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.

For the reason of environmental problems connected with the use of furan binders, attention is increasingly being paid to self-setting mixtures using alkali resols. A resol binder stabilized with KOH, NaOH is hardened by liquid esters with the formation of alkaline salts. The increase of their concentration affects the shortening of the mixture bench life, it also decreases strength, increases abrasive wear to moulds and cores, and results in uneconomical dilution of the reclaim with expensive new base sand. The length of life (bench life) of mixtures plays an important role in the manufacture of huge and voluminous moulds and cores in self-setting mixtures. This study aims at analyzing the function of reactive alkaline salts in the reclaim, monitors the consequences of its thermal exposure on the properties of selfsetting mixtures, and deals with development of methods evaluating its qualities.