Sensitivity of multi-product two-stage economic lotsizing models and their dependency on change-over and product cost ratio's

This study considers the production and inventory management problem of a two-stage semi-process production system. In case both production stages are physically connected it is obvious that materials are forced to flow. The economic lotsize depends on the holding cost of the end-product and the combined change-over cost of both production stages. On the other hand this 'flow shop' is forced to produce at the speed of the slowest stage. The benefit of this approach is the low amount of Work In Process inventory. When on the other hand, the involved stages are physically disconnected, a stock of intermediates acts as a decoupling point. Typically for the semi-process industry are high change-over costs for the process oriented first stage, which results in large lotsize differences for the different production stages. Using the stock of intermediates as a decoupling point avoids the complexity of synchronising operations but is an additional reason to augment the intermediate stock position. The disadvantage of this model is the high amount of Work-In-Process inventory. This paper proposes the 'synchronised planning model' realising a global optimum instead of the combination of two locally optimised settings. The mathematical model proves (for a two-stage single-product setting) that the optimal two-stage production frequency corresponds with the single EOQ solution for the first stage. A sensitivity study reveals, within these two-stage lotsizing models, the economical cost dependency on product and change-over cost ratio‟s. The purpose of this paper is to understand under which conditions the „joined setup‟ or the „two-stage individual eoq model‟ remain close to the optimal model. Numerical examples prove that the conclusions about the optimal settings remain valid when extending the model to a two-stage multi-product setting. The research reveals that two-stage individually optimized EOQ lotsizing should only be used when the end-product stage has a high added value and small change-over costs, compared to the first stage. Physically connected operations should be used when the end-product stage has a small added value and low change-over costs, or high added value and large change-over costs compared to the first production stage. The paper concludes with suggesting a practical common cycle approach to tackle a two-stage multi-product production and inventory management problem. The common cycle approach brings the benefit of a repetitive and predictable production schedule

A model of JIT make-to-stock inventory with stochastic demand

We consider a firm that manages its internal manufacturing operations according to a just-in-time (JIT) system but maintains an inventory of finished goods as a buffer against random demands from external customers. We formulate a model in which finished goods are replenished by a small fixed quantity each time period. In the interest of schedule stability, the size of the replenishment quantity must remain fixed for a predetermined interval of time periods. We analyse the single-interval problem in depth, showing how to compute a cost-minimising value of the replenishment quantity for a given interval length, and characterising the optimal cost, inventory levels and service as functions of the interval length and initial inventory. The model displays significant cost and service penalties for schedule stability. A dynamic version of the problem is also formulated, and shown to be convex in nature with relatively easily computed optima

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Purchasing Functions and MRP in Foodservice Firms

The study examines the purchasing functions and material requirement planning (MRP) in food service and hotel purchasing. Bill of materials (BOM) is one of the three main inputs to the MRP programme (the other two are the master schedule and the inventory record file). Purchasing food and supplies is somewhat unique in that it goes beyond specifying products and placing orders. In foodservice, the functions related to purchasing should be generally done by the same people who do the purchasing. The paper looks at array of purchasing functions in foodservice and offers an effective management of materials approach. It addresses situation where materials planning are established in consultation with other departments, the resources needed and translation of the master plan into specific materials requirement.  A detailed example in the foodservice firm is presented to explain the concept of MRP. Keywords: Bill-of-materials, materials requirement planning, master production schedule, purchasing functio

Meta-heuristic & hyper-heuristic scheduling tools for biopharmaceutical production

The manufacturing of biopharmaceuticals requires substantial investments and necessitates long-term planning. Complicating the task of determining optimal production plans are large portfolios of products and facilities which limit the tractability of exact solution methods, and uncertainties & stochastic events which often render plans obsolete when reality deviates from the expectation. This thesis therefore describes decisional tools that are able to cope with these complexities. First, a capacity planning problem for a network of facilities and multiple products was tackled. Inspired by meta-heuristic approaches to job shop scheduling, a tailored construction heuristic that builds a production plan based on a sequence — optimised by a genetic algorithm—of product demands was proposed. Comparisons to a mathematical programming model demonstrated its competitiveness on certain scenarios and its applicability to a multi-objective problem. Next, a custom object-oriented model was introduced for a manufacturing scheduling system that utilised a failure-prone perfusion-based bioprocess. With this, process design decisions such as cell culture run time and process configuration, and single-product facility scheduling strategies were evaluated whilst incorporating simulations of stochastic failure events and uncertain demand. This model was then incorporated into a larger hyper-heuristic to determine optimal scheduling policies for a multi-product problem. Various policy representations are tested and a few policies are adapted from the literature to fit this specific problem. In addition, a novel policy utilising a look-ahead heuristic is proposed. The benefit of parameter tuning using evolutionary algorithms is demonstrated and shows that tuned policies perform much better than a policy that estimates parameters based on service level considerations. In addition, the disadvantages of relying on a fixed or rigid production sequence policy in the face of uncertainty is highlighted

Joint cell loading and scheduling approach to cellular manufacturing systems

Cataloged from PDF version of article.A hierarchical multi-objective heuristic algorithm and pricing mechanism are developed to first determine the cell loading decisions, and then lot sizes for each item and to obtain a sequence of items comprising the group technology families to be processed at each manufacturing cell that minimise the setup, inventory holding, overtime and tardiness costs simultaneously. The linkage between the different levels is achieved using the proposed pricing mechanism through a set of dual variables associated with the resource and inventory balance constraints, and the feasibility status feedback information is passed between the levels to ensure internally consistent decisions. The computational results indicate that the proposed algorithm is very efficient in finding a compromise solution for a set of randomly generated problems compared with a set of competing algorithms. © 2011 Taylor & Francis

Applying Operations Research techniques to planning of train shunting

In this paper, we discuss a model-based algorithmic approach for supporting planners in the creation of shunt plans for passenger trains. The approach provides an example of a mathematical model and a corresponding solution approach for model based support. We introduce a four-step solution approach and we discuss how the planners are supported by this approach. Finally, we present computational results for these steps and give some suggestions for further research.A* search;railway optimization;real world application;routing

Optimising the climate resilience of shipping networks

Climate catastrophes (e.g. hurricane, flooding and heat waves) are generating increasing impact on port operations and hence configuration of shipping networks. This paper formulates the routing problem to optimise the resilience of shipping networks, by taking into account the disruptions due to climate risks to port operations. It first describes a literature review with the emphasis on environmental sustainability, port disruptions due to climate extremes and routing optimisation in shipping operations. Second, a centrality assessment of port cities by a novel multi-centrality-based indicator is implemented. Third, a climate resilience model is developed by incorporating the port disruption days by climate risks into shipping route optimisation. Its main contribution is constructing a novel methodology to connect climate risk indices, centrality assessment, and shipping routing to observe the changes of global shipping network by climate change impacts

A hierarchical approach to multi-project planning under uncertainty

We survey several viewpoints on the management of the planning complexity of multi-project organisations under uncertainty. A positioning framework is proposed to distinguish between different types of project-driven organisations, which is meant to aid project management in the choice between the various existing planning approaches. We discuss the current state of the art of hierarchical planning approaches both for traditional manufacturing and for project environments. We introduce a generic hierarchical project planning and control framework that serves to position planning methods for multi-project planning under uncertainty. We discuss multiple techniques for dealing with the uncertainty inherent to the different hierarchical stages in a multi-project organisation. In the last part of this paper we discuss two cases from practice and we relate these practical cases to the positioning framework that is put forward in the paper