Hybrid approach for solving the integrated planning and scheduling production problem

2019 ◽  
Vol 18 (1) ◽  
pp. 172-189 ◽  
Author(s):  
Zineb Ibn Majdoub Hassani ◽  
Abdellah El Barkany ◽  
Abdelouahhab Jabri ◽  
Ikram El Abbassi ◽  
Abdel Moumen Darcherif
Keyword(s):  
Simulated Annealing ◽  
Production Planning ◽  
Job Shop ◽  
Hybrid Approach ◽  
Production Costs ◽  
Local Optimum ◽  
Total Production ◽  
Local Optima ◽  
Planning And Scheduling ◽  
Content Type

Purpose This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their complexity. Scheduling depends on the lot sizes calculated at the tactical level and ignoring scheduling constraints generates unrealistic and inconsistent decisions. Therefore, integrating more detail scheduling constraint in production planning is important for managing efficiently operations. Therefore, an integrated model was developed, and two evolutionary optimization approaches were suggested for solving it, namely, genetic algorithm (GA) and the hybridization of simulated annealing (SA) with GA HSAGA. The proposed algorithms have some parameters that must be adjusted using Taguchi method. Therefore, to evaluate the proposed algorithm, the authors compared the results given by GA and the hybridization. The SA-based local search is embedded into a GA search mechanism to move the GA away from being closed within local optima. The analysis shows that the combination of simulated annealing with GA gives better solutions and minimizes the total production costs. Design/methodology/approach The paper opted for an approached resolution method particularly GA and simulated annealing. The study represents a comparison between the results found using GA and the hybridization of simulated annealing and GA. A total of 45 instances were studied to evaluate job-shop problems of different sizes. Findings The results illustrate that for 36 instances of 45, the hybridization of simulated annealing and GA HSAGA has provided best production costs. The efficiency demonstrated by HSAGA approach is related to the combination between the exploration ability of GA and the capacity to escape local optimum of simulated annealing. Originality/value This study provides a new resolution approach to the integration of planning and scheduling while considering a new operational constrain. The model suggested aims to control the available capacity of the resources and guaranties that the resources to be consumed do not exceed the real availability to avoid the blocking that results from the unavailability of resources. Furthermore, to solve the MILP model, a GA is proposed and then it is combined to simulated annealing.

Convergence of Simulated Annealing with Feedback Temperature Schedules

1997 ◽  
Vol 11 (3) ◽  
pp. 279-304 ◽  
Author(s):  
M. Kolonko ◽  
M. T. Tran
Keyword(s):  
Simulated Annealing ◽  
Cost Function ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Method ◽  
Search Process ◽  
Local Optimum ◽  
Fixed Sequence ◽  
Temperature Parameter ◽  
The Cost

It is well known that the standard simulated annealing optimization method converges in distribution to the minimum of the cost function if the probability a for accepting an increase in costs goes to 0. α is controlled by the “temperature” parameter, which in the standard setup is a fixed sequence of values converging slowly to 0. We study a more general model in which the temperature may depend on the state of the search process. This allows us to adapt the temperature to the landscape of the cost function. The temperature may temporarily rise such that the process can leave a local optimum more easily. We give weak conditions on the temperature schedules such that the process of solutions finally concentrates near the optimal solutions. We also briefly sketch computational results for the job shop scheduling problem.

An agent-based architecture for production scheduling in dynamic job-shop manufacturing system

2018 ◽  
Vol 66 (6) ◽  
pp. 492-502 ◽  
Author(s):  
Om Ji Shukla ◽  
Gunjan Soni ◽  
Rajesh Kumar ◽  
Sujil A
Keyword(s):  
Production Planning ◽  
Production Scheduling ◽  
Manufacturing System ◽  
Job Shop ◽  
Shop Floor ◽  
Efficient Production ◽  
Planning And Scheduling ◽  
Agent Based ◽  
Production Planning And Scheduling ◽  
Key Issues

Abstract In a highly competitive environment, effective production is one of the key issues which can be addressed by efficient production planning and scheduling in the manufacturing system. This paper develops an agent-based architecture which enables integration of production planning and scheduling. In addition, this architecture will facilitate real time production scheduling as well as provide a multi-agent system (MAS) platform on which multiple agents will interact to each other. A case study of job-shop manufacturing system (JMS) has been considered in this paper for implementing the concept of MAS. The modeling of JMS has been created in SimEvents which integrates an agent-based architecture developed by Stateflow to transform into dynamic JMS. Finally, the agent-based architecture is evaluated using utilization of each machine in the shop floor with respect to time.

scholarly journals Effects of Eliminating Pyruvate Node Pathways and of Coexpression of Heterogeneous Carboxylation Enzymes on Succinate Production by Enterobacter aerogenes

2014 ◽  
Vol 81 (3) ◽  
pp. 929-937 ◽  
Author(s):  
Yoshinori Tajima ◽  
Yoko Yamamoto ◽  
Keita Fukui ◽  
Yousuke Nishio ◽  
Kenichi Hashiguchi ◽  
...  
Keyword(s):  
Phosphoenolpyruvate Carboxykinase ◽  
Enterobacter Aerogenes ◽  
Major Product ◽  
Production Costs ◽  
Total Production ◽  
Acid Fermentation ◽  
Succinate Production ◽  
Content Type ◽  
Acidic Conditions ◽  
The Impact

ABSTRACTLowering the pH in bacterium-based succinate fermentation is considered a feasible approach to reduce total production costs. Newly isolatedEnterobacter aerogenesstrain AJ110637, a rapid carbon source assimilator under weakly acidic (pH 5.0) conditions, was selected as a platform for succinate production. Our previous work showed that the ΔadhE/PCK strain, developed from AJ110637 with inactivated ethanol dehydrogenase and introducedActinobacillus succinogenesphosphoenolpyruvate carboxykinase (PCK), generated succinate as a major product of anaerobic mixed-acid fermentation from glucose under weakly acidic conditions (pH <6.2). To further improve the production of succinate by the ΔadhE/PCK strain, metabolically engineered strains were designed based on the elimination of pathways that produced undesirable products and the introduction of two carboxylation pathways from phosphoenolpyruvate and pyruvate to oxaloacetate. The highest production of succinate was observed with strain ES04/PCK+PYC, which had inactivated ethanol, lactate, acetate, and 2,3-butanediol pathways and coexpressed PCK andCorynebacterium glutamicumpyruvate carboxylase (PYC). This strain produced succinate from glucose with over 70% yield (gram per gram) without any measurable formation of ethanol, lactate, or 2,3-butanediol under weakly acidic conditions. The impact of lowering the pH from 7.0 to 5.5 on succinate production in this strain was evaluated under pH-controlled batch culture conditions and showed that the lower pH decreased the succinate titer but increased its yield. These findings can be applied to identify additional engineering targets to increase succinate production.

Production Planning

2017 ◽  
pp. 50-72 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Production Planning ◽  
Radio Frequency Identification ◽  
Production Costs ◽  
Planning And Scheduling ◽  
Chemical Process Industry ◽  
Planning And Control ◽  
Business Operations ◽  
Frequency Identification ◽  
The Individual ◽  
Advanced Production

This chapter explains the overview of production planning; the issues of remanufacturing production planning and control; Advanced Production Planning and Scheduling (APPS) and Radio Frequency Identification (RFID); production planning conflict resolution and optimization models; production planning and emission constraints; production planning and quality management; production planning and Cellular Manufacturing System (CMS); production planning in the steel industry; production planning in the energy industry; and production planning in the chemical process industry. The purpose of production planning is to organize the resources in order to efficiently manage the production costs, time, and staffing in the business operations. The individual in charge of production planning adjusts the workforce and process flow to obtain the regular utilization of organizational resources with minimal downtime, minimal bottlenecks, and a level of output consistent with all the resources being put into the manufacturing processes.

scholarly journals Process planning and scheduling optimisation with alternative recipes

2020 ◽  
Vol 68 (2) ◽  
pp. 140-147
Author(s):  
Piotr Dziurzanski ◽  
Shuai Zhao ◽  
Sebastian Scholze ◽  
Albert Zilverberg ◽  
Karl Krone ◽  
...  
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Total Production ◽  
Flexible Job Shop Scheduling ◽  
Planning And Scheduling ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
New Variant ◽  
Human Operators ◽  
Selection Of

AbstractThis paper considers an application of a new variant of a multi-objective flexible job-shop scheduling problem, featuring multisubset selection of manufactured recipes, to a real-world chemical plant. The problem is optimised using a multi-objective genetic algorithm with customised mutation and elitism operators that minimises both the total production time and the produced commodity surplus. The algorithm evaluation is performed with both random and historic manufacturing orders. The latter demonstrated that the proposed system can lead to more than 10 % makespan improvements in comparison with human operators.

A fuzzy imperialistic competitive algorithm for optimizing convex functions

2014 ◽  
Vol 7 (2) ◽  
pp. 192-208 ◽  
Author(s):  
Mahsan Esmaeilzadeh Tarei ◽  
Bijan Abdollahi ◽  
Mohammad Nakhaei
Keyword(s):  
Fuzzy Logic ◽  
Convex Functions ◽  
Optimization Problems ◽  
Imperialist Competitive Algorithm ◽  
Optimization Techniques ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Local Optima ◽  
Content Type ◽  
Competitive Algorithm

Purpose – The purpose of this paper is to describe imperialist competitive algorithm (ICA), a novel socio-politically inspired optimization strategy for proposing a fuzzy variant of this algorithm. ICA is a meta-heuristic algorithm for dealing with different optimization tasks. The basis of the algorithm is inspired by imperialistic competition. It attempts to present the social policy of imperialisms (referred to empires) to control more countries (referred to colonies) and use their sources. If one empire loses its power, among the others making a competition to take possession of it. Design/methodology/approach – In fuzzy imperialist competitive algorithm (FICA), the colonies have a degree of belonging to their imperialists and the top imperialist, as in fuzzy logic, rather than belonging completely to just one empire therefore the colonies move toward the superior empire and their relevant empires. Simultaneously for balancing the exploration and exploitation abilities of the ICA. The algorithms are used for optimization have shortcoming to deal with accuracy rate and local optimum trap and they need complex tuning procedures. FICA is proposed a way for optimizing convex function with high accuracy and avoiding to trap in local optima rather than using original ICA algorithm by implementing fuzzy logic on it. Findings – Therefore several solution procedures, including ICA, FICA, genetic algorithm, particle swarm optimization, tabu search and simulated annealing optimization algorithm are considered. Finally numerical experiments are carried out to evaluate the effectiveness of models as well as solution procedures. Test results present the suitability of the proposed fuzzy ICA for convex functions with little fluctuations. Originality/value – The proposed evolutionary algorithm, FICA, can be used in diverse areas of optimization problems where convex functions properties are appeared including, industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning (optimization techniques; fuzzy logic; convex functions).

Optimizing production while reducing machinery lockout/tagout circumvention possibilities

2016 ◽  
Vol 22 (2) ◽  
pp. 188-201 ◽  
Author(s):  
Abdoulaye Badiane ◽  
Sylvie Nadeau ◽  
Jean-Pierre Kenné ◽  
Vladimir Polotski
Keyword(s):  
Production Planning ◽  
Manufacturing System ◽  
Production Costs ◽  
Worker Safety ◽  
Finite Difference Approximation ◽  
Difference Approximation ◽  
Inventory Level ◽  
Maintenance Policy ◽  
Content Type ◽  
Bellman Equations

Purpose – The optimization of production imposes a review of facility maintenance policies. Accidents during maintenance activities are frequent, sometimes fatal and often associated with deficient or absent machinery lockout/tagout. Lockout/tagout is often circumvented in order to avoid what may be viewed as unnecessary delays and increased production costs. To reduce the dangers inherent in such practice, the purpose of this paper is to propose a production strategy that provides for machinery lockout/tagout while maximizing manufacturing system availability and minimizing costs. Design/methodology/approach – The joint optimization problem of production planning, maintenance and safety planning is formulated and studied using a stochastic optimal control methodology. Hamilton-Jacobi-Bellman equations are developed and studied numerically using the Kushner approach based on finite difference approximation and an iterative policy improvement technique. Findings – The analysis leads to a solution that suggests increasing the “comfortable” inventory level in order to provide the time required for lockout/tagout activities. It is also demonstrated that the optimization of lockout/tagout procedures is particularly important when the equipment is relatively new and the inventory level is minimal. Research limitations/implications – This paper demonstrates that it is possible to integrate production, maintenance and lockout/tagout procedures into production planning while keeping manufacturing system cost objectives attainable as well as ensuring worker safety. Originality/value – This integrated production and maintenance policy is unique and complements existing procedures by explicitly accounting for safety measures.

A hybrid meta-heuristic scheduler algorithm for optimization of workflow scheduling in cloud heterogeneous computing environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Reza Noorian Talouki ◽  
Mirsaeid Hosseini Shirvani ◽  
Homayon Motameni
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Methodology ◽  
Heterogeneous Computing ◽  
Hybrid Approach ◽  
Heuristic Optimization ◽  
Workflow Scheduling ◽  
Engineering Systems ◽  
Computing Environment ◽  
Content Type

Purpose Improvement of workflow scheduling in distributed engineering systems Design/methodology/approach The authors proposed a hybrid meta heuristic optimization algorithm. Findings The authors have made improvement in hybrid approach by exploiting of genetic algorithm and simulated annealing plus points. Originality/value To the best of the authors’ knowledge, this paper presents a novel theorem and novel hybrid approach.

scholarly journals Planning and Scheduling in Additive Manufacturing

2018 ◽  
Vol 21 (62) ◽  
pp. 40 ◽  
Author(s):  
Filip Dvorak ◽  
Maxwell Micali ◽  
Mathias Mathieug
Keyword(s):  
Additive Manufacturing ◽  
Bin Packing ◽  
Job Shop ◽  
Materials Science ◽  
Job Shop Scheduling ◽  
Formal Model ◽  
Search Algorithm ◽  
Production Costs ◽  
Planning And Scheduling ◽  
Science Operations

Recent advances in additive manufacturing (AM) and 3D printing technologies have led to significant growth in the use of additive manufacturing in industry, which allows for the physical realization of previously difficult to manufacture designs. However, in certain cases AM can also involve higher production costs and unique in-process physical complications, motivating the need to solve new optimization challenges. Optimization for additive manufacturing is relevant for and involves multiple fields including mechanical engineering, materials science, operations research, and production engineering, and interdisciplinary interactions must be accounted for in the optimization framework. In this paper we investigate a problem in which a set of parts with unique configurations and deadlines must be printed by a set of machines while minimizing time and satisfying deadlines, bringing together bin packing, nesting (two-dimensional bin packing), job shop scheduling, and constraints satisfaction. We first describe the real-world industrial motivation for solving the problem. Subsequently, we encapsulate the problem within constraints and graph theory, create a formal model of the problem, discuss nesting as a subproblem, and describe the search algorithm. Finally, we present the datasets, the experimental approach, and the preliminary results.

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