Integrated Production Planning Research Articles

An integrated production planning and inventory management problem for a perishable product: optimization and Monte Carlo simulation as a tool for planning in scenarios with uncertain demands

An integrated production planning and inventory management problem for a perishable product: optimization and Monte Carlo simulation as a tool for planning in scenarios with uncertain demands

Aggregate production planning and energy supply management in steel industry with an onsite energy generation system: A multi-objective robust optimization model

This paper introduces an optimization model developed for the steel industry, the most energy-intensive industrial sector in many countries. New methodologies for both aggregate production planning and energy supply management are developed to maximize profit while minimizing energy consumption and greenhouse gas (GHG) emissions. Moreover, uncertainties are considered, and robustly optimized strategies are identified for a grid-connected steel production company with an onsite energy generation system. The performance of the developed model is evaluated through extensive computational experiments. The results show how energy tariffs affect integrated production planning. Also, the selected production planning time depends on several factors such as electricity, labor, and maintenance costs, and pending orders that need to be fulfilled. The onsite power plant produces electricity when the electricity production cost is lower than the price of electricity provided by the connected grid. Furthermore, the company’s environmental policies influence the choice of energy generation technology. However, implementing an onsite energy generation is a viable policy that benefits both profitability and pollutant reduction. This study helps decision-making in production planning and energy supply management in the iron and steel industry (which can be applied to other industries too), ensuring environmental improvement at the lowest costs.

A Lagrangian heuristic for integrated production planning, material ordering and investment multi-project scheduling in a project-driven supply chain

Effective decision making in large project planning presents a significant challenge. Despite progress in developing mathematical models, few studies comprehensively address project and supply-chain management aspects in an integrated framework. This article presents an integrated approach for multi-project resource investment, material ordering and production planning in a project-driven supply chain. A mixed-integer programming model is developed to optimize the total cost of renewable resource investment, production, transportation, and material ordering and holding, with an opportunity to share mobile renewable resources across multiple projects. A Lagrangian relaxation heuristic algorithm is proposed to determine the upper and lower bounds for the objective function, with an additional set of valid cuts to reduce the distance between bounds. The results of numerical experiments demonstrate the favourable performance of the proposed algorithm compared to the GAMS solver. This article provides insights into supply-chain participant expenses and activity scheduling based on these findings.

Identifying the promising production planning and scheduling method for manufacturing in Industry 4.0: a literature review

ABSTRACT Industry 4.0 technologies create a connected ecosystem that allows for real-time monitoring, control, and optimization. This enables the manufacturing industry to move the ultimate goal of their businesses from lean to leagile. Then, to meet the needs of leagile manufacturing, an integrated production planning and scheduling system is needed. The academic world responds to the need by the growth of papers. Many methodologies, models, frameworks, and methods have been proposed. Literature abounds on classifying them, yet it remains inconclusive mainly on their applicability to Industry 4.0. In this regard, this paper conducts a literature review aiming to show the state of the art and discover the most promising production planning and scheduling method for Industry 4.0 among the many. Lastly, on the basis of this study, recommendations on possible future research directions are provided for researchers, and the design of the next-generation production planning and scheduling system for Industry 4.0 is also suggested.

How Integrated Production Planning and Cross Employee Planning Increases Productivity

How Integrated Production Planning and Cross Employee Planning Increases Productivity

Integrated Production Planning and Model Predictive Control of a Fluidized Bed Catalytic Cracking-Fractionator Unit

The present study proposes a new production planning framework that considers the effect of closed-loop control and process disturbances/disruptions. The production planning layer is augmented to include process control degrees of freedom, process operating constraints, operating uncertain parameters (related to closed-loop feedback), and disturbance information. This results in a stochastic production planning problem, whose solution successfully reduces the economic gap between production planning predictions and realized production. The proposed approach is applied to a large-scale model of a refinery section comprising a fluid catalytic cracker (FCC) and a fractionator. Extensive simulations involving different disturbances, operating modes, models, and production planning formulations are performed. The results demonstrate the benefits of the proposed framework in terms of economic performance, computational tractability, and ease of application. Finally, the impact of the economic model and plant-model mismatches between production planning and model predictive control (MPC), which arise in practice, is investigated.

A deep learning approach for integrated production planning and predictive maintenance

This paper considers a multi-period multi-product capacitated lot-sizing problem. It develops an integrated predictive maintenance and production planning framework using deep learning and mathematical programming. The objective is to minimise the sum of maintenance, setup, holding, backorder, and production costs, while satisfying the demand for all products over the horizon under consideration. Based on a rolling horizon approach, the model dynamically integrates data-driven predictive maintenance and production planning. The used maintenance policy includes replacements and minimal repairs that are considered as preventive and corrective maintenance, respectively. To select preventive maintenance actions, a long short-term memory model is employed to accurately predict the health condition of the machine. Each rolling horizon consists of ordinary and forecast stages, and by collecting new sensor data, the maintenance and production decisions are simultaneously updated. The resulting integrated framework is validated using a benchmarking data set. The results are compared for different approaches to highlight the advantages of the proposed framework.

Simulation-based Optimization of Flexible Energy Systems in Manufacturing with Local Energy Production and Storage Components

This paper introduces a simulation-based planning method that metaheuristically optimizes production planning and the control of components of a flexible energy system for energy intensive production. Significantly increasing energy efficiency and energy flexibility are key requirements and success factors, especially for energy-intensive manufacturers, amid rising energy prices, volatility, and the overall need to reduce the energy and pollution footprint. The considerable potential for an optimized integrated planning and control of production and energy system components remains unexhausted, due to the underlying planning complexity, combined with a lack of available planning methods. This paper aims to introduce a novel planning approach that provides an automated integrated planning functionality to improve energy efficiency and flexibility alongside production goals. The method considers fluctuating short-term energy markets as well as local hydrogen-based energy storage components. Both method development and evaluation are based on a case study in energy-intensive steel production. The achieved results show a significant benefit from the developed dynamic planning and optimization for different variables particularly in two different heat-treatment process technologies.

Research on uncertain integrated production planning and scheduling with risk management based on improved collaborative optimization

The optimization of production planning and scheduling is important for modern process industry. Due to different time dimensions of them, it is easy to create conflicts between the optimization results if they are solved separately, and the final results are not feasible. When facing a complex problem like production model, Collaborative optimization could be applied, but it still has shortcomings. Therefore, the improvement of collaborative optimization is proposed and improved collaborative optimization is applied to solve the uncertain production model. At last, the simulation results show that improved collaborative optimization can achieve better global optimization capability and practicability. This paper not only shows that the application scope of collaborative optimization has been expanded, but also provides a new idea for solving the uncertain production model.

Integrated optimization of production planning and scheduling in uncertain re-entrance environment for fixed-position assembly workshops

For the fixed-position assembly workshop, the integrated optimization problem of production planning and scheduling in the uncertain re-entrance environment is studied. Based on the situation of aircraft assembly workshops, the characteristics of fixed-position assembly workshop with uncertain re-entrance are abstracted. As the re-entrance repetition obeys some type of probability distribution, the expected value is used to describe the repetition, and a bi-level stochastic expected value programming model of integrated production planning and scheduling is constructed. Recursive expressions for start time and completion time of assembly classes and teams are confirmed. And the relation between the decision variable in the lower-level model of scheduling and the overtime and earliness of assembly classes and teams in the upper-level model of production planning is identified. Addressing the characteristics of bi-level programming model, an alternate iteration method based on Improved Genetic Algorithm (AI-IGA) is proposed to solve the models. Elite Genetic Algorithm (EGA) is introduced for the upper-level model of production planning, and Genetic Simulated Annealing Algorithm based on Stochastic Simulation Technique (SS-GSAA) is developed for the lower-level model of scheduling. Results from our experiments demonstrate that the proposed method is feasible for production planning and optimization of the fixed-position assembly workshop with uncertain re-entrance. And algorithm comparison verifies the effectiveness of the proposed algorithm.

Condition-based maintenance optimization via stochastic programming with endogenous uncertainty

In this work we address the challenge of integrating production planning and maintenance optimization for a process plant. We consider uncertain predictions of the equipment degradation by adopting a stochastic programming formulation with decision-dependent uncertainty. The probability of the uncertain parameters, in this work the remaining useful time of the plant, depends on the operating conditions of the plant which is modeled by embedding a prognosis model, the Cox model, into the optimization problem. A separation of the variables is suggested to decompose the MINLP formulation via two different primal decomposition algorithms. We provide computational results and compare the performance of the proposed decompositions with the global solver BARON enhanced with a custom branching priority strategy.

Supply chain-oriented two-stage assembly flowshops with sequence-dependent setup times

Supply chain-oriented scheduling problems have received recent recognition among production research scholars due to their ability in integrating production planning and control across manufacturing systems. This study contributes to the literature of the distributed scheduling problems developing an original Mixed-Integer Linear Programming (MILP) formulation to the Distributed Two-Stage Assembly Flowshop Scheduling Problem with Sequence-Dependent Setup Times (DTSAFSP-SDSTs). Besides, the Iterated Greedy algorithm is extended to effectively solve this relatively complex production scheduling situation considering the makespan as the optimization criterion. Extensive numerical tests and statistical analyses are conducted to evaluate the effectiveness of the developed solution algorithm. Results showed that the Improved Iterated Greedy (IIG) algorithm yields the best solution in nearly all of the large-scale instances. The statistical test of significance confirmed that IIG is superior to the current-best-performing algorithm. This study contributes to the transition from standalone optimization to integrated production planning and control of distributed manufacturing systems.

Integrated production planning and preventive maintenance scheduling for synchronized parallel machines

Production synchronization is an emerging paradigm in advanced manufacturing systems, with which the inventory space and cost of finished products can be significantly reduced. This paradigm has been studied in the past years, and several mathematical models have been developed to evaluate production synchronization for a diversity of manufacturing systems. Most of the existing studies on production synchronization, however, assume that machines are always functioning without malfunctions in production process. It is no doubt that machine failures can definitely lead to unexpected breakdowns that may produce non-ignorable impact on production synchronization. In this study, a new evaluation model for production synchronization is put forth for parallel machines. The production time uncertainty caused by unexpected breakdowns of machines is explicitly quantified, and the distribution of the job completion time on a machine is approximated by the Gamma distribution through the moment match method. By integrating production planning and preventive maintenance scheduling together, a new joint optimization problem is formulated for synchronized parallel machines, and it is resolved by a modified cooperative co-evolutionary genetic algorithm with a heuristic initialization. Two illustrative examples along with a set of comparative studies are presented to examine the effectiveness of the proposed model and solution algorithm.

An integrated approach to joint production planning and reliability-based multi-level preventive maintenance scheduling optimisation for a deteriorating system considering due-date satisfaction

This paper presents a new bi-objective model to deal with an integrated production planning and reliability-based multi-level preventive maintenance (PM) scheduling problem. The production system includes a set of parallel deteriorating machines. The model aims to find the production lot sizes and the sequence and interval of PM activities over a multi-shift planning horizon. The PM activities are classified as the adjustment and replacement ones, each of which has a specific cost and effect on machines. The model serves to minimise the total cost while maximising customer satisfaction. Also, the most profitable customers are identified by applying a hybrid multi-attribute decision making (MADM) approach (BWM-TOPSIS) in order to fulfil their orders in their desirable time windows. Three efficient meta-heuristic algorithms are developed to solve the proposed model for large-scale problems. Besides, TOPSIS method is employed to select the most desirable solution among the obtained Pareto solutions. Finally, a case study is provided to show the applicability of the proposed approach.

IE Tools for Boosting Competitiveness in Iron and Steel Industry: A Review

Integrated steelmaking operations, from ironmaking to final steel categories/forms, are a complex industrial system. Decision-makers, in addition to the domain knowledge, need to understand many facets related to operations management, process execution, involved risk factors and material handling aspects, etc. The solution approach must address trade-offs, uncertainties, multiple stages, multiple objectives, many decision variables, and constraints. Literature has presented several Industrial Engineering approaches like multi-objective optimisation, heuristics, evolutionary algorithm, Simulations, lean six-sigma, etc., to address the iron and steel industry's complex problems like integrated production planning. The manuscript reviews recent application of these IE tools/solution approaches in iron and steel industries to enhanced productivity/competitiveness. A Systematic Literature Review (SLR) methodology has been adopted. The review will help the practitioners and academicians to know the different problem areas about iron and steel industries discussed in the literature, and the solution approaches used to solve the problem.

A two-level optimisation-simulation method for production planning and scheduling: the industrial case of a human–robot collaborative assembly line

In this work, a novel optimisation-simulation based on the Recursive Optimisation-Simulation Approach (ROSA) methodology is developed to provide effective decision-support for integrated production planning and scheduling. The proposed iterative approach optimises production plans while satisfying complex scheduling constraints, such as robots' allocation in collaborative tasks. The plans are determined through a two-level MILP model and are iteratively evaluated by a detailed discrete-event simulation model to guarantee capacity-feasible solutions at the scheduling level. Through an industrial case study of a multistage assembly line design collaboratively operated by humans and mobile shared robots, near-optimal solutions comprise lot-sizing decisions, the release schedule of production orders, the allocation of tasks to humans or robots, and the number of robots per period. Moreover, by addressing a set of propositions to assess the methodology, the results highlight the advantages of the hybrid approach to converge into optimised operational decisions and analyse the process dynamics.

Integrated production planning and warehouse storage assignment problem: An IoT assisted case

This study is motivated by a real-world problem in a food company, where production planning is restricted by the available warehouse space for the finished goods. A novel integrated strategy that combines production planning with a randomized storage assignment policy is presented. The strategy takes advantage of greater visibility and traceability of items provided by IoT-enabled tracking systems in order to increase space utilization. An integer linear programming model is developed to formulate the strategy to minimize the total cost of production and warehouse operations. Our model is the first dynamic model for a randomized storage assignment policy. The model's feasibility, complexity, and its lower bound are presented. A heuristic algorithm is developed to obtain the near-optimal solution for the large-scale real-world problem. Based on numerical experiments, comparisons between our solutions and the solutions to model with a dedicated storage policy are also presented. The results show that the integrated strategy with a randomized storage policy can significantly reduce the total cost (up to 16.84% with an average of 9.95%) and increase space utilization (up to 26.1% with an average of 14.8%), compared to the strategy with a dedicated policy. Such results provide evidence that may justify the cost of applying the new technologies, such as IoT-enabled tracking systems, in warehouse management.

A Multi-objective Particle Swarm Optimization Based on Pareto Archive for Integrated Production and Distribution Planning in A Green Supply Chain

ABSTRACT This paper presents a bi-objective model for integrated production and distribution planning in a multi-period multi-product green supply chain. This paper considers many near-real-world factors, such as: normal and overtime production shifts, limited storage capacity of the finished product and raw materials, different vehicle types, direct and indirect shipping along with considering different production methods that influence the production costs and sale prices, simultaneously. Mixed integer programming model is proposed in which the profit maximization and the CO2 emission minimization are formulated. A particle swarm optimization algorithm based on Pareto archive that utilizes the genetic algorithm operators is proposed to solve the model.

Production planning and scheduling problem of continuous parallel lines with demand uncertainty and different production capacities

Abstract Production planning and scheduling problems are highly interdependent as scheduling provides optimum allocation of resources and planning is an optimum utilization of these allocated resources to serve multiple customers. Researchers have solved production planning and scheduling problems by the sequential method. But, in this case, the solution obtained by the production planning problem may not be feasible for scheduling method. Hence, production planning and scheduling problems must be solved simultaneously. Therefore, in this work, a mathematical model is developed to integrate production planning and scheduling problems. The solution to this integrated planning and scheduling problem is attempted by using a discrete artificial bee colony (DABC) algorithm. To speed up the DABC algorithm, a k-means clustering algorithm is used in the initial population generation phase. This k-means clustering algorithm will help to converge the algorithm in lesser time. A real-life case study of a soap manufacturing industry is presented to demonstrate the effectiveness of the proposed approach. An objective function to minimize overall cost, which comprises the processing cost, material cost, utility cost, and changeover cost, is considered. The results obtained by using DABC algorithm are compared with those obtained by CPLEX software. There is a saving of ₹2 23 324 for weeks 1–4 in overall cost compared with the results obtained by using CPLEX software.

Integrated production planning and scheduling under uncertainty: A fuzzy bi-level decision-making approach

Production planning and scheduling are two core decision layers, constrained and affected by one another in manufacturing systems. Owing to different time scales and objectives, planning and scheduling are often separately handled in a sequential way, which frequently results in infeasible or suboptimal solutions. Moreover, uncertain issues, e.g. the fuzzy startup time of a machine and the fuzzy processing time for a task, are inherent to manufacturing systems due to mechanized and/or man-made factors. Motivated by these challenges, this paper aims to develop fuzzy bi-level decision-making techniques to handle integrated planning and scheduling problems in the fuzzy manufacturing system. First, the integrated problem is formulated into a fuzzy bi-level decision model in which solving the higher-level planning problem has to take into account lower-level implicit scheduling reactions in advance. Second, a hybrid solution method is developed to solve the resulting bi-level decision model, in which a particle swarm optimization (PSO) algorithm is applied to update planning decisions, and then, in view of each given planning decision, a heuristic algorithm is presented to find an optimal schedule under fuzzy manufacturing conditions. Lastly, a set of computational study is constructed to demonstrate the effectiveness of the proposed fuzzy bi-level decision-making techniques. Compared with existing works, they can find better planning decisions fulfilled by schedules and perform much better in terms of computational efficiency.