The article presents a study on the effectiveness of the foundries using Data Envelopment Analysis (DEA) method. The aim of the article

is to analyze the usefulness of DEA method in the study of the relative efficiency of the foundries. DEA is a benchmarking technique

based on linear programming to evaluate the effectiveness of the analyzed objects. The research was conducted in four Polish and two

foreign plants. Evaluated foundries work in similar markets and have similar production technology. We created a DEA model with two

inputs (fixed assets and employment) and one output (operating profit). The model was produced and solved using Microsoft Excel

together with its Solver add-in. Moreover, we wrote a short VBA script to perform automating calculations. The results of our study

include a benchmark and foundries’ ranking, and directions to improve the efficiency of inefficient units. Our research has shown that

DEA can be a very valuable method for evaluating the efficiency of foundries.

The paper presents a novel Iterated Local Search (ILS) algorithm to solve multi-item multi-family capacitated lot-sizing problem with setup costs independent of the family sequence. The model has a direct application to real production planning in foundry industry, where the goal is to create the batches of manufactured castings and the sequence of the melted metal loads to prevent delays in delivery of goods to clients. We extended existing models by introducing minimal utilization of furnace capacity during preparing melted alloy. We developed simple and fast ILS algorithm with problem-specific operators that are responsible for the local search procedure. The computational experiments on ten instances of the problem showed that the presence of minimum furnace utilization constraint has great impact on economic and technological conditions of castings production. For all test instances the proposed heuristic is able to provide the results that are comparable to state-of-the art commercial solver.

The problem considered in the paper is motivated by production planning in a foundry equipped with a furnace and a casting line,

which provides a variety of castings in various grades of cast iron/steel for a large number of customers. The goal is to create the order of

the melted metal loads to prevent delays in delivery of goods to customers. This problem is generally considered as a lot-sizing and

scheduling problem. However, contrary to the classic approach, we assumed the fuzzy nature of the demand set for a given day. The paper

describes a genetic algorithm adapted to take into account the fuzzy parameters of simultaneous grouping and scheduling tasks and

presents the results achieved by the algorithm for example test problem.

In the paper, we present a coordinated production planning and scheduling problem for three major shops in a typical alloy casting

foundry, i.e. a melting shop, molding shop with automatic line and a core shop. The castings, prepared from different metal, have different

weight and different number of cores. Although core preparation does not required as strict coordination with molding plan as metal

preparation in furnaces, some cores may have limited shelf life, depending on the material used, or at least it is usually not the best

organizational practice to prepare them long in advance. Core shop have limited capacity, so the cores for castings that require multiple

cores should be prepared earlier. We present a mixed integer programming model for the coordinated production planning and scheduling

problem of the shops. Then we propose a simple Lagrangian relaxation heuristic and evolutionary based heuristic to solve the coordinated

problem. The applicability of the proposed solution in industrial practice is verified on large instances of the problem with the data

simulating actual production parameters in one of the medium size foundry.

The paper presents a production scheduling problem in a foundry equipped with two furnaces and one casting line, where the line is a bottleneck and furnaces, of the same capacity, work in parallel. The amount of produced castings may not exceed the capacity of the line and the furnaces, and their loads determine metal type from which the products are manufactured on the casting line. The purpose of planning is to create the processing order of metal production to prevent delays in the delivery of the ordered products to the customers. The problem is a mix of a lot-sizing and scheduling problems on two machines (the furnaces) run in parallel. The article gives a mathematical model that defines the optimization problem, and its relaxed version based on the concept of a rolling-horizon planning. The proposed approaches, i.e. commercial solver and Iterated Local Search (ILS) heuristic, were tested on a sample data and different problem sizes. The tests have shown that rolling horizon approach gives the best results for most problems, however, developed ILS algorithm gives better results for the largest problem instances with tight furnace capacity.

Mathematical programming, constraint programming and computational intelligence techniques, presented in the literature in the field of operations research and production management, are generally inadequate for planning real-life production process. These methods are in fact dedicated to solving the standard problems such as shop floor scheduling or lot-sizing, or their simple combinations such as scheduling with batching. Whereas many real-world production planning problems require the simultaneous solution of several problems (in addition to task scheduling and lot-sizing, the problems such as cutting, workforce scheduling, packing and transport issues), including the problems that are difficult to structure. The article presents examples and classification of production planning and scheduling systems in the foundry industry described in the literature, and also outlines the possible development directions of models and algorithms used in such systems.

The problem considered in the paper is motivated by production planning in a foundry equipped with the furnace and casting line, which

provides a variety of castings in various grades of cast iron/steel for a large number of customers. The quantity of molten metal does not

exceed the capacity of the furnace, the load is a particular type of metal from which the products are made in the automatic casting lines.

The goal is to create the order of the melted metal loads to prevent delays in delivery of goods to customers. This problem is generally

considered as a lot-sizing and scheduling problem. The paper describes two computational intelligence algorithms for simultaneous

grouping and scheduling tasks and presents the results achieved by these algorithms for example test problems.

A novel approach for treating the uncertainty about the real levels of finished products during production planning and scheduling process

is presented in the paper. Interval arithmetic is used to describe uncertainty concerning the production that was planned to cover potential

defective products, but meets customer’s quality requirement and can be delivered as fully valuable products. Interval lot sizing and

scheduling model to solve this problem is proposed, then a dedicated version of genetic algorithm that is able to deal with interval

arithmetic is used to solve the test problems taken from a real-world example described in the literature. The achieved results are compared

with a standard approach in which no uncertainty about real production of valuable castings is considered. It has been shown that interval

arithmetic can be a valuable method for modeling uncertainty, and proposed approach can provide more accurate information to the

planners allowing them to take more tailored decisions.

The problem considered in the paper is motivated by production planning in a foundry equipped with the furnace and casting line, which

provides a variety of castings in various grades of cast iron/steel for a large number of customers. The quantity of molten metal does not

exceed the capacity of the furnace, the load is a particular type of metal from which the products are made. The goal is to create the order

of the melted metal loads to prevent delays in delivery of goods to customers. This problem is generally considered as a lot-sizing and

scheduling problem. The paper describes a mathematical programming model that formally defines the optimization problem and its

relaxed version that is based on the conception of rolling-horizon planning

The size and complexity of decision problems in production systems and their impact on the economic results of companies make it

necessary to develop new methods of solving these problems. One of the latest methods of decision support is business rules management.

This approach can be used for the quantitative and qualitative decision, among them to production management. Our study has shown that

the concept of business rules BR can play at most a supporting role in manufacturing management, but alone cannot form a complete

solution for production management in foundries.

One of the most popular heuristics used to solve the permutation flowshop scheduling problem (PFSP) is the NEH algorithm. The reasons for the NEH popularity are its simplicity, short calculation time, and good-quality approximations of the optimal solution for a wide range of PFSP instances. Since its development, many works have been published analysing various aspects of its performance and proposing its improvements. The NEH algorithm includes, however, one unspecified and unexamined feature that is related to the order of jobs with equal values of total processing time in an initial sequence. We examined this NEH aspect using all instances from Taillard’s and VRF benchmark sets. As presented in this paper, the sorting operation has a significant impact on the results obtained by the NEH algorithm. The reason for this is primarily the input sequence of jobs, but also the sorting algorithm itself. Following this observation, we have proposed two modifications of the original NEH algorithm dealing with sequencing of jobs with equal total processing time. Unfortunately, the simple procedures used did not always give better results than the classical NEH algorithm, which means that the problem of sequencing jobs with equal total processing time needs a smart approach and this is one of the promising directions for further research.

The paper outlines the methodology of virtual design of a foundry plant as a system. The most important stage in the procedure involves the development of a model defined as a set of data about the system. Model development involves two stages: defining the model’s architecture and specifying the model data in the form of parameters and input-output relationships. The structure is understood as configuration of machines and transport units, representing the sub-systems and system components. As the main purpose of the simulation procedure is to find the characteristics of the system’s behaviour, the merits of the iterative method involving analysis, synthesis and evaluation of results are fully explored.

The article presents a case study on the effectiveness of photovoltaic farm and battery energy storage in one of the Polish foundries. In the study, we consider two investment options: stand-alone PV farm of 1MWp and the farm together with battery energy storage with a maximum capacity of 4MWh. The Payback Period and Net Present Value were used as measures of investment profitability. The paper provides a detailed presentation of the assumptions made, as well as the PV electricity production model of the farm and the optimization model that determines the operation cycle of the energy storage. The case study presented in the article shows that the PV farm is economically sensible and profitable, but the battery energy storage is too costly to give a positive economic effect. Energy storage is an important element that provides flexibility in the energy supply system, so it is necessary to find a technical solution that gives this flexibility. Such a solution could be a virtual power plant, which could include a foundry energy system with a RES installation inside.

The market of consumer goods requires nowadays quick response to customer needs. As a consequence, this is transferred to the time restrictions that the semi-finished product manufacturer must meet. Therefore the cost of manufacturing cannot determine how production processes are designed, and the main evaluation function of manufacturing processes is the response time to customers’ orders. One of the ideas for implementing this idea is the QRM (Quick Response Manufacturing) production organization system. The purpose of the research undertaken by the authors was to develop an innovative solution in the field of production structure, allowing for the implementation of the QRM concept in a Contract Manufacturer, which realizes its tasks according to engineering-to-order (ETO) system in conditions defined as High Mix, Low Volume, High Complexity. The object of the research was to select appropriate methods for grouping products assuming that certain operations will be carried out in traditional but well-organized technological and/or linear cells. The research was carried out in one of the largest producers of sheet metal components in Europe. Pre-completed groupings for data obtained from the company had indicated that – among the classical methods – the best results had been given by the following methods: King’s Algorithm (otherwise called: Binary Ordering, Rank Order Clustering), k-means, and Kohonen’s neural networks. The results of the tests and preliminary simulations based on the data from the company proved that the implementation of the QRM concept does not have to be associated with the absolute formation of multi-purpose cells. It turned out that the effect of reducing the response time to customer needs can be obtained by using hybrid structures that combine solutions characteristic of cellular systems with traditional systems such as a technological, linear, or mixed structure. However, this requires the application of technological solutions with the highest level of organization.