

Multi Objective Flexible Jobshop Scheduling Problem using Heuristic Algorithm

Flexible workshop problem (FJSP) is an extension of the classic job shop problem (JSP) that allows one operation that can be performed from a collection of alternative machines on a single machine. It is closer to the actual condition in manufacturing. Due to the additional conditions to assess the allocation of system operations, FJSP is more It's also a typical problem in combinatorial optimization. But the difference is that all the workers in the shop floor may or may not be handled in all the computers. In just one machine or two machines, a job can be processed or a separate task in all machines may have to go through the processing in order to be finished. Each computer has different work sequences. So it's an internet str complex. The classical workshop scheduling problem varies from the problem of the flow shop and the work flow is not unidirectional. It is more complex than JSP, combining all JSP's problems and complexities. All workers have the same operations series. In this field, in the objective of minimizing "make period time" and mean flow time, the problem is considered with bi-criteria.. nitially manual calculation is done with the question of literature and then with the method of Gantt chart for collecting industrial data.

Metaheuristic algorithms for the flexible job-shop scheduling problem

The problem with flexible job planning (FJSP) is a modification o f the classic job booking problem. This paper deals with the problem o f flexible job deployment and processing o f operations on one machine from a set o f alternative machines. The problem o f deploying flexible jobs in real time is one o f a group o f difficult NP problems (Non-deterministic polynomial time). The motive o f this paper is to show the application o f meta-heuristic algorithms on the example of flexible job schedules and present the appropriate method to future studies. To solve this problem, two meta-heuristic algorithms were used: Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The aim o f the paper is to achieve the speed o f the conversion solution in a series o f iterations, minimizing the total time o f deployment of jobs on an alternative set of machines, as well as minimizing the total schedule time. The problem o f deploying flexible jobs has great application, and with this test we propose an algorithm for solving this problem.

A Hybrid Genetic Scatter Search Algorithm to Solve Flexible Job Shop Scheduling Problems

Scheduling is one of the most important problems in production planning systems. It is a decision-making process that plays a crucial role in many Industries. Different scheduling environments were addressed in the literature. Among them Flexible job-shop problem (FJSP) is an important one and it is an extension of the classical JSP that allows one operation which can be processed on one machine out of a set of alternative machines. It is closer to the real manufacturing situation. Because of the additional needs to determine the assignment of operations on the machines, FJSP is more complex than JSP, and incorporates all the difficulties and complexities of JSP. This chapter addresses a hybrid genetic scatter search algorithm for solving multi-objective FJSP. Makespan and flow time are the objective functions considered in this chapter. The computational results prove the effectiveness of the proposed algorithm for solving flexible job-shop scheduling problem.