Rodney L. Martin

An FCM-FAHP approach for managing readiness-relevant activities for ERP implementation

An approach for managing readiness-relevant activities using FCM and FAHP is proposed.The approach models causal relationships between readiness-relevant activities.It also assesses how these activities contribute to the overall readiness.A management matrix for effective allocation of limited management efforts is provided.Scenario analysis was accomplished to work out a readiness improvement plan. Any organization which plans to introduce a new enterprise resource planning (ERP) system will carry out a range of activities to improve its readiness for the new system. This paper develops a new approach for managing these interrelated activities using fuzzy cognitive maps (FCMs) and the fuzzy analytical hierarchy process (FAHP). This approach enables the organization to (1) identify the readiness-relevant activities, (2) determine how these activities influence each other, (3) assess how these activities will contribute to the overall readiness and (4) prioritize these activities according to their causal interrelationships to allocate management effort for the overall readiness improvement. The approach first uses FCMs and a fuzzy connection matrix to represent all possible causal relationships between activities. It then uses FAHP to determine the contribution weights and uses FCM inference to include the effects of feedback between the activities. Based on the contribution and interrelationships between activities, a management matrix is developed to categorize them into four management zones for effective allocation of limited management efforts. An empirical study is conducted to demonstrate how the approach works.

ICLA imperialist competitive learning algorithm for fuzzy cognitive map: Application to water demand forecasting

In this paper, we develop a new Fuzzy Cognitive Map (FCM) learning method using the imperialistic competitive learning algorithm (ICLA). An FCM seems like a fuzzy signed directed graph with feedback, and models complex systems as a collection of concepts and causal relations between concepts. Conventional FCMs are mainly constructed by human experts who have experience in the specific problem domain. However, large problems need automated methods. We develop an automated method for FCM construction inspired by the socio-political behavior of countries as imperialists with colonies. In the real world imperialists extend their territories and change the socio attributes of their colonies. The ICLA is an evolutionary algorithm and simulates this behavior. We explain the algorithm for FCM learning and demonstrate its performance advantages through synthetic and real data of water demand. The results of the new algorithm were compared to that of a genetic algorithm, which is the most commonly used and well-known FCM learning algorithm.

Finite capacity scheduling using a predetermined placement sequence

This paper presents a new heuristic for the finite capacity scheduling of factories. It treats the task of scheduling a factory the same as scheduling a large project that has many delivery points. The job placement sequence on machines is used as a link between infinite capacity schedules and finite capacity schedules. An ideal placement sequence is proposed from the infinite capacity backward schedule and this is embedded into the project network for finite capacity scheduling. This allows a finite capacity algorithm whose boundary condition is the most tightly packed schedule possible, and is also ideal from a cash flow perspective. The paper proposes a preference list of machines as an integral part of the scheduling process and shows how to switch between machines using preferences. It also shows how to integrate infinite and finite capacity schedules within the same algorithm by using a parameter called the constraint horizon. The heuristic is explained using input-output matrices and by working through an example of a project network. The example includes a discussion of circuits and a detailed explanation of how to implement the heuristic in a computer program.

Finite capacity scheduling: packed placement sequence schedule

This is one of a series of papers which describe the placement sequence approach to finite capacity scheduling of factories. A central aim of the placement sequence approach is to achieve well-packed and stable schedules, with multiple and alternative tools and machines, without the need for an iterative schedule improvement stage within the calculations. This means that calculations are bounded and the technique is suitable for large industrial applications. In this paper we treat schedule packing as the main goal and show how to build a ‘packed placement sequence schedule’. We use a two-stage process to choose the machines and placement sequence constraints. The first stage is a backward infinite capacity schedule and the second stage is a backward finite capacity schedule. While building the backward finite schedule we schedule using the order of event times from the infinite capacity schedule, and read the machine loading from the infinite capacity schedule so we can spread the load and push jobs away from the most heavily loaded machines in the finite capacity schedule. The final result is a well-packed, backward, finite capacity schedule. The paper explains the method, discusses the issues, and works through a detailed example of the calculations.

Finite capacity scheduling with multiple tools and machines

This is one of a series of papers that describe the placement sequence approach to finite-capacity scheduling of factories. We explain the concept of event networks and use input–output matrices so that complex scheduling calculations can be formalized as operations on the rows and columns of matrices. A central aim of the placement sequence approach is to achieve well-packed and stable schedules without the need for an iterative schedule improvement stage within the calculations. This means that calculations are bounded and it is feasible to apply the technique to large industrial applications. This paper addresses the issue of finite-capacity scheduling where activities can have multiple and alternative combinations of tools and machines. This is often called the alternate routing problem. We represent the tooling combinations as Boolean expressions that include the preferences between tools and machines. These are converted into an input–output matrix, which we call the tooling matrix. The description is presented partly in terms of principles and partly in terms of how to write a computer program to implement the calculations. The paper explains the principles of scheduling multiple and alternative tools and machines and then works through a detailed example of the calculations.

Finite capacity scheduling with mixed duration tooling trees

Finite capacity scheduling is a research area which has much practical application and the quality of the schedules has a major impact on the delivery performance, profitiability and finance requirements of companies. This paper proposes a method of finite capacity scheduling where activities have a range of mandatory and alternative tools which can be used, and some of the less preferred tools run at a slower rate. The calculations are presented by example, and in the context of a computer program to implement the algorithms.

Finite capacity scheduling with the placement sequence method, specification for a computer program

This is one of a series of papers which describes the placement sequence approach to finite capacity scheduling of factories. In this paper, the author gives a detailed description of how to write a computer program to do finite capacity scheduling using the placement sequence method. The main advantage of the placement sequence method is that it treats multiple and alternative tools and machines as the normal situation, with single machine scheduling as a special case. The author presents a series of flow charts, file structure definitions and a project plan which should enable a research group to get started on an industrial scale finite capacity scheduling program. The author also shows how researchers can tap into the program to write their own higher level algorithms to drive the lower level scheduling calculations. This paper, combined with the explanation of the earlier papers in IJPR, provides a framework for further research into finite capacity scheduling with multiple and alternative tools and machines.This is one of a series of papers which describes the placement sequence approach to finite capacity scheduling of factories. In this paper, the author gives a detailed description of how to write a computer program to do finite capacity scheduling using the placement sequence method. The main advantage of the placement sequence method is that it treats multiple and alternative tools and machines as the normal situation, with single machine scheduling as a special case. The author presents a series of flow charts, file structure definitions and a project plan which should enable a research group to get started on an industrial scale finite capacity scheduling program. The author also shows how researchers can tap into the program to write their own higher level algorithms to drive the lower level scheduling calculations. This paper, combined with the explanation of the earlier papers in IJPR, provides a framework for further research into finite capacity scheduling with multiple and alternative tools and ...

Finite Capacity Scheduling - Large System Design

This paper presents the design principles for a large finite capacity scheduling system which takes into account alternative tools and machines. The system will produce tightly packed and stable schedules which in many cases will be close to optimal. The system can then be used as a platform for research into the application of decision making techniques, mathematical optimization, and other modern techniques.