Intelligent scheduling model and algorithm for manufacturing

This paper puts forward an intelligent scheduling model based on Hopfield neural network and a unified algorithm for manufacturing. The energy computation function and its dynamic state equation are derived and discussed in detail about their coefficients (parameters) and steps (Delta t) in iteration towards convergence. The unified model is focused on the structure of the above function and equation, in which the goal and penalty items must be involved and meet different schedule models. The applications to different schedule mode including jobshop static scheduling, scheduling with due-date constraint or priority constraint, dynamic scheduling, and JIT (just in time) scheduling are discussed, and a series of examples with Gantt charts are illustrated.     

MRP in a job shop environment using a resource constrained project scheduling model

One of the most difficult tasks in a job shop manufacturing environment is to balance schedule and capacity in an ongoing basis. MRP systems are commonly used for scheduling, although their inability to deal with capacity constraints adequately is a severe drawback. In this study, we show that material requirements planning can be done more effectively in a job shop environment using a resource constrained project scheduling model. The proposed model augments MRP models by incorporating capacity constraints and using variable lead time lengths. The efficacy of this approach is tested on MRP systems by comparing the inventory carrying costs and resource allocation of the solutions obtained by the proposed model to those obtained by using a traditional MRP model. In general, it is concluded that the proposed model provides improved schedules with considerable reductions in inventory carrying costs.     

A pairwise interchange algorithm for parallel machine scheduling

This paper considers the problem of non-preemptive scheduling n tasks on m identical parallel processors to minimize makespan for simultaneous arrivals. Based on a pairwise interchange method, an efficient algorithm ispresented which is able to give a near-optimal schedule in a short time through suitable pairwise interchange between tasks, after an initial solution is constructed. The behaviour of the algorithm is discussed. Testing results prove its high performance in comparison with available simple heuristic procedures. Finally, the algorithm is generalized for the problems of non-identical processors and non-simultaneous arrivals.     

Simulation-based production schedule generation

Abstract

While simulation has been used in manufacturing for many years, predominantly for facility design, it is within the last few years, that simulation languages have been developed to a point where they can be used on a day-to-day basis to generate schedules and predict their performance. This paper describes our use of different modelling techniques to develop a production schedule generation system. An example describing a system for a small- to medium-sized order producing company using SIMAN/CINEMA is included. While addressing the shortcomings of existing scheduling systems it is shown how the approach taken is a feasible way of creating a dynamic goal-driven simulation-based production scheduler. The paper does not aim to describe an ‘off the shelf’ scheduling system product, but rather to give an overview to methods, techniques and experiences which enable us rapidly to tailor a simulation-based scheduling system to the specific needs of a company.     

Combinatorial Optimization in Real-Time Scheduling: Theory and Algorithms

Real-time computer systems are essential for many applications, such as robot control, avionics, medical instrumentation, manufacturing, etc. The correctness of the system depends on the temporal correctness as well as the functional correctness of the task executions. In order to assure temporal correctness it is necessary that the resources be scheduled to meet the temporal requirements of applications. When we consider the problem of nonpreemptive scheduling of a set of tasks in a processor for which no feasible solution exists, some tasks may have to be rejected so that a schedule can be generated for the rest. In this paper, we consider the problem of generating an optimal schedule such that the number of rejected tasks is minimized, and then the finish time is minimized for the accepted tasks. We propose to use an analytic approach to solve this problem. We first discuss the super sequence based technique which was originally proposed for reducing the search space in testing the feasibility of a task set. Then we show by the Conformation theorem that the super sequence constructed from the task set also provides a valid and reduced search space for the optimization problem. While the complexity of our scheduling algorithm in the worst case remains exponential, our simulation results show that the cost is reasonable for the average case.     

Scheduling algorithms for computer-aided line balancing in printed circuit board assembly

Generalized flexible flow line (GFFL) is a scheduling environment comprising several machine banks which the products visit in the same order but can skip some machine banks. The type of machines in a bank can differ but they are suitable for performing the same manufacturing tasks. To change one product to another demands a set-up operation of the machine. This paper describes several scheduling algorithms for the GFFL problem. The overall structure of these algorithms is similar, consisting of machine allocation and sequencing phases. The algorithms have been integrated into an interactive production scheduling system for electronics assembly. Sample cases are used to illustrate the operation of the system in practice.     

Flow shop scheduling by transportation model

This paper describes the classical problem of scheduling n jobs on m machines in a flow shop. A schedule evaluation algorithm is presented, which for job-pairs, generates a schedule evaluation matrix. The matrix is the input data to a transportation problem, the solution of which gives near-optimal jobsequence and makespan. The performance of the algorithm is discussed.     

A Model for Master Production Scheduling in Uncertain Environments

In uncertain environments, the master production schedule (MPS) is usually developed using a rolling schedule. When utilizing a rolling schedule, the MPS is replanned periodically and a portion of the MPS is frozen in each planning cycle. The cost performance of a rolling schedule depends on three decisions: the choice of the replanning interval (R), which determines how often the MPS should be replanned; the choice of the frozen interval (F), which determines how many periods the MPS should be frozen in each planning cycle; and the choice of the forecast window (T), which is the time interval over which the MPS is determined using newly updated forecast data. This paper uses an analytical approach to study the master production scheduling process in uncertain environments without capacity constraints, where the MPS is developed using a rolling schedule. It focuses on the choices of F, R, and T for the MPS. A conceptual framework that includes all important MPS time intervals is described. The effects of F, R, and T on system costs, which include the forecast error, MPS change, setup, and inventory holding costs, are also explored. Finally, a mathematical model for the MPS is presented. This model approximates the average system cost as a function of F, R, T, and several environmental factors. It can be used to estimate the associated system costs for any combination of F, R, and T.     

Flexible reasoning using sensible agent-based systems: A case study in job flow scheduling

Planning and control systems for highly dynamic and uncertain manufacturing environments require adaptive flexibility and decision-making capabilities. Modern distributed manufacturing systems assess the utility of planning and executing solutions for both system goals (e.g. minimize manufacturing production time for all parts or minimize WIP) and local goals (e.g. expedite part A production schedule or maximize machine X utilization). Sensible Agents have the ability to alter their autonomy levels to choose among a set of decision models in order to handle the differences between local and system goals. In this paper, Sensible Agents are applied to a production planning and control problem in the context of job shop scheduling and decision model theory. Sensible Agents provide for trade-off reasoning mechanisms among system and local utilities that are flexible and responsive to an agent's abilities, situational context and position in the organizational structure of the system.     

Analysis of the List Scheduling Algorithm for Precedence Constrained Parallel Tasks

Given P processors, and a set of precedence constrained parallel tasks with their processor requirements and execution times, the problem of scheduling precedence constrained parallel tasks on multiprocessors is to find a nonpreemptive schedule of the tasks on a multiprocessor with P processors, such that the schedule length is minimized. We show that for many heuristic choices of the initial priority list, the list scheduling algorithm has worst-case performance ratio P, which is unbounded as P gets large. However, it is also shown that when task sizes are bounded from above by a fraction of P, the list scheduling algorithm has finite worst-case performance ratio. In particular, we prove that if all tasks request for no more than qP processors, where 0 < q < 1, then the worst-case performance ratio of the list scheduling algorithm is no larger than

A variability taxonomy to support automation decision-making for manufacturing processes

Abstract

Although many manual operations have been replaced by automation in the manufacturing domain in various industries, skilled operators still carry out critical manual tasks such as final assembly. The business case for automation in these areas is difficult to justify due to increased complexity and costs arising out of process variabilities associated with those tasks. The lack of understanding of process variability in automation design means that industrial automation often does not realize the full benefits at the first attempt, resulting in the need to spend additional resource and time, to fully realize the potential. This article describes a taxonomy of variability when considering the automation of manufacturing processes. Three industrial case studies were analyzed to develop the proposed taxonomy. The results obtained from the taxonomy are discussed with a further case study to demonstrate its value in supporting automation decision-making.     

Evaluation of safety stock methods in multilevel material requirements planning (MRP) systems

This paper evaluates alternative methods of establishing the safety stock level taking into consideration of historical measures of forecasting accuracy and the needs for master production scheduling and material requirement planning under a rolling time horizon. A computer model is used to simulate the forecasting, master production scheduling and material planning activities in a company that produces to stock and the production activities are managed by multilevel MRP systems. The simulation output is analysed to evaluate the impact of safety stock methods on MRP system performance. The result of the study shows that using safety stock can help to reduce total cost, schedule instability and improve service level in the MRP systems. Guidelines are developed to help managers select methods to determine safety stock in MRP system operations.     

Experimental earliness/tardiness production planning with the due window system

In order to use the philosophy of JIT to improve the production planning method of MRP-II, we propose the experimental software system of the earliness/tardiness produc tion planning problem with due window. By means of the approaches and model reported in this paper, the optimal production planning can be achieved. The recommended model extends the problem of due window from the shop scheduling level into the aggregated planning level of mass manufacturing systems. Simulation results have demonstrated that the experimental software is a useful tool for the production management of repetitive manufacturing enterprises.     

Development of demand pull type control methods: The design of block action production control methods to reduce throughput time and improve supply chain synchronization

In this paper the implementation of time based manufacturing strategies and pull type control systems in an organization manufacturing electrical switchgear built to customers individual requirements are discussed in detail. The concept of block control to synchronize the production time of all internally and externally sourced components and materials is also introduced in an attempt to simplify production control methods and reduce throughput time. Savings in time and cost are described during a twenty months period of restructuring. Foundations were laid for a second objective which is an attempt to gain some linkage between throughput time reduction and resultant production costs.     

带工期指派的产品服务系统订单随机调度问题研究

针对由一个制造工厂和多个区域服务中心组成的服务型制造企业,研究了考虑生产时间和服务时间均具有随机性且工期可指派的产品服务系统（PSS）订单调度问题。首先以最小化订单提前、误工和工期指派费用的期望总额为目标构建问题的优化模型,然后分析目标函数近似值的最优性条件,据此提出加权最短平均生产时间排序规则,并结合该规则与插入邻域局部搜索设计了启发式算法对问题进行求解,最后通过数值仿真验证算法的可行性和有效性。研究表明,提前费用偏差对PSS订单调度与工期指派决策的影响很小,因此企业管理者无需准确估计库存费用也能制定出比较有效的PSS订单调度策略;而工期指派费用偏差对决策结果的影响非常大,因此企业管理者在决策时必须谨慎估计该项费用。     

A closed loop automatic scheduling system (CLASS)

Abstract

CLASS is a production scheduling system, that is designed to function in either a stand-alone manner, or in conjunction with an MRP system. MRP systems innately do not have 'closed loop’ capability in the sense of being able to produce master schedules and order releases that are consistent and that respect capacity constraints. True closed loop performance requires detailed scheduling. In addition to interfacing with MRP systems, CLASS is designed to produce schedules that can be used in conventional shops or can be downloaded to automated facilities. The design goals for the system, its internal architecture, and its role in manufacturing control systems are described. The modelling and decision capabilities     

Productivity gains by cellular manufacturing

This paper discusses some of the major factual results and discussions of the effects of implementation of a cellular manufacturing environment on the productivity of people, management and facilities, as was experienced in this study. It is suggested that significant improvements at very low costs are possible at managerial, and other work force levels, by introduction of appropriate production management systems. This is based on analysis of the changes in production costs and more importantly the behaviour of employees brought about by a first time implementation of cellular manufacturing in a company.     

A New Framework for Manufacturing Planning and Control Systems*

This paper presents a new framework for manufacturing planning and control systems which we call iterative manufacturing planning in continuous time (IMPICT) that appears to have several advantages over the well-known material requirements planning (MRP) framework. IMPICT explicitly considers capacity constraints and total system cost (including tardiness) to determine order sizes, order release/due dates, and operation schedules in a deterministic, multi-level, finite horizon, dynamic demand environment. Continuous time scheduling variables allow setups to be carried over from one period to the next. Three new heuristics built on the IMPICT framework are presented and tested in a simulation-based, full-factorial experiment with a wide variety of problem environments. The benchmark for the experiment was materials requirements planning with operations sequencing (MRP/OS) implemented with best-case, fixed planned lead times. The experiment showed that all three heuristics were statistically better than MRP/OS. The total cost for the order merging (OM) heuristic was 25 percent better than the total cost for MRP/OS. Computational times for OM were substantially larger than for MRP/OS; however, the computational times in the experiment suggest that OM is still computationally viable for large-scale batch manufacturing environments found in industry. IMPICT is superior to standard MRP systems because it explicitly considers capacity constraints and total system costs when it creates a materials plan. IMPICT is superior to linear programming-based approaches to finite loading and scheduling found in the literature because it allows setups to be carried over from one period to another and because it is computationally viable for realistic-sized problems.     

Is the scheduling task context-dependent? A survey investigating the presence of constraints in different manufacturing contexts

Abstract

Most studies regarding real scheduling constraints only consider the constraints related to the specific manufacturing environment studied, limiting the possibility of drawing general conclusions. A survey of 50 companies was conducted in order to discover which constraints were present and what their relationship was with the scheduling context. This paper investigates which practical scheduling constraints are present in the manufacturing industry and whether the scheduling task is context-dependent. Results of this study show that some practical production constraints are context-dependent.     

Priority Rules for Multi‐Task Due‐Date Scheduling under Varying Processing Costs

We study the scheduling of multiple tasks under varying processing costs and derive a priority rule for optimal scheduling policies. Each task has a due date, and a non‐completion penalty cost is incurred if the task is not completely processed before its due date. We assume that the task arrival process is stochastic and the processing rate is capacitated. Our work is motivated by both traditional and emerging application domains, such as construction industry and freelance consulting industry. We establish the optimality of Shorter Slack time and Longer remaining Processing time (SSLP) principle that determines the priority among active tasks. Based on the derived structural properties, we also propose an effective cost‐balancing heuristic policy and demonstrate the efficacy of the proposed policy through extensive numerical experiments. We believe our results provide operators/managers valuable insights on how to devise effective service scheduling policies under varying costs.