Production Scheduling Problems Research Articles

An energy-aware scheduling algorithm under maximum power consumption constraints

This research investigates the production scheduling problems under maximum power consumption constraints. Probabilistic models are developed to model dispatching-dependent and stochastic machine energy consumption. A multi-objective scheduling algorithm called the energy-aware scheduling optimization method is proposed in this study to enhance both production and energy efficiency. The explicit consideration of the probabilistic energy consumption constraint and the following factors makes this work distinct from other existing studies in the literature: 1) dispatching-dependent energy consumption of machines, 2) stochastic energy consumption of machines, 3) parallel machines with different production rates and energy consumption pattern, and 4) maximum power consumption constraints. The proposed three-stage algorithm can quickly generate near-optimal solutions and outperforms other algorithms in terms of energy efficiency, makespan, and computation time. While minimizing the total energy consumption in the first and second stages, the proposed algorithm generates a detailed production schedule under the probabilistic constraint of peak energy consumption in the third stage. Numerical results show the superiority of the scheduling solution with regard to quality and computational time in real problems instances from manufacturing industry. While the scheduling solution is optimal in total energy consumption, the makespan is within 0.6 % of the optimal on average.

Crowdsource-enabled integrated production and transportation scheduling for smart city logistics

With city logistics becoming more and more important, increasing attention has been paid to the ‘last-mile delivery’ in urban areas. We investigate a novel crowdsource-enabled integrated production and transportation scheduling problem in the paper. The problem is first formulated into a mixed-integer linear program and its strong NP-hardness is proved. To better understand this complex problem, two sub-problems: a production and transportation scheduling problem and a crowdsourced bid selection problem are analysed. Based on problem properties, a Genetic Algorithm (GA) and a lower bound (LB) are developed to solve the original problem. Experimental results with up to 100 customers show that the GA outperforms the well-known commercial MIP solver CPLEX. Especially, (1) the GA can yield near-optimal solutions for all the tested instances with an average gap of 10.17% from the lower bound, while CPLEX provides feasible solutions only for instances with no more than 30 customers; (2) the average computation time of the GA is only 0.93% of that required by CPLEX; Besides, sensitivity analysis demonstrates advantages of introducing crowdsourced delivery into city logistics.

Improving performance of open-pit mine production scheduling problem under grade uncertainty by hybrid algorithms

One of the surface mining methods is open-pit mining, by which a pit is dug to extract ore or waste downwards from the earth’s surface. In the mining industry, one of the most significant difficulties is long-term production scheduling (LTPS) of the open-pit mines. Deterministic and uncertainty-based approaches are identified as the main strategies, which have been widely used to cope with this problem. Within the last few years, many researchers have highly considered a new computational type, which is less costly, i.e., meta-heuristic methods, so as to solve the mine design and production scheduling problem. Although the optimality of the final solution cannot be guaranteed, they are able to produce sufficiently good solutions with relatively less computational costs. In the present paper, two hybrid models between augmented Lagrangian relaxation (ALR) and a particle swarm optimization (PSO) and ALR and bat algorithm (BA) are suggested so that the LTPS problem is solved under the condition of grade uncertainty. It is suggested to carry out the ALR method on the LTPS problem to improve its performance and accelerate the convergence. Moreover, the Lagrangian coefficients are updated by using PSO and BA. The presented models have been compared with the outcomes of the ALR-genetic algorithm, the ALR-traditional sub-gradient method, and the conventional method without using the Lagrangian approach. The results indicated that the ALR is considered a more efficient approach which can solve a large-scale problem and make a valid solution. Hence, it is more effectual than the conventional method. Furthermore, the time and cost of computation are diminished by the proposed hybrid strategies. The CPU time using the ALR-BA method is about 7.4% higher than the ALR-PSO approach.

Color-Coating Scheduling With a Multiobjective Evolutionary Algorithm Based on Decomposition and Dynamic Local Search

The color-coated steel coil is a high value-added product for steel enterprises, and its production process is affected by multiple factors. How to provide operators with appropriate scheduling schemes is the key to improve the economic benefits of enterprises. In this article, for the scheduling of a single color-coating turn, we establish a multiobjective optimization model that minimizes the number of insertions of transition coils, the thickness jump penalty of adjacent coils, and the switching times of the backup rollers. To address this problem, we propose a piecewise coding approach to ensure that each individual meets the production constraints. Besides, a multiobjective evolutionary algorithm (MOEA) based on decomposition and dynamic local search (D-DLS) strategy is proposed (MOEA/D-DLS). More specifically, the color-coating multiobjective scheduling problem is decomposed into a series of single-objective subproblems and optimized simultaneously. Furthermore, based on the speed of evolution of these subproblems, local search is performed on partial subproblems dynamically. The proposed algorithm is used to solve eight multiobjective scheduling problem instances of color-coating with different scales, and the experimental results demonstrate that the proposed algorithm is very effective compared with four state-of-the-art algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Practical production scheduling problems in iron & steel industry generally need to optimize conflicting objectives simultaneously, which is very hard for practitioners to make appropriate decisions with manual experience. The decomposition-based multiobjective evolutionary algorithm (MOEA) can help practitioners of color-coating scheduling to achieve a set of Pareto optimal decisions with good distribution and tradeoff among three objectives. Since the scheduling of the other production lines shares many similarities with our problem, the proposed model and algorithm can also be applicable to these problems.

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.

A Comprehensive Study of Several Meta-Heuristic Algorithms for Open-Pit Mine Production Scheduling Problem Considering Grade Uncertainty

It is significant to discover a global optimization in the problems dealing with large dimensional scales to increase the quality of decision-making in the mining operation. It has been broadly confirmed that the long-term production scheduling (LTPS) problem performs a main role in mining projects to develop the performance regarding the obtainability of constraints, while maximizing the whole profits of the project in a specific period. There is a requirement for improving the scheduling methodologies to get a good solution since the production scheduling problems are non-deterministic polynomial-time hard. The current paper introduces the hybrid models so as to solve the LTPS problem under the condition of grade uncertainty with the contribution of Lagrangian relaxation (LR), particle swarm optimization (PSO), firefly algorithm (FA), and bat algorithm (BA). In fact, the LTPS problem is solved under the condition of grade uncertainty. It is proposed to use the LR technique on the LTPS problem and develop its performance, speeding up the convergence. Furthermore, PSO, FA, and BA are projected to bring up-to-date the Lagrangian multipliers. The consequences of the case study specifies that the LR method is more influential than the traditional linearization method to clarify the large-scale problem and make an acceptable solution. The results obtained point out that a better presentation is gained by LR–FA in comparison with LR-PSO, LR-BA, LR-Genetic Algorithm (GA), and traditional methods in terms of the summation net present value. Moreover, the CPU time by the LR-FA method is approximately 16.2% upper than the other methods.

Bir Metal Fabrikası İçin İş Çizelgeleme Yazılımı Hazırlanması

In order to maintain continuity in today's competitive environment, companies provide products and services that are produced with higher quality and cheaper. Continuous production techniques and production systems technology production plant has become more complex and control of simple production activities has become more difficult. The production planning option is capable of planning and planning the production of the production, available, identified and required equipment, wherever and wherever we want. The production scheduling problem is to determine the order in which a certain number of jobs must be loaded on a certain number of machines. Different scheduling options to solve production scheduling problems. The common goal of all methods is to use all existing orders. The solution of these methods is done with Gantt diagrams. On this page a work scheduling package program has been implemented in a factory produces software, metal production. The program is C# language software and the program is Access. The aim of the program is to facilitate the scheduling process so that firms can examine the charts according to different methods, make the comparison, use the most accurate method.

Joint optimization of opportunistic maintenance and production scheduling considering batch production mode and varying operational conditions

The joint optimization of production scheduling and maintenance has been a hot topic. Most of the existing publications assume that the operational condition (OC) of a machine is constant during the entire production task. However, it is practical that a machine may experience several different OCs due to the requirement of different jobs. The varying OC may impact the deterioration of machines and their maintenance decisions. In addition, in a batch production system, a maintenance action has to be advanced before or postponed after a processing batch. Even if a machine is maintained preventively, it cannot be renewed since preventive maintenance (PM) is usually imperfect. In addition, minimal repair should be considered, which is costly and time consuming upon machine failure. The above concerns are practical but insufficiently considered in an integrated manner. Due to this integration, the complexity of the joint optimization problem is enhanced. An improved genetic algorithm (GA) based on random keys, convex set theory and the Jaya algorithm is proposed to solve the joint optimization problem of opportunistic PM and production scheduling in a batch production system under varying OCs. A series of comparative cases are conducted to illustrate the effectiveness of the proposed methods.

Two calibrated meta-heuristics to solve an integrated scheduling problem of production and air transportation with the interval due date

Contrary to previous methods in production management, today’s approaches mainly focus on the whole supply chain parties’ considerations. Considering production planning and distribution, as the two main functions in supply chain (SC) management, in an integrated manner in order to enhance the SC advantages is one of today’s main dilemma. Here, we have firstly proposed and investigated the integrated production and air transportation scheduling problem with time windows for the due date to minimize the total SC costs. Since the problem was NP-hard, two new coordinated and integrated solution procedures have been presented based on meta-heuristics. Four algorithms (i.e., simulated annealing (SA), genetic algorithm, particle swarm optimization/district PSO (PSO/DPSO), and hybrid variable neighborhood search–simulated annealing (H-VNS–SA)) have been developed in both procedures. For the first time in literature, we probe different encoding schemes in the proposed algorithms. In addition, by using Taguchi experimental design, the parameters of the algorithms have been tuned. Besides, to study the behavior of the algorithms, different problem sizes have been generated and the results of two procedures have been compared together and discussed. Finally, a comparison of the proposed algorithms with some state-of-art optimized algorithms has been presented to prove statistically better performance of the proposed algorithms in most cases.

Application of an improved Lagrangian relaxation approach in the constrained long-term production scheduling problem under grade uncertainty

In open-pit mines, the long-term production scheduling (LTPS) problem is a mixed-integer programming problem and is considered as a class of NP-hard problems that has to be solved in a reasonably small time owing to the operational requirements. The LTPS problem cannot be considered a well-solved problem. The current article presents hybrid models to elucidate the LTPS problem regarding grade uncertainty with the involvement of Lagrangian relaxation and augmented Lagrangian relaxation (ALR) with metaheuristic methods, firefly algorithm (FA) and bat algorithm. The results demonstrate that the ALR-FA has the best results in terms of net present value, average ore grade and computational time, and it is significantly better than the conventional method. Finally, analysis of the results shows that the proposed method generates a near-optimal solution within a reasonable time; thus, it could be a good proposition for use in the industry.

Design of mathematical models for the integration of purchase and production lot-sizing and scheduling problems under demand uncertainty

This study addresses the multi-level lot-sizing and scheduling problem with complex setups and considers supplier selection with quantity discounts and multiple modes of transportation. The present research proposes a mixed-integer linear programming (MILP) model in which the purchase lot-sizing from multiple suppliers, production lot-sizing with multiple machines and scheduling of various products of different families are accomplished at the same time. However, these decisions are not integrated in traditional environments and are taken separately. In this study, two different types of lot-sizing models called aggregated and disaggregated are developed for the problem to evaluate and compare the computational efficiency of them under deterministic and stochastic demands and provide some managerial insights. To deal with the stochastic demands, Chance-Constrained Programming (CCP) approach is applied. Based on the results of this study, the average profit of the separated (purchase from production) lot-sizing model under demand choice flexibility and stochastic demand is 24% and 22% less than the integrated model, respectively. Moreover, the results also confirm the effect of discount structure on the amount of purchases, productions, revenues and costs.

Deep Learning-Based Dynamic Scheduling for Semiconductor Manufacturing With High Uncertainty of Automated Material Handling System Capability

Recently, the transportation capability of the automated material handling system (AMHS) has emerged as a major barrier to the semiconductor fabrication facility (FAB), because it can limit the FAB production capacity. In this study, we propose a prediction method for a machine allocation problem of production scheduling in consideration with the AMHS’s constraints. The proposed method dynamically targets a machine for the next process by identifying diverse production conditions. We use a deep neural network-based dynamic scheduling method considering the overall production environment, which includes the remaining processing time, facility states, transportation time and traffic congestion, work-in-process distribution, and intermediate buffer states. To demonstrate the superiority and efficiency of the proposed method, we conducted experimental studies to compare the proposed model with the existing priority rule-based and analytic models under the static and dynamic environments. From the results, we verify that the proposed dynamic scheduling system can enhance the performance of existing AMHS and reduce machine starvation and production losses.

Production scheduling problem in a factory of automobile component primer painting

In a factory of automobile component primer painting, various automobile parts are attached to overhead hangers in a conveyor line and undergo a series of coating processes. Thereafter, the components are wrapped at a packaging station. The packaging process should be fully balanced by an appropriate sequence of components to prevent the bottleneck effect because each component requires different packaging times and materials. An overhead hanger has a capacity limit and can hold varying numbers of components depending on the component type. Capacity loss can occur if the hanger capacity is not fully utilized. To increase hanger utilization, companies sometimes mix two or more component types on the same hangers, and these hangers are called mixed hangers. However, mixed hangers generally cause heavy workload because different items require additional setup times during hanging and packing processes. Hence, having many mixed hangers is not recommended. A good production schedule requires a small number of mixed hangers and maximizes hanger utilization and packaging workload balance. We show that the scheduling problem is NP-hard and develop a mathematical programming model and efficient solution approaches for the problem. When applying the methods to solve real problems, we also use an initial solution-generating method that minimizes the mixing cost, set a rule for hanging the items on hangers considering eligibility constraint, and decrease the size of tabu list in proportion to the remaining computational time for assuring intensification in the final iterations of the search. Experimental results demonstrate the effectiveness of the proposed approaches.

Speed scaling technique integrated in scheduling of production and maintenance under energy constraints using genetic algorithms

Recently, energy scarcity and environmental pollution have become increasingly serious problems, while the scheduling of energy savings is attracting increasing attention from industries to minimize energy consumption. This article deals with the problem of scheduling production and maintenance under energy constraints in the flow shop. Two mixed binary integer programming models are provided to derive optimal scheduling to minimize total energy consumption (TEC). To solve this problem, we use an approximate method as a genetic algorithm (GA).

Integrált termelésütemezési és műszakbeosztási feladatok megoldása

A cikk bemutatja egy integrált termelésütemezési és műszakbeosztási feladat megoldását. A feladat jellegzetessége, hogy összeszerelő gépsorok és alkatrészgyártó gépek működését kell összehangolni megfelelő ütemtervek készítésével. Ezen túlmenően az ütemezés és a műszakbeosztás egy integrált feladatát kell megoldani. A cikk összefoglalja a vizsgált feladat legfontosabb jellemzőit és a kifejlesztett megoldási módszer koncepcióját.

A new mathematical model for production and delivery scheduling problem with common cycle in a supply chain with open-shop system

A new mathematical model for production and delivery scheduling problem with common cycle in a supply chain with open-shop system

A new mathematical model for production and delivery scheduling problem with common cycle in a supply chain with open-shop system

In order to coordinate the supply chain, reordering strategy of needed goods must be synchronised and sequence of production and replenishment cycle time must be optimised in terms of cost. Therefore, this paper studies the economic lot and delivery scheduling problem for multi-stage supply chain. The common cycle time and integer multiplier policies were adopted to accomplish the desired synchronisation. In this regard, a new mathematical model has been presented where a manufacturer with open-shop system purchases raw materials from suppliers and sends them to packaging companies after converting them into the final product and then they are sold. Since this is a non-deterministic polynomial-time hard (NP-hard) problem, simulated annealing algorithms have been developed for it. For this algorithm, two different scenarios have been proposed for solving the study problem and at the end the numerical results have been applied on problems with different dimensions by the algorithm.

Hybrid spider monkey optimisation algorithm for multi-level planning and scheduling problems of assembly lines

The production planning and scheduling problems of printed circuit board (PCB) assembly line robustly influence the production efficiency of PCB industries. The current study focuses on the optimisation of the multi-level planning and scheduling problem by minimising the cycle time of the PCB assembly lines. Two levels of planning problems i.e. component allocation problem (CAP) and component placement sequence problem (CPSP) are solved simultaneously using mixed integer linear programming model. In CAP, a model is formulated with the objective of balancing the workload among the surface mounted machines (SMM), while in CPSP, a model is formulated to find optimum sequencing for allocated components at each SMM. A novel hybrid spider monkey optimisation (HSMO) algorithm is proposed with the addition of new sorting food sources and genetic operators in the standard spider monkey optimisation (SMO) algorithm. The performance of the proposed HSMO algorithm is validated by comparing the solutions with well-known algorithms, i.e. genetic algorithm (GA), particle swarm optimisation (PSO), simulated annealing (SA) and artificial bee colony (ABC) algorithms. The proposed HSMO algorithm is tested on different problem set instances scaled based on the realistic production of PCB industries. The detailed analysis of results indicates that the proposed HSMO algorithm outperforms the compared algorithms in efficiency and effectiveness.

Less is more: variable neighborhood search for integrated production and assembly in smart manufacturing

This paper investigates an integrated production and assembly scheduling problem with the practical manufacturing features of serial batching and the effects of deteriorating and learning. The problem is divided into two stages. During the production stage, there are several semi-product manufacturers who first produce ordered product components in batches, and then these processed components are sent to an assembly manufacturer. During the assembly stage, the assembly manufacturer will further process them on multiple assembly machines, where the product components are assembled into final products. Through mathematical induction, we characterize the structures of the optimal decision rules for the scheduling problem during the production stage, and a scheme is developed to solve this scheduling problem optimally based on the structural properties. Some useful lemmas are proposed for the scheduling problem during the assembly stage, and a heuristic algorithm is developed to eliminate the inappropriate schedules and enhance the solution quality. We then prove that the investigated problem is NP-hard. Motivated by this complexity result, we present a less-is-more-approach-based variable neighborhood search heuristic to obtain the approximately optimal solution for the problem. The computational experiments indicate that our designed LIMA-VNS (less is more approach–variable neighborhood search) has an advantage over other metaheuristics in terms of converge speed, solution quality, and robustness, especially for large-scale problems.

Multi-agent supply chain scheduling problem by considering resource allocation and transportation

Today in the global market competition, integration issue in supply chain is considered as an important principle. In this study, for the first time, different requirements of the customers and different aims of the manufacturer are simultaneously addressed in an integrated problem of production scheduling, transportation, and resource allocation. The problem consists of two types of customers, considered as the agents. The first agent accepts tardiness in delivery of orders provided that the manufacturer pays the tardiness penalty; whereas, the second agent does not accept the tardy orders. The purpose is to minimize the sum of batch delivery cost, resource allocation, tardiness penalty cost, and lost sale cost (the total number of tardy orders). To solve the problem, two mathematical programming models, including a Mixed Integer Non-Linear Programming (MINLP) and a Mixed Integer Linear Programming (MILP) are proposed. Also, due to NP-hard nature of the problem, two meta-heuristic algorithms of Adaptive Genetic Algorithm (AGA) and Ant Lion Optimization (ALO), as well as a heuristic algorithm are proposed. To assess the merits of the solution methods, small and large-scale tests are designed. The results indicate the superiority of adaptive genetic algorithm in comparison with other algorithms.