Multi-objective integrated scheduling optimization of semi-combined marine crankshaft structure production workshop for green manufacturing

Abstract

In order to realize green manufacturing in the production process of semi-combined marine crankshaft structural parts, good job scheduling and reasonable workshop layout are the key. In traditional method, flexible job shop scheduling problem (FJSP) and the multi-row workshop layout problem (MRWLP) are regarded as separate tasks. However, the separate optimization method ignores the interaction between FJSP and MRWLP. Because the process sequencing of FJSP affects the layout results of processing machines, while the layout scheme of MRWLP affects the scheduling completion time through the transportation between processes. Therefore, it is very important to establish an integrated mathematical model for optimization of both layout and scheduling simultaneously to explore the common influence of the two resource constraints on scheduling results. At the same time, the transportation task is also a manufacturing process that cannot be ignored, which affects the completion time and energy consumption of the workshop, especially the heavy industrial manufacturing workshop with crane as transportation equipment. According to the established model, a five-segment coding including transportation information, layout information and processing information is designed, and two heuristic selection strategies are integrated into non-dominated sorting genetic algorithm II (NSGA-II) to optimize the iterative results twice. Finally, the effectiveness of the integrated mathematical model is verified by an example, which provides guidance for green manufacturing in the shipbuilding industry.

Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives

“…Apart from that, the probability values used more were the higher ones (i.e., in the Q4 range), as expected. EDA GA [52] Own algorithm -- [53] 2-echelon iMOEA/D MOEA/D NSGA-II; MOGLS [90] HEA -NSGA-II; NNIA [59] iIMOALO ALO NSGA-II; MOPSO [60] NMA MA MA [73] NSGA-II NSGA-II SPEA2 [74] NSGA-II NSGA-II - [75] EE-VBIH; EE-IG; IG-ALL [76] MDSS-MOGA-DE MOABC; MOACO; MOCS [77] NSGA-II; SPEA-2 NSGA-II; SPEA-II NSGA-II; SPEA-2 [78] NSGA-II NSGA-II [81] PSO_SWS; PSO_LWR PSO PSO_SWS; PSO_LWR; PSO [72] HPSO PSO i NSGA-II; HPSO-LS…”

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

“…Nevertheless, there are a few exceptions. Some authors [37,38,85,[87][88][89][90][91][92] schedule transport and take into account the energy required to load/unload vehicles, and others [93][94][95][96][97][98] consider the energy required to transport the jobs between geographically distributed facilities.…”

“…Other subproblems that impact the total energy consumption have been considered, although less frequently: layout optimization (LOP) [86,89]-machine location (and reallocation) is determined at the same time that the manufacturing operations are scheduled; job process planning (JPP) [14]-job routing is chosen from a set of predefined job routes; batch scheduling (BS) [62]-machines are set up to process a batch of similar operations; and distributed manufacturing scheduling (DMS) [93,94,98,121,122]-operations may be processed on machines that are located in different factories, which are geographically distributed. Among these, we highlight the last two, since they address non-identical factories and consider the three pillars of sustainability (economic, social, and environmental), thus complying with the triple bottom line principle.…”

“…Several authors introduce and discuss specific case studies on problems that can be cast as energy-efficient (flexible) job shop problems. The problem instances associated with such studies involve the FJSP [10,19,56], the FJSP and tool allocation [7], the FJSP and WoS [13], the FJSP with TrT [11,18,20], the FJSP with LOP and TrS [89], the DS FJSP [106], and the JSP with TrT [116].…”

Twenty‐year retrospection on green manufacturing: A bibliometric perspective

“…In the work by [34]; a mathematical model considering new job arrivals and turn on/off strategy is formulated to minimise the makespan, energy consumption, and system instability, where an improved backtracking search algorithm is employed to solve the problem. In addition to the flexible job shop, [35] also considered the multi‐row workshop layout problem and established an integrated mathematical model to optimise both the layout and scheduling simultaneously. [36] addressed an energy‐efficient scheduling of the distributed permutation flow shop to minimise both the makespan and total energy consumption.…”