Models for Solving Integrated Planning and Scheduling Problem: Computational Comparison

2018 ◽  
Vol 34 ◽  
pp. 161-170 ◽  
Author(s):  
Zineb Ibn Majdoub Hassani ◽  
Abdellah El Barkany ◽  
Abdelouahhab Jabri ◽  
Ikram El Abbassi
Keyword(s):  
Production Planning ◽  
Production System ◽  
Production Plan ◽  
Scheduling Problem ◽  
Integrated Planning ◽  
System Planning ◽  
Planning And Scheduling ◽  
Computational Comparison

This article concerns the integration of planning and scheduling production system. Planning and scheduling are usually treated separately because of their complexity. Scheduling largely depends on the production quantities computed at the production planning level. However, ignoring scheduling constraints in the tactical level leads to inconsistent decisions. So, it is important to integrate planning and scheduling to efficiently manage operations and to determine a realistic production plan for a given sequence of jobs on each machine. In this paper, we present some approaches proposed to solve the problem and we realize a comparison between the two most interesting ones, using the standard solver CPLEX.

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.

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

2020 ◽  
Vol 7 (6) ◽  
pp. 761-774
Author(s):  
Kailash Changdeorao Bhosale ◽  
Padmakar Jagannath Pawar
Keyword(s):  
Production Planning ◽  
Demand Uncertainty ◽  
Clustering Algorithm ◽  
Real Life ◽  
Initial Population ◽  
Planning Problem ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Planning And Scheduling ◽  
Production Planning And Scheduling

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 Surrogate Model to Predict Production Performance in Digital Twin based Smart Manufacturing

2021 ◽  
pp. 1-55
Author(s):  
Ping Chong Chua ◽  
Seung Ki Moon ◽  
Yen Ting Ng ◽  
Huey Yuen Ng
Keyword(s):  
Production Planning ◽  
Production System ◽  
Surrogate Model ◽  
Production Performance ◽  
Smart Manufacturing ◽  
Planning And Scheduling ◽  
Digital Twin ◽  
Production Planning And Scheduling ◽  
Input Parameters ◽  
Production Orders

Abstract With the dynamic arrival of production orders and unforeseen changes in shop-floor conditions within a production system, production scheduling presents a challenge for manufacturing firms to ensure production demands are met with high productivity and low operating cost. Before a production schedule is generated to process the incoming production orders, production planning is performed. Given the large number of input parameters involved in production planning, it poses the challenge on how to systematically and accurately predict and evaluate the production performance. Hence, it is important to understand the interactions of the input parameters between production planning and scheduling. This is to ensure that the production planning and scheduling are coordinated and can be performed to achieve the optimal production performance such as minimizing cost effectively and efficiently. Digital twin presents an opportunity to mirror the real-time production status and analyze the input parameters affecting the production performance in smart manufacturing. In this paper, using the capabilities of real-time synchronization of production data in digital twin, we propose an approach to develop a surrogate model to predict the production performance using input parameters from a production plan. Multivariate adaptive regression spline (MARS) is applied to construct the surrogate model based on three categories of input parameters, such as current production system load, machine-based and product-based parameters. The effectiveness of the proposed MARS model is demonstrated using an industrial case study of a wafer fabrication production based on random sampling of varying numbers of training data set.

Production Planning Considering Transfer Lot Size

2010 ◽  
Vol 44-47 ◽  
pp. 552-556
Author(s):  
Zhi Cong Zhang ◽  
Kai Shun Hu ◽  
Hui Yu Huang ◽  
Shuai Li
Keyword(s):  
Production Planning ◽  
Performance Measures ◽  
Lot Sizing ◽  
Mixed Integer ◽  
Production Plan ◽  
Lot Size ◽  
Planning And Scheduling ◽  
Production Planning And Scheduling ◽  
Lot Sizing Problem ◽  
Plan Change

Traditional methods conduct production planning and scheduling separately and solve transfer lot sizing problem between these two steps. Unfortunately, this may result in infeasibility in planning and scheduling. We take into account transfer lot size in production planning to obtain the consistency and to eliminate the gap between planning and real production. We present the detailed Transfer Lot-Based Model with mixed integer programming. Experiments show that performance measures of a production plan change remarkably with increasing of transfer lot size.

Multi-site planning and scheduling: state-of-the-art review and future research directions

2019 ◽  
Vol 13 (1) ◽  
pp. 17-37
Author(s):  
Gaurav Kumar Badhotiya ◽  
Gunjan Soni ◽  
M.L. Mittal
Keyword(s):  
Relevant Literature ◽  
Future Research ◽  
Scheduling Problem ◽  
Integrated Planning ◽  
Planning And Scheduling ◽  
Content Type ◽  
Solution Strategies ◽  
Research Findings ◽  
Future Research Directions ◽  
Site Planning

Purpose This paper aims to deal with integrated planning and scheduling problem in multi-site manufacturing environment and provides a comprehensive review of literature. Classification schemes and various aspects of planning and scheduling problem in multi-site manufacturing are highlighted. Design/methodology/approach A structured review methodology is adopted to classify the relevant literature. Taxonomy for classification of the problem is presented, followed by review of modelling approaches, solution strategies and challenges faced in multi-site integrated planning and scheduling problem. Findings The paper is concluded with interesting research findings and a short view on directions related to modelling approach, solution strategy and technique for further developments in the area of multi-site integrated planning and scheduling. Research limitations/implications The findings of this study would be helpful for future researchers and practitioners to provide a knowledge base and to further work in this area. Originality/value This study attempts to consolidate the diverse literature available and highlight the various aspects of planning and scheduling in multi-site manufacturing.

Research on production planning and scheduling based on improved collaborative optimization

2019 ◽  
Vol 27 (2) ◽  
pp. 99-111 ◽  
Author(s):  
Song Zheng ◽  
Jiaxin Gao ◽  
Jian Xu
Keyword(s):  
Production Planning ◽  
Production Scheduling ◽  
Production Efficiency ◽  
Collaborative Optimization ◽  
Production Plan ◽  
The Novel ◽  
Integration Algorithm ◽  
Planning And Scheduling ◽  
Integrated Optimization ◽  
Production Planning And Scheduling

The production planning is aimed at the formulation and distribution of the overall production plan, while the production scheduling focuses on the implementation of the specific production plan. It is very important to coordinate each other in order to promote the production efficiency of enterprises, but the integrated optimization of production planning and scheduling has great challenges. This article proposes the novel integrated optimization method of planning and scheduling based on improved collaborative optimization. An integrated model of planning and scheduling with collaborative optimization structure is established, and the detailed solution strategy of the novel integrated optimization algorithm is presented. At last, the simulation results show that the proposed integration algorithm of planning and scheduling is competitive in global optimization and practicality.

A NEW APPROACH FOR SOLVING INTEGRATED PLANNING AND SCHEDULING PROBLEM

2006 ◽  
Vol 39 (3) ◽  
pp. 191-196 ◽  
Author(s):  
Cathy Wolosewicz ◽  
Stéphane Dauzère-Pérès ◽  
Riad Aggoune
Keyword(s):  
Scheduling Problem ◽  
Integrated Planning ◽  
Planning And Scheduling ◽  
New Approach

The Importance of Asset Management and Hidden Factory for Integrated Planning

2014 ◽  
Vol 1039 ◽  
pp. 577-584 ◽  
Author(s):  
Harald Rødseth ◽  
Per Schjølberg
Keyword(s):  
Production Planning ◽  
Asset Management ◽  
Production System ◽  
Maintenance Planning ◽  
Integrated Planning

The purpose of this paper is to investigate in Asset Management and the metaphor “hidden factory” and evaluate to what extent this is of importance for Integrated Planning (IPL). Integrated Planning is facing the challenges in the industry with “silo thinking”, which hinders the organisation to optimise the cooperation between the different functions in the production system, e.g. the cooperation between production planning and maintenance planning. The result shows that there are indeed elements both in Asset Management and from the hidden factory concept that supports the IPL concept. In particular it has been proposed specific KPIs and a dashboard for the planners in IPL.

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