Numerical Simulation and Assessment of Meta Heuristic Optimization Based Multi Objective Dynamic Job Shop Scheduling System

In today's world of manufacturing, cost reduction becomes one of the most important issues. A successful business needs to reduce its cost to be competitive. The programming of the machine is playing an important role in production planning and control as a tool to help manufacturers reduce their costs maximizing   the   use   of   their   resources.   The   programming problem is not only limited to the programming of the machine, but also covers many other areas such such as computer and information technology and communication. From the definition, programming is an art that involves allocating, planning the allocation and utilization of resources to achieve a goal. The aim of the program is complete tasks in a reasonable amount of time. This reasonableness is a performance measure of how well the resources   are   allocated   to   tasks.   Time   or   time-dependent functions are always it used as performance measures. The objectives of this research are to develop Intelligent Search Heuristic algorithms (ISHA) for equal and variable size sub lot for  m  machine  flow  shop  problems,  to  Implement  Particle Swarm Optimization algorithm (PSO) in matlab, to develop PSO based Optimization program for efficient job shop scheduling problem. The work also address solution to observe and verify results of PSO based Job Shop Scheduling with help of graft chart

Evolving Neural Networks to Solve a Two-Stage Hybrid Flow Shop Scheduling Problem with Family Setup Times

We present a novel strategy to solve a two-stage hybrid flow shop scheduling problem with family setup times. The problem is derived from an industrial case. Our strategy involves the application of NeuroEvolution of Augmenting Topologies - a genetic algorithm, which generates arbitrary neural networks being able to estimate job sequences. The algorithm is coupled with a discrete-event simulation model, which evaluates different network configurations and provides training signals. We compare the performance and computational efficiency of the proposed concept with other solution approaches. Our investigations indicate that NeuroEvolution of Augmenting Topologies can possibly compete with state-of-the-art approaches in terms of solution quality and outperform them in terms of computational efficiency

An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan

This paper deals with a variant of flowshop scheduling, namely, the hybrid or flexible flowshop with sequence dependent setup times. This type of flowshop is frequently used in the batch production industry and helps reduce the gap between research and operational use. This scheduling problem is NP-hard and solutions for large problems are based on non-exact methods. An improved genetic algorithm (GA) based on software agent design to minimise the makespan is presented. The paper proposes using an inherent characteristic of software agents to create a new perspective in GA design. To verify the developed metaheuristic, computational experiments are conducted on a well-known benchmark problem dataset. The experimental results show that the proposed metaheuristic outperforms some of the well-known methods and the state-of-art algorithms on the same benchmark problem dataset.The translation of this paper was funded by Universidad Politecnica de Valencia, Spain.Gómez Gasquet, P.; Andrés Romano, C.; Lario Esteban, FC. (2012). An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan. Expert Systems with Applications. 39(9):8095-8107. https://doi.org/10.1016/j.eswa.2012.01.158S8095810739

Flow Shop Scheduling for Energy Efficient Manufacturing

A large number of new peaking power plants with their associated auxiliary equipment are installed to meet the growing peak demand every year. However, 10% utility capacity is used for only 1%~2% of the hours in a year. Thus, to meet the demand and supply balance through increasing the infrastructure investments only on the supply side is not economical. Alternatively, demand-side management might cut the cost of maintaining this balance via offering consumers incentives to manage their consumption in response to the price signals. Time-varying electricity rate is a demand-side management scheme. Under the time-varying electricity rate, the electricity price is high during the peak demand periods, while it is low during the off-peak times. Thus, consumers might get the cost benefits through shifting power usages from the high price periods to the low price periods, which leading to reduce the peak power of the grid. The current research works on the price-based demand-side management are primarily focusing on residential and commercial users through optimizing the “shiftable” appliance schedules. A few research works have been done focusing manufacturing facilities. However, residential, commercial and industrial sectors each occupies about one-third of the total electricity consumption. Thus, this thesis investigates the flow shop scheduling problems that reduce electricity costs under time-varying electricity rate. A time-indexed integer programming is proposed to identify the manufacturing schedules that minimize the electricity cost for a single factory with flow shops under time-of-use (TOU) rate. The result shows that a 6.9% of electricity cost reduction can be reached by shifting power usage from on-peak period to other periods. However, in the case when a group of factories served by one utility, each factory shifting power usage from on-peak period to off-peak hours independently, which might change the time of peak demand periods. Thus, a TOU pricing combined with inclining block rate (IBR) is proposed to avoid this issue. Two optimization problems are studied to demonstrate this approach. Each factory optimizes manufacturing schedule to minimize its electricity cost: (1) under TOU pricing, and (2) under TOU-IBR pricing. The results show that the electricity cost of each factory is minimized, but the total electricity cost at the 2nd hour is 6.25% beyond the threshold under TOU pricing. It also shows that factories collaborate with each other to minimize the electricity cost, and meanwhile, the power demand at each hour is not larger than the thresholds under TOU-IBR pricing. In contrast to TOU rate, the electricity price cannot be determined in ahead under real-time price (RTP), since it is dependent on the total energy consumption of the grid. Thus, the interactions between electricity market and the manufacturing schedules bring additional challenges. To address this issue, the time-indexed integer programming is developed to identify the manufacturing schedule that has the minimal electricity cost of a factory under the RTP. This approach is demonstrated using a manufacturing facility with flow shops operating during different time periods in a microgrid which also served residential and commercial buildings. The results show that electricity cost reduction can be achieved by 6.3%, 10.8%, and 24.8% for these three time periods, respectively. The total cost saving of manufacturing facility is 15.1% over this 24-hour period. The results also show that although residential and commercial users are under “business-as-usual” situation, their electricity costs can also be changed due to the power demand changing in the manufacturing facilities. Furthermore, multi-manufacturing factories served by one utility are investigated. The manufacturing schedules of a group of manufacturing facilities with flow shops subject to the RTP are optimized to minimize their electricity cost. This problem can be formulated as a centralized optimization problem. Alternatively, this optimization problem can be decomposed into several pieces. A heuristic approach is proposed to optimize the sub-optimization problems in parallel. The result shows that both the individual and total electricity cost of factories are minimized and meanwhile the computation time is reduced compared with the centralized algorithm

A Unified Framework for Solving Multiagent Task Assignment Problems

Multiagent task assignment problem descriptors do not fully represent the complex interactions in a multiagent domain, and algorithmic solutions vary widely depending on how the domain is represented. This issue is compounded as related research fields contain descriptors that similarly describe multiagent task assignment problems, including complex domain interactions, but generally do not provide the mechanisms needed to solve the multiagent aspect of task assignment. This research presents a unified approach to representing and solving the multiagent task assignment problem for complex problem domains. Ideas central to multiagent task allocation, project scheduling, constraint satisfaction, and coalition formation are combined to form the basis of the constrained multiagent task scheduling (CMTS) problem. Basic analysis reveals the exponential size of the solution space for a CMTS problem, approximated by O(2n(m+n)) based on the number of agents and tasks involved in a problem. The shape of the solution space is shown to contain numerous discontinuous regions due to the complexities involved in relational constraints defined between agents and tasks. The CMTS descriptor represents a wide range of classical and modern problems, such as job shop scheduling, the traveling salesman problem, vehicle routing, and cooperative multi-object tracking. Problems using the CMTS representation are solvable by a suite of algorithms, with varying degrees of suitability. Solution generating methods range from simple random scheduling to state-of-the-art biologically inspired approaches. Techniques from classical task assignment solvers are extended to handle multiagent task problems where agents can also multitask. Additional ideas are incorporated from constraint satisfaction, project scheduling, evolutionary algorithms, dynamic coalition formation, auctioning, and behavior-based robotics to highlight how different solution generation strategies apply to the complex problem space

Finding Optimal Cayley Map Embeddings Using Genetic Algorithms

Genetic algorithms are a commonly used metaheuristic search method aimed at solving complex optimization problems in a variety of fields. These types of algorithms lend themselves to problems that can incorporate stochastic elements, which allows for a wider search across a search space. However, the nature of the genetic algorithm can often cause challenges regarding time-consumption. Although the genetic algorithm may be widely applicable to various domains, it is not guaranteed that the algorithm will outperform other traditional search methods in solving problems specific to particular domains. In this paper, we test the feasibility of genetic algorithms in solving a common optimization problem in topological graph theory. In the study of Cayley maps, one problem that arises is how one can optimally embed a Cayley map of a complete graph onto an orientable surface with the least amount of holes on the surface as possible. One useful application of this optimization problem is in the design of circuit boards since such a process involves minimizing the number of layers that are required to build the circuit while still ensuring that none of the wires will cross. In this paper, we study complete graphs of the form K_12m + 7 for positive integers m and we work on mappings with the finite cyclic group Z_n. We develop several baseline search algorithms to first gain an understanding of the search space and its complexity. Then, we employ two different approaches to building the genetic algorithm and compare their performances in finding optimal Cayley map embeddings