An Integrated Simulation Optimisation Decision Support Tool for Multi-Product Production Systems

Abstract

Over the past decades, the rising energy prices and imposing environmental regulations have motivated manufacturers to improve the energy efficiency of their manufacturing processes. Manufacturers need to also consider energy efficiency in addition to classical performance measures. The additional performance dimension (energy-related indicators) significantly increases the complexity of classical production planning problems (e.g. scheduling), already known as NP-hard problem).To overcome the inherited complexity, an integrated simulation-optimization framework is proposed. The proposed approach tackles scheduling problem in a multi-product/multi-machine manufacturing environment and optimizes several production objectives simultaneously. A case study is presented to demonstrate the applicability of the proposed approach in a real-life industrial facility.

Energy Efficiency Improvement through Optimal Batch Sizing in Job Shop

“…To address the gap in literature, the focus of this paper is on energy-oriented optimisation of job shop through simulation using the integrated simulation-optimisation framework presented and published earlier (Alvandi et al, 2017). To this end, a real-life job shop industrial facility is chosen, involving more than 100 different job orders for variety of product types passing through multiple routes.…”

“…Integrated Framework of Simulation-based Optimisation(Alvandi et al, 2017) A simulation model and an optimisation engine work in tandem and exchange information. The simulation model represents the manufacturing system and consists of the physical components of the system and existing logic.…”

Productivity Improvement Through Multi-Objective Simulation Optimization—A Case Study

“…The approach integrates DESS, DEA, design of experiments, artificial neural networks, and radial basis function to provide a real-time scheduling of the problem. Alvandi et al [23] developed an integrated simulation-based optimization framework for a multi-product, multi-machine manufacturing system. The framework optimizes multi-objective functions, product routing, throughput, and energy consumption, and was demonstrated via a case study.…”

“…which includes the design of material handling systems [5]- [7], flexible manufacturing systems [8], cellular manufacturing systems [9], and sustainable manufacturing systems [10], [11]. Other applications include planning and scheduling operations [12]- [16]; planning and scheduling maintenance operations in manufacturing systems [17]- [19]; planning and optimizing quality inspection strategies in manufacturing systems [20]- [22]; analyzing and controlling energy and power consumption in manufacturing operations [11], [23]- [25]; and planning and operations of remanufacturing systems [26]- [28]. For comprehensive reviews on the applications of simulation in manufacturing, the reader is referred to [2]- [4] and [29].…”

A simulation based optimization approach to combine two hedging control policies in a transported degrading failure-prone manufacturing system

“…In subject of simulation-based optimization approach, some meta-heuristic algorithm such as neural network, scatter search and taboo search becomes more efficient, in market, OPTQUEST software combines mentioned algorithm in a separated single package along with many simulation soft wares, which enables us to optimize large scale problems, in terms of number of decision variables and also the ability of involving mathematical limitations. Many researchers, utilized OPTQUEST, and yielded great results [20][21][22] . Since current paper deals with large decision variables ( nine variable), according to [2] applying RSM becomes time-consuming, And also with regard to the good responses offered by many researchers, optimization procedure is applied on the basis of OPTQUEST package software.…”