Kiln Drying Operations Scheduling with Dynamic Composition of Loading Patterns

2021 ◽  
Vol 71 (2) ◽  
pp. 101-110
Author(s):  
Philippe Marier ◽  
Jonathan Gaudreault ◽  
Thomas Noguer
Keyword(s):  
Physical Stability ◽  
Difficult Problem ◽  
Mixed Integer ◽  
Planning And Scheduling ◽  
Operations Planning ◽  
Kiln Drying ◽  
Dynamic Composition ◽  
Mip Model ◽  
Loading Patterns ◽  
Lumber Drying

Abstract Planning and scheduling wood lumber drying operations is a very difficult problem. The literature proposes different methods aiming to minimize order lateness. They all make use of pre-established kiln loading patterns that are known to offer good physical stability in the kiln and allow full kiln space utilization. Instead, we propose a mixed integer programming (MIP) model, which can be used to generate loading patterns “on the fly.” This MIP model can be integrated into existing kiln drying operation planning/scheduling systems in order to improve their solutions. We show how this integration can be done by adapting a state of the art drying operations planning and scheduling methodology from the literature. We compare the solutions obtained by this system using the predefined loading patterns versus the solutions it generates if it is connected to our loading patterns generator MIP model. The study shows it is much better to dynamically create loading patterns than to use predefined ones, as most North American sawmills do.

A high performance scalable fuzzy based modified Asymmetric Heterogene Multiprocessor System on Chip (AHt-MPSOC) reconfigurable architecture

2021 ◽  
pp. 1-12
Author(s):  
Arun Prasath Raveendran ◽  
Jafar A. Alzubi ◽  
Ramesh Sekaran ◽  
Manikandan Ramachandran
Keyword(s):  
High Performance ◽  
Standard Technique ◽  
System On Chip ◽  
Mixed Integer ◽  
Multiprocessor System ◽  
Compound System ◽  
Available Bandwidth ◽  
Mip Model ◽  
Fpga Chip ◽  
On Chip

This Ensuing generation of FPGA circuit tolerates the combination of lot of hard and soft cores as well as devoted accelerators on a chip. The Heterogene Multi-Processor System-on-Chip (Ht-MPSoC) architecture accomplishes the requirement of modern applications. A compound System on Chip (SoC) system designed for single FPGA chip, and that considered for the performance/power consumption ratio. In the existing method, a FPGA based Mixed Integer Programming (MIP) model used to define the Ht-MPSoC configuration by taking into consideration the sharing hardware accelerator between the cores. However, here, the sharing method differs from one processor to another based on FPGA architecture. Hence, high number of hardware resources on a single FPGA chip with low latency and power targeted. For this reason, a fuzzy based MIP and Graph theory based Traffic Estimator (GTE) are proposed system used to define New asymmetric multiprocessor heterogene framework on microprocessor (AHt-MPSoC) architecture. The bandwidths, energy consumption, wait and transmission range are better accomplished in this suggested technique than the standard technique and it is also implemented with a multi-task framework. The new Fuzzy control-based AHt-MPSoC analysis proves significant improvement of 14.7 percent in available bandwidth and 89.8 percent of energy minimized to various traffic scenarios as compared to conventional method.

Intelligente Arbeitsvorbereitung in der Cloud/Smarter operations planning and scheduling in the cloud - Cloud application reduces setup and non-productive times by the use of virtual machine tools

2016 ◽  
Vol 106 (01-02) ◽  
pp. 77-82
Author(s):  
G. Rehage ◽  
F. Isenberg ◽  
R. Reisch ◽  
J. Weber ◽  
B. Jurke ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Machine Tools ◽  
Distributed Simulation ◽  
Cloud Service ◽  
Planning And Scheduling ◽  
Operations Planning ◽  
Manufacturing Operations ◽  
Cloud Application ◽  
Cognitive Information ◽  
Selection Of

Auf dem Weg zu Industrie 4.0 wird die Arbeitsvorbereitung zunehmend von kognitiver Informationstechnik unterstützt. Der Beitrag präsentiert die bisherigen Ergebnisse des Forschungsprojekts „Intelligente Arbeitsvorbereitung auf Basis virtueller Werkzeugmaschinen“. Projektziel ist eine Cloud-Dienstleistungsplattform zur Reduzierung der Rüst- und Nebenzeiten durch eine intelligente Planung. Hierzu zählen unter anderem die Auswahl und Validierung alternativer Maschinen sowie die automatische Optimierung der Einrichtungsparameter durch verteilte Simulationen.   On the way to industry 4.0, the operations planning and scheduling will be aided by cognitive information systems. This contribution presents the previous findings of a research project called “Smart operations planning and scheduling on the basis of virtual machine tools” (translated from German). The aim of the project is the development of a cloud service for the smart planning of manufacturing operations; that will reduce the setup and non-productive times of machine tools. This is achieved by the automatic selection of alternative CNC machines, as well as the optimization of setup parameters via distributed simulation.

scholarly journals A bi-objective model for sustainable logistics and operations planning of WEEE recovery

2019 ◽  
Vol 9 (2) ◽  
pp. 89-99 ◽  
Author(s):  
Emine Nisa Kapukaya ◽  
Alperen Bal ◽  
Sule Itir Satoglu
Keyword(s):  
Electronic Waste ◽  
Hazardous Materials ◽  
Mixed Integer ◽  
Economic Aspects ◽  
Bottom Line ◽  
Operations Planning ◽  
Sustainable Logistics ◽  
Unique Aspect ◽  
Objective Model ◽  
Recovered Material

The Triple-bottom-line concept suggests that firms must consider the environmental and social impacts of their decisions, beside the economic aspects. Hence, the sustainability of the firms’ operations can be reached. The purpose of this study is to develop a bi-objective, multi-product and multi-period mixed-integer model for the operations planning of electrical-electronic waste (WEEE) recovery facilities, by considering social (workforce) constraints. Main objective is the minimization of net recycling and logistics costs offset by the profit earned by recovered material sales, and second objective is the maximization of hazardous materials recovery.  Collection of used products from the specified regions is decided and the required machine-hours, inventory and workforce decisions are made. Besides, both weight-based and unit-based WEEE recovery targets are separately considered, as a unique aspect. A sensitivity analysis is conducted with various scrap prices to understand operations planning in changing conditions. Results show that weight-based targets enhance recovery amounts.

A note on bi-objective optimization for sustainable supply chain network design in omnichannel

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Weidong Lei ◽  
Dandan Ke ◽  
Pengyu Yan ◽  
Jinsuo Zhang ◽  
Jinhang Li
Keyword(s):  
Mixed Integer ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Sustainable Supply Chain ◽  
Content Type ◽  
Product Flow ◽  
Flow Balance ◽  
Mip Model ◽  
Milp Model ◽  
Facility Capacity

PurposeThis paper aims to correct the existing mixed integer programming (MIP) model proposed by Yadav et al. (2019) [“Bi-objective optimization for sustainable supply chain network design in omnichannel.”, Journal of Manufacturing Technology Management, Vol. 30 No. 6, pp. 972–986].Design/methodology/approachThis paper first presents a counterexample to show that the existing MIP model is incorrect and then proposes an improved mixed integer linear programming (MILP) model for the considered problem. Last, a numerical experiment is conducted to test our improved MILP model.FindingsThis paper demonstrates that the formulations of the facility capacity constraints and the product flow balance constraints in the existing MIP model are incorrect and incomplete. Due to this reason, infeasible solutions could be identified as feasible ones by the existing MIP model. Hence, the optimal solution obtained with the existing MIP model could be infeasible. A counter-example is used to verify our observations. Computational results verify the effectiveness of our improved MILP model.Originality/valueThis paper gives a complete and correct formulation of the facility capacity constraints and the product flow balance constraints, and conducts other improvements on the existing MIP model. The improved MILP model can be easily implemented and would help companies to have more effective distribution networks under the omnichannel environment.

Production and Inter-Facility Transportation with Shipment Size Restrictions

2013 ◽  
pp. 74-92
Author(s):  
Mohamed K. Omar
Keyword(s):  
Integer Programming ◽  
Transportation Problem ◽  
Mixed Integer ◽  
Actual Performance ◽  
Industrial Data ◽  
Chemical Company ◽  
Mip Model ◽  
Proposed Model ◽  
Production Inventory ◽  
End Products

This chapter studies production and transportation problem confronting a speciality chemical company that has two manufacturing facilities. Facility I produces intermediate products which are then transported to Facility II where the end products are to be manufactured to meet customers’ demand. The author formulated the problem as a mixed integer programming (MIP) model that integrates the production and transportation decisions between the two facilities. The developed MIP aims to minimize the production, inventory, manpower, and transportation costs. Real industrial data are used to test and validate the developed MIP model. Comparing the model’s results and the company’s actual performance indicate that, if the company implemented the proposed model, significant costs savings could be achieved.

scholarly journals Backbone—An Adaptable Energy Systems Modelling Framework

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3388 ◽  
Author(s):  
Niina Helistö ◽  
Juha Kiviluoma ◽  
Jussi Ikäheimo ◽  
Topi Rasku ◽  
Erkka Rinne ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Unit Commitment ◽  
Energy Systems ◽  
Mixed Integer ◽  
Investment Planning ◽  
Planning And Scheduling ◽  
Modelling Framework ◽  
Wide Range ◽  
Systems Modelling

Backbone represents a highly adaptable energy systems modelling framework, which can be utilised to create models for studying the design and operation of energy systems, both from investment planning and scheduling perspectives. It includes a wide range of features and constraints, such as stochastic parameters, multiple reserve products, energy storage units, controlled and uncontrolled energy transfers, and, most significantly, multiple energy sectors. The formulation is based on mixed-integer programming and takes into account unit commitment decisions for power plants and other energy conversion facilities. Both high-level large-scale systems and fully detailed smaller-scale systems can be appropriately modelled. The framework has been implemented as the open-source Backbone modelling tool using General Algebraic Modeling System (GAMS). An application of the framework is demonstrated using a power system example, and Backbone is shown to produce results comparable to a commercial tool. However, the adaptability of Backbone further enables the creation and solution of energy systems models relatively easily for many different purposes and thus it improves on the available methodologies.

scholarly journals A Hybrid Personnel Scheduling Model for Staff Rostering Problems

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1702
Author(s):  
Jiun-Yan Shiau ◽  
Ming-Kung Huang ◽  
Chu-Yi Huang
Keyword(s):  
Airline Industry ◽  
Real Life ◽  
Integrated Approach ◽  
Manpower Planning ◽  
Mixed Integer ◽  
Aviation Industry ◽  
Personnel Scheduling ◽  
Staff Scheduling ◽  
Mip Model

The problem of staff scheduling in the airline industry is extensively investigated in operational research studies because efficient staff employment can drastically reduce the operational costs of airline companies. Considering the flight schedule of an airline company, staff scheduling is the process of assigning all necessary staff members in such a way that the airline can operate all its flights and construct a roster line for each employee while minimizing the corresponding overall costs for the personnel. This research uses a rostering case study of the ground staff in the aviation industry as an example to illustrate the application of integrating monthly and daily schedules. The ground staff in the aviation industry case is a rostering problem that includes three different types of personnel scheduling results: fluctuation-centered, mobility-centered, and project-centered planning. This paper presents an integrated mixed integer programming (MIP) model for determining the manpower requirements and related personnel shift designs for the ground staff at the airline to minimize manpower costs. The aim of this study is to complete the planning of the monthly and daily schedules simultaneously. A case study based on real-life data shows that this model is useful for the manpower planning of ground services at the airline and that the integrated approach is superior to the traditional two-stage approach.

scholarly journals Coordinated Production Planning and Scheduling Problem in a Foundry

2017 ◽  
Vol 17 (3) ◽  
pp. 133-138
Author(s):  
A. Stawowy ◽  
J. Duda
Keyword(s):  
Production Planning ◽  
Programming Model ◽  
Mixed Integer ◽  
Medium Size ◽  
Scheduling Problem ◽  
Planning And Scheduling ◽  
Alloy Casting ◽  
Production Planning And Scheduling ◽  
Multiple Cores ◽  
Molding Shop

Abstract In the paper, we present a coordinated production planning and scheduling problem for three major shops in a typical alloy casting foundry, i.e. a melting shop, molding shop with automatic line and a core shop. The castings, prepared from different metal, have different weight and different number of cores. Although core preparation does not required as strict coordination with molding plan as metal preparation in furnaces, some cores may have limited shelf life, depending on the material used, or at least it is usually not the best organizational practice to prepare them long in advance. Core shop have limited capacity, so the cores for castings that require multiple cores should be prepared earlier. We present a mixed integer programming model for the coordinated production planning and scheduling problem of the shops. Then we propose a simple Lagrangian relaxation heuristic and evolutionary based heuristic to solve the coordinated problem. The applicability of the proposed solution in industrial practice is verified on large instances of the problem with the data simulating actual production parameters in one of the medium size foundry.

An Integrated Multi-Period Layout Planning and Scheduling Model for Sustainable Reconfigurable Manufacturing Systems

2020 ◽  
Vol 19 (01) ◽  
pp. 31-64
Author(s):  
S. Ghanei ◽  
T. AlGeddawy
Keyword(s):  
Manufacturing Systems ◽  
Planning Horizon ◽  
Mixed Integer ◽  
Reconfigurable Manufacturing Systems ◽  
Reconfigurable Manufacturing ◽  
Production Environment ◽  
Energy Sustainability ◽  
Planning And Scheduling ◽  
Energy Pricing ◽  
Cost Of Energy

In a dynamic production environment, not only the customer’s needs change with time, but the economic aspects of that environment, such as energy pricing, also change. Reconfigurable Manufacturing Systems (RMSs) are designed to respond to such changes by reconfiguring system components efficiently. This paper presents a novel mathematical model to maximize energy sustainability of RMS. The novelty aspect of the model is the consideration of energy sustainability concurrently with system configuration and scheduling decisions in each period of the planning horizon. The objective of this mixed integer linear model is to minimize the total cost of energy consumption, system reconfiguration, and part transportation between machines, depending on fluctuations of energy pricing and demand during different periods. Several case studies are solved by GAMS Software to illustrate the performance of the presented model and analyze its sensitivity to the volatility of energy pricing and demand to show their effect on system changeability. An efficient genetic algorithm (GA) has been developed to solve the model in larger scale due to its NP-hardness and compared to GAMS for validation. Results show that the presented GA finds near-optimal solutions in 70% shorter time than GAMS on average.

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