scholarly journals Two meta-heuristic algorithms for optimizing a multi-objective supply chain scheduling problem in an identical parallel machines environment

2021 ◽  
Vol 12 (3) ◽  
pp. 249-272 ◽  
Author(s):  
Nima Farmand ◽  
Hamid Zarei ◽  
Morteza Rasti-Barzoki
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Parallel Machines ◽  
Heuristic Algorithms ◽  
Small Scale ◽  
Scheduling Problem ◽  
Identical Parallel Machines ◽  
Nsga Ii ◽  
Multi Objective ◽  
Large Scale Problems

Optimizing the trade-off between crucial decisions has been a prominent issue to help decision-makers for synchronizing the production scheduling and distribution planning in supply chain management. In this article, a bi-objective integrated scheduling problem of production and distribution is addressed in a production environment with identical parallel machines. Besides, two objective functions are considered as measures for customer satisfaction and reduction of the manufacturer’s costs. The first objective is considered aiming at minimizing the total weighted tardiness and total operation time. The second objective is considered aiming at minimizing the total cost of the company’s reputational damage due to the number of tardy orders, total earliness penalty, and total batch delivery costs. First, a mathematical programming model is developed for the problem. Then, two well-known meta-heuristic algorithms are designed to spot near-optimal solutions since the problem is strongly NP-hard. A multi-objective particle swarm optimization (MOPSO) is designed using a mutation function, followed by a non-dominated sorting genetic algorithm (NSGA-II) with a one-point crossover operator and a heuristic mutation operator. The experiments on MOPSO and NSGA-II are carried out on small, medium, and large scale problems. Moreover, the performance of the two algorithms is compared according to some comparing criteria. The computational results reveal that the NSGA-II performs highly better than the MOPSO algorithm in small scale problems. In the case of medium and large scale problems, the efficiency of the MOPSO algorithm was significantly improved. Nevertheless, the NSGA-II performs robustly in the most important criteria.

scholarly journals A multi-objective flexible job-shop cell scheduling problem with sequence-dependent family setup times and intercellular transportation by improved NSGA-II

2021 ◽  
pp. 095440542110446
Author(s):  
Yaoyao Han ◽  
Xiaohui Chen ◽  
Minmin Xu ◽  
Youjun An ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Job Shop ◽  
Heuristic Algorithms ◽  
Setup Times ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Nsga Ii ◽  
Multi Objective ◽  
Sequence Dependent ◽  
Cell Scheduling ◽  
Dependent Setup Times

With the development of Industry 4.0 and requirement of smart factory, cellular manufacturing system (CMS) has been widely concerned in recent years, which may leads to reducing production cost and wip inventory due to its flexibility production with groups. Intercellular transportation consumption, sequence-dependent setup times, and batch issue in CMS are taken into consideration simultaneously in this paper. Afterwards, a multi-objective flexible job-shop cell scheduling problem (FJSCP) optimization model is established to minimize makespan, total energy consumption, and total costs. Additionally, an improved non-dominated sorting genetic algorithm is adopted to solve the problem. Meanwhile, for improving local search ability, hybrid variable neighborhood (HVNS) is adopted in selection, crossover, and mutation operations to further improve algorithm performance. Finally, the validity of proposed algorithm is demonstrated by datasets of benchmark scheduling instances from literature. The statistical result illustrates that improved method has a better or an equivalent performance when compared with some heuristic algorithms with similar types of instances. Besides, it is also compared with one type scalarization method, the proposed algorithm exhibits better performance based on hypervolume analysis under different instances.

DECOMPOSITION ALGORITHMS FOR THE INTERVAL SCHEDULING PROBLEM

2010 ◽  
Vol 27 (04) ◽  
pp. 517-537 ◽  
Author(s):  
SHIDONG WANG ◽  
LI ZHENG ◽  
ZHIHAI ZHANG
Keyword(s):  
Large Scale ◽  
Heuristic Algorithms ◽  
Integer Program ◽  
Computational Effort ◽  
Small Scale ◽  
Test Problems ◽  
Decomposition Algorithms ◽  
Scheduling Problem ◽  
Interval Scheduling ◽  
Average Percentage

Scheduling track lines at a marshalling station where the objective is to determine the maximal weighted number of trains on the track lines can be modeled as an interval scheduling problem: each job has a fixed starting and finishing time and can only be carried out by an arbitrarily given subset of machines. This scheduling problem is formulated as an integer program, which is NP-Complete when the number of machines and jobs are unfixed and the computational effort to solve large scale test problems is prohibitively large. Heuristic algorithms (HAs) based on the decomposition of original problem have been developed and the benefits lie in both conceptual simplicity and computational efficiency. Genetic algorithm (GA) to address the scheduling problem is also proposed. Computational experiments on low and high utilization rates of machines are carried out to compare the performance of the proposed algorithms with Cplex. Computational results show that the HAs and GA perform well in most condition, especially HA2 with the maximum of average percentage deviation on average 3.5% less than the optimal solutions found by Cplex in small-scale problem. Our methodologies are capable of producing improved solutions to large-scale problems with reasonable computing resources, too.

scholarly journals Fairness Time-Slot Allocation and Scheduling with QoS Guarantees in Multihop WiMAX Mesh Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Chien-Yu Wu ◽  
Hann-Jang Ho ◽  
Sing-Ling Lee ◽  
Liang Lung Chen
Keyword(s):  
Large Scale ◽  
Heuristic Algorithms ◽  
Mesh Networks ◽  
Time Slot ◽  
Optimal Solution ◽  
Small Scale ◽  
Scheduling Problem ◽  
Slot Allocation ◽  
Long Distance ◽  
Time Slot Allocation

The WiMAX technology has been defined to provide high throughput over long distance communications and support the quality of service (QoS) control applied on different applications. This paper studies the fairness time-slot allocation and scheduling problem for enhancing throughput and guaranteeing QoS in multihop WiMAX mesh networks. For allocating time slots to multiple subscribe stations (SSs), fairness is a key concern. The notion of max-min fairness is applied as our metric to define the QoS-based max-min fair scheduling problem for maximizing the minimum satisfaction ratio of each SS. We formulate an integer linear programming (ILP) model to provide an optimal solution on small-scale networks. For large-scale networks, several heuristic algorithms are proposed for better running time and scalability. The performance of heuristic algorithms is compared with previous methods in the literatures. Experimental results show that the proposed algorithms are better in terms of QoS satisfaction ratio and throughput.

Minimizing Energy Consumption and Line Length of Mixed-Model Multi-Robotic Disassembly Line Systems Using Multi-Objective Evolutionary Optimization

2019 ◽  
Author(s):  
Yilin Fang ◽  
Hongkai Wei ◽  
Quan Liu ◽  
Yongliang Li ◽  
Zude Zhou ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Line Length ◽  
Economic Benefits ◽  
Small Scale ◽  
Medium Scale ◽  
Multi Objective ◽  
Large Scale Problems ◽  
Robotic Disassembly ◽  
Disassembly Line

Abstract Robot helps to increase automation and economic benefits of disassembly line systems, and reduce risk to the human worker. For the robotic disassembly line, its energy consumption can be further optimized to reduce carbon dioxide emissions. In this paper, energy consumption of disassembly line systems is considered to be one of optimization objectives of disassembly line balancing problem. In the proposed model, the optimization objectives are to minimize the energy consumption and the line length (number of multi-robotic workstations and number of opened disassembly robots). To solve this multi-objective optimization problem, an improved NSGA-III optimization algorithm which consists of problem-dependent global and local variation operators is proposed. Several experiments are conducted to verify the effectiveness of the proposed method. In terms of hypervolume indicator, compared with three other state-of-art multi-objective evolutionary algorithms, the proposed method outperforms the best in small-scale, medium-scale, and large-scale problems. The proposed method also performs better on the problem of all scales than MOEA\D and NSGA-II in inverted generational distance metric, the proposed approach outperforms NSGA-III in most small-scale, some medium-scale and large-scale problems. The Friedman test based on the indicators of hypervolume and inverted generational distance is also conducted to verify the effectiveness of the proposed method.

Multi-Objective Optimization Approach with Job-Based Encoding Method for Semiconductor Final Testing Scheduling Problem

2012 ◽  
Vol 622-623 ◽  
pp. 152-157
Author(s):  
Yi Sun ◽  
Xin Wei ◽  
Shigeru Fujimura ◽  
Gen Ke Yang
Keyword(s):  
Parallel Machines ◽  
Flow Shop ◽  
Optimization Approach ◽  
Scheduling Problem ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Penalty Cost ◽  
Multi Objective ◽  
Testing Stage ◽  
Encoding Method

The semiconductor final testing scheduling problem (SFTSP) is a variation of the complex scheduling problem, which deals with the arrangement of the job sequence for the final testing process. In this paper, we present an actual SFTSP case includes almost all the flow-shop factors as reentry characteristic, serial and batch processing stages, lot-clusters and parallel machines. Since the critical equipment needs to be utilized efficiently at a specific testing stage, the scheduling arrangement is then playing an important role in order to reduce both the makespan and penalty cost of all late products in total final testing progress. On account of the difficulty and long time it takes to solve this problem, we propose a multi-objective optimization approach, which uses a lot-merging procedure, a new job-based encoding method, and an adjustment to the non-dominated sorting genetic algorithm II (NSGA-II). Simulation results of the adjusted NSGA-II on this SFTSP problem are compared with its traditional algorithm and much better performance of the adjusted one is observed.

scholarly journals Green Supply Chain Management Optimization Based on NSGA-II Method

2019 ◽  
Vol 9 (2) ◽  
pp. 210-215
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Large Scale ◽  
Optimization Technique ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Small Scale ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Chain Management

Green Supply Chain Management (GSCM) is the adopted by many companies due to the government policies of various countries. The optimization technique can be applied in the GSCM to increase the profit of the company. In this research, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) technique is applied for the optimization of GSCM to increase the performance. The NSGA-II method has the advantage of choosing the solution closer to the pareto-solution and uses the elitist technique to preserve the best solution in the next generation. Mathematical model of the GSCM system is established and data is provided as input to the mathematical mode. Data is generated in three types, small scale, medium scale and large scale. The proposed NSGA-II method has high performance in the optimization technique compared to existing method. The proposed NSGA-II method has the Number of Pareto Solution (NPS) metrics of 17 for large scale data, while existing method has 14.

scholarly journals Parallel machines scheduling with time-dependent deterioration, using meta-heuristic algorithms

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Jaber Kalaki Juybari ◽  
Somayyeh Kalaki Juybari ◽  
Reza Hasanzadeh
Keyword(s):  
Large Scale ◽  
Processing Time ◽  
Parallel Machines ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Time Dependent ◽  
Mixed Integer ◽  
Small Scale ◽  
Simulating Annealing ◽  
Parallel Machines Scheduling

AbstractIn this paper, we consider the identical parallel machines scheduling problem with exponential time-dependent deterioration. The meaning of time-dependent deterioration is that the processing time of a job is not a constant and depends on the scheduled activities. In other words, when a job is processed later, it needs more processing time compared to the jobs processed earlier. The main purpose is to minimize the makespan. To reach this aim, we developed a mixed integer programming formulation for the problem. We solved problem in small scale using GAMS software, while due to the fact that in larger scales the aforesaid case is a complex and intricate optimized problem which is NP-hard, it is not possible to solve it by standard calculating techniques (in logical calculating times); we applied the meta-heuristic genetic algorithm, simulating annealing and artificial immune system, and their performance has been evaluated. In the end, we showed that solving the problem in small scale, with the meta-heuristic algorithms (GA, SA, and AIS) equals the optimal solution (GAMS), And on a large scale, at a time of approximately equal solution, meta-heuristic algorithm simulating annealing, provides a more optimal solution.

scholarly journals Supply chain resilience in a developing country context: a case study on the interconnectedness of threats, strategies and outcomes

2017 ◽  
Vol 22 (6) ◽  
pp. 486-505 ◽  
Author(s):  
Benjamin Tukamuhabwa ◽  
Mark Stevenson ◽  
Jerry Busby
Keyword(s):  
Supply Chain ◽  
Developing Country ◽  
Large Scale ◽  
Empirical Studies ◽  
Small Scale ◽  
Supply Network ◽  
Catastrophic Events ◽  
Content Type ◽  
Supply Chain Resilience

Purpose In few prior empirical studies on supply chain resilience (SCRES), the focus has been on the developed world. Yet, organisations in developing countries constitute a significant part of global supply chains and have also experienced the disastrous effects of supply chain failures. The purpose of this paper is therefore to empirically investigate SCRES in a developing country context and to show that this also provides theoretical insights into the nature of what is meant by resilience. Design/methodology/approach Using a case study approach, a supply network of 20 manufacturing firms in Uganda is analysed based on a total of 45 interviews. Findings The perceived threats to SCRES in this context are mainly small-scale, chronic disruptive events rather than discrete, large-scale catastrophic events typically emphasised in the literature. The data reveal how threats of disruption, resilience strategies and outcomes are inter-related in complex, coupled and non-linear ways. These interrelationships are explained by the political, cultural and territorial embeddedness of the supply network in a developing country. Further, this embeddedness contributes to the phenomenon of supply chain risk migration, whereby an attempt to mitigate one threat produces another threat and/or shifts the threat to another point in the supply network. Practical implications Managers should be aware, for example, of potential risk migration from one threat to another when crafting strategies to build SCRES. Equally, the potential for risk migration across the supply network means managers should look at the supply chain holistically because actors along the chain are so interconnected. Originality/value The paper goes beyond the extant literature by highlighting how SCRES is not only about responding to specific, isolated threats but about the continuous management of risk migration. It demonstrates that resilience requires both an understanding of the interconnectedness of threats, strategies and outcomes and an understanding of the embeddedness of the supply network. Finally, this study’s focus on the context of a developing country reveals that resilience should be equally concerned both with smaller in scale, chronic disruptions and with occasional, large-scale catastrophic events.

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