Production Sustainability via Supermarket Location Optimization in Assembly Lines

Fathi, Masood; Nourmohammadi, Amir; Ghobakhloo, Morteza; Yousefi, Milad

doi:10.3390/su12114728

Cited by 15 publications

(12 citation statements)

References 32 publications

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“…At the same time, it was observed how the cost of shipping material across the assembly line is the most influential factor in reducing the total cost of the supermarket. It was through simulation that the optimum location of these supermarkets on the assembly lines was optimized [ 50 ]. Hence, the simulation allows us to optimize from a particular point of view any necessary movement by making iterations until the optimal solution is reached.…”

Section: Resultsmentioning

confidence: 99%

Sustainability in the Aerospace, Naval, and Automotive Supply Chain 4.0: Descriptive Review

et al. 2020

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The search for sustainability in the Supply Chain (SC) is one of the tasks that most concerns business leaders in all manufacturing sectors because of the importance that the Supply Chain has as a transversal tool and due to the leading role that it has been playing lately. Of all the manufacturing sectors, this study focuses on the aerospace, shipbuilding, and automotive sectors identified as transport. The present study carries out a descriptive review of existing publications in these three sectors in relation to the sustainability of the Supply Chain in its 4.0 adaptation as an update in matters that are in constant evolution. Among the results obtained, Lean practices are common to the three sectors, as well as different technologies focused on sustainability. Furthermore, the results show that the automotive sector is the one that makes the greatest contribution in this sense through collaborative programs that can be very useful to the other two sectors, thus benefiting from the consequent applicable advantages. Meanwhile, the Aerospace and Shipbuilding sectors do not seem to be working on promoting a sustainable culture in the management of the Supply Chain or on including training programs for their personnel in matters related to Industry 4.0.

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Section: Resultsmentioning

confidence: 99%

Sustainability in the Aerospace, Naval, and Automotive Supply Chain 4.0: Descriptive Review

et al. 2020

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“…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 .…”

Section: Methodsmentioning

confidence: 99%

Energy‐efficient and sustainable supply chain in the manufacturing industry

Beheshtinia

Fathi

2022

Energy Science & Engineering

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This study aims at reducing energy consumption in supply chain networks by providing optimal integrated production and transportation scheduling. The considered supply chain consists of one main manufacturing center, multiple production units (i.e., suppliers), and multiple heterogeneous vehicles as the transportation fleet. To schedule this complex supply chain network in an energy-efficient way, several decisions should be made concerning the assignment of orders to suppliers and determining their production sequence, splitting orders, assigning orders to vehicles, and assigning delivery priority to orders. To cope with the problem, a mixed-integer linear programming model is presented. Due to the complexity of the problem, a novel development of the genetic algorithm named the Multiple Reference Group Genetic Algorithm (MRGGA) is also proposed. Four objectives are considered to be optimized to meet both suitability and energy-efficiency aspects in the supply chain network. These optimization objectives are to minimize the total orders' delivery times to the manufacturing center, fuel consumption by the vehicles, energy consumption at supplies, and maximize orders' quality. To analyze the performance of the proposed algorithm, a real case and a set of generated instances are solved. The results obtained by the proposed algorithm are compared with an existing genetic algorithm in the literature. Moreover, the results are also compared with the optimal solutions obtained from the mathematical model for small-size problems. The results of the comparisons show the efficiency of the proposed MRGGA in finding energy-efficient solutions for the considered supply chain network.

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“…Consumer demand, perception, and buying behavior can be approached with a probabilistic system [ 140 , 141 , 142 ], for instance, uncertainty concerning what food products are expected to be bought. Meanwhile, daily demand prediction and inventory management can be addressed with an optimization paradigm [ 143 , 144 ]. For this case, the aim is to optimize stock levels in such a way that food waste can be decreased or even to avoiding over-stocking issues completely.…”

Section: A Taxonomy Of Ci-based Problems In the Food Supply Chainmentioning

confidence: 99%

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Angarita-Zapata

Alonso-Vicario

Masegosa

et al. 2021

Sensors

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In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.

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Production Sustainability via Supermarket Location Optimization in Assembly Lines

Cited by 15 publications

References 32 publications

Sustainability in the Aerospace, Naval, and Automotive Supply Chain 4.0: Descriptive Review

Sustainability in the Aerospace, Naval, and Automotive Supply Chain 4.0: Descriptive Review

Energy‐efficient and sustainable supply chain in the manufacturing industry

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

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