A survival duration‐guided NSGA‐III for sustainable flexible job shop scheduling problem considering dual resources

Abstract

Considering the increasing concern on sustainable development from manufacturers, focus is given to three kinds of indicators of sustainable development, that is, economy, environment, and society, and schedule two types of resource, that is machines and workers, simultaneously in the classical flexible job shop scheduling problem. The authors define it as a sustainable flexible job shop scheduling problem considering dual resources (SFJSPCDR). First, a model of the SFJSPCDR is formulated to optimise the makespan, the energy consumption, and the ergonomic risk simultaneously. Second, an improved survival duration-guided NSGA-III algorithm (SDG-NSGA-III) is proposed to solve SFJSPCDR. The survival duration of each individual determines whether it takes part in generating offspring. In order to balance the energy consumption and ergonomic risk while minimising the makespan, a double-low decoding algorithm is proposed, which is composed of two decoding algorithms. Cross-generation selection is employed with the non-dominated sorting, and the next-generation population is selected according to the reference point-based selection strategy. In addition, a restart strategy is also integrated to improve the exploration and exploitation performance of the SDG-NSGA-III algorithm. Finally, a group of experiments are carried out and the results prove the effectiveness of the proposed algorithm. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Social Sustainability in Production Planning: A Systematic Literature Review

“…Thus, decision-making problems extending the regarded planning hierarchy (e.g., warehousing, order picking) and alternative modeling and solutions (e.g., simulation approaches) were not covered. Lot sizing X X analytical [65] Lot sizing X (2) X analytical [66] Lot sizing (2) X MOP [67] Lot sizing (6) X MONLP [68] Lot sizing (6) NLP [69] s-m X X heuristic [70] f-s X X GA [71] j-s X X heuristic [72] j-s (1) GA [73] j-s (1) GA [74] f-s (1) DMORA [75] f-s (1) MOCGWO [76] j-s (5) GA [77] p-m X -con. [78] p-m X X heuristic [79] p-m X heuristic [81] ALBP X X GA, PSO ALBP X GA, PSO…”

“…GA [73] j-s (1) GA [74] f-s (1) DMORA [75] f-s (1) MOCGWO [76] j-s (5) GA [77] p-m X -con. [78] p-m X X heuristic [79] p-m X heuristic AP-Affecting Productivity, con.-constraint, DMORA-Discrete Multi-Objective Rider optimization Algorithm, EE-Energy Expenditure, EM-Effect Modeling, EP-Employee Preferences, ER-Ergonomic Risks, f-s-flow shop scheduling, GA-Genetic Algorithm, j-s-job shop scheduling, MOCGWO-Multi-Objective Cellular Grey Wolf Optimizer, p-m-parallel-machine scheduling, s-m-single-machine scheduling, VL-Value Limiting, (1)noise emission, (5)-combined/general assessment methods.…”

“…A general ergonomic risk includes [76] for job shop scheduling (assessed according to EAWS). Besides this social aspect, the makespan and energy consumption are minimized within a multi-objective function.…”

“…However, within the identified approaches, the DM is not further supported regarding a suitable specification of these parameters and weights. For approaches using an a posteriori method, first, (Pareto) efficient frontiers are identified [62,63,70,[72][73][74][75][76]78,85,97]. Accordingly, a suitable solution can be selected from the set of identified (non-)dominated points to meet the DM's preferences.…”

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

“…Besides the performance and economy objectives, a few other objectives have been considered, although in just a very small number of papers. These include machine performance goals such as machines workload (ML) [16,31,35], rescheduling disruption (RD) [57,103], machines' fault probability (FP) [117], and system reliability (Rel) [8,51]; product-related goals such as work in process (W IP) [16], raw material usage (RM) [64], quality and defective rate (Q) [19,22], and customer satisfaction (CuS) [35]; and social objectives, included mainly through working conditions such as noise level (N) [32,101,118], ergonomic risk (Erg) [119], and vibration (Vib) [101].…”