Process Planning Optimization With Energy Consumption Reduction From a Novel Perspective: Mathematical Modeling and a Dynamic Programming-Like Heuristic Algorithm

Abstract

Process planning can be deemed as an important component in manufacturing systems. It bridges the gap between designing and manufacturing by specifying the manufacturing requirements and details to convert a part from raw materials to the finished form. For the purpose of low carbon emission, this paper pays attention to both technical performance measures and environmental impact criteria. In this problem, a part may have more than one process plans and only one process plan can finally be adopted. Due to the non-deterministic polynomial-hardness, it is rather difficult to conduct operation selection, machine determination, operation sequencing, and energy consumption reduction simultaneously with various constraints from technical requirements or the shop floor status. A novel position-based mixed integer linear programming model is developed to reduce total production time and the total energy consumption. The energy consumption coefficient matrix is proposed to evaluate the energy consumption in process planning. Because of the complexity in solving the model, this research proposes dynamic programminglike heuristic algorithm to tackle this problem. The weighted sum method is applied in multi-objective optimization and three typical instances with operation flexibility, sequencing flexibility, and processing flexibility are used to test the proposed algorithm. According to the results, both the total production time and the energy consumption criteria are optimized; in the best case, the energy consumption after optimization takes only 21.2% of the one before optimization. On average, about 40.9% of the total energy consumption can be reduced after optimization. Computational results also show that the proposed algorithm is generally better than the genetic algorithm. This research gives a novel perspective to reduce energy consumption in the process planning stage. INDEX TERMS Energy consumption reduction, dynamic programming, green process planning, heuristic algorithms, operations selection & sequencing, production time minimization.

Hybrid Particle Swarm Optimization Algorithm for Process Planning

“…Algorithms in the above literatures include genetic algorithm (GA), particle swarm optimization (PSO) algorithm, simulated annealing (SA) algorithm, tabu search (TS) algorithm, ant colony optimization (ACO) algorithm, artificial immune algorithm (IA) and so on. There are some more novel methods except those common heuristic algorithms for solving process planning, such as the intelligent water drop algorithm used by Gao et al [19], and DP-like heuristic algorithm used by Jin et al [1] etc.…”

“…(3) Machine selection flexibility, that is, some operations can be processed by different machines or equipment. There may be more than one process route for a product, but only one can be adopted in the end [1]. Flexible process routes can improve the flexibility of production activities, provide more options for manufacturing resources, and expand the optimization space of manufacturing process.…”

Stochastic Multi-Objective Optimized Dispatch of Combined Cooling, Heating, and Power Microgrids Based on Hybrid Evolutionary Optimization Algorithm

“…Because a high-power unit consumes less raw material than a low-power unit for the same active power output, the raw material consumption coefficient of a high-power unit is relatively small [37].…”

A neutrosophic set-based TLBO algorithm for the flexible job-shop scheduling problem with routing flexibility and uncertain processing times

“…As two crucial components in a flexible manufacturing system, the job-shop scheduling module and the process planning module received a number of research attentions [1][2][3][4][5]. In tradition, process planning specifies the technical details [6,7], e.g., cutting parameters; the scheduling module, on the other hand, arranges operations on machines to shorten the maximum completion time (makespan) or meet other criteria [8,9].…”

“…Thus, this will lead to inefficiencies of a flexible manufacturing system [15,16], such as resource conflicts and unbalanced utilization of machines [17]. Lots of efforts have been paid to eliminate resource conflicts and improve the performance of a manufacturing system by taking the advantage of the flexibilities in the two modules [4,8,13,17]. Fruitful results have been observed and the meta-heuristics have garnered wide research interests due to the high cost performance and the accessibility.…”