Short-term Production Planning Research Articles

A new composite heuristic to minimize the total tardiness for the single machine scheduling problem with variable and flexible maintenance

Inspired by a real-world maintenance/job scheduling issue coming from the semiconductor industry, the present paper proposes a new heuristic algorithm structure for the single machine scheduling T-problem with flexible and variable maintenance, job release dates and sequence dependence setup times. Considering the typical short-term production planning needs, a single maintenance problem has to be scheduled within a certain time interval, along with a set of jobs so as to minimize the total tardiness. A twofold contribution emerges from the present paper. First, four mixed-integer linear programming models are developed for the problem at hand and compared in terms of time to convergence and computational complexity. Second, a novel heuristic algorithm, which has been configured into three distinct variants, has been compared with 17 alternative heuristics from the relevant literature based on a comprehensive experimental campaign. The numerical results allow the selection of the most suitable MILP model and confirm the effectiveness of the proposed heuristic approach.

Analysis of the truck haulage system in an underground mine using long-term log data

ABSTRACT This paper addresses the collection, analysis, and optimization of log data related to the truck haulage system in a mine. After selecting an underground mine in South Korea as the research area, the Mine Production Management System (MPMS) using Bluetooth beacons and tablet PCs was utilized to collect log data related to the haulage system over 18 months. Through the analysis of the collected log data, strategies to enhance the productivity and efficiency of the haulage system were derived. Weekly analysis of log data files was performed, identifying periods with abnormal truck cycle times and conducting an analysis of the underlying causes. It was revealed that the truck cycle time is influenced by the haulage system, equipment operation, and the log data collection system. To shorten truck cycle times and improve the productivity and efficiency of transportation systems, continuous collection and regular analysis of log data and dynamic system and equipment operation based on this are important. Analyzing log data collected over an extended period can help predict future trends in the haulage system and formulate measures and strategies in response. Therefore, it can serve as a valuable decision-making tool for establishing short-term, medium-term, and long-term production and operation plans.

Sustainable Short-Term Production Planning Optimization

This study proposes a framework for short-term production planning of a Portuguese company operating as a tier 2 supplier in the automotive sector. The framework is intended to support the decision-making process regarding a single progressive hydraulic press, which is used to manufacture cold-stamped parts for exhaust systems. The framework consists of two sequential levels: (1) a Mixed-Integer Linear Programming (MILP) model to determine the optimal production quantities per week while minimizing the total cost; (2) a dynamic production sequencing rule for scheduling operations on the hydraulic press. The two levels are combined and implemented in Excel, where the MILP model is solved using the Solver add-in, and the second level uses the optimal production quantities as inputs to determine the production sequence using a dynamic priority rule. To validate the framework, a proposed optimal plan was compared to a real plan executed by the company, and it was found that the framework could save up to 22.1% of the total cost observed in reality while still satisfying demand. To address uncertainties, the framework requires a rolling weekly planning horizon.

Short-term planning of open pit mines with Semi-Mobile IPCC: a shovel allocation model

ABSTRACT In-pit crushing and conveying (IPCC) is considered a suitable alternative to truck haulage in open pit mines because it offers a lower operating cost than a truck-shovel system. It also reduces truck haulage distance and truck requirements. One of the IPCC variations is the semi-mobile system, which is relocated every two to five years. The short-term plan needs to be updated accordingly, based on the crusher’s optimal location and relocation time. To the best of our knowledge, short-term planning with IPCC is an area of research that has not been explored extensively yet and hardly any model can generate short-term schedules considering an IPCC in place. This research work proposes a mixed integer programming model to generate short-term production plans and near-optimal shovel allocation to mining faces, within a time horizon of 12 months. The objective of the model is to minimise the cost of material handling and maximise revenue, with respect to plant requirement, maximum allowable tonnage variation and IPCC location constraints, and the production and NPV targets set by the strategic plan. An iron ore mine case including a semi-mobile IPCC (SMIPCC) system with one relocation is used as the case study to verify the proposed model. The comparison of results between scenarios with and without IPCC justifies the use of IPCC in the iron ore mine from a short to medium-term perspective. The project can be considered a pioneering work in the arena of short-term mine planning with the IPCC.

Simultaneous shovel allocation and grade control decisions for short-term production planning of industrial mining complexes – an actor-critic approach

ABSTRACT Short-term planning typically involves optimising different mining complex components individually, preventing unlocking value from intercorrelated activities. This paper proposes an integrated framework that defines diglines and shovel allocation decisions to improve the operation’s financial performance. This involves a clustering approach, defining diglines and material destination; a simulator of the mining complex operations, forecasting the material flow from benches to processors; and an actor-critic reinforcement learning approach, assigning shovels to mining areas given operating requirements. A case study at a copper mining complex shows 27% cash flow improvements compared to a non-adaptive baseline approach provided by recent advances in the field.

A comprehensive approach to selecting mine transportation system using AHP and FUZZY-TOPSIS

Mine transportation approximately accounts for more than half of operating costs. Therefore selection of the appropriate mine transportation system by considering the effective criteria has become an important issue in mining engineering. A hybrid decision support system based on Analytical Hierarchy Process (AHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) was proposed to assess criteria and alternatives for balancing haulage distance, continuity, flexibility and reliability, which could provide the suitable balance between different goals, such as costs and safety. An application of this approach is carried out through the Sungun copper mine as case study. For this purpose, the production capacity, medium and short-term production plan, conditions of mining benches and ramps and future development plans collected from chosen mine located at the East Azerbaijan Province, Iran. This research has been studied comprehensively in terms of technical, economic, environmental, society, site information, geological and geo-mechanically criteria. More than 170 criteria were identified and classified under six main categories. To select the mine transportation system, the importance of each criteria is first measured relative to each other, then the importance of each option is evaluated for each criteria and were filtered to 55 items. The proposed model is based on the combination of the AHP and fuzzy TOPSIS. The hybrid model has advantages such as the possibility of using paired comparison and considering uncertainty. Finally, the in-pit crushing and conveying is selected as the best alternative, the truck-front shovel and truck-backhoe system are in the next priorities, respectively 45.2, 28.4 and 26.4%. In summary, from the results of the research, it can be mentioned to present the effective criteria in the selection of the transport system of open pit mines, to calculate their weight, to provide a framework for the selection of the transport system of open pit mines by considering the uncertainties.

The Concept of an Intelligent Decision Support System for Ore Transportation in Underground Mine

The management of in-mine ore transportation systems is a crucial challenge due to the constant development of underground mines. The transport network's stability and efficiency directly affect enterprise profits and growth. Managing key areas such as transportation asset maintenance, object control, and ore extraction planning is essential to ensuring high transportation standards. For larger, multi-site enterprises, transportation supervision is divided among many managers. To address short-term production planning and management support during unplanned events, a decision support system is proposed in this article to meet the expectations of both lower and higher-level managers. The proposed concept is based on the simulation deployed from real-time operational data from mine, in which an agent is placed and taught through reinforcement learning. The simulation as well as agent possible actions have a block structure, where each means of ore transport is a separate add-on. In addition, it is assumed that the solution will be further improved by the implementation of various algorithms found in the literature (e.g. early fault detection algorithms). When created and implemented, such a system will bring improvements in multiple levels of operation, such as the safety of employees, overall machine health, cost reduction, energy efficiency, etc. As a result of this work, a conception of an intelligent decision-support system is presented, along with the specification of areas of its influence and a description of tasks and algorithms proposed to be incorporated within the system itself.

Metamodeling the optimal total revenues of the short-term optimization of a hydropower cascade under uncertainty

This paper deals with the optimization of the short-term production planning of a real cascade of run-of-river hydropower plants. Water inflows and electricity prices are subject to data uncertainty and they are modeled by a finite set of joint scenarios. The optimization problem is written with a two-stage stochastic dynamic mixed-integer linear programming formulation. This problem is solved by replacing the value function of the second stage with a surrogate model. We propose to evaluate the feasibility of fitting the surrogate model by supervised learning during a pre-processing step. The learning data set is constructed by Latin hypercube sampling after discretizing the functional inputs. The surrogate model is chosen among linear models and the dimension of the functional inputs is reduced by principal components analysis. Validation results for one simplified case study are encouraging. The methodology could however be improved to reduce the prediction errors and to be compatible with the time limit of the operational process.

On planning production and distribution with disrupted supply chains

This paper presents a model for short-term time-horizon production and distribution planning of a manufacturing company located in the middle of a supply chain. The model focuses on an unbalanced market with broken supply chains. This reflects the state of the current post-COVID-19 economy, which is additionally struggling with even more uncertainty and disruptions due to the Russian aggression against Ukraine. The manufacturer, operating on the post-pandemic and post-war market, on the one hand observes a soaring demand for its products, and on the other faces uncertainty regarding the availability of components (parts) used in the manufacturing process. The goal of the company is to maximise profits despite the uncertain availability of intermediate products. In the short term, the company cannot simply raise prices, as it is bound by long-term contracts with its business partners. The company also has to maintain a good relationship with its customers, i.e. businesses further in the supply chain, by proportionally dividing its insufficient production and trying to match production planning with the observed demand. The post-COVID-19 production-planning problem has been addressed with a robust mixed integer optimisation model along with a dedicated heuristic, which makes it possible to find approximate solutions in a large-scale real-world setting.

An Application of a Decision Support System Enabled by a Hybrid Algorithmic Framework for Production Scheduling in an SME Manufacturer

In this research, we present a hybrid algorithmic framework and its integration into the precise production scheduling system of a Greek metal forming factory. The system was created as a decision support tool to assist production planners in arranging weekly production orders to work centers and other manufacturing cells. The functionality offered includes dispatching priority rules, bottleneck identification for capacity planning, production order reallocation to alternate work centers and planning periods, interchangeable scheduling scenarios, and work-in-process availability checks based on bill of materials (BOM) precedence constraints. As a consequence, a solid short-term production plan is created, capable of absorbing shop floor risks such as machine failures and urgent orders. The primary design ideas are simplicity, ease of use, a flexible Gantt-chart-based graphical user interface (GUI), controllable report creation, and a modest development budget. The practical application takes place in a make-to-stock (MTS) environment with a complicated multi-level production process, defined due dates, and parallel machines. A critical component is the integration with legacy applications and the existing enterprise resource planning (ERP) system. The method adopted here avoids both overburdening the existing information system architecture with software pipeline spaghetti, as is common with point-to-point integration, and overshooting implementation costs, as is often the case with service-oriented architectures.

Efficiency comparison of proactive approaches to deal with machine failures*

Due to the innovations brought by Industry 4.0, decision-makers must rethink the premisses and methods used to solve production planning problems. We study short-term production planning and scheduling problems subject to disruptions, incorporating characteristics of Industry 4.0 into the analyzed scenario. We propose and compare two proactive approaches for dealing with machine failures on the shop floor. The methods aim to find sequences with low probabilities of infeasibility at the scheduling level even with the occurrence of disruptions. One proactive approach performs simulations before the resolution of the planning problems to define slacks added to the capacity constraints of the short-term production planning problem. On the other hand, the second approach determines these slacks based on the probability distributions of the failures. According to the experiments, we conclude that the proposed approaches find similar results for most instance sets when comparing the averages and standard deviations. The results indicate that is not necessary to perform simulations before the resolution of the planning problems; a quick analysis of the probability distributions could be used by the decision-maker.

Overview on an open pit mine planning of the pickstone peerless in a volatile environment

The prevailing mining climate is highly characterized by unstable consumables pricing systems, a volatile economy and skyrocketing operational costs, and exacerbated by a steady decline and intermittence in the availability of electricity. Zimbabwean mines need to extensively capitalize on the opportunity to improve productivity by emphasizing variables they can control, predominantly operational efficiency to avoid resizing of operations or facilitating downsizing by relatively insignificant factors. In this study, cycle times, rig penetration, utilization, availability and payloads were used to evaluate the mining cycles, operator costs and the information was compared against the life of mine plans with block models. Shifts were restructured to be concordant to the schedule provided by the utility company to save fuel that was being used to power metallurgical plant. Several challenges have been identified as the principal reasons behind discrepancies between the theoretical capabilities of equipment were proven to be achievable by a trial schedule which reduced the 7 days/month to less than 2 days a month. The new schedule reflects a theoretical improvement of close to 25% and significantly lower operational costs. The current mining fleet is capable of meeting the stipulated targets and even achieving more even within tough working environments characterized by harsh load shedding schedules and volatile inflation rates; however, this requires stringent monitoring and evaluation of unit process. By adopting the recommended short term production plans will avoid resizing of operations as it automatically reduces the operational costs by US $3 million annually whilst coercing both operators and management to improve their operational efficiency.

Dispatch Optimization Model for Haulage Equipment between Stopes Based on Mine Short-Term Resource Planning

The working environment of underground mines is complicated, making it difficult to construct an underground mine production plan. In response to the requirements for the preparation of a short-term production plan for underground mines, an optimization model for short-term resource planning was constructed, with the goal of maximizing the total revenue during the planning period. The artificial bee colony optimization algorithm is used to solve the model using MATLAB. According to the basic requirements of underground mine ore haulage and ore hoisting, a haulage equipment inter-stopes dispatch plan model was constructed, with the primary goal of minimizing the haulage equipment wait time. A non-dominated sorting genetic algorithm is used to solve the optimization model. An underground mine is examined using the two models, and the optimization results are compared and verified with the scheme obtained by using traditional optimization algorithms. Results show that based on the improved optimization algorithm, the use of short-term production planning schemes to guide mine production operations can increase the haulage equipment utilization rate, thereby increasing mine production revenue.

Design of Optimal Energy Management System in a Residential Microgrid Based on Smart Control

ABSTRACT Optimal use of microgrids for efficient and economic management of energy resources is of special importance. At the residential level, energy costs can be reduced by using the concepts of price-based intelligent control (response to demand). In this paper, an energy management problem that proposed to decrease the cost of operating a residential microgrid with Micro-CHP equipped by a smart meter. The problem objective function formulation in the form of short-term production planning is based on the total cost of Micro-CHP gas consumption and auxiliary burner and the cost of electricity purchased from the network or sell to it in 24 hours. The aim is to present the ideal energy management strategy of the resources available based on smart control, and due to the non-linear problem, GAMS software by Mixed Integer Non-Linear Programming (MINLP) was used to solve the problem. Finally, the proposed method is implemented on the sample microgrid and the results are compared with Heat-Led Control, the effect of battery on microgrid participation in load response programs was demonstrated. Also, the effect of different tariffs of the market price on the cost of microgrid operation and how to exchange with the grid in different cases are evaluated that results showed the proper performance of the proposed method.

Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

In furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of a smart PPC system. A smart PPC system uses emerging technologies such as the internet of things, big-data analytics tools and machine learning running on the cloud or on edge devices to enhance performance of PPC processes. It achieves this by using a wider range of data sources from the production system, capturing and utilizing the experience of production planners, using analytics and machine learning to harness insights from the data and allowing dynamic and near real-time action to the continuously changing production system. The proposed methodology is illustrated with a case study in a sweets and snacks manufacturing company, to highlight the key considerations and challenges production managers might face during its application. The case further demonstrates considerations for scalability and flexibility via a loosely coupled, service-oriented architecture and the selection of fitting algorithms respectively to address a business requirement for a short-term, multi-criteria and event-driven production planning and control solution. Finally, the paper further discusses the challenges of PPC in smart manufacturing and the importance of fitting smart technologies to planning environment characteristics.

Effect of work-force availability on manufacturing systems operations of job shops

In manufacturing systems characterized by job shop architectures, long lead times, and multiple part types, operators are important resources of the system, because they supervise and actively support the technical operations of machines. However, Industry 4.0 tools, as digital twins of manufacturing systems, rarely consider the human aspect. As a consequence, the real impact of workforce on production performance cannot be assessed precisely. In this paper, the effect of work-force availability on manufacturing systems operations is shown, with a special focus on job-shop systems. Results show that there is a strong relation between the operators management and the short-term production planning. The method is applied and validated within a real industrial case in the aeronautics sector, characterized by high value-added complex parts.

A Digital Twin-based approach for multi-objective optimization of short-term production planning

Abstract Short-term multi-product production planning strongly depends on manifold considerations which can be related to backlog production, product-type prioritization, machine capacity, and set-ups. Different manufacturing systems conditions may lead to different objectives. In this context, it is rather difficult to define a-priori optimal production plans. This paper presents an effective approach for short-term production planning of multi-product systems based on the integration of a Digital Twin and a multi-objective optimization method. The proposed approach has been implemented in a real industrial case of the railway sector. Numerical results show that useful insights can be inferred from the proposed methodology.

ОПТИМІЗАЦІЯ ЖИТТЄВОГО ЦИКЛУ СТВОРЕННЯ НОВОЇ ТЕХНІКИ В УМОВАХ КОНКУРЕНЦІЇ ТА СТОХАСТИЧНОЇ ПОВЕДІНКИ РИНКУ ЗБУТУ ВИСОКОТЕХНОЛОГІЧНОЇ ПРОДУКЦІЇ

The task to optimize the life cycle of new technique (aerospace, engineering products, etc.) in difficult economic conditions is stated and solved. The aim of the study is to develop a method to reduce the life cycle of complex technique creation. The subject of the research is the planning and management of the life cycle of complex technique in a highly competitive environment and stochastic behavior of high-tech products market. The paper shows the contradictions between the planned nature of modern production, which operates in conditions of Industry 4.0 and the stochastic behavior of the market. This contradiction leads to the relevance of short-term production plans with minimal risks. Therefore, the planning of the production system is carried out based on the portfolio that can be done in the short term. When planning new orders, it is necessary to shorten the life cycle of new equipment creation by analyzing the main stages: design, preparation of production, production. The optimization model to select the measures (project actions) at the initial stage of the life cycle is proposed. In order to generate the set from which the alternative options for activities are selected, expert assessments being ordered on the basis of the importance of time indicators, competitiveness, innovation, costs and risks are used. Simulation of the shortening of the new technique creation life cycle at a time of capacity constraints of production that creates high-tech products is carried out. The following mathematical models and methods are used: system analysis, optimization using integer programming, multi-criteria optimization, expert assessments, simulation modeling, agent-based modeling, risk assessment. The method allows to create competitive products at a time of capacity constraints by means of planning to shorten the life cycle of new technique creation and resources management.

Low-carbon electricity generation-based dynamic equilibrium strategy for carbon dioxide emissions reduction in the coal-fired power enterprise.

Climate change is already resulting in extreme devastation in the earth, with carbon dioxide emissions produced by coal-fired power plants being the largest contributor. Therefore, integrated coal purchasing, blending, and distribution strategies are playing a more important role in large-scale coal-fired power enterprises due to the need to reduce carbon dioxide emissions and operational costs. In this study, a dynamic equilibrium strategy for integrated coal purchasing, blending, and distribution under an uncertain environment is proposed to reduce carbon dioxide emissions in large-scale coal-fired powered enterprises; the practicality and efficiency of which are verified using a real-world case. Sensitivity analyses under different carbon dioxide emissions levels and satisfactory degrees were also conducted to give insights into the conflict between economic development and environmental protection for large-scale coal-fired power enterprises, and balance short-term and long-term production plans. The results indicated that the proposed method was able to achieve economic-environmental coordination and sustainable development. Compared to previous studies, the developed model was found to be able to reduce carbon emissions by about 30% compared with the maximum carbon emissions and improve carbon emissions reduction performance to assist in mitigating climate change.

Low-risk bypassing of machine failure scenarios in automotive industry press shops by releasing overall capacity of the production networks

Driven by lean manufacturing and technological progresses, the heterarchic and highly interdependent press-shop production networks in the automotive industry hold heavily-reduced redundancies at machine level. In these environments, even short-term machine or tool failure carry risks that can hardly be predicted, since single sites cannot usually continue to compensate incidents autonomously.This paper introduces a new optimization model for integrated machine scheduling with logistics planning at press-shop network level. The approach enables optimal capacity utilization for overcoming machine unavailability, and supports decision management for reconfiguring short-term production plans. Furthermore, the comprehensive integration of suppliers and subcontractors within the holistic concept is illustrated.