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

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.

Major manufactures are moving towards a sustainability goal. This paper introduces the results of collaboration with the leading company in the packaging and advertising industry in Germany and Poland. The problem addresses the manufacturing planning problem in terms of minimizing the total cost of production. The challenge was to bring a new production planning method into cardboard manufacturing and paper processing which minimizes waste, improves the return of expenses, and automates daily processes heavily dependent on the production planners’ experience. The authors developed a module that minimizes the total cost, which reduces the overproduction and is used by the company’s manufacturing planning team. The proposed approach incorporates planning allowances rules to compromise the manufacturing requirements and production cost minimization.

The aim of the work was to develop a prioritizing and scheduling method to be followed in small and medium-sized companies operating under conditions of non-rhythmic and nonrepeatable production. A system in which make to stock, make to order and engineer to order (MTS, MTO and ETO) tasks are carried out concurrently, referred to as a non-homogenous system, has been considered. Particular types of tasks have different priority indicators. Processes involved in the implementation of these tasks are dependent processes, which compete for access to resources. The work is based on the assumption that the developed procedure should be a universal tool that can be easily used by planners. It should also eliminate the intuitive manner of prioritizing tasks while providing a fast and easy to calculate way of obtaining an answer, i.e. a ready plan or schedule. As orders enter the system on an ongoing basis, the created plan and schedule should enable fast analysis of the result and make it possible to implement subsequent orders appearing in the system. The investigations were based on data from the non-homogenous production system functioning at the Experimental Plant of the Łukasiewicz Research Network – Institute of Ceramics and Building Materials, Refractory Materials Division – ICIMB. The developed procedure includes the following steps: 1 – Initial estimation of resource availability, 2 – MTS tasks planning, 3 – Production system capacity analysis, 4 – ETO tasks planning, 5 – MTO orders planning, 6 – Evaluation of the obtained schedule. The scheduling procedure is supported by KbRS (Knowledge-based Rescheduling System), which has been modified in functional terms for the needs of this work assumption.

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.

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 paper considers the negative pandemic-type demand shocks in the mean-variance newsvendor problem. It extends the previous results to investigate the case when the actual additive demand may attain negative values due to high prices or considerable, negative demand shocks. The results indicate that the general optimal solution may differ to the solution corresponding exclusively to the non-negative realizations of demand.

Value Stream Mapping has been a key Lean tool since its publication in 1988, offering a strategic view on the reconfiguration of an organization’s processes to reduce overall lead time. It has since been used in many different domains beyond (car) manufacturing. However, the potential offered by its concise representation of both material flow and its controlling information flow seems to have been largely underused. Most literature reports on VSM in the context of waste detection and local improvements. VSM also supports redesigning the material flow (even on a supply chain level) towards (pure) pull systems. However, it fails to adequately give guidance on how to gradually evolve towards this ultimate ideal state. This paper wants to offer a significant contribution to practitioners on how to use VSM to bridge this gap. Another key challenge that remains largely unpublished is how to adapt the planning systems accordingly at each reconfiguration of the material flow. This paper presents extensions to the basic VSM tool to meet these challenges. It includes a more comprehensive 5-level hierarchy that allows to position most lean flow-related techniques. It also extends the basic “door-to-door” VSM with new symbols to accommodate these techniques into the map. Finally, it introduces a new set of 13 questions to support redesigning not only the material flow, but also the information flow. The resulting richer future state maps better support the gradual evolution towards a leaner future shop floor, as illustrated with an example.

In this article conclusions from nearly 10 years of collaboration with Polish and German Engineer-to-Order (ETO) small and medium-sized enterprises (SMEs) from mechanical sector was presented. Research objective was to highlight common organizational problems they are dealing with, which prevent them from transition to Mass Customizers. As a result, a concept of 5 foundations for robust process design was proposed: procedures, product selection, machining philosophy, planning and storage, cross-functional teams. More practical solutions from this field have to be published to fill the research gap.

Traditionally the aggregate production plan helps in determining the inventory, production, and work-force, based on the demand forecasts without considering the productivity loss at a tactical level in supply chain planning. In this paper, we include the productivity loss into traditional aggregate production plan and the prescriptive analytics technique, linear programming, is used to solve this problem of practical interest in the domain of multifarious businesses and industries. In this study, we discussed two model variations of the aggregate production planning problem with and without productivity loss, i) fixed work-force, and ii) variable Work Force. The mathematical models were designated to be solved by using an open-source python pulp package in order to evaluate the impacts of the productivity loss on both the models. PuLP is an open-source modeling framework provided by the COIN-OR Foundation (Computational Infrastructure for Operations Research) for linear and integer Programing problems written in Python. The computational results indicate that the productivity loss has direct impact on the workforce hiring and firing.

Environmental awareness among the masses compelled many companies to adopt sustainable practices in their business operations. Remanufacturing is a well-tested and successful business model practiced in many European countries. But in many African and Asian countries, it is still nascent, including India. This research study tries to identify the critical factors in the “Operational Management” area for the viability of remanufacturing business in India. For this purpose, a questionnaire was developed based on the important factors identified from the extensive literature review. An online questionnaire survey was conducted among Indian white goods appliance manufacturing companies and their suppliers. The responses were analyzed statistically and ranked based on their criticality in initiating remanufacturing business in India. The findings may help the Indian government and manufacturing firms to frame proper strategies related to the operational management issues of remanufacturing business in India.

The paper presents the analysis of IT tools selection to develop a system of deposits geological modelling as well as production designing and scheduling in a hard coal mine. The presented concept creates a subject-matter foundation of the solution supporting the decision making system in the field of production activities performance, with the use of IT solutions and monitoring of end product quality, implemented under the paradigm of so-called Intelligent Mine.
A technological dialogue carried out by questionnaire surveys, supported with experts’ opinions, was applied to select the software for designing a system of deposit modelling, and for designing and scheduling of mining operations. Questionnaires originated based on presentations, covering the functionality in the field of geological data gathering, developing a geological spatial model of a bedded deposit, as well as designing and scheduling. The presented solutions were next evaluated, via questionnaires, by the employees of the company. In addition, 4 groups of criteria were prepared: technical (questionnaires), technical (experts), business, and IT, based on which the final evaluation was carried out. Ultimately, Solution 2 was selected as that, which to the highest degree satisfied technical, business, and IT requirements of the planned system.
The indicated IT solution was implemented and became one of basic tools for modelling hard coal deposits, an also for designing and scheduling of the mining operations in the company.

The paper presents multi-criteria optimization method allowing for selection of the best production scenarios in underground coal mines. We discuss here the dilemma between strategies maximizing economic targets and rational resources depletion. Elaborated method combines different geological and mining parameters, structure of the deposit, mine’s infrastructure constrains with economic criteria such as the net present value (NP V), earnings before deducting interest and taxes (EBIT ) and the free cash flows to firm (FCFF). It refers to strategic production planning. Due to implementation of advanced IT software in underground coal mines (digital model, automated production scheduling) we were able to identify millions of scenarios finally reduced to a few – the best ones. The method was developed and tested using data from mine operation “X” (a real project – an example of a coking coal mine located in Poland). The reliability of the method was approved; we were able to identify multiple production scenarios better than the one chosen for implementation in the “X” mine. The final product of the method were rankings of scenarios grouped according to economic decision criteria. The best scenarios reached NP V nearly 50% higher than the Base Case, which held only 52. position out of 60. According to EBIT and FCFF criteria, 10 scenarios achieved results higher than the Base Case, but the percentage differences were very small, below 2 and 4%, respectively. The developed method is of practical importance and can be successfully applied to many other coal projects.

The article is to present the application of genetic algorithm in production scheduling in a production company. In the research work the assumptions of the methodology were described and the operation of the proposed genetic algorithm was presented in details. Genetic algorithms are useful in complex large scale combinatorial optimisation tasks and in the engineering tasks with numerous limitations in the production engineering. Moreover, they are more reliable than the existing direct search algorithms. The research is focused on the effectivity improvement and on the methodology of scheduling of a manufacturing cell work. The genetic algorithm used in the work appeared to be robust and fast in finding accurate solutions. It was shown by experiment that using this method enables obtaining schedules suitable for a model. It

The manufacturing industry has been reshaping its operations using digital technologies for a smart production towards a more customized demand. Nevertheless, the flexibility to attend the production plan changes in real time is still challenging. Although the Internet of Services (IoS) has been addressed as a key element for Industry 4.0, there is still a lack of clarity about the IoS contribution for advanced manufacturing. Through a case study, the paper aims to validate the adherence of a theoretical model named Service-Oriented Manufacturing Architecture (SOMA) in two manufacturing companies that have been already engaged in Industry 4.0. As main results, it was concluded that IoS could suit in one case of Industry 4.0 flexible production process but not in a mass production one. Considering the scarcity of research that exemplifies the IoS contribution, the present paper brings an important assessment on a real manufacturing scenario.