Hybrid meta-heuristic algorithms for optimising a sustainable agricultural supply chain network considering CO2 emissions and water consumption

A Bi-Level Model for a Closed-Loop Agricultural Supply Chain Considering Biogas and Compost

10.21203/rs.3.rs-876356/v1 ◽

2021 ◽

Author(s):

Armin Cheraghalipour ◽

Emad Roghanian

Keyword(s):

Supply Chain ◽

Closed Loop ◽

Heuristic Algorithms ◽

Distribution Centers ◽

Chain Management ◽

Logistics Network ◽

Solution Methods ◽

Level Model ◽

Network Costs ◽

Agricultural Supply

Abstract In today's competitive marketplace, to increase customer satisfaction and profitability, supply chain management has become more prominent. Therefore, thorough planning and designing the supply chain by seeing all levels and units are essential to growing the efficiency of the entire supply chain. In the present study, an eight-echelon network is designed for a closed-loop agricultural supply chain. These eight echelons are consist of suppliers, farms, distribution centers (DCs), customers, recycling depots, biogas centers, compost production centers, and biogas applicants. To design the agricultural logistics network, a bi-level programming mathematical model is presented that optimizes the network costs and profits. Also, some meta-heuristics and hybrid meta-heuristics are applied for solving the formulated problem. It should be noted that bi-level programming problems are part of the NP-hard class and due to the computational complexity of the problems, the meta-heuristic algorithms are utilized. Finally, various comparisons and analyses are performed to evaluate the model's performance and the capabilities of the solution methods.

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Multi-Period Multi-Product Supply Chain Network Design in the Competitive Environment

Mathematical Problems in Engineering ◽

10.1155/2020/8548150 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Jian Wang ◽

Xueyan Wang ◽

Mingzhu Yu

Keyword(s):

Supply Chain ◽

Network Design ◽

Price Competition ◽

Heuristic Algorithms ◽

Programming Model ◽

Mixed Integer ◽

Competitive Environment ◽

Market Area ◽

Supply Chain Network Design ◽

Supply Chain Network

This paper studies a supply chain network design model with price competition. The supply chain provides multiple products for a market area in multiple periods. The model considers the location of manufacturers and retailers and assumes a probabilistic customer behavior based on an attraction function depending on both the location and the quality of the retailers. We aim to design the supply chain under the capacity constraint and maximize the supply chain profit in the competitive environment. The problem is formulated as a mixed integer nonlinear programming model. To solve the problem, we propose two heuristic algorithms—Simulated Annealing Search (SA) and Particle Swarm Optimization (PSO)—and numerically demonstrate the effectiveness of the proposed algorithms. Through the sensitivity analysis, we give some management insights.

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Multi Criteria Simulation Model for Lead Times, Costs and CO2 Emissions in a Low-carbon Supply Chain Network

Procedia CIRP ◽

10.1016/j.procir.2014.07.182 ◽

2015 ◽

Vol 26 ◽

pp. 329-334 ◽

Cited By ~ 8

Author(s):

Tetsu Kawasaki ◽

Tetsuo Yamada ◽

Norihiro Itsubo ◽

Masato Inoue

Keyword(s):

Supply Chain ◽

Simulation Model ◽

Co2 Emissions ◽

Lead Times ◽

Supply Chain Network ◽

Low Carbon ◽

Carbon Supply

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A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints

Computers & Industrial Engineering ◽

10.1016/j.cie.2018.04.007 ◽

2018 ◽

Vol 119 ◽

pp. 338-352 ◽

Cited By ~ 21

Author(s):

Zhuo Dai ◽

Faisal Aqlan ◽

Xiaoting Zheng ◽

Kuo Gao

Keyword(s):

Supply Chain ◽

Network Model ◽

Heuristic Algorithms ◽

Perishable Products ◽

Supply Chain Network ◽

Fuzzy Constraints

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Designing a new Medicine Supply Chain Network considering production technology policy using two novel heuristic algorithms

RAIRO - Operations Research ◽

10.1051/ro/2021031 ◽

2021 ◽

Author(s):

Fariba Goodarzian ◽

Hassan Hoseini-Nasab ◽

Mehdi Toloo ◽

Mohammad Bagher Fakhrzad

Keyword(s):

Supply Chain ◽

Production Technology ◽

Technology Policy ◽

Heuristic Algorithms ◽

Mixed Integer ◽

Computational Time ◽

Model Assessment ◽

Supply Chain Network ◽

High Quality ◽

Medicine Supply

The role of medicines in health systems is increasing day by day. The medicine supply chain is a part of the health system that if not properly addressed, the concept of health in that community is unlikely to experience significant growth. To fill gaps and available challenging in the medicine supply chain network (MSCN), in the present paper, efforts have been made to propose a location-production-distribution-transportation-inventory holding problem for a multi-echelon multi-product multi-period bi-objective MSCN network under production technology policy. To design the network, a mixed-integer linear programming (MILP) model capable of minimizing the total costs of the network and the total time the transportation is developed. As the developed model was NP-hard, several meta-heuristic algorithms are used and two heuristic algorithms, namely, Improved Ant Colony Optimization (IACO) and Improved Harmony Search (IHS) algorithms are developed to solve the MSCN model in different problems. Then, some experiments were designed and solved by an optimization solver called GAMS (CPLEX) and the presented algorithms to validate the model and effectiveness of the presented algorithms. Comparison of the provided results by the presented algorithms and the exact solution is indicative of the high-quality efficiency and performance of the proposed algorithm to find a near-optimal solution within reasonable computational time. Hence, the results are compared with commercial solvers (GAMS) with the suggested algorithms in the small-sized problems and then the results of the proposed meta-heuristic algorithms with the heuristic methods are compared with each other in the large-sized problems. To tune and control the parameters of the proposed algorithms, the Taguchi method is utilized. To validate the proposed algorithms and the MSCN model, assessment metrics are used and a few sensitivity analyses are stated, respectively. The results demonstrate the high quality of the proposed IACO algorithm.

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Modeling and Balancing for Costs and CO2 Emissions in Global Supply Chain Network among Asian Countries

Procedia CIRP ◽

10.1016/j.procir.2014.07.107 ◽

2015 ◽

Vol 26 ◽

pp. 664-669 ◽

Cited By ~ 9

Author(s):

Tomoyuki Urata ◽

Tetsuo Yamada ◽

Norihiro Itsubo ◽

Masato Inoue

Keyword(s):

Supply Chain ◽

Co2 Emissions ◽

Global Supply Chain ◽

Supply Chain Network ◽

Asian Countries

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A bi-objective production-distribution problem in a supply chain network under grey flexible conditions

RAIRO - Operations Research ◽

10.1051/ro/2021003 ◽

2021 ◽

Author(s):

Fariba Goodarzian ◽

davood shishebori ◽

Hadi Nasseri ◽

Faridreza Dadvar

Keyword(s):

Mathematical Model ◽

Linear Programming ◽

Supply Chain ◽

Heuristic Algorithms ◽

Mixed Integer ◽

Supply Chain Network ◽

Distribution Problem ◽

Supply Chain Networks ◽

Multi Objective ◽

Production Distribution

One of the main topics discussed in a supply chain is the production-distribution problem. Producing and distributing the products plays a key role in reducing the costs of the chain. To design a supply chain, a network of efficient management and production-distribution decisions is essential. Accordingly, providing an appropriate mathematical model for such problems can be helpful in designing and managing supply chain networks. Mathematical formulations must be drawn close to the real world due to the importance of supply chain networks. This makes those formulations more complicated. In this study, a novel multi-objective formulation is devised for the production-distribution problem of a supply chain that consists of several suppliers, manufacturers, distributors, and different customers. Also, a Mixed Integer Linear Programming (MILP) mathematical model is proposed for designing a multi-objective and multi-period supply chain network. In addition, grey flexible linear programming (GFLP) is done for a multi-objective production-distribution problem in a supply chain network. The network is designed for the first time to cope with the uncertain nature of costs, demands, and capacity parameters. In this regard, due to the NP-hardness and complexity of problems and the necessity of using meta-heuristic algorithms, NSGA-II and Fast PGA algorithm are applied and compared in terms of several criteria that emphasize the quality and diversity of the solutions.

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A bi-objective production-distribution problem in a supply chain network under grey flexible conditions

RAIRO - Operations Research ◽

10.1051/ro/2020111 ◽

2020 ◽

Author(s):

Fariba Goodarzian ◽

Davood Shishebori ◽

Hadi Nasseri ◽

Faridreza Dadvar

Keyword(s):

Mathematical Model ◽

Linear Programming ◽

Supply Chain ◽

Heuristic Algorithms ◽

Mixed Integer ◽

Supply Chain Network ◽

Distribution Problem ◽

Supply Chain Networks ◽

Multi Objective ◽

Production Distribution

One of the main topics discussed in a supply chain is the production-distribution problem. Producing and distributing the products plays a key role in reducing the costs of the chain. To design a supply chain, a network of efficient management and production-distribution decisions is essential. Accordingly, providing an appropriate mathematical model for such problems can be helpful in designing and managing supply chain networks. Mathematical formulations must be drawn close to the real world due to the importance of supply chain networks. This makes those formulations more complicated. In this study, a novel multi-objective formulation is devised for the production-distribution problem of a supply chain that consists of several suppliers, manufacturers, distributors, and different customers. Also, a Mixed Integer Linear Programming (MILP) mathematical model is proposed for designing a multi-objective and multi-period supply chain network. In addition, grey flexible linear programming (GFLP) is done for a multi-objective production-distribution problem in a supply chain network. The network is designed for the first time to cope with the uncertain nature of costs, demands, and capacity parameters. In this regard, due to the NP-hardness and complexity of problems and the necessity of using meta-heuristic algorithms, NSGA-II and Fast PGA algorithm are applied and compared in terms of several criteria that emphasize the quality and diversity of the solutions.

Download Full-text