Heuristic procedures for reactive project scheduling

This paper describes new heuristic reactive project scheduling procedures that may be used to repair resource-constrained project baseline schedules that suffer from multiple activity duration disruptions during project execution. The objective is to minimize the deviations between the baseline schedule and the schedule that is actually realized.We discuss computational results obtained with priority-rule based schedule generation schemes, a sampling approach and a weighted-earliness tardiness heuristic on a set of randomly generated project instances.     

Heuristic search for scheduling flexible manufacturing systems using lower bound reachability matrix

For scheduling flexible manufacturing systems efficiently, we propose new heuristic functions for A^* algorithm that is based on the T-timed Petri net. In minimizing makespan, the proposed heuristic functions are usually more efficient than the previous functions in the required number of states and computation time. We prove that these heuristic functions are all admissible and one of them is more informed than that using resource cost reachability matrix. We also propose improved versions of these heuristic functions that find a first near-optimal solution faster. In addition, we modify the heuristic function of Yu, Reyes, Cang, and Lloyd (2003b) and propose an admissible version in all states. The experimental results using a random problem generator show that the proposed heuristic functions perform better as we expected.     

Fuzzy-based methodology for multi-objective scheduling in a robot-centered flexible manufacturing cell

A fuzzy logic based methodology for generating the sequence of part movements in a multi-product batch processing through a computerized machine cell is presented in this paper. A number of production objectives are taken into account. Two fuzzy based strategies: fuzzy-job and fuzzy-machine are proposed and their performance is compared to two well known dispatching rules such as SPT (Shortest Processing Time) and WEED (Weighted Earliest Due Date). The sequencing algorithm was implemented on a standard personnel computer and the scheduler was interfaced to a robot controller for implementing loading and unloading strategy within the cell. The proposed fuzzy-based methodologies especially fuzzy-job shows a superior performance compared to the traditional dispatching rules considered.     

An agent-based framework for global purchasing and manufacturing in a shoe industry

Global competition is forcing the present day industry to produce high quality product more fast and inexpensively. The need for fast information exchange has increased among the remote locations for the cooperation and coordination. In this study, a virtual global manufacturing system in a shoe company is constructed that distributes production schedule among remote places, acts as a bridge between the headquarters and manufacturing plants, distributes tasks and collates different solutions between demand and supply using an agent. It generates an order and a procurement plan. An order-supply agent is proposed and implemented to automate the process of ordering and supplying of goods in a shoe making company from the Internet environment.     

Application of videotex in a manufacturing company

The article describes the use of videotex throughout ICI, a major UK manufacturing company. Among the applications discussed are access to up-to-date technical information and customer sales statistics and the use of videotex as a messaging system for brief exchanges of all kinds of information.     

Heuristic approaches for master planning in semiconductor manufacturing

In this paper, we propose heuristic approaches for solving master planning problems that arise in semiconductor manufacturing networks. The considered problem consists of determining appropriate wafer quantities for several products, facilities, and time periods by taking demand fulfillment (i.e., confirmed orders and forecasts) and capacity constraints into account. In addition, fixed costs are used to reduce production partitioning. A mixed-integer programming (MIP) formulation is presented and the problem is shown to be NP-hard. As a consequence, two heuristic procedures are proposed: a product based decomposition scheme and a genetic algorithm. The performance of both heuristics is assessed using randomly generated test instances. It turns out that the decomposition scheme is able to produce high-quality solutions, while the genetic algorithm achieves results with reasonable quality in a short amount of time.     

Heuristic algorithms for assigning and scheduling flight missions in a military aviation unit

We consider an operations planning problem in a military aviation unit that performs a number of flight missions with multiple identical aircrafts. The problem is to assign the flight missions to the aircrafts and to schedule these assigned missions on each aircraft. Sequence-dependent setup times are required between the missions, and multiple aircrafts may be needed for a mission, but the aircrafts assigned to the same mission should start the mission simultaneously. We develop heuristic algorithms for the problem with the objective of minimizing makespan, i.e., the time by which all the missions have been completed. For evaluation of the performance of the algorithms, a series of computational tests was performed on a number of problem instances, and results show that the proposed algorithms give good or near optimal solutions in a reasonable amount of time.     

Effect of cell based team work in productivity improvement at a manufacturing company

This paper report is part of a comprehensive study aiming at establishing a productivity improvement model for an Iranian manufacturing company. We firstly describe the state of affairs encompassing the effects of the prevailing behavioural system which is in turn affected by cultural and environmental conditions, impact of history, geographical situation, state of technology and people's attitude. A team work effort is organised around a cellular manufacturing exercise to demonstrate an effective change in the performance and esisting conditions.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

Knowledge based system for scheduling and control of an automated manufacturing cell

Cell Manager (Cm) is a prototype knowledge based system for real-time control and short-term planning of an automated manufacturing facility. It uses background of mathematical programming, simulation, and heuristic search. The Cm engine allows tracking of time related events and facts in discrete or continuous time. The use of Cm is illustrated on a cell that processes discrete batches of parts. There are four workstations and the material handling system consists of automated guided vehicles.     

Metaheuristics for scheduling a non-permutation flowline manufacturing cell with sequence dependent family setup times

The broad applications of cellular manufacturing make flowline manufacturing cell scheduling problems with sequence dependent family setup times a core topic in the field of scheduling. Due to computational complexity, almost all published studies focus on using permutation schedules to deal with this problem. To explore the potential effectiveness of treating this argument using non-permutation schedules, three prominent types of metaheuristics—a simulated annealing, a genetic algorithm and a tabu search—are proposed and empirically evaluated. The experimental results demonstrate that in general, the improvement made by non-permutation schedules over permutation schedules for the due-date-based performance criteria were significantly better than that for the completion-time-based criteria. The results of this study will provide practitioners a guideline as to when to adopt a non-permutation schedule, which may exhibit better performance with additional computational efforts.     

Design of a suitable production management system for a manufacturing company

This paper reports on the design of a suitable production management system (PMS) for a manufacturing company, located in Western Australia. The company was experiencing problems in scheduling and plant layout leading to further problems in materials flow, labour control, inventory and purchasing, material handling system, and production space.

Group technology (GT) was used to design a new plant layout. A G.T. algorithm was developed to minimise machine duplications. A Kanban system was designed to assist execution of scheduling based on a pull system. The design was tested by simulation using actual data collected on existing operations.     

Combined mathematical programming and heuristics for a radiotherapy pre-treatment scheduling problem

Demand for radiotherapy treatment services has increased due to higher cancer incidence, ageing population, and a larger number of prescribed radiotherapy fractions. This higher demand can, in turn, produce longer waiting times for radiotherapy treatment. We introduce a real-world radiotherapy pre-treatment scheduling problem at a hospital in the UK. This scheduling problem is modelled as an optimisation problem with multiple objectives. The objectives are hierarchical, thus the solution approach is based on solving a series of single-objective optimisation scheduling problems. Each of these problems is formulated as a mathematical programming model. Dispatching rules were introduced to produce an initial solution for the mathematical programming model. Their performance is compared to the mathematical programming approach.     

A mathematical programming model for manufacturing cell formation to develop multiple configurations

This paper presents and analyses a mathematical model for the design of manufacturing cells which considers two conflicting objectives such as the heterogeneity of cells and the intercell moves. A genetic algorithm (GA) based solution methodology is developed for the model which is also solved using an optimization package. The model is suitable for getting multiple potential solutions in a structured way for the cell formation problem by making a trade-off between the two objectives, instead of reaching at a single negotiating solution. This model provides the decision maker the flexibility of choosing a suitable cell design from different alternatives by considering the practical constraints. A part assignment heuristic is also developed by which part-families can be identified and is integrated with the GA based solution procedure. A comparison of the proposed method is made with other seven methods using 36 problems from the literature. Grouping efficacy is the basis for comparison and it is found to give reasonably good results.     

Network modeling and evolutionary optimization for scheduling in manufacturing

Scheduling is one of the most important fields in Advanced Planning and Scheduling or a manufacturing optimization. In this paper, we propose a network modeling technique to formulate the complex scheduling problems in manufacturing, and focus on how to model the scheduling problems to mathematical formulation. We propose a multi-section evolutionary algorithm for the scheduling models formulated by network modeling. Through a combination of the network modeling and this multi-section evolutionary algorithm, we can implement the auto-scheduling in the manufacturing system. The effectiveness and efficiency of proposed approach are investigated with various scales of scheduling problems by comparing with recent related researches. Lastly, we introduced service-oriented evolutionary computation architecture software. It help improved the evolutionary computation??s availability in the variable practical scheduling in manufacturing.     

A decision support system for personnel scheduling in a manufacturing environment

A Decision Support System (DSS) is developed to support managers in the task of scheduling labor in the area of manufacturing. The DSS is designed to generate labor requirements by worker category and work center based on master production schedules. It is a PC-based, menu-driven program that generates a capacity plan based on data supplied by the user of the system.     

Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment

This paper proposes a fuzzy rule-based system for an adaptive scheduling, which dynamically selects and applies the most suitable strategy according to the current state of the scheduling environment. The adaptive scheduling problem is generally considered as a classification task since the performance of the adaptive scheduling system depends on the effectiveness of the mapping knowledge between system states and the best rules for the states. A rule base for this mapping is built and evolved by the proposed fuzzy dynamic learning classifier based on the training data cumulated by a simulation method. Distributed fuzzy sets approach, which uses multiple fuzzy numbers simultaneously, is adopted to recognize the system states. The developed fuzzy rules may readily be interpreted, adopted and, when necessary, modified by human experts. An application of the proposed method to a job-dispatching problem in a hypothetical flexible manufacturing system (FMS) shows that the method can develop more effective and robust rules than the traditional job-dispatching rules and a neural network approach.     

Project-driven planning and scheduling support for virtual manufacturing

The paper addresses the issue of decision-making support for small and medium-size enterprises operating within a virtual project-driven enterprise environment. The problem considered here can be defined in terms of finding a feasible schedule that satisfies the constraints imposed by the work-order duration, the price, and the time-constrained resource availability. The problem belongs to the class of multi-mode case problems of project scheduling, where finding a feasible solution is NP-hard. A heuristic method for process planning and scheduling is proposed. The method is based on a critical path approach and the branch and bound search scheme. It has been implemented in a web-enabled interactive software package, and is illustrated using the example of a virtual construction enterprise. Received: February 2005 / Accepted: January 2006     

A supply network-modelling system for a small- to medium-sized manufacturing company

During the late 1990s and early 2000s, the profile of global manufacturing has experienced many changes. There is anecdotal evidence that many western manufacturing companies have chosen to expand their manufacturing base across geographical boundaries. The common reasons sited for these ventures are to exploit less expensive labour markets, to establish a presence in expanding markets and in response to the threat of new competition. Whilst a global manufacturing base can prove to have many cost and sales benefits, there are also many disadvantages. Logistics operations can often increase in complexity leading to higher reliance on planning and effective interpretation of demand data. In response, systems modelling has remerged as a fertile research area after many years. Many modelling and simulation techniques have been developed, but these have had very limited practical success. The authors have identified that majority of these simulation techniques rely upon a detailed market structure being known, when this is rarely the case. This paper describes the outcome of a research project to develop of a pragmatic set of tools to gather, assess and verify supply chain structure data. A hybrid collection of technologies are utilised to assist these operations and to build a dynamic supply network model.     

Production scheduling and inventory control for multi-stage manufacturing systems

In this paper, singular perturbation methods are used to synthesize high-gain error-actuated production control policies for a class of multi-stage manufacturing systems in which each manufacturing sub-system comprises either one or several production facilities. It is shown that when the manufacturing sub-systems are connected in cascade, each sub-system can be controlled in isolation, but that production control policies based on this approach lead to undesirable transient behaviour if the value of the feedback gain parameter is small. An alternative approach whereby production control policies are synthesized for the composite system leads to improved transient behaviour. However, if the value of the feedback gain parameter is large, both approaches are shown to yield good transient behaviour. In addition, it is demonstrated that trade-off between rapid adjustments in production rates and large inventory-level deviation can be achieved by selecting an appropriate value for the feedback gain parameter.