Multi-objective Supply Chain Network Research Articles

A bi-objective integrated transportation and inventory management under a supply chain network considering multiple distribution networks

In order to respond to the customer’s needs effectively and efficiently, logistics is characterized as a part of the supply chain that executes and handles forward and reverse movement and storage of products, services, and related data. An efficient logistic network is needed for the supply chain that executes forward and reverses products’ movement. This study resolves the supply chain network’s logistic problem to determine the appropriate order allocation of products from multiple plants, warehouses, and distributors to minimize total transportation and inventory costs by simultaneously determining optimal locations, flows, shipment composition, and shipment cycle times. The multi-objective logistic cost minimizes through the value function approach for obtaining the optimal order allocation. An actual data-based case study has been applied to examine the effectiveness of the multi-objective supply chain network. These results are very relevant for the manufacturing sectors, particularly those facing the logistics issue in the supply chain network. The findings indicate the optimal logistic costs. The results enable managers to cope with various types of logistics risks.

Customer Prioritization Integrated Supply Chain Optimization Model with Outsourcing Strategies.

Pre-COVID-19, most of the supply chains functioned with more capacity than demand. However, COVID-19 changed traditional supply chains' dynamics, resulting in more demand than their production capacity. This article presents a multiobjective and multiperiod supply chain network design along with customer prioritization, keeping in view price discounts and outsourcing strategies to deal with the situation when demand exceeds the production capacity. Initially, a multiperiod, multiobjective supply chain network is designed that incorporates prices discounts, customer prioritization, and outsourcing strategies. The main objectives are profit and prioritization maximization and time minimization. The introduction of the prioritization objective function having customer ranking as a parameter and considering less capacity than demand and outsourcing differentiates this model from the literature. A four-valued neutrosophic multiobjective optimization method is introduced to solve the model developed. To validate the model, a case study of the supply chain of a surgical mask is presented as the real-life application of research. The research findings are useful for the managers to make price discounts and preferred customer prioritization decisions under uncertainty and imbalance between supply and demand. In future, the logic in the proposed model can be used to create web application for optimal decision-making in supply chains.

A supply chain network reconfiguration under partial disruptions with capacity recovery

This paper presents a multi-objective supply chain network (SCN) reconfiguration model under disruptions considering cost minimisation and resilience maximisation objectives. Potential disruption scenarios are modelled considering the decision maker's (DM) risk-attitude. At first, the single-objective cost minimisation problem is formulated and extended as a multi-objective problem incorporating responsiveness as another objective. A set of Pareto solutions is obtained using the augmented ε-constraint (AUGMECON) method. The major inferences include: 1) the structural decisions are highly influenced by the SCN facility's failure probability and the DM's risk-attitude; 2) the cost-effectiveness is achieved by incorporating a portfolio of reactive strategies during the post disruption periods, whereas redundancy at design phase is preferred to maximise the resilience; 3) a rapid and radical albeit costly recovery option is favoured to maximise resilience while a slow and incremental recovery is preferred to minimise the cost.

A coevolutionary algorithm based on the auxiliary population for constrained large-scale multi-objective supply chain network.

Supply chain network is important for the enterprise to improve the operation and management, but has become more complicated to optimize in reality. With the consideration of multiple objectives and constraints, this paper proposes a constrained large-scale multi-objective supply chain network (CLMSCN) optimization model. This model is to minimize the total operation cost (including the costs of production, transportation, and inventory) and to maximize the customer satisfaction under the capacity constraints. Besides, a coevolutionary algorithm based on the auxiliary population (CAAP) is proposed, which uses two populations to solve the CLMSCN problem. One population is to solve the original complex problem, and the other population is to solve the problem without any constraints. If the infeasible solutions are generated in the first population, a linear repair operator will be used to improve the feasibility of these solutions. To validate the effectivity of the CAAP algorithm, the experiment is conducted on the randomly generated instances with three different problem scales. The results show that the CAAP algorithm can outperform other compared algorithms, especially on the large-scale instances.

A Fuzzy Goal Programming Approach for Solving Multi-Objective Supply Chain Network Problems with Pareto-Distributed Random Variables

Uncertainty is unavoidable and addressing the same is inevitable. That everything is available at our doorstep is due to a well-managed modern global supply chain, which takes place despite its efficiency and effectiveness being threatened by various sources of uncertainty originating from the demand side, supply side, manufacturing process, and planning and control systems. This paper addresses the demand- and supply-rooted uncertainty. In order to cope with uncertainty within the constrained multi-objective supply chain network, this paper develops a fuzzy goal programming methodology, with solution procedures. The probabilistic fuzzy goal multi-objective supply chain network (PFG-MOSCN) problem is thus formulated and then solved by three different approaches, namely, simple additive goal programming approach, weighted goal programming approach, and pre-emptive goal programming approach, to obtain the optimal solution. The proposed work links fuzziness in transportation cost and delivery time with randomness in demand and supply parameters. The results may prove to be important for operational managers in manufacturing units, interested in optimizing transportation costs and delivery time, and implicitly, in optimizing profits. A numerical example is provided to illustrate the proposed model.

Sustainable production network design for city multi-floor manufacturing cluster

The most important aspects of supply chain network design implicated companies, are quality, time and effectiveness of their processes. Those aspects are issued not only by users’ requirements, but also by applicable benchmarks, specifications as well as economic and financial conditions. Thus, among others, the most desirable tools that fulfill such demands the knowledge assessment methods and applications of modern computing methods in engineering and life science. The following draft presents a method used to explain and asses the knowledge about an enterprises infrastructure system. System that allows allocation of production components and analyze their role in maintaining that process.The aim of this article is to present an approach for sustainable supply chain network design, as well as the proposal for big data repository analysis and a way to use a multicriteria method to solve a multi-objective supply chain network problems.

Recent advances in hybrid priority-based genetic algorithms for logistics and SCM network design

Abstract Design and optimization of logistics and supply chain management (SCM) network is very important issue, which plans, implements and controls the efficient, effective forward and reverse flow and storage of goods, services and related information between the point of origin and the point of consumption to meet customers’ requirements. A multistage based logistics or SCM network can be modeled by means of a sequence of stages, each consisting of a set of functions, i.e. the existing or potential facilities located in several countries or regions of the world where goods are transformed or manufactured or stocked and delivered: suppliers, plants, distribution centers (DCs) and customers. The structure of each stage of the supply chain is completed by a set of arcs connected to the nodes and representing the flow of goods: each arc has a weight proportional to the cost incurred in between the two nodes it connects together. In this paper, we survey a recent advance on hybrid priority-based genetic algorithms for solving a multistage logistics or SCM network problems. In particular, the following multistage based logistics or SCM network models will be introduced: (1) the sugarcane SCM network model, (2) multiobjective supply chain network model, (3) flexible multistage logistics network model, and (4) multiobjective reverse logistics network model. In each model, we summarize the background, mathematical model, hybrid priority-based genetic algorithm and numerical experiment. We also enhance a hybrid genetic algorithm (HGA) by combining a local search (LS) technique and tuning GA parameters by a fuzzy logic control (FLC) to fast the search ability of GA. Finally, numerical experiment of each case study is carried out to show the effectiveness of the proposed approach by the hybrid priority-based genetic algorithms.

An integrated multi-objective supply chain network and competitive facility location model

In this study, a multi-objective supply chain (SC) network optimization model based on the joint SC network optimization and competitive facility location models is proposed to analyse the results of ignoring the impacts of SC network decisions on customer demand. The objectives utilized in the model are profit maximization, sales maximization and SC risk minimization. The unique unknown variable within the model is the demand. The demand at each customer zone is assumed to be determined by price and the utility function. The utility function is defined as the availability of same-day transportation from the distribution centre (DC) to the customer zone. The application of the proposed model is illustrated through a real-world problem and is solved as single and multi-objective models. The results of single and multi-objective models are subsequently compared. After solving the problem, a sensitivity analysis is also conducted to test the applicability of the model with respect to various parameter coefficients, such as price elasticity, one–day replenishment coverage impact, risk factors (disruption probabilities) and the relative weights of the objectives.

Risk-pooling strategy, lead time, delivery reliability and inventory control decisions in a stochastic multi-objective supply chain network design

A three-echelon supply chain network design is considered and a multi-objective mixed integer non-linear problem is modeled. Risk-pooling, lead time between facilities and the concept of the reliability are used to modeling. The objectives of the model are minimization of the supply chain’s total cost and maximization the delivery reliability, which can be equal to minimize the risk of not delivering the products with regard to stochastic demand. To solve the model a heuristic hierarchical algorithm is presented. Numerical examples are considered to test and verify the Pareto front. The results show the effectiveness and efficiency of the heuristic algorithm.

Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art

Supply chain network designing and programming is a momentous issue that many practitioners have focused on and contributed numerous novelties for this prompt. This paper puts forward a fuzzy multi-agent system according to which compatible with the decision makers’ interests and environmental survey, identifies the parameters of the mathematical model. An embedded optimization party including evolutionary-based optimizer intelligent agents, obtains non-dominated potential solutions. The output of these optimizer agents during the calibration process is an underpinning for evaluating the performance of the party. The system makes the policy of optimization complying with the results evaluation as well as the decision makers’ elaborated desires. Afterwards, in step with this policy, it sets a pool from obtained Pareto Fronts and aggregates them to extract a set of the best individuals. It interactively represents this set to the decision makers and catches their desired circumstance amongst these optional solutions. Proposing the network graph and program—which its generic morphography is determined—for decision makers is contrived as the system last stage. The main competencies of this system could be contemplated regarding the facts that it interactively fulfills the decision makers’ utilities relying on its robustness in optimization, self-tuning, training loop, ambient intelligence and consciousness toward the changes in environment.

Multi-objective biogeography-based optimization for supply chain network design under uncertainty

This paper proposes a new two-stage optimization method for multi-objective supply chain network design (MO-SCND) problem with uncertain transportation costs and uncertain customer demands. On the basis of risk-neutral and risk-averse criteria, we develop two objectives for our SCND problem. We introduce two solution concepts for the proposed MO-SCND problem, and use them to define the multi-objective value of fuzzy solution (MOVFS). The value of the MOVFS measures the importance of uncertainties included in the model, and helps us to understand the necessity of solving the two-stage multi-objective optimization model. When the uncertain transportation costs and customer demands have joined continuous possibility distributions, we employ an approximation approach (AA) to compute the values of two objective functions. Using the AA, the original optimization problem becomes an approximating mixed-integer multi-objective programming model. To solve the hard approximating optimization problem, we design an improved multi-objective biogeography-based optimization (MO-BBO) algorithm integrated with LINGO software. We also compare the improved MO-BBO algorithm with the multi-objective genetic algorithm (MO-GA). Finally, a realistic dairy company example is provided to demonstrate that the improved MO-BBO algorithm achieves the better performance than MO-GA in terms of solution quality.

Research on Supply Chain Network System Based on Industrial Cluster

The reliability of supply chain network system design and optimization based on the industrial cluster was discussed, that is, the operational platform of multi-objective supply chain network was moved to industrial cluster, so the platform has the effects of both regional radiation and scale of economy. Instead of some static research methods such as former traditional mathematic planning, systematic and dynamic analysis, this paper put forward a coordinative mechenism construction, this mechenism used dynamic simulation method which combined the supply chain system with industrial cluster multi-objective weighing in the region, the mechenism should be cost efficient and satisfy the application in the industrial cluster radiation region, the mechenism should integrate all possible resources in the industrial cluster and consider heterogeneity, layout, nonlinear, limitation and dynamics of the supply chain network system.

A fuzzy goal programming approach to solve multi-objective supply chain network design problems

A fuzzy goal programming approach to solve multi-objective supply chain network design problems