Dynamic agent-based bi-objective robustness for tardiness and energy in a dynamic flexible job shop

Nowadays, manufacturing systems are shifting rapidly with the significant change in technology, business, and industry to become more complex and involved in more difficult issues, customised products, variant services and products, unavailable machines, and rush jobs. In the current practices, there are limited models or approaches that are dealing with these complexities. Most of the scheduling models in literature are proposed as centralised approaches. Researchers recently started to pay attention to reduce energy consumption in manufacturing due to the rising cost and the environmental impact. The energy consumption factor has been lately introduced into scheduling research among other traditional objectives such as time, cost and quality. Although reducing energy in manufacturing systems is very important, few researchers have considered energy consumption factor into scheduling in dynamic flexible manufacturing systems. This paper proposes an agent-based dynamic bio-objective robustness for energy and time in a job shop. Two types of agent are introduced which are machine agent and product agent. A new decision making and negotiation model for multi-agent systems is developed. Two types of dynamic unexpected events in the shop floor are introduced: dynamic job arrival and machines breakdown. A case study is provided in order to verify the result

Selected heuristic algorithms for solving job shop and flow shop scheduling problems

Importance of job shop and flow shop scheduling has increased to a high extent. Nowadays, each and every industry focuses largely on how to schedule their machine working, since it is an important factor which decides the net productivity. All manufacturing systems including flexible manufacturing system follow a planned schedule of machine operation depending on the demand criterion. With the increase in number of machines and jobs to be scheduled, complexity of the problem increases which demands the need of a proper scheduling technique. Here this thesis shows some of the essential methods of solving a job shop and flow shop scheduling problems. This thesis focuses on finding the most efficient way of scheduling in a flow shop environment with the help of heuristics algorithms that include Palmer’s algorithm, CDS algorithm and NEH algorithm. Comparison was also done between the various heuristics algorithms. For getting the optimum make span for job shop scheduling we have used branch and bound algorithm and shifting bottleneck algorithm. Basis input parameters are given in the problem which are then used for computing the make span. A C programming code was generated to find the optimum results of the scheduling problem

Heuristic approaches to scheduling problems in a flexible job shop environment

Thesis (Master)--Izmir Institute of Technology, Energy Engineering, Izmir, 2004Includes bibliographical references (leaves: 81-85)Text in English, Abstract: Turkish and Englishx, 85 pages, [245] leavesModern production factories, to obtain high profits, usually maximize their profits through streamlining their productivity. This goal can be achieved, among others, by optimal or almost optimal scheduling of jobs in production process. Scheduling is a key factor for manufacturing productivity and energy save. Effective scheduling can improve on-time delivery of products, reduce inventory, reduce processing times, and utilize bottleneck resources, therefore energy is saved as a result.Process plants typically produce a family of related products that require similar processing techniques. The most important problem encountered in such manufacturing systems is scheduling of operations so that demand is fulfilled within a pre-described time horizon imposed by production planning. The typical scheduling operation that process plants involve can be formulated as a general job shop scheduling problem. Due to production flexibility, it is possible to generate many feasible process plans for each job. The two functions, process planning and scheduling are tightly interwoven with each other. The optimality of scheduling depends on the result of process planning. The integration of process planning and scheduling is therefore important for an efficient utilization of manufacturing resources.In this study, we present real cases taken from manufacturing industry, which were modeled and solved using theoretical tools of scheduling theory. According to this idea, this study was motivated by the design and implementation of a flexible job shop scheduling system for the manufacturing of Teba Oven.s Press Workshop.The manufacturing is characterized by significant machine setup times, strict local capacities, the option of choosing a few alternative processing routes, and long horizon as compared to the time resolution required by the scheduling models. Our goal is thus to obtain near-optimal schedules with quantifiable quality in computationally efficient manner. For achieving this goal, dispatching rules and shifting bottleneck heuristics are used, and solution methodology based on a combined dynamic programming. The methods have been implemented by using the object-oriented generic programming, LEKIN [43], and the outputs show that the methods generate high-quality schedules in a timely fashion to achieve on-time delivery of products and low in work-in-process inventory. Finally, the integrated treatment of machines and buffers facilitates the smooth flow of parts through the system

Design choices for agent-based control of AGVs in the dough making process

In this paper we consider a multi-agent system (MAS) for the logistics control of Automatic Guided Vehicles (AGVs) that are used in the dough making process at an industrial bakery. Here, logistics control refers to constructing robust schedules for all transportation jobs. The paper discusses how alternative MAS designs can be developed and compared using cost, frequency of messages between agents, and computation time for evaluating control rules as performance indicators. Qualitative design guidelines turn out to be insufficient to select the best agent architecture. Therefore, we also use simulation to support decision making, where we use real-life data from the bakery to evaluate several alternative designs. We find that architectures in which line agents initiate allocation of transportation jobs, and AGV agents schedule multiple jobs in advance, perform best. We conclude by discussing the benefits of our MAS systems design approach for real-life applications

An expert system for a local planning environment

In this paper, we discuss the design of an Expert System (ES) that supports decision making in a Local Planning System (LPS) environment. The LPS provides the link between a high level factory planning system (rough cut capacity planning and material coordination) and the actual execution of jobs on the shopfloor, by specifying a detailed workplan. It is divided in two hierarchical layers: planning and scheduling. At each level, a set of different algorithms and heuristics is available to anticipate different situations.\ud \ud The Expert System (which is a part of the LPS) supports decision making at each of the two LPS layers by evaluating the planning and scheduling conditions and, based on this evaluation, advising the use of a specific algorithm and evaluating the results of using the proposed algorithm.\ud \ud The Expert System is rule-based while knowledge (structure) and data are separated (which makes the ES more flexible in terms of fine-tuning and adding new knowledge). Knowledge is furthermore separated in algorithmic knowledge and company specific knowledge. In this paper we discuss backgrounds of the expert system in more detail. An evaluation of the Expert system is also presented

Abandoned project restoration model (APRM) for residential construction projects

Incompletion of construction projects is a common phenomenon in Malaysia. Project abandonment has given an adverse consequences to the economy, society and environment. In the best interest of the end users and other parties involved in the contract, the best resolution for this abandoned projects is to successfully revive them, which has its’ stages and barriers along the way as well. The main aim of this research is to develop an effective model as a guide towards project restoration which could be used to mitigate the issue of abandoned residential construction projects in Malaysia. Identifying the factors contributing towards the restoration of the abandoned projects are important to have a successful completed project. This research was conducted in the purpose of identifying those significant factors in order to obtain the restoration process for abandoned projects where lastly the Abandoned Project Restoration Model (APRM) was developed. The research focuses on residential construction projects. This research comprises of both quantitative and qualitative approaches and process, where a pilot survey and full survey, and as well as interview analysis were conducted. Factor model was developed using AMOS and lastly the developed model was validated and tested by related officials. The outcome of this research showed that the most significant factor for abandoned project restoration is Management Aspects. A complete restoration process based on the significant factors identified were also obtained. This model is seen as useful in contributing and as well as assisting the restoration of the abandoned projects in Malaysia and could be used as a guideline for that purpose

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods