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.     

Route‐Independent Analysis of Available Capacity in Flexible Manufacturing Systems

In a job shop, because of large setup times, each operation is assigned to only one machine. There is no alternative routing. In a flexible manufacturing system, each manufacturing operation can often be performed on several machines. Therefore, with automated equipment, the capacity of a machine to perform certain operations is not independent of the capacity of other machines. Often, however, operations managers can use a route‐independent answer to production planning questions. For example, how much can be produced of a certain part type and when are important capacity questions in business negotiations, when the detailed routing and scheduling are not yet of interest or cannot be known. This paper provides a mathematical model for the route‐independent analysis of the capacity of flexible manufacturing systems based on a concept of operation types. An example is provided both to illustrate the use of operation types and to highlight the differences between the traditional route‐dependent and the proposed route‐independent formulations of capacity constraints. Some computational results are also given. Finally, a sensitivity analysis is developed to analyze the feasibility of production plans when production requirements and machine capacities can change.     

Shop floor characteristics influencing the use of advanced planning and scheduling systems

The purpose of this article is to investigate how the manufacturing process, the shop type and the data quality, i.e. the shop floor characteristics, influence the use of advanced planning and scheduling (APS) systems in production activity and control (PAC). The methodology implemented is a multiple case study at three case companies. Each company has different shop floor characteristics, but all use a scheduling module in an APS system, which supports production scheduling. A theoretical framework is developed suggesting how APS system are used in the PAC activities, and which major aspect to consider. The case analysis shows that the scheduling module in APS system, foremost supports sequencing and dispatching. In particular, the shop type is influenced by the decision of how often the APS runs and what freedom is given to the shop floor. The manufacturing process influences how the dispatch list is created. Contrary to the literature presuming that APS systems are most suitable in job shop processes, it is found that the manufacturing process is not a crucial factor when deciding whether APS systems are an appropriate investment. It is found that the level of data quality needed in the APS system depends to a large extent on how the dispatch list is used. For example, is the dispatch list used as a guideline, not a regulation, the need for accurate data in the module is reduced. This article extends the previous literature concerning APS systems by analysing how APS systems influence PAC as a whole and increase the understanding of the challenges of using APS systems in PAC.     

A Virtual Cellular Manufacturing Approach to Batch Production*

In this paper, Virtual Cellular Manufacturing (VCM), an alternative approach to implementing cellular manufacturing, is investigated. VCM combines the setup efficiency typically obtained by Group Technology (GT) cellular manufacturing (CM) systems with the routing flexibility of a job shop. Unlike traditional CM systems in which the shop is physically designed as a series of cells, family-based scheduling criteria are used to form logical cells within a shop using a process layout. The result is the formation of temporary, virtual cells as opposed to the more traditional, permanent, physical cells present in GT systems. Virtual cells allow the shop to be more responsive to changes in demand and workload patterns. Production using VCM is compared to production using traditional cellular and job shop approaches. Results indicate that VCM yields significantly better flow time and due date performance over a wide range of common operating conditions, as well as being more robust to demand variability.     

A mathematical model for job shop scheduling with multiple process plan consideration per job

In this paper, a mixed integer programming model is formulated for scheduling a set of jobs through a shop when each job is supplied or provided with multiple process plans or process routings. Simultaneous selection of a process plan for each job and the sequencing of the jobs through the machines in the shop based on the set of selected process plans is addressed. The procedure developed seeks to integrate the selection of machines for each job and the sequencing of jobs on each machine based on the objective of minimizing production makespan. the application of the procedure is demonstrated with an example problem. The following conclusions were drawn as a result of the research: (1) the procedure developed produces optimal or near optimal solution; (2) the benefit from the developed approach is that it allows a shop to adaptively select process plans for jobs to optimize on production makespan. By combining solution quality with scheduling flexibility and efficiency, the productivity of a shop can be greatly enhanced.     

Evaluation of scheduling strategies for a dynamic job shop in a tool-shared,flexible manufacturing environment

A two-phase approach is used to examine the impact of job scheduling rules and tool selection policies for a dynamic job shop system in a tool-shared, flexible manufacturing environment. The first phase develops a generalized simulation model and analyses 'simple' job scheduling rules and tool selection policies under various operating scenarios. The results from this investigation are then used to develop and analyse various bi-criteria rules in the second phase of this study. The results show that the scheduling rules have the most significant impact on system performance, particularly at high shop load levels. Tool selection policies affect some of the performance measures, most notably, proportion of tardy jobs, to a lesser degree. Higher machine utilizations can be obtained at higher tool duplication levels but at the expense of increased tooling costs and lower tool utilization. The results also show that using different processing time distributions may have a significant impact on shop performance.     

Minimizing tardiness on a single processor with sequence-dependent setup times: a simulated annealing approach

In today's fast-paced Just-In-Time and mass customization manufacturing in a sequence-dependent setup environment, the challenge of making production schedules to meet due-date requirements is becoming a more complex problem. Unfortunately, much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. This paper considers the problem of minimizing tardiness, a common measure of due-date performance, in a sequence-dependent setup environment. Simulated annealing was used to solve the sequencing problem, and its performance was compared with random search. Our experimental results show that the algorithm can find a good solution fairly quickly, and thus can rework schedules frequently to react to variations in the schedule. The algorithm is invaluable for ‘on-line’ production scheduling and ‘last-minute’ changes to production schedule. The results of this research also suggest ways in which more complex and realistic job shop environments, such as multiple machines with a higher number of jobs in the sequence, and other scheduling objectives can be modeled. This research also investigates computational aspects of simulated annealing in solving complex scheduling problems.     

Order Dispatching and Labor Assignment in Cellular Manufacturing Systems

Although order and labor dispatching in the job shop manufacturing setting have been investigated extensively over the last three decades, its representation of actual processes found in practice today is limited due to the move to cellular manufacturing (CM). Manufacturing cells have become an important approach to batch manufacturing in the last two decades, and their layout structure provides a dominant flow structure for the part routings. The flow shop nature of manufacturing cells adds a simplifying structure to the problem of planning worker assignments and order releases, which makes it more amenable to the use of optimization techniques. In this paper we exploit this characteristic and present two mathematical modeling approaches for making order dispatching and labor assignment/reassignment decisions in two different CM settings. The two formulations are evaluated in a dynamic simulation setting and compared to a heuristic procedure using tardiness as the primary performance measure. The formulations are superior to the heuristic approach and can be incorporated into detail scheduling systems that are being implemented by corporations employing enterprise resource planning (ERP) systems today.     

Interactive Job Shop Scheduling: An Experiment

An experiment is discussed in which three computer-aided, interactive job shop scheduling approaches are compared using an interactive job shop scheduling simulator (JOB) developed for the project. All three approaches use a combination of computer and human capabilities to develop job shop schedules, but differ in terms of the timing and degree of human involvement required. The three scheduling approaches are (1) the successive approach, (2) the interactive approach, and (3) the semi-interactive approach. The successive approach is characterized by the computer scheduling all work orders without any human intervention. The interactive approach is distinguished by the human scheduling one work order at a time until all work orders are scheduled. The schedule is developed interactively by the person who must simultaneously consider work-order scheduling needs and machine group load capacities. The semi-interactive approach may be viewed as a combination of the successive and interactive approaches. Work orders are automatically scheduled one at a time using the successive approach criteria, but with prespecified machine-group load thresholds. As long as the load threshold is not exceeded, the successive approach is used to schedule work orders. When a threshold is exceeded, the algorithm (successive approach) pauses and human rescheduling (interactive approach) is required to rectify the overload situation. A second (reallocation) phase, identical for all three approaches, is used to overcome any scheduling problems generated in phase one. Experimental results based on nine different performance criteria (including scheduling time, makespan, machine group utilization, and work-in-process inventory) and 45 experimental runs indicate that there are differences between the results produced by the three scheduling approaches. The interactive approach yields the best overall scheduling results, but the other two approaches are clearly better than the interactive approach in some situations. The success of the interactive approach indicates that it is usually best for the human scheduler to become involved early in the computer-based job shop scheduling process.     

A STOCHASTIC DOMINANCE ORDERING OF SCHEDULING RULES

This paper applies stochastic dominance (SD) preference-ordering criteria to job shop scheduling rules. A simulation model of a hypothetical dual-constrained job shop is used to derive several measures of shop performance for a number of dispatching/due-date scheduling policies. The results presented suggest that previous research conclusions concerning the relative performance of dispatching scheduling rules may need to be reconsidered if production schedulers are risk-averse utility maximizers.     

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.     

Priority class scheduling: Production scheduling for multi-objective environments

A new scheduling framework (priority class scheduling) is devised which reduces the conflict in the manufacturingmarketing interface. Priority class scheduling rewards manufacturing for making small improvements in efficiency, involves marketing in the setting of production priorities, improves customer service by putting in place realistic production timetables, allows manufacturing to concentrate on improving both quality and efficiency, and improves both the communication and cooperation between the shop floor, the production scheduler, marketing, and customer service. Within a production period, all jobs are classified into priority classes based on both their due dates and importance. Production is scheduled within each production period to maximize throughput subject to the priority class constraints- if any job in priority class i is started in the production period, then all jobs in priority class i - 1 must be completed within the production periods.     

MULTI-MACHINE,MULTI-PRODUCT PRODUCTION SCHEDULING AND INVENTORY CONTROL

This paper deals with a multi-machine, multi-product lot size determination and scheduling problem. The model developed here considers not only the usual inventory-related operational cost, but also the costs that depend on under- or over-utilization of available men and machines. It penalizes overtime or idle time at any facility. The solution minimizes the inventory and resource-related costs and not just inventory costs. A heuristic is developed to determine the solution from the model and to modify it, as necessary, to obtain a conflict-free, repetitive, and cyclic production schedule for an infinite horizon. Although this model is developed for a manufacturing situation, the words machine, job, and machine shop are used in a symbolic sense, and hence the model can be used in practice in a variety of circumstances.     

Operation-based flowtime estimation in a dynamic job shop

In the scheduling literature, estimation of job flowtimes has been an important issue since the late 1960s. The previous studies focus on the problem in the context of due date assignment and develop methods using aggregate information in the estimation process. In this study, we propose a new flowtime estimation method that utilizes the detailed job, shop and route information for operations of jobs as well as the machine imbalance information. This type of information is now available in computer-integrated manufacturing systems. The performance of the proposed method is measured by computer simulation under various experimental conditions. It is compared with the existing flowtime estimation methods for a wide variety of performance measures. The results indicate that the proposed method outperforms all the other flowtime estimation methods. Moreover, it is quite robust to changing shop conditions (i.e., machine breakdowns, arrival rate and processing time variations, etc.). A comprehensive bibliography is also provided in the paper.     

An intelligent workstation controller for integrated planning and scheduling of FMS cell

This paper presents an experimental design developed to determine a combination of robust planning and scheduling rules for an intelligent workstation controller (IWC). The IWC is used as part of the control system for an automated flexible manufacturing system. A three-level hierarchical control structure (shop, workstation and equipment)is adopted in order effectively to control a shop-floor. At the top level is a shop controller which receives orders and their associated manufacturing information, and manages interactions among workstations. The IWC defines and resolves the production control activities necessary to coordinate a group of equipment controllers so as to ensure the completion of orders. Specifically, the IWC is responsible for selecting a specific process routeing for each part, allocating resources, scheduling and coordinating the activities across the equipment, monitoring the progress of activities, detecting and recovering from errors, and preparing reports. These activities are accomplished using planning, scheduling, and execution functions. In order to demonstrate the effectiveness and robustness of the IWC, all the controllable and uncontrollable factors need to be identified and detailed. Controllable factors are those which are set by the controller and cannot be directly affected by the production environment. Uncontrollable factors are those which vary with the production environment and cannot be directly changed by the controller developer. The objective of the paper is to illustrate how a good set of controllable factors that dictate the IWC's effectiveness and robustness over various uncontrollable factors can be determined. Due to the number of possible combinations of all the factors, a complete validation of the effectiveness and robustness of the IWC is extremely time consuming and far beyond the scope of this paper. Therefore, a few planning and scheduling strategies are selected and a formal experiment is conducted. The experiment illustrates how significant performance dependencies for various planning and scheduling strategies can be identified.     

Multi-objective permutation flow shop scheduling problem: Literature review,classification and current trends

The flow shop scheduling problem is finding a sequence given n jobs with same order at m machines according to certain performance measure(s). The job can be processed on at most one machine; meanwhile one machine can process at most one job. The most common objective for this problem is makespan. However, many real-world scheduling problems are multi-objective by nature. Over the years there have been several approaches used to deal with the multi-objective flow shop scheduling problems (MOFSP). Hence, in this study, we provide a brief literature review of the contributions to MOFSP and identify areas of opportunity for future research.     

Shop Performance Implications of Using Cells,Partial Cells,and Remainder Cells*

This paper considers the application of cellular manufacturing (CM) to batch production by exploring the shop floor performance trade‐offs associated with shops employing different levels of CM. The literature has alluded to a continuum that exists between the purely departmentalized job shop and the completely cellular shop. However, the vast majority of CM research exists at the extremes of this continuum. Here, we intend to probe performance relationships by comparing shops that exist at different stages of CM adoption. Specifically, we begin with a hypothetical departmentalized shop found in the CM literature, and in a stepwise fashion, form independent cells. At each stage, flow time and tardiness performance is recorded. Modeling results indicate that, depending on shop conditions and managerial objectives, superior shop performance may be recorded by the job shop, the cell shop, or by one of the shops between these extreme points. In fact, under certain conditions, shops that contain partially formed cells perform better than shops that use completely formed cells. Additional results demonstrate that in order to achieve excellent performance, managers investigating specific layouts need to pay especially close attention to changes in machine utilization as machine groups are partitioned into cells.     

A DECISION THEORY APPROACH TO PRIORITY DISPATCHING FOR JOB SHOP SCHEDULING

Recent developments in the design of job shop scheduling systems have inspired a new approach to priority dispatching. The basis for the approach is in elementary decision theory: at each decision juncture define the alternative courses of action, evaluate the consequences of each alternative according to a given criterion, and choose the best alternative. The experimental results of a simulated single machine queueing system reinforce earlier findings that a decision theory approach represents a significant advance over conventional priority dispatching.     

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.     

A new three-machine shop scheduling: complexity and approximation algorithm

The paper investigates a new three-machine shop scheduling problem that arises from many production systems, such as the garment assembly line, etc. In such scenarios, each job consists of three operations, each of which has to be non-preemptively processed by one specific machine. In contrast with the classical three-machine shop scheduling, the processing order of the operations of each job is partially restricted. In particular, the first two operations are ordered and all the same for all jobs, while the third operation is not restricted. The objective is to minimize the makespan. We show the problem is NP-hard in the ordinary sense and present a polynomial time approximation algorithm with a worst case performance ratio of $\frac{3}{2}$ .