Multi-objective Supply Chain Model Research Articles

Presenting a two-echelon multi-objective supply chain model considering the expiration date of products and solving it by applying MODM

The supply chain (SC) as the most significant factor influencing competitive advantage has a great impact on the organization's life and progress. Considering the expiration date of products in supply chain modeling is essential for ensuring operational efficiency, reducing waste, maintaining product quality, and ultimately driving business success. Therefore, this paper provides a two-level supply chain including manufacturer and distributor then chain modeling with considered conditions and applying the concept of Markowitz's theory. Finally, drawing the optimal cost curve of producer and distributor as an efficient frontier. For this purpose, the main contribution of the paper is determining the optimal cost of each level of the chain if the cost of another level is known and increasing the power of analysis. The proposed model applied in Darupakhsh Manufacturing Company and Mahya Daroo Distribution Company, a two-echelon supply chain design, including a manufacturer and a distributor is developed. At first, this chain is modeled, and then, implementing the concept of Markowitz theory, the optimal cost curve of producer and distributor is drawn as an efficient frontier. Findings show it is found that the optimal cost of the two echelons of the chain is inversely correlated and decreases with the increase of another one. Furthermore, it is found that the producer warehouse limit is redundant and has no effect on the final response. Also, with the increase in the cost of the manufacturer, the number of deliveries decreases, and instead, the number of products in each delivery increases, which results in a reduction in the costs of the distributor.

Optimizing The Transportation of Petroleum Products in A Possible Multi-Level Supply Chain

The goal of many supply chain optimization problems is to minimize the costs of the entire supply chain network. However, since environmental protection is one of the main concerns, the green supply chain network has been seriously considered as a solution to this concern in order to minimize its effects on nature. This article refers to the modeling and solution of a green supply chain network for the transportation of petroleum products in order to reduce the annual costs, considering the environmental effects. In this article, the cost elements of the supply chain such as the transportation costs of each petroleum product, operating costs, the cost of purchasing crude oil products and the fixed costs of building oil centers as well as the components of the environmental effects of the supply chain such as the amount of gas emissions and volatile organic particles produced by transportation options in the supply chain. considered green. Considering these two components (cost and environmental impact), we have proposed a multi-objective supply chain model. In this facility model, oil centers have limited capacity and at each level of the chain, there are several types of transportation options with different costs. To solve the problem, we have used two multi-objective particle swarm optimization algorithms and genetic multi-objective optimization algorithm with non-dominant sorting II with a priority-based decoding to encode the chromosome. Finally, we have used TOPSIS method to compare these two algorithms.

A novel interactive approach for solving uncertain bi-level multi-objective supply chain model

In this paper, we adopt an interactive approach for bi-level multi-objective supply chain model (BL-MOSCM). The essential target is to decide the ideal request designation of items where the client's demands and supply for the items are vague demand. This study considers two decision-makers (DMs) operating at two separate groups of supply chain network (SCN), that is, a bi-level decision-making process. In the current BL-MOSCM, the leader locates quantities dispatched to retailers, and afterward, the follower chooses his amounts reasonably. The pioneer's goal is to reduce the all-out conveyance expenses, also, the follower’s goal is to reduce the all-out conveyance time of the SCN and simultaneously adjusting the optimal request allotment from each source, plant, retailer, and distribution center, respectively. The BL-MOSCM is defuzzified and changed into a valent crisp structure based on the α-level methodology. Then the interactive methodology works on the α-(BL-MOSCM) by changing it into discrete multi-objective programming problems (MOPP). Also, each separate MOPP thinks through the ∊-constraint methodology and the idea of satisfactoriness. The ∊-constraint method aims at optimising one objective function, while considering all other objective functions as constraints. By obtaining the solution of the first level SCN utilizing the ∊-constraint method, the second level SCN is also optimized considering the controlled variables of the first level. A novel test function is introduced to decide the compromise solution of the BL-MOSCM. Procedures for solving the uncertain BL-MOSCM via the interactive approach are introduced. A real-life case study was used to illustrate the proposed interactive methodology for the BL-MOSCM with fuzzy parameters. The obtained result shows the optimal quantities transported from the various sources to the various destinations that could enable managers to detect the optimum quantity of the product when hierarchical decision-making involving two levels. Finally, a comparison with the past studies is used to display the practicality and efficiency of the suggested methodology.

Smart Scheduling: An Integrated First Mile and Last Mile Supply Approach

Supply chain management applies more and more Industry 4.0 innovations to increase their availability, elasticity, sustainability, and efficiency. In interconnected logistics networks, operations are integrated from suppliers through 3rd party logistics providers to customers. There are different delivery models depending on the time and cost. In the last few years, a wide range of customers is willing to pay an extra fee for the same delivery or instant delivery. This fact led to the increased importance of the optimized design and control of first mile/last mile (FMLM) delivery solutions. Cyberphysical system-based service innovations make it possible to enhance the productivity of FMLM delivery in the big data environment. The design and operation problems can be described as NP-hard optimization problems. These problems can be solved using sophisticated models and methods based on heuristic and metaheuristic algorithms. This research proposes an integrated supply model of FMLM delivery. After a careful literature review, this paper introduces a mathematical model to formulate the problem of real-time smart scheduling of FMLM delivery. The integrated model includes the assignment of first mile and last mile delivery tasks to the available resources and the optimization of operations costs, while constraints like capacity, time window, and availability are taken into consideration. Next, a black hole optimization- (BHO-) based algorithm dealing with a multiobjective supply chain model is presented. The sensitivity of the enhanced algorithm is tested with benchmark functions. Numerical results with different datasets demonstrate the efficiency of the proposed model and validate the usage of Industry 4.0 inventions in FMLM delivery.

Cuckoo search algorithm for multi-objective supply chain problem

A meta-heuristic algorithm called, the cuckoo search algorithm is proposed in dealing with the multi-objective supply chain model to find the optimum configuration of a given supply chain problem which minimizes the total cost and the total lead-time. The supply chain problem utilized in this study is taken from literature to show the performance of the proposed model; in addition, the results have been compared to those achieved by the bee colony optimization algorithm and genetic algorithm. Those obtained results indicate that the proposed cuckoo search algorithm is able to get better Pareto solutions (non-dominated set) for the supply chain problem.

Optimization of In-Plant Production Supply with Black Hole Algorithm

Logistic process is a basic factor in the success of manufacturing plants’ operation and has a direct impact on its efficiency, flexibility and reliability. Today’s successful operation of manufacturing processes views logistics as a high priority to ensure maximum utilization of resources. The material supply of manufacturing processes in the automotive industry is usually based on supermarkets and milk runs. This paper proposes an integrated supply model of manufacturing processes, which includes facility location and assignment. After a careful literature review, this paper introduces a mathematical model to formulate the problem of supermarkets and milk run based supply of machines. The model seeks the optimal location of buffers as well as the optimal assignment of buffers and machines so as to minimize the material handling costs while taking into account order limits of machines and capacities of resources. Next, we demonstrate an enhanced black hole algorithm dealing with multi-objective supply chain model to find the optimal structure of the system. Numerical results demonstrate how the proposed model supports the efficiency, flexibility and reliability of the manufacturing process.

Optimization of Consignment-Store-Based Supply Chain with Black Hole Algorithm

The globalization of economy and market led to increased networking in the field of manufacturing and services. These manufacturing and service processes including supply chain became more and more complex. The supply chain includes in many cases consignment stores. The design and operation of these complex supply chain processes can be described as NP-hard optimization problems. These problems can be solved using sophisticated models and methods based on metaheuristic algorithms. This research proposes an integrated supply model based on consignment stores. After a careful literature review, this paper introduces a mathematical model to formulate the problem of consignment-store-based supply chain optimization. The integrated model includes facility location and assignment problems to be solved. Next, an enhanced black hole algorithm dealing with multiobjective supply chain model is presented. The sensitivity analysis of the heuristic black hole optimization method is also described to check the efficiency of new operators to increase the convergence of the algorithm. Numerical results with different datasets demonstrate how the proposed model supports the efficiency, flexibility, and reliability of the consignment-store-based supply chain.

A fuzzy stochastic multi-objective optimization model to configure a supply chain considering new product development

This study aims to design a multi-echelon, multi-objective supply chain model that incorporates new product development and its effects on supply chain configuration. To survive in a highly competitive industry, strategies to either collaborate or compete with rival firms within a network should be considered in the new product development process, and it is crucial to pay great attention to customers’ needs and interests. Considering the imprecise nature of some critical parameters plays an important role in making suitable strategic decisions. This fact requires considering uncertainties of the environment such as customer demands and supplier capacities. In this study, a supply chain involving multiple suppliers, manufacturers, distributors and customers and addressing a multi-objective, multi-period and multi-product aggregate procurement and production planning problem is considered. The first objective function aims to maximize the profit of the supply chain, including that associated with new product development. The second objective function considers customer satisfaction, and the third one maximizes the production of the developed and new products. To address real-world planning problems involving noisy, incomplete or erroneous data, the supplier capacity parameters of the supply chain and demand fluctuations are subject to uncertainty, which is modeled by fuzzy stochastic programming. Finally, the proposed multi-objective model is solved as a single-objective mixed integer programming model by applying the revised multi-choice goal programming method. In addition, a numerical example is provided to demonstrate the applicability of the proposed model.

Heuristic Supply Chain Optimization of Networked Maintenance Companies

This paper considers the problem of networked maintenance companies and the connected purchasing processes. Today's successful operation of companies views maintenance as a high priority to ensure maximum utilisation of resources. In this article, a mathematical model is first developed to formulate the problem of finding the optimal make-or-buy solution for complex spare part production. The model seeks the optimal proportion of in-house and outsourced productions, as well as the order quantity so as to minimize the total costs while taking into account order limits. Next, we demonstrate an enhanced harmony search algorithm dealing with multi-objective supply chain model to find the optimum make-or-buy solution of a given maintenance related supply chain problem. We also discuss the sensibility analysis of the heuristic optimisation method and propose optimal process parameters for a high convergence. Numerical results demonstrate how the proposed model supports the make-or-buy decision and helps to reduce the total costs while keeping high availability to fulfil maintenance tasks.

A study of the LCA based biofuel supply chain multi-objective optimization model with multi-conversion paths in China

In this paper we present a life cycle assessment (LCA) based biofuel supply chain model with multi-conversion pathways. This model was formulated as a mixed integer linear programming (MILP) problem which took economic, energy, and environmental criteria (3E) into consideration. The economic objective was measured by the total annual profit. The energy objective was measured by using the average fossil energy input per megajoule (MJ) of biofuel. The environmental objective was measured by greenhouse gas (GHG) emissions per MJ of biofuel. After carefully consideration of the current situation in China, we chose to examine three conversion pathways: bio-ethanol (BE), bio-methanol (BM) and bio-diesel (BD). LCA was integrated to a multi-objective supply chain model by dividing each pathway into several individual parts and analyzing each part. The multi-objective MILP problem was solved using a ε-constraint method by defining the total annual profit as the optimization objective and assigning the average fossil energy input per MJ biofuel and GHG emissions per MJ biofuel as constraints. This model was then used to design an experimental biofuel supply chain for China. A surface of the Pareto optimal solutions was obtained by linear interpolation of the non-inferior solutions. The optimal results included the choice of optimal conversion pathway, biomass type, biomass locations, facility locations, and network topology structure in the biofuel supply chain. Distributed and centralized systems were also factored into our experimental system design. In addition, the influence of price change on the optimal solutions was investigated. The optimal solutions obtained in this study reveal a tradeoff between the impact of the 3E criteria. These results indicate that our model will be extremely useful for the design and planning of biofuel supply chains in China.

A multi-objective supply chain optimisation using enhanced Bees Algorithm with adaptive neighbourhood search and site abandonment strategy

In this paper, an enhanced version of the Bees Algorithm is proposed in dealing with multi-objective supply chain model to find the optimum configuration of a given supply chain problem in order to minimise the total cost and the total lead-time. The new Bees Algorithm includes an adaptive neighbourhood size change and site abandonment (ANSSA) strategy which is an enhancement to the basic Bees Algorithm. The supply chain case study utilised in this work is taken from literature and several experiments have been conducted in order to show the performances, the strength, the weaknesses of the proposed method and the results have been compared to those achieved by the basic Bees Algorithm and Ant Colony optimisation. The results show that the proposed ANSSA-based Bees Algorithm is able to achieve better Pareto solutions for the supply chain problem.

A multi-objective supply chain model integrated with location of distribution centers and supplier selection decisions

Today, in the competitive markets, supply chain works as an interrelating network of suppliers, manufacturers, distributors, and customers, to satisfy customer demands. Coordinating all parts of the supply chain is a multi-level process and needs important decisions that affect on the performance of each part and whole supply chain and some conflicting objectives should be considered. In this paper, the supplier selection problem is integrated with production decision and distributor location problems and a new mathematical model is proposed. Delivery time, total cost, and quality are the objective functions considered and weighting method is used to solve the multi-objective problem. A modified genetic algorithm is proposed to obtain Pareto optimal solution sets. Computational experiments are included for validation of proposed algorithm. The results show that the proposed algorithm gives high-quality solutions as well as better computational times.

A Multi-Objective Optimization for Supply Chain Network Using the Bees Algorithm

A supply chain is a complex network which involves the products, services and information flows between suppliers and customers. A typical supply chain is composed of different levels, hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between the multi-objectives such as cost minimization and lead-time minimization. There are several multi-objective optimization methods which have been applied to find the optimum solutions set based on the Pareto front line. In this study, a swarm-based optimization method, namely, the bees algorithm is proposed in dealing with the multi-objective supply chain model to find the optimum configuration of a given supply chain problem which minimizes the total cost and the total lead-time. The supply chain problem utilized in this study is taken from literature and several experiments have been conducted in order to show the performance of the proposed model; in addition, the results have been compared to those achieved by the ant colony optimization method. The results show that the proposed bees algorithm is able to achieve better Pareto solutions for the supply chain problem.