Optimizing decentralized production–distribution planning problem in a multi-period supply chain network under uncertainty (original) (raw)
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Fuzzy Sets and Systems, 2017
In today's business environments, the high importance of economic benefits and environmental impacts of using scrapped products has caused most companies to move to designing the closed loop supply chain network. This paper considers the closed loop supply chain network design problem under hybrid uncertainty, while there are two sources of uncertainty for most parameters, thus require fortifying of the robustness of the decision. The first source is that some uncertain parameters may be based on the future scenarios which are considered according to the probability of their occurrence. The second source is that the values of these parameters in each scenario are usually imprecise and can be specified by possibilistic distributions. In this case, the best robust decision has some additional properties in terms of mean value and variability of the objective function. We introduced two types of the variability named scenario variability and possibilistic variability. Possibility theory is used to choose a solution in such a problem and a novel robust fuzzy stochastic programming approach is proposed that has significant advantages. The performance of the proposed model is also compared with that of other models in term of the mean cost and variability by simulation.
Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty
International Journal of Mechanical Engineering and Applications, 2017
This research is a development of a stochastic mixed integer linear programming (SMILP) model considering stochastic customer demand, to tackle the multi-product SCND problems. It also considers multi-period, multi-echelons, products inventories, considering locations capacities and associated cost elements. The model represents both location and allocation decisions of the supply chain which maximize the total expected profit. The effect of demand mean on the total expected profit and the effect of the number of scenarios on the CPU time are studied. The results have shown the effect of customers' demands for each product in each period on the quantities of material delivered from each supplier to each factory, the quantities of products delivered from each factory and factory store to each distributor, the inventory of each product in each factory and distributor, the quantities of each type of product delivered from each distributor to each customer in each period. The model has been verified through a detailed example.
This paper proposes the network design and optimization of a multi-product, multi-time, multi-echelon capacitated closed-loop supply chain in an uncertain environment. The uncertainty related to ill-known parameters like product demand, return volume, fraction of parts recovered for different product recovery processes, purchasing cost, transportation cost, inventory cost, processing, and set-up cost at facility centers is handled with fuzzy numbers. A fuzzy mixed-integer linear programming model is proposed to decide optimally the location and allocation of products/parts at each facility, number of products to be remanufactured, number of parts to be purchased from external suppliers and inventory level of products/parts in order to maximize the profit to the organization. The proposed solution methodology is able to generate a balanced solution between the feasibility degree and the degree of satisfaction of the decision maker. The proposed model has been tested with an illustrative example.
A stochastic programming approach for supply chain network design under uncertainty
European Journal of Operational Research, 2005
This paper proposes a stochastic programming model and solution algorithm for solving supply chain network design problems of a realistic scale. Existing approaches for these problems are either restricted to deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Our solution methodology integrates a recently proposed sampling strategy, the Sample Average Approximation scheme, with an accelerated Benders decomposition algorithm to quickly compute high quality solutions to large-scale stochastic supply chain design problems with a huge (potentially infinite) number of scenarios. A computational study involving two real supply chain networks are presented to highlight the significance of the stochastic model as well as the efficiency of the proposed solution strategy. of supply chain networks (see for example ). However, the majority of this research assumes that the operational characteristics of, and hence the design parameters for, the supply chain are deterministic. Unfortunately, critical parameters such as customer demands, prices, and resource capacity are quite uncertain. Moreover, the arrival of regional economic alliances, for instance the Asian Pacific Economic Alliance and the European Union, have prompted many corporations to move more and more towards global supply chains, and therefore to become exposed to risky factors such as exchange rates, reliability of transportation channels, and transfer prices . Unless the supply chain is designed to be robust with respect to the uncertain operating conditions, the impact of operational inefficiencies such as delays and disruptions will be larger than necessary. A recent study found that after a company announces a supply chain disruption, such as a production or shipment delay, its stock price can decrease significantly, with an average decrease of 8.6% on the day of the announcement, and is often followed by further decreases, as much as 20% over the next six months.
Supply Chain Network Design under Uncertainty
This paper proposes a fuzzy programming model and a hybrid intelligent algorithm to design supply chain network. Existing researches on these problems are either restricted on deterministic environment or only address stochastic parameters. In this paper, we consider supply chain network design problem in fuzzy environment. In practise, there is generally a predetermined cost which decision-maker can accept, the objective of this paper is to maximize the degree of credibility of satisfying the event that the total cost is less than that given cost. Moreover, a genetic algorithm based on fuzzy simulation is developed to solve the proposed fuzzy models.
Supply Chain Optimization Under Uncertainty
2014
In this real world case study, I study the distribution plan for a major North American building supplies manufacturer under both demand and cost uncertainty. The sponsoring company seeks to minimize the total cost of production and freight for 37 items produced at 3 plants and distributed through 54 intermediate warehouses to customers in 162 geographical areas. The large-scale nature of this problem, including over 800 uncertainties and 1200 decision variables, provides an opportunity for novel methods in solving this problem. I show that the existing method of using point-in-time data for all of the uncertain parameters produces a sub-optimal plan, and that a plan based on chance constraint methods can lower the average total cost of production and distribution up to 4.9%.
A hybrid fuzzy approach for the closed-loop supply chain network design under uncertainty
Journal of Intelligent & Fuzzy Systems, 2015
A closed-loop supply chain (CLSC) network consists of both forward and reverse supply chains. In this paper a CLSC network is investigated that involves four echelons in a forward direction including suppliers, manufacturer, distribution center and demand market, and three echelons in a backward direction including disposal, rework and collection centers. This paper presents a bi-objective model in order to design a network of bi-directional facilities in logistics network under uncertainties. Its objectives are to minimize the total costs as well as the total defective rate, disposal rate and pollution production rate. To solve the model, a hybrid solution approach is applied that combines fuzzy possibilistic programming and fuzzy multi-objective programming. Furthermore, in order to illustrate the validity of the model and applicability of the proposed solution approach, numerical experiments and the related sensitivity analysis are provided. Finally, the conclusion is provided.
2018
We study a multi-time, multi-product and multi-echelon supply chains aggregate procurement, production and distribution planning problem and discuss the implications of formulating a tri-level model to integrate procurement, production and distribution, maintaining the existing hierarchy in the decision process. In our model, there are three different decision makers controlling the procurement, production and the distribution processes in the absence of cooperation because of different optimization strategies. First, we present a hierarchical tri-level programming model to deal with decentralized supply chain problems. Then, an algorithm is presented to solve the proposed model. A numerical illustration is provided to show the applicability of the optimization model and the proposed algorithm. In order to evaluate the application of the model and the proposed algorithm, ten sets of small and large problems are randomly generated and tested The experimental results show that our pro...
Supply chains are one of the key tools in optimizing production and distribution simultaneously. However, information uncertainty is always a challenge in production and distribution management. The main purpose of this paper is to design a two-echelon supply chain in a multi-cycle state and in conditions of demand uncertainty. The task includes determining the number and location of distribution centers, planning capacity for active distribution centers, and determining the amount of shipments between different levels so that the total costs of the chain are minimized. Uncertainty is applied through discrete scenarios in the model and the problem is formulated by multi-stage stochastic programming method in the form of a mixed integer linear model. The results acquired using two indicators called VMS and VSS demonstrated that modeling the supply chain design problem with the multi-stage stochastic approach can result in significant costs reduction. Plus, utilizing mathematical expectation can generate misleading results, therefore resulting in the development of supply chain designs incapable of satisfying demand due to its overlooked limitations.
Pomorstvo, 2021
In this paper, we devoted a design under uncertainty of a four-echelon supply chain network including multiple suppliers, multiple plants, multiple distributors and multiple customers. The proposed model is a bi-objective mixed integer linear programming which considers several constraints and aims to minimize the total costs including the procurement, production, storage and distribution costs as well as to maximize on-time deliveries (OTD). To bring the model closer to real-world planning problems, the objective function coefficients (e.g. procurement cost, production cost, inventory holding and transport costs) and other parameters (e.g., demand, production capacity and safety stock level), are all considered triangular fuzzy numbers. Besides, a hybrid mathematical model-based on credibility approach is constructed for the problem, i.e., expected value and chance constrained models. Moreover, to build the crisp equivalent model, we use different property of the credibility measure. The resulted crisp equivalent model is a bi-objective mixed integer linear programs (BOMILP). To transform this crisp BOMILP into a single objective mixed integer linear programs (MILP) model, we apply three different aggregation functions. Finally, numerical results are reported for a real case study to demonstrate the efficiency and applicability of the proposed model.