Simultaneous scheduling of machines and tools in a multi machine FMS with alternative routing using symbiotic organisms search algorithm

This paper deals with simultaneous scheduling of machines and tools with alternate machines in a multi machine flexible manufacturing system (FMS) to minimize makespan (MS). Only one copy of each type of tools is made available due to economic restrictions and the tools are stored in a central tool magazine (CTM) that shares with and serves for several machines. The problem is to select machines from alternate machines for job-operations, allocation of tools to job-operations and job-operations’ sequencing on machines for MS minimization. This paper presents a nonlinear mixed integer programming (MIP) formulation to model the combined scheduling of machines and tools with alternate machines and a symbiotic organisms search algorithm (SOSA) built on the symbiotic interaction strategies that organisms employ to continue to exist in the ecosystem for solving the scheduling of machines and tools with alternate machines. The results have been tabulated, analyzed. It is observed that there is a reduction in MS when the alternate machines are considered for job-operation.

MANUFACTURING INDUSTRY JOB SHOP FLOOR DECISIONS THROUGH HYBRID GENETIC ALGORITHM

This article addresses flexible manufacturing system (FMS) Performance is likely to improve with employment of various resources efficiently. Initially simultaneous scheduling problems are solved by means of priority rules like first come first serve (FCFS), shortest processing time (SPT) and longest processing time (LPT) to find out the operational completion time for 120 problems. Later gene rearrangement genetic algorithm (HGA) is implemented for same set of problems with makespan as objective and the results are compared with the results of priority rules. The results are performed well by using HGA. The same HGA is used to find the finest optimal sequence that minimize the operational completion time.

SEGMENT RANDOM INSERTION PERTURBATION SCHEME (SRIPS) GENETIC ALGORITHM FOR MANUFACTURING INDUSTRY

This paper lays down a formal framework for simultaneous scheduling of machines- automated guided vehicles (AGVs) and tools in a multi-machine flexible manufacturing system (FMS) while accounting for transport times of parts to minimise makespan. To minimize tooling costs- a central tool magazine (CTM) is suggested so that the tools are ‘shared’. AGVs and tool transporter (TT) carry jobs and tools between machines. The complexity of including sequencing of job operations on machines- assignment of AGVs and tools to job operations and corresponding trip operations such as the empty trip and loaded trip times of AGVs and a CTM in scheduling is greater. The scope of this paper is to propose a nonlinear Mixed Integer Programming (MIP) model to minimize makespan. Since the problem is known to be NP hard- it is conjectured and then verified that the intelligent behaviour of chromosomes and genes can be effectively used to lay down a metaheuristic algorithm known as a segment random insertion perturbation scheme genetic algorithm (SRIPSGA) suitable for the problem at hand- and the results have been tabulated and analyzed.

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.