Optimization of delay time and environmental pollution in scheduling of production and transportation system: a novel multi-society genetic algorithm approach

Abstract

Purpose The purpose of this study is to investigate the optimization of the scheduling of production and transportation systems while considering delay time (DT) and environmental pollution (EP) concurrently. To this, an integrated multi-site manufacturing process using a cumulative transportation system is investigated. Additionally, a novel multi-society genetic algorithm is developed to reach the best answers. Design/methodology/approach A bi-objective model is proposed to optimize the production and transportation process with the objectives of minimizing DT and EP. This is solved by a social dynamic genetic algorithm (SDGA), which is a novel multi-society genetic algorithm, in scenarios of equal and unequal impacts of each objective. The impacts of each objective are calculated by the analytical hierarchical process (AHP) using experts’ opinions. Results are compared by dynamic genetic algorithm and optimum solution results. Findings Results clearly depict the efficiency of the proposed algorithm and model in the scheduling of production and transportation systems with the objectives of minimizing DT and EP concurrently. Although SDGA’s performance is acceptable in all cases, in comparison to other genetic algorithms, it needs more process time which is the cost of reaching better answers. Additionally, SDGA had better performance in variable weights of objectives in comparison to itself and other genetic algorithms. Research limitations/implications This research is an improvement which allows both society and industry to elevate the levels of their satisfaction while their social responsibilities have been glorified through assuaging the concerns of customers on distribution networks’ emission, competing more efficient and effective in the global market and having the ability to make deliberate decisions far from bias. Additionally, implications of the developed genetic algorithm help directly to the organizations engaged with intelligent production and/or transportation planning which society will be merited indirectly from their outcomes. It also could be utilitarian for organizations that are engaged with small, medium and big data analysis in their processes and want to use more effective and more efficient tools. Originality/value Optimization of EP and DT are considered simultaneously in both model and algorithm in this study. Besides, a novel genetic algorithm, SDGA, is proposed. In this multi-society algorithm, each society is focused on a particular objective; however, in one society all the feasible answers will have been integrated and optimization will have been continued.

Energy‐efficient and sustainable supply chain in the manufacturing industry

“…Because of the probability of falling into an optimal local trap, there is no guarantee that a final solution provided by a computerized algorithm like MRGGA is optimum 35 . Using proper crossover, mutation, and selection operators could reduce this probability but not necessarily eliminate it 29,36 . The proposed MRGGA tries to reduce this probability by the concept of reference group theory proposed by the sociologist Robert K. Merton 19 .…”

“…It has already been proven in the literature that a special case of the problem presented in this study, in which order splitting is not permitted, is NP-hard. 29 Therefore, the problem in this study is also NP-hard. In this regard, resorting to heuristic or meta-heuristic algorithms to tackle the problem is inevitable.…”

“…Their considered supply chain comprises a set of suppliers with different production speeds, vehicles with different capacities and speeds, and a manufacturer. Moghimi and Beheshtinia 29 addressed the supply chain scheduling problem and proposed a novel multisociety genetic algorithm to minimize total delay times and environmental pollution produced by vehicles. They assumed multiple suppliers with identical production speed that produces some orders and deliver them to multiple manufacturers with multiple heterogeneous vehicles.…”

Supply Chain Optimization Considering Sustainability Aspects

“…After proposing the mixed-integer linear mathematical model of the problem, a heuristic algorithm is proposed to solve large-scale problems. Moghimi and Beheshtinia [27] discussed the integration of production and scheduling problems in a supply chain considering the multi-site manufacturing system. They considered two objective functions of minimizing total tardiness and environmental pollution and proposed a genetic algorithm with multiple parallel populations two solve the problem.…”

“…The current research also differs from the previous studies by addressing the SCCP while integrating transportation and production scheduling and considering multiple objectives, namely minimizing the total orders' delivery time, transportation and production costs, pollution levels, and maximizing the quality of completed orders. Single machine Heuristic [6] Single machine GA [7] Flexible flow shop [8] Single machine [9] Single machine GA [10] Single machine [11] Parallel machines Heuristic [12] Single machine Heuristic [13] Single machine GA [14] Single machine GA [15] Single machine GA [16] Single machine MLCA [17] Single machine GA [18] Single machine Heuristic Single machine GA [20] Single machine Heuristic [21] Assembly Flowshop Heuristic and IGA [22] Single machine [23] Single machine GA [24] Single machine GA [25] Assembly Flowshop IGA [26] Single machine [27] Single machine GA [28] Single machine PSO…”