Some complexity results in cyclic scheduling

In this chapter, we deal with the cyclic scheduling problem usually observed in the FMS producing multi-type parts where the AGVS plays a role of a material handling system. Finding the conditions guaranteeing the AGVs deadlock-free and collision-free movement policy is the aim of this work. The AGVs co-sharing the common parts of the transportation route while executing repetitive processes, can be modeled in terms of Cyclic Concurrent Process Systems (CCPSs). The chapter suggests a novel approach for schedulability analysis employing the declarative modeling. In turn, the schedulability analysis for a given CCPS answers the question whether a cyclic schedule exists or not. A reference model of constraint satisfaction cyclic scheduling problem shows that unschedulability can be caused by a relation among an initial state and dispatching rules selected. The sufficient conditions guaranteeing CCPS schedulability are discussed and the recursive approach to their designing is proposed. Possible implementations are illustrated on example of the flexible manufacturing system operation.

Flexible Assembly Systems - Hardware Components and Features: Basic components of a FAS.- Classification of flexible assembly systems.- Examples of industrial installations.- Issues in Design, Planning and Scheduling of Flexible Assembly: FAS design issues.- Network design for material flow systems.- Design for assembly.- Assembly planning.- Planning and scheduling.- Loading and Routing Decisions in Flexible Assembly Systems: Description of a flexible assembly system.- Optimization of station workloads and product movements.- Design and balancing of flexible assembly lines.- Numerical examples.- Simultaneous loading and routing.- Sequential loading and routing.- Loading and Routing Decisions with Assembly Plan Selection: Simultaneous loading, routing and assembly plan selection in a general flexible assembly system.- Simultaneous loading, routing and assembly plan selection in a flexible assembly line.- Loading and Scheduling in Flexible Assembly Cells: Loading and scheduling in a robot assembly cell.- Loading and sequencing in printed circuit board assembly.- Production Scheduling in Flexible Assembly Lines: Flexible assembly line with limited intermediate buffers.- Flexible assembly line with no in-process buffers.- Global lower bounds for flexible flow lines with unlimited buffers.- Just-in-time scheduling of flexible assembly lines.- Multilevel scheduling of flexible assembly lines with limited intermediate buffers.- Machine and Vehicle Scheduling in Flexible Assembly Systems: Dispatching scheduling.- Machine and vehicle scheduling - a multi-level approach.- Machine and vehicle scheduling - a single-level approach.

In this paper, we address some issues on the interface of buffer design and cyclic scheduling decisions in a multi-product deterministic flow line. We demonstrate the importance of the above interface for the throughput performance of the flow line. In particular, we point out that the use of sequence-independent information, such as workload distribution and variability in processing times among stations, is not adequate to decide the optimal buffer configuration of the flow line. We formulate the buffer design problem for a fixed sequence of jobs as a general resource allocation problem, and suggest two effective heuristics for its solution. For the simultaneous buffer design and cyclic scheduling problem, we suggest an iterative scheme that builds on the effectiveness of the above heuristics. One of the side results of our extensive computational studies on this problem is that the general guidelines of buffer design in single-product flow lines with stochastic processing times are not directly transferable to the multiproduct deterministic flow line environment.

A scheduling problem of flexible mixed-flow assembly line with non-tight operations priority constraints is studied in this paper. A mathematic model is established to minimize the makespan. Considering the products assembly sequence and the assignment and sequence of product operations on each workstation, a genetic algorithm combined with neighborhood search strategy is designed. The optimal parameter configuration of the algorithm is found through the calculation experiments of different scale problems, and the effectiveness of the proposed algorithm is verified.

A multi-job cyclic scheduling problem for an automated job-shop-type manufacturing system was analyzed. In the cyclic scheduling problem, an optimum schedule is a non-idle schedule, where a bottleneck machine produces jobs without idle time in the steady state. A dividing method of the jobs was introduced and the job output interval in the steady state was clarified. A lower bound on the buffer capacity was obtained to construct a non-idle schedule.

Converging to periodic schedules for cyclic scheduling problems with resources and deadlines

Scheduling is key towards improving the performance of a Flexible Assembly Line (FAL). In this paper, a Bilevel Differential Evolution (BiDE) algorithm to solve a FAL scheduling problem is proposed. The BiDE algorithm optimizes the performance of the FAL with respect to two criteria: the weighted sum of Earliness/Tardiness (E/T) penalties and the balance of the FAL. The performance of BiDE is evaluated using the data sets available in the literature and an evolutionary heuristic algorithm published earlier called BiGA. The experimental results show that the BiDE algorithm can solve the FAL scheduling problem effectively and exhibits a superior performance over BiGA.Note to Practitioners-This paper was motivated by the problem of allocating time and equipment resources efficiently in a flexible assembly line to improve its performance. Among the different methods to solve this problem, heuristic search techniques are becoming more popular. Current work in literature have proposed a heuristic search algorithm to solve this problem. However, there is ambiguity in the model. This paper aims to clarify the ambiguity in the proposed flexible assembly line model, as well as applying a different heuristic search algorithm for comparison purposes. Experimental results show that the proposed technique can solve the problem effectively.

Two efficient cyclic scheduling heuristics for re-entrant job shop environments were developed. Each heuristic generated an efficient and feasible cyclic production schedule for a job shop in which a single product was produced repetitively on a set of machines was to determine an efficient and feasible cyclic schedule which simultaneously minimized flow time and cycle time. The first heuristic considered a repetitive production re-entrant job shop with a predetermined sequence of operations on a single product with known processing times, set-up and material handling times. The second heuristic was a specialization of the first heuristic where the set-up for an operation could commence even while the preceding operation was in progress. These heuristics have been extensively tested and computational results are provided. Also, extensive analysis of worst-case and trade-offs between cycle time and flow time are provided. The results indicate that the proposed heuristics are robust and yield efficient and superior cyclic schedules with modest computational effort.

This paper concerns the problem of cyclic job shop scheduling problem with linear constraints. The main characteristic of this problem is that the tasks of each job are generic, that is, they have an infinite number of occurrences; moreover, these tasks are constrained by linear precedence constraints. The general approach to solving this scheduling problem, is based on the coupling of a genetic algorithm and a scheduler. The scheduler uses a Petri net to construct a feasible solution step by step, respecting the linear constraints between the tasks. The genetic algorithm aims at proposing heuristics for solving the resource conflicts that occur during the simulation process achieved by the scheduler. Finally, a benchmark and some preliminary results of this approach are presented.

A re-entrant shop describes a manufacturing environment in which a machine can process a job more than once. A re-entrant shop can be classified into a re-entrant job-shop (RJS) and a re-entrant flow-shop (RFS). This article presents the development of the mixed binary integer programming (BIP) technique for the RJS and the RFS scheduling problems. Eight mixed BIP models are proposed for solving re-entrant shop scheduling problems to minimize makespan. The models RJS-1, RJS-2, RJS-3, and RJS-4 are for the RJS problems, while the other four models (RFS-1, RFS-2, RFS-3, and RFS-4) are for the RFS problems. Computational results show that the RJS-4 model is the best of the RJS models, and the RFS-3 model is the best of the RFS models.

In this study, we consider the production environment of no-wait reentrant flow shop with the objective of minimizing makespan of the jobs. In a reentrant flow shop, at least one job should visit at least one of the machines more than once. In a no-wait flowshop scheduling problem, when the process of a specific job begins on the first machine, it should constantly be processed without waiting in the line of any machine until its processing is completed on the last one. Integration of the properties of both of these environments, which is applied in many industries such as robotic industries, is not investigated separately. First, we develop a mathematical model for the problem and then we present three methods to solve it. Therefore, we construct simulated annealing (SA), genetic algorithm (GA) and a bottleneck based heuristic (BB) algorithms that solve the problem. Finally, the efficiency of the proposed methods is numerically analyzed.

A natural starting strategy for Aronson's heuristic 1 for solving the deterministic, single operator, multiple machine, multiple run cyclic scheduling

Online scheduling of operations is essential to optimize productivity of flexible manufacturing systems (FMSs) where manufacturing requests arrive on the fly. An FMS processes products according to a particular flow through processing stations. This work focusses on online scheduling of re-entrant FMSs with flows using processing stations where products pass twice and with limited buffering between processing stations. This kind of FMS is modelled as a re-entrant flow shop with due dates and sequence-dependent set-up times. Such flow shops can benefit from minimization of the time penalties incurred from set-up times. On top of an existing greedy scheduling heuristic we apply a meta-heuristic that simultaneously explores several alternatives considering trade-offs between the used metrics by the scheduling heuristic. We identify invariants to efficiently remove many infeasible scheduling options so that the running time of online implementations is improved. The resulting algorithm is much faster than the state of the art and produces schedules with on average 4.6% shorter makespan.

With the combination of the extension method and multi-agent, the assemble process of the motor and its subassembly was analyzed to build the corresponding Agent and multi-Agent System model; The architecture of the flexible assembly line was established based on the established model. Finally, with the technology of fieldbus and industrial control, a flexible assembly line control system was developed based on the Profibus-DP and it provided the effective solution for the combination of theory and practical application and the applicable model for the establishment of the architecture of the flexible assembly line.

After the research of schedule problem of one kind of flexible motorcycle assembly lines, a kind of parallel conveyor belt flow shop schedule model using JIT (just-in-time) is given. The impacts of the different max process paces of different work pieces in different stations are considered. Besides, some actual situations, including unstable information and structural constrain, are also considered. Then a kind of improved artificial fish swarm algorithm based on polar coordinate coding is presented to optimise this schedule model. Lastly, this model is tested with the real data from two motorcycle assembly lines, and the result shows that the total production fee is reduced.