Multi-product Supply Chain Research Articles

Optimisation of distribution quantity in a multi-product multi-period supply chain using genetic algorithm

This research is about optimising the distribution quantity of products in a three-stage supply chain system. The stages consist of manufacturers, distribution centres and retailers. This study also considers the different number of products and time periods in a planning horizon. Optimising the quantity of products shipped through the stages of a supply chain has a significant effect on the total cost of the system and generally, it is a key factor for decision making. The costs studied in this paper are transportation cost and inventory holding cost. Transportation cost itself includes fixed cost and variable cost. A mathematical model was formulated to represent the problem and it was solved by using genetic algorithm. Besides this, a programme was written in a way that can be used with different number of manufacturers, distribution centres, retailers, products and time periods in a planning horizon.

Clustering Return Items in a Multi-Layer Multi-Product Reverse Supply Chain Using Data Mining

In the reverse supply chain the basic concern is to categorize the returned materials in the usable or non-usable ones for performing the required operations to resend them in the supply chain. Companies can obtain more confidence from their consumers to carry out continuous revers logistics removing the defective products. To begin the reverse multi-layer multi-product supply chain and after collecting the returning commodities, we cluster them via k-mean algorithm in Matlab software environment. The results are used to perform a sampling process to deliver the commodities to the related layer for rework and repair operations. The computations show an extensive reduction in the human resource costs for separation of the returning items and increasing the efficiency.

A fuzzy multi-objective multi-follower linear Bi-level programming problem to supply chain optimization

Article history: Received June 10, 2013 Received in revised format 25 August 2013 Accepted September 15 2013 Available online September 17 2013 In today's world, many planning problems include a hierarchical decision structure with independent and often conflicting objectives. Therefore, the optimization of supply chains with hierarchical structure is essential. In this paper, we investigate a fuzzy multi-objective multi-products supply chain optimization problem in a bi-level structure with one level corresponding to a manufacturer planning problem, while the other to K distribution centers problem. In our model, customer demand and supply chain costs are considered uncertain and will be modeled with use of fuzzy sets. We first describe how different kinds of problems can be modeled as bi-level programming problems. Then, this fuzzy model is first converted into an equivalent crisp model by using α-cut method in each level, and then by applying extended Kuhn–Tucker approach, we have a linear multi-objective programming problem. Fuzzy goal programming technique is applied to solve the multi-objective linear programming problem to obtain a set of Pareto-optimal solutions. Finally, a numerical example is illustrated to demonstrate the feasibility of the proposed approach. © 2013 Growing Science Ltd. All rights reserved.

Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case

We have developed a stochastic mathematical formulation for designing a network of multi-product supply chains comprising several capacitated production facilities, distribution centres and retailers in markets under uncertainty. This model considers demand-side and supply-side uncertainties simultaneously, which makes it more realistic in comparison to models in the existing literature. In this model, we consider a discrete set as potential locations of distribution centres and retailing outlets and investigate the impact of strategic facility location decisions on the operational inventory and shipment decisions of the supply chain. We use a path-based formulation that helps us to consider supply-side uncertainties that are possible disruptions in manufacturers, distribution centres and their connecting links. The resultant model, which incorporates the cut-set concept in reliability theory and also the robust optimisation concept, is a mixed integer nonlinear problem. To solve the model to attain global optimality, we have created a transformation based on the piecewise linearisation method. Finally, we illustrate the model outputs and discuss the results through several numerical examples, including a real-life case study from the agri-food industry.

A highly optimised tolerance-based approach for multi-stage, multi-product supply chain network design

In this paper we propose an algorithm called Highly Optimised Tolerance (HOT) for solving a multi-stage, multi-product supply chain network design problem. HOT is based on power law and control theory. The proposed approach takes its traits from the local incremental algorithm (LIA), which was initially employed to maximise the design parameter (i.e. yield), particularly in the percolation model. The LIA is somewhat analogous to the evolution by natural selection schema. The proposed methodology explores a wide search space and is computationally viable. The HOT algorithm tries to make the system more robust at each step of the optimisation. The objective of this paper is to reduce the total cost of supply chain distribution by selecting the optimum number of facilities in the network. To examine the effectiveness of the HOT algorithm we compare the results with those obtained by applying simulated annealing on a supply chain network design problem with different problem sizes and the same data sets.

Equity in multiproduct supply chain network

In this paper, multiproduct supply chain network is investigated with equity consideration,namely, obtaining the optimal flow pattern, in such a way that no user in the network can increasehis(her) benefit with change in product’s sending path. However, each kind of products, has anindividual cost function and, at the same time, contributes to its own and other product’s cost functionin an individual way. An algorithm is developed to find optimal flow pattern for such multiproductsupply chain network.

The optimization of lot sizing with supplier selection problem in multi-echelon defective supply chain network

A new problem called lot sizing with supplier selection problem in the multi-product multi-echelon defective supply chain network (MDSCN) is proposed in this study. We explain the problem by a case study. We take the multi-product MDSCN of X enterprise into account. Back and front engine blocks are products of X enterprise. The aim of this study is to identify how many components will be purchased from which supplier while meeting the demands of the customers for these two products. The supply chain (SC) network of X enterprise is formed by mixed-integer linear programming (MILP). The optimization of current SC network of X enterprise is carried out by using Linear, INeractive, Discrete Optimizer (LINDO) program. The customer expectations of X enterprise are met at the highest level, and it gives the opportunity to have the knowledge, which reduces the total cost, of purchasing–production–distribution strategy with this work.

A multi-period and multi-product optimizer for cooperative supply chains

Members in a traditional supply chain compete to reduce their individual costs. But total cost is minimized in a cooperative, or a corporate managed, supply chain. A lower average cost and a lower cost variation are achieved by cooperative individual members in the long-run. The problem is formulated and solved as an integrated flow network. Previous research is expanded to include multi-period and multi-product cooperative supply chain with possibility of holding inventory in a multi-stage, multi-member setup. A Cooperative Supply Optimizer System (CSOS), a software-based coordination mechanism, is developed for large chains. It gathers operational information from members of the supply chain, and then guides them on ordering decisions for a minimum cost of the entire supply chain. Simulation results indicate an approximately 26% reduction in total supply chain costs, utilizing this formulation over a competitive setup. As the holding costs increase, the problem decomposes into single period (Just-in-time) again. The disturbing bullwhip effect disappears in cooperative supply chains.

A grid-based collaborative supply chain with multi-product multi-period production–distribution

A collaborative supply chain seeks to coordinate its members to produce and distribute products along the chain for minimum overall costs to satisfy customer demand. An integrated multi-period, multi-product and multi-stage supply chain model is developed. A collaborative supply chain grid (CSCG) system gathers necessary information on operations and resources from members of the supply chain. It then guides the members on ordering decisions for a minimum overall cost, while continuously re-directing them as new data becomes available. An actual CSCG system has been implemented in a collaborative auto industry with diverse and dynamic membership, yielding an approximately 26% reduction in total supply chain costs.

Research on Integrated Optimization Problem in a Multi-product Supply Chain Based on Markov Decision Processes

Research on Integrated Optimization Problem in a Multi-product Supply Chain Based on Markov Decision Processes

Optimization Modelling for Multi-Objective Supply Chains, A Case Study of the Oil and Gas Sector

One of the key problems managers of companies face is the high cost of distributing multiple products to downstream locations. They often rely on their intuition and rule of thumb while making allocation decisions. In this study a mathematical model was developed for minimizing the distribution cost in a multi-product supply chain system. The oil and gas sector was studied to understand the underlying supply chain system. Attempt was made to identify system parameters, variables, limitations, criteria so as to be able to define the distribution problem. This study was done using information from one of the major source in the oil and gas sector in Nigeria. The interactions and flow of products in the system were properly studied. It was then observed that the source distribute products to downstream at a higher cost without fulfilling demand at some destination. The data gotten from the plant shows that a total distribution cost of N 2,353,050 was incurred at the source for the fortnight. The model was solved for a 12 product 8 destination case. The problem developed was an LP linear program formulation with three major constraints defined; demand, availability and company policies. And at the end of the analyses, it was observed that a LP minimization process would distribute a set of products to all the downstream without any suffering a zero product at an optimal cost of N 2,025,200 . About 14% of the optimal cost was reduced. It is concluded that the model is effective to reduce or minimize distribution expenses for any multi-product multiple destination system and fulfilling demand at various destinations. Keywords : Optimization, Modelling, Supply chain, Oil and gas,

Supply chain design considering economies of scale and transport frequencies

In this paper we consider a 3-echelon, multi-product supply chain design model with economies of scale in transport and warehousing that explicitly takes transport frequencies into consideration. Our model simultaneously optimizes locations and sizes of tank farms, material flows, and transport frequencies within the network. We consider all relevant costs: product cost, transport cost, tank rental cost, tank throughput cost, and inventory cost. The problem is based on a real-life example from a chemical company. We show that considering economies of scale and transport frequencies in the design stage is crucial and failing to do so can lead to substantially higher costs than optimal. We solve a wide variety of problems with branch-and-bound and with the efficient solution heuristics based on iterative linearization techniques we develop. We show that the heuristics are superior to the standard branch-and-bound technique for large problems like the one of the chemical company that motivated our research.

Optimization of Supply Chain Systems with Price Elasticity of Demand

A centralized multiechelon, multiproduct supply chain network is presented in a multiperiod setting with products that show varying demand against price. An important consideration in such complex supply chains is to maintain system performance at high levels for varying demands that may be sensitive to product price. To examine the price-centric behavior of the customers, the concept of price elasticity of demand is addressed. The proposed approach includes many realistic features of typical supply chain systems such as production planning and scheduling, inventory management, transportation delay, transportation cost, and transportation limits. In addition, the proposed system can be extended to meet unsatisfied demand in future periods by backordering. Effects of the elasticity in price demand in production and inventory decisions are also examined. The supply chain model is formulated as a convex mixed-integer nonlinear programming problem. Reformulations are presented to make the problem tractable. The differential equations are reformulated as difference equations, and unbounded derivatives in the nonlinear objective function are handled with an approximation, with guaranteed bounds on the loss of optimality. The approach is illustrated on a multiechelon, multiproduct supply chain network.

Supply Chain Design Considering Economies of Scale and Transport Frequencies

In this paper we consider a 3-echelon, multi-product supply chain design model with economies of scale in transport and warehousing that explicitly takes transport frequencies into consideration. Our model simultaneously optimizes locations and sizes of tank farms, material flows, and transport frequencies within the network. We consider all relevant costs: product cost, transport cost, tank rental cost, tank throughput cost, and inventory cost. The problem is based on a real-life example from a chemical company. We show that considering economies of scale and transport frequencies in the design stage is crucial and failing to do so can lead to substantially higher costs than optimal. We solve a wide variety of problems with branch-and-bound and with the efficient solution heuristics based on iterative linearization techniques we develop. We show that the heuristics are superior to the standard branch-and-bound technique for large problems like the one of the chemical company that motivated our research.

Determining the optimal number of Kanban in multi-products supply chain system

Kanban, a key element of just-in-time system, is a re-order card or signboard giving instruction or triggering the pull system to manufacture or supply a component based on actual usage of material. There are two types of Kanban: production Kanban and withdrawal Kanban. This study uses optimal and meta-heuristic methods to determine the Kanban quantity and withdrawal lot sizes in a supply chain system. Although the mix integer programming method gives an optimal solution, it is not time efficient. For this reason, the meta-heuristic methods are suggested. In this study, a genetic algorithm (GA) and a hybrid of genetic algorithm and simulated annealing (GASA) are used. The study compares the performance of GA and GASA with that of the optimal method using MIP. The given problems show that both GA and GASA result in a near optimal solution, and they outdo the optimal method in term of run time. In addition, the GASA heuristic method gives a better performance than the GA heuristic method.

Optimization of warehousing and transportation costs, in a multi-product multi-level supply chain system, under a stochastic demand

The centralized management approach provides a general view to set a better coordination between the ele- ments of the supply chain, and look for the equilibrium between the stock and the shipped quantities. This paper describes a stochastic model predictive control algorithm to optimize the warehousing cost and the shipping cost on the same time. We consider a multi-level and multi-product supply chain dealing with a stochastic demand. We solve the stochastic model predictive control problem by a dynamic programming to determine the optimal policies to minimize the costs of shipping and warehousing products.

Supply chain network design with considerations for modular assembly

We present a supply chain optimisation model that simultaneously considers sourcing decisions for each part in a complex multi-level bill of materials (BOM) but decides on which should be assembled into subassemblies or modules. Indeed, some parts in the BOM are flexible in the sense that they can be grouped with other parts or subassemblies to form modules. The problem is to find the composition of the modules and the assignment of modules and parts to subcontractors while minimising the overall supply chain cost. The problem considered is one faced by a jet engine manufacturer when designing its multi-echelon, multi-period, multi-product supply chain network with deterministic demand. The mixed integer programming model considers multiple sourcing where the number of parts sourced from a business partner must exceed a lower bound as dictated by purchasing contracts. Computational results are presented for different scenarios allowing the combined analysis of supply chain design and supplier relationships.

The value of information sharing in a multi-product supply chain with product substitution

The extant literature on the value of information sharing within a supply chain has investigated only the case in which the supply chain manufactures and distributes a single product to customers. In this paper, the case in which a supply chain distributes multiple products is considered. These products may also be substitutable in the sense that a consumer may be willing to buy an alternate product when the customer's preferred product is out of stock. It is shown that substitutability among products generally reduces the value of information sharing and that the reduction is increasing in the degree of substitution. This result occurs because of two reasons. First, the demand-pooling effect of substitution reduces demand variance, and the reduction in demand variance because of substitution is higher when the degree of substitution is higher. Second, substitution increases the base level profit when information is not shared. The reduction in the value of information sharing because of substitutability is higher when the number of substitutable products is lower, the demands of products are less correlated or when the number of products whose information is shared is higher. When information about the demands of only a subset of products is shared, the value of information sharing under substitution is higher than that under no substitution under certain conditions. However, in these conditions the inventory holding and shortage costs are significantly low, relative to the profit margin, causing the value of information sharing to be low. The key implication of these findings is that if substitution effects are ignored, then there is a risk of overestimating the value of information sharing. The overestimate can be very significant when the degree of substitution is higher, the number of substitutable products is smaller, demands of these products are less correlated and more independent or when the number of products whose information is shared is higher.

Fuzzy scheduling of a build-to-order supply chain

The overwhelming majority of the literature in the area of supply chain planning and scheduling considers the traditional make-to-stock (MTS) environment. However, manufacturers of assembled products such as cars, computers, furniture, etc. adopt the build-to-order supply chain (BOSC) to become agile in a mass customization process in order to meet diversified customer requirements. In this paper we propose an integrated production–distribution planning model for a multi-echelon, multi-plant and multi-product supply chain operating in a build-to-order (BTO) environment. The uncertainties associated with estimation of the various operational cost parameters are represented by fuzzy numbers. The BOSC scheduling model is thus constructed as a mixed-integer fuzzy programming (MIFP) problem with the goal of reducing the overall operating costs related to component fabrication, procurement, assembling, inspection, logistics and inventory, while improving customer satisfaction by allowing product customization and meeting delivery promise dates at each market outlet. An efficient compromise solution approach by transforming the problem into an auxiliary multi-objective linear programming model is also suggested.

Midterm Supply Chain Planning under Uncertainty: A Multiobjective Chance Constrained Programming Framework

Uncertainty issues associated with a multisite, multiproduct supply chain planning problem has been analyzed in this paper, using the chance constrained programming (CCP) approach. In the literature, such problems have been addressed using the scenario-based two-stage stochastic programming approach. Although this approach has merits, in terms of decomposition, the computational complexity, even for small-size planning problems, is generally quite large, leading to either huge time consumption in solving the problem or an inability to solve big instances of problems under a standard solver environment. To make the aforementioned lacunea of two-stage stochastic programming more tractable, the problems under uncertainty have been recast in this paper in a CCP framework that uses a more suitable representation of uncertainty. Addressing uncertainty issues in product demands and machine uptime, using the CCP approach, leads to the evaluation of multiobjective tradeoffs that are analyzed here in the Pareto sense, and the ε-constraint approach is used to generate those Pareto optimal (PO) points. Different aspects of uncertainty issues are analyzed in detail by taking a few PO points among the total set of PO solutions found for this problem. It is seen that this CCP-based approach is quite generic, relatively simple to use and can be adapted for bigger size planning problems, as the equivalent deterministic problem does not blow up in size with the CCP approach. We demonstrate the analysis on a relatively moderate size midterm planning problem taken from published work [McDonald, C. M.; Karimi, I. A. Ind. Eng. Chem. Res. 1997, 36, 2691] and discuss various aspects of uncertainty in the context of this problem.