scholarly journals Modeling and Optimization for Multi-Objective Nonidentical Parallel Machining Line Scheduling with a Jumping Process Operation Constraint

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1521
Author(s):  
Guangyan Xu ◽  
Zailin Guan ◽  
Lei Yue ◽  
Jabir Mumtaz ◽  
Jun Liang
Keyword(s):  
Production Line ◽  
Mixed Model ◽  
Parallel Line ◽  
Grey Wolf Optimizer ◽  
Scheduling Problem ◽  
Due Dates ◽  
Penalty Cost ◽  
Multi Objective ◽  
Process Operation ◽  
Machining Lines

This paper investigates the nonidentical parallel production line scheduling problem derived from an axle housing machining workshop of an axle manufacturer. The characteristics of axle housing machining lines are analyzed, and a nonidentical parallel line scheduling model with a jumping process operation (NPPLS-JP), which considers mixed model production, machine eligibility constraints, and fuzzy due dates, is established so as to minimize the makespan and earliness/tardiness penalty cost. While the physical structures of the parallel lines in the NPPLS-JP model are symmetric, the production capacities and process capabilities are asymmetric for different models. Different from the general parallel line scheduling problem, NPPLS-JP allows for a job to transfer to another production line to complete the subsequent operations (i.e., jumping process operations), and the transfer is unidirectional. The significance of the NPPLS-JP model is that it meets the demands of multivariety mixed model production and makes full use of the capacities of parallel production lines. Aiming to solve the NPPLS-JP problem, we propose a hybrid algorithm named the multi-objective grey wolf optimizer based on decomposition (MOGWO/D). This new algorithm combines the GWO with the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to balance the exploration and exploitation abilities of the original MOEA/D. Furthermore, coding and decoding rules are developed according to the features of the NPPLS-JP problem. To evaluate the effectiveness of the proposed MOGWO/D algorithm, a set of instances with different job scales, job types, and production scenarios is designed, and the results are compared with those of three other famous multi-objective optimization algorithms. The experimental results show that the proposed MOGWO/D algorithm exhibits superiority in most instances.

scholarly journals Multi-Objective Approach for Production Line Equipment Selection

2012 ◽  
Vol 3 (1) ◽  
pp. 4-17 ◽  
Author(s):  
H. Chehade ◽  
A. Dolgui ◽  
F. Dugardin ◽  
L. Makdessian ◽  
F. Yalaoui
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Local Search ◽  
Production Line ◽  
Nsga Ii ◽  
Equipment Selection ◽  
Multi Objective ◽  
Numerical Tests ◽  
Machining Lines ◽  
Direct Measures

Multi-Objective Approach for Production Line Equipment Selection A novel problem dealing with design of reconfigurable automated machining lines is considered. Such lines are composed of workstations disposed sequentially. Each workstation needs the most suitable equipment. Each available piece of equipment is characterized by its cost, can perform a set of operations and requires skills of a given level for its maintenance. A multi-objective approach is proposed to assign tasks, choose and allocate pieces of equipment to workstations taking into account all the problem parameters and constraints. The techniques developed are based on a genetic algorithm of type NSGA-II. The NSGA-II suggested is also combined with a local search. These two genetic algorithms (with and without local search) are tested for several line examples and for two versions of the considered problem: bi-objective and four-objective cases. The results of numerical tests are reported. What is the most interesting is that the assessment of these algorithms is accomplished by using three measuring criteria: the direct measures of gaps, the measures proposed by Zitzler and Thiele in 1999 and the distances suggested by Riise in 2002.

scholarly journals A Modified PSO Algorithm for Minimizing the Total Costs of Resources in MRCPSP

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammad Khalilzadeh ◽  
Fereydoon Kianfar ◽  
Ali Shirzadeh Chaleshtari ◽  
Shahram Shadrokh ◽  
Mohammad Ranjbar
Keyword(s):  
Project Scheduling ◽  
Renewable Resource ◽  
Pso Algorithm ◽  
Scheduling Problem ◽  
Due Dates ◽  
Resource Constrained ◽  
Nonrenewable Resource ◽  
Penalty Cost ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem

We introduce a multimode resource-constrained project scheduling problem with finish-to-start precedence relations among project activities, considering renewable and nonrenewable resource costs. We assume that renewable resources are rented and are not available in all periods of time of the project. In other words, there is a mandated ready date as well as a due date for each renewable resource type so that no resource is used before its ready date. However, the resources are permitted to be used after their due dates by paying penalty costs. The objective is to minimize the total costs of both renewable and nonrenewable resource usage. This problem is called multimode resource-constrained project scheduling problem with minimization of total weighted resource tardiness penalty cost (MRCPSP-TWRTPC), where, for each activity, both renewable and nonrenewable resource requirements depend on activity mode. For this problem, we present a metaheuristic algorithm based on a modified Particle Swarm Optimization (PSO) approach introduced by Tchomté and Gourgand which uses a modified rule for the displacement of particles. We present a prioritization rule for activities and several improvement and local search methods. Experimental results reveal the effectiveness and efficiency of the proposed algorithm for the problem in question.

scholarly journals Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 447
Author(s):  
Sharif Naser Makhadmeh ◽  
Mohammed Azmi Al-Betar ◽  
Zaid Abdi Alkareem Alyasseri ◽  
Ammar Kamal Abasi ◽  
Ahamad Tajudin Khader ◽  
...  
Keyword(s):  
Dynamic Pricing ◽  
Smart Home ◽  
Grey Wolf Optimizer ◽  
Scheduling Problem ◽  
Optimal Solutions ◽  
Grey Wolf ◽  
Multi Objective ◽  
Home Appliances ◽  
Power Scheduling ◽  
Smart Home Appliances

The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) minimizing the cost of the power consumed by home appliances, which refers to electricity bills, (II) balance the power consumed during a time horizon, particularly at peak periods, which is known as the peak-to-average ratio, and (III) maximizing the satisfaction level of users. Several approaches have been proposed to address PSPSH optimally, including optimization and non-optimization based approaches. However, the set of restrictions inhibit the approach used to obtain the optimal solutions. In this paper, a new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions. SHB can enhance the scheduling of smart home appliances by storing power at unsuitable periods and use the stored power at suitable periods for PSPSH objectives. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. A robust swarm-based optimization algorithm inspired by the grey wolf lifestyle called grey wolf optimizer (GWO) is adapted to address PSPSH. GWO has powerful operations managed by its dynamic parameters that maintain exploration and exploitation behavior in search space. Seven scenarios of power consumption and dynamic pricing schemes are considered in the simulation results to evaluate the proposed multi-objective PSPSH using SHB (BMO-PSPSH) approach. The proposed BMO-PSPSH approach’s performance is compared with that of other 17 state-of-the-art algorithms using their recommended datasets and four algorithms using the proposed datasets. The proposed BMO-PSPSH approach exhibits and yields better performance than the other compared algorithms in almost all scenarios.

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.

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