A heuristic approach for big bucket multi-level production planning problems

Multi-level production planning problems in which multiple items compete for the same resources frequently occur in practice, yet remain daunting in their difficulty to solve. In this paper, we propose a heuristic framework that can generate high quality feasible solutions quickly for various kinds of lot-sizing problems. In addition, unlike many other heuristics, it generates high quality lower bounds using strong formulations, and its simple scheme allows it to be easily implemented in the Xpress-Mosel modeling language. Extensive computational results from widely used test sets that include a variety of problems demonstrate the efficiency of the heuristic, particularly for challenging problems

The development of a methodology for the evaluation of installed CAPM system’s effectiveness and efficiency

The objective of this work was to design, develop and evaluate an audit for a Computer Aided Production Management (CAPM) system. Such systems, despite their costs of purchase and implementation, find wide application in industry but there is still considerable debate as to their contribution to the overall performance of a company. A variety of possible methodologies were explored. However, it was found that most of the existing analytical techniques tended to focus on a comparison of systems with respect to best practice or to require data that a company was unlikely to have. Best practice is not an absolute measure, nor does it take account of different company types and their individual requirements. A flexible methodology, 'the CAPM Audit', designed to establish the effectiveness and efficiency of any installed CAPM system, has been developed. The audit is a development of the Delphi approach and is designed to establish the contribution of the CAPM system to the company's overall competitive position. In its development, a generic model for any CAPM system was devised to facilitate analysis without reference to any particular technology, management mode, or manufacturing control system. The audit developed (in the form of a workbook) consists of four stages: stage one establishes the context; stage two determines the underlying architecture of the system; stage three quantifies the contribution to the company's competitive position; and stage four identifies the causes of any failure of the CAPM system. The design of the audit is such that: it enables a systematic investigation of the effectiveness and efficiency of an installed CAPM system to be completed; it enables the CAPM system's contribution to the company to be identified; and it also enables any inadequacies to be determined

Intelligent design of manufacturing systems.

The design of a manufacturing system is normally performed in two distinct stages, i.e. steady state design and dynamic state design. Within each system design stage a variety of decisions need to be made of which essential ones are the determination of the product range to be manufactured, the layout of equipment on the shopfloor, allocation of work tasks to workstations, planning of aggregate capacity requirements and determining the lot sizes to be processed. This research work has examined the individual problem areas listed above in order to identify the efficiency of current solution techniques and to determine the problems experienced with their use. It has been identified that for each design problem. although there are an assortment of solution techniques available, the majority of these techniques are unable to generate optimal or near optimal solutions to problems of a practical size. In addition, a variety of limitations have been identified that restrict the use of existing techniques. For example, existing methods are limited with respect to the external conditions over which they are applicable and/or cannot enable qualitative or subjective judgements of experienced personnel to influence solution outcomes. An investigation of optimization techniques has been carried out which indicated that genetic algorithms offer great potential in solving the variety of problem areas involved in manufacturing systems design. This research has, therefore, concentrated on testing the use of genetic algorithms to make individual manufacturing design decisions. In particular, the ability of genetic algorithms to generate better solutions than existing techniques has been examined and their ability to overcome the range of limitations that exist with current solution techniques. IIFor each problem area, a typical solution has been coded in terms of a genetic algorithm structure, a suitable objective function constructed and experiments performed to identify the most suitable operators and operator parameter values to use. The best solution generated using these parameters has then been compared with the solution derived using a traditional solution technique. In addition, from the range of experiments undertaken the underlying relationships have been identified between problem characteristics and optimality of operator types and parameter values. The results of the research have identified that genetic algorithms could provide an improved solution technique for all manufacturing design decision areas investigated. In most areas genetic algorithms identified lower cost solutions and overcame many of the limitations of existing techniques

Critical path analysis type scheduling in a finite capacity environment

In order to cope with more realistic production scenarios, scheduling theory has been increasingly considering assembly job shops. Such an effort has raised synchronization of operations and components as a major scheduling issue. Most effective priority rules designed for assembly shops have incorporated measures to improve coordination when scheduling assembly structures. However, by assuming a forward loading, the priority rules designed by these studies schedule all operations as soon as possible, which often leads to an increase of the workin- progress level. This study is based on the assumption that synchronization may be improved by sequencing rules that incorporate measures to cope with the complexity of product structures. Moreover, this study favours the idea that, in order to improve synchronization and, consequently, reduce waiting time, backward loading should be considered as well. By recognizing that assembly shop structures are intrinsically networks, this study investigates the feasibility of adopting the Critical Path Method as a sequencing rule for assembly shop. Furthermore, since a Critical Path type scheduling requires a precise determination of production capacity, this study also includes Finite Capacity as a requisite for developing feasible schedules. In order to test the above assumptions, a proven and effective sequencing rule is selected to act as a benchmark and a simulation model is developed. The simulation results from several experiments showed significant reduction on the waiting time performance measure due to the adoption of the proposed critical path type priority rule. Finally, a heuristic procedure is proposed as a guideline for designing scheduling systems which incorporate Critical Path based rules and Finite Capacity approach

Achieving manufacturing excellence through the integration of enterprise systems and simulation

This paper discusses the significance of the enterprise systems and simulation integration in improving shop floor’s short-term production planning capability. The ultimate objectives are to identify the integration protocols, optimisation parameters and critical design artefacts, thereby identifying key ‘ingredients’ that help in setting out a future research agenda in pursuit of optimum decision-making at the shop floor level. While the integration of enterprise systems and simulation gains a widespread agreement within the existing work, the optimality, scalability and flexibility of the schedules remained unanswered. Furthermore, there seems to be no commonality or pattern as to how many core modules are required to enable such a flexible and scalable integration. Nevertheless, the objective of such integration remains clear, i.e. to achieve an optimum total production time, lead time, cycle time, production release rates and cost. The issues presently faced by existing enterprise systems (ES), if properly addressed, can contribute to the achievement of manufacturing excellence and can help identify the building blocks for the software architectural platform enabling the integration

Inventory drivers in a pharmaceutical supply chain

In recent years, inventory reduction has been a key objective of pharmaceutical companies, especially within cost optimization initiatives. Pharmaceutical supply chains are characterized by volatile and unpredictable demands –especially in emergent markets-, high service levels, and complex, perishable finished-good portfolios, which makes keeping reasonable amounts of stock a true challenge. However, a one-way strategy towards zero-inventory is in reality inapplicable, due to the strategic nature and importance of the products being commercialised. Therefore, pharmaceutical supply chains are in need of new inventory strategies in order to remain competitive. Finished-goods inventory management in the pharmaceutical industry is closely related to the manufacturing systems and supply chain configurations that companies adopt. The factors considered in inventory management policies, however, do not always cover the full supply chain spectrum in which companies operate. This paper works under the pre-assumption that, in fact, there is a complex relationship between the inventory configurations that companies adopt and the factors behind them. The intention of this paper is to understand the factors driving high finished-goods inventory levels in pharmaceutical supply chains and assist supply chain managers in determining which of them can be influenced in order to reduce inventories to an optimal degree. Reasons for reducing inventory levels are found in high inventory holding and scrap related costs; in addition to lost sales for not being able to serve the customers with the adequate shelf life requirements. The thesis conducts a single case study research in a multi-national pharmaceutical company, which is used to examine typical inventory configurations and the factors affecting these configurations. This paper presents a framework that can assist supply chain managers in determining the most important inventory drivers in pharmaceutical supply chains. The findings in this study suggest that while external and downstream supply chain factors are recognized as being critical to pursue inventory optimization initiatives, pharmaceutical companies are oriented towards optimizing production processes and meeting regulatory requirements while still complying with high service levels, being internal factors the ones prevailing when making inventory management decisions. Furthermore, this paper investigates, through predictive modelling techniques, how various intrinsic and extrinsic factors influence the inventory configurations of the case study company. The study shows that inventory configurations are relatively unstable over time, especially in configurations that present high safety stock levels; and that production features and product characteristics are important explanatory factors behind high inventory levels. Regulatory requirements also play an important role in explaining the high strategic inventory levels that pharmaceutical companies hold

A Digital Twin Framework for Production Planning Optimization: Applications for Make-To-Order Manufacturers

In this dissertation, we develop a Digital Twin framework for manufacturing systems and apply it to various production planning and scheduling problems faced by Make-To-Order (MTO) firms. While this framework can be used to digitally represent a particular manufacturing environment with high fidelity, our focus is in using it to generate realistic settings to test production planning and scheduling algorithms in practice. These algorithms have traditionally been tested by either translating a practical situation into the necessary modeling constructs, without discussion of the assumptions and inaccuracies underlying this translation, or by generating random instances of the modeling constructs, without assessing the limitations in accurately representing production environments. The consequence has been a serious gap between theory advancement and industry practice. The major goal of this dissertation is to develop a framework that allows for practical testing, evaluation, and implementation of new approaches for seamless industry adoption. We develop this framework as a modular software package and emphasize the practicality and configurability of the framework, such that minimal modelling effort is required to apply the framework to a multitude of optimization problems and manufacturing systems. Throughout this dissertation, we emphasize the importance of the underlying scheduling problems which provide the basis for additional operational decision making. We focus on the computational evaluation and comparisons of various modeling choices within the developed frameworks, with the objective of identifying models which are both effective and computationally efficient. In Part 1 of this dissertation, we consider a class of Production Planning and Execution problems faced by job shop manufacturing systems. In Part 2 of this dissertation, we consider a class of scheduling problems faced by manufacturers whose production system is dominated by a single operation

An integrated MRP and finite scheduling system to derive detailed daily schedules for a manufacturing shop

Many companies rely on Material Requirements Planning (MRP) to support their Production Scheduling and Control (PS&C) functions. Since MRP does not provide a detailed shop floor schedule, these users have to implement either a third party procedure or an internally developed procedure for shop floor controls. In this thesis we consider a class of user shops which are characterized by the following features: Homogenous machines, that is all machines can produce all products. Each product requires a setup, but several products may have a common setup. MRP requirements are specified on a weekly basis while actual requirements are specified on a hourly basis. Specifically, we develop a MRP and Finite Scheduling System (MFSS) which calculates the weekly net change requirements of products, then generates the detailed daily job order schedules, and finally sequences jobs on machine queues. The objectives of the system are to maximize the utilization of the machines and to minimize setup times. The MFSS was programmed on a personal computer-based system utilizing off-the-shelf relational database software