Multi-echelon Distribution Research Articles

Hybrid analytical and simulation optimization approach for production and distribution supply chain planning

Supply chain planning consists of designing an optimal and feasible production and distribution plan for the whole supply chain. Traditionally, two common methods of optimization are analytical and simulation-based optimization, and each of them has pros and cons. In this paper, both methods are combined to consolidate the strengths of each, also known as the hybrid analytical and simulation approach. A case study of a multi-period, multi-echelon, and multi-product production and distribution problem that maximizes the whole supply chain’s profit is introduced, to demonstrate the proposed hybrid approach. The analytical model is solved to find the optimal production-distribution plan, and then the plan is inputted into a simulation model, where uncertainties are incorporated. The proposed algorithm is then applied to identify a feasible plan that meets makespan limitation and service level requirements. Safety stock is incorporated to satisfy the service level requirements and maximize the supply chain’s profit. This procedure continues iteratively until the production-distribution plan is feasible and optimized. The results show that the proposed approach can solve for a near or possibly optimal as well as feasible solution with relatively fast computational time.

Integrating collection-and-delivery points in the strategic design of urban last-mile e-commerce distribution networks

The use of collection-and-delivery points (CDPs) within multi-echelon distribution networks is becoming an increasingly popular last-mile distribution strategy. In this work, we provide a method to integrate CDPs in the design of multi-echelon distribution networks. We formulate a non-linear optimization model that includes CDP location decisions and accounts for changes in demand patters occurring with their placement. To enable its application to large-scale problem instances, we formulate extended routing cost approximation formulae. We then propose a heuristic solution method and apply the model and solution approach to a real-world case study of last-mile distribution by a major Brazilian e-commerce player.

Multi Echelon Distribution Model for Electric Market Deregulation Collaboration Strategy in East Kalimantan

Electric market deregulation aims to provide flexibility for customers to have many suppliers and low prices. However, market deregulation also provides opportunity for certain parties to manipulate supply, so there is a scarcity of products that result in price increases. Therefore, this research proposes a collaborative strategy for electricity market deregulation using the Multi Echelon distribution model applied in East Kalimantan with dummy data. Collaboration strategy made based on optimization of mathematical models in two stages and three scenarios. The simulation was carried out using Excel Solver covering three regions, dynamic time and estimated price fluctuations over three periods. As a result, Gencos gets the biggest profit when serving basic load. Whereas wheeling occurs, the D4 region is a strategic area that generates the largest profit compared to the D1 and D6 regions.

The two echelon open location routing problem: Mathematical model and hybrid heuristic

Multi echelon distribution systems have become more common in recent years. This paper addresses the two echelon open location routing problem (2E-OLRP) which is a variant of the two echelon location routing problem (2E-LRP). This problem seeks to find a minimum-cost set of vehicle routes that do not return to the depot in the first echelon and do not return to satellites in the second echelon due to the presence of individual contractors and third party logistics (3PL) providers. In spite of the large amount of research on LRPs, the 2E-OLRP has received very little attention. Three flow-based mixed-integer linear programs and a hybrid heuristic algorithm are proposed to deal with this problem. Extensive experiments evaluate the effectiveness of these methods.

A simulation–optimization approach for a service-constrained multi-echelon distribution network

Academic research on (s,S) inventory policies for multi-echelon distribution networks with deterministic lead times, backordering, and fill rate constraints is limited. Inspired by a real-life Dutch food retail case we develop a simulation-optimization approach to optimize (s,S) inventory policies in such a setting. We compare the performance of a Nested Bisection Search (NBS) and a novel Scatter Search (SS) metaheuristic using 1280 instances from literature and we derive managerial implications from a real-life case. Results show that the SS outperforms the NBS on solution quality. Additionally, supply chain costs can be saved by allowing lower fill rates at upstream echelons.

Cost analysis of an electricity supply chain using modification of price based dynamic economic dispatch in wheeling transaction scheme

Deregulation of the electricity market requires coordination between parties to synchronize the optimization on the production side (power station) and the transport side (transmission). Electricity supply chain presented in this article is designed to facilitate the coordination between the parties. Generally, the production side is optimized with price based dynamic economic dispatch (PBDED) model, while the transmission side is optimized with Multi-echelon distribution model. Both sides optimization are done separately. This article proposes a joint model of PBDED and multi-echelon distribution for the combined optimization of production and transmission. This combined optimization is important because changes in electricity demand on the customer side will cause changes to the production side that automatically also alter the transmission path. The transmission will cause two cost components. First, the cost of losses. Second, the cost of using the transmission network (wheeling transaction). Costs due to losses are calculated based on ohmic losses, while the cost of using transmission lines using the MW - mile method. As a result, this method is able to provide best allocation analysis for electrical transactions, as well as emission levels in power generation and cost analysis. As for the calculation of transmission costs, the Reverse MW-mile method produces a cheaper cost than the Absolute MW-mile method

Effective bullwhip metrics for multi-echelon distribution systems under order batching policies with cyclic demand

A large number of problems in a distribution supply chain require that decisions are made in the presence of the bullwhip effect phenomenon. The impact of the order batching policies on the bullwhip effect is analysed in this paper, when cycle demand on a multi-echelon supply chain operating is considered. While investigating which bullwhip effect metrics are more adequate to measure the bullwhip effect in these type of systems, the optimal reordering plan that minimises the operation costs of the overall system is calculated. A Mixed Integer Linear Programming (MILP) model is developed that takes into account an inventory and distribution system formed by multiple warehouses and retailers with lateral transshipments. The bullwhip effect is measured through four metrics: the echelon average inventory; the echelon inventory variance ratio; the echelon average order; and the echelon order rate variance ratio. As conclusion the inventory metrics suggest that (i) using batching policy reduces instability; (ii) batching may reduce in general order variance if using larger batches and (iii) cycle demand length has no major impact in the bullwhip effect. A motivational example and a real word case study are used and tested.

On Integral Policies in Deterministic and Stochastic Distribution Systems

The “integrality” question for dynamic optimization models of inventory control asks if there exists an integral optimal policy, given integral initial inventory levels, capacities, and demand realizations. One practical implication of this question lies in whether or not full-truckload (FTL) shipping is optimal if customer demand is in integral number of truckloads. In this paper, we investigate the integrality question in single-product, multiechelon distribution systems and show that integrality holds under deterministic demand but fails to hold under stochastic demand. In distribution systems with stochastic demand, less-than-truckload (LTL) shipping can be significantly cheaper than the cost of the optimal FTL shipping policy, even in the presence of economies of scale. For instance, this occurs in settings where shipping costs are expected to increase in the future and/or inventories are more expensive to hold upstream than downstream. In such situations, our results highlight the importance of strategically positioning inventory: LTL shipments can offer a more balanced allocation of inventory across the distribution network, leading to benefits that can exceed the savings from FTL shipments due to economies of scale. However, when the cost parameters are fairly constant across time and inventory holding costs are not significantly higher upstream than downstream, then the difference between the costs of optimal FTL and optimal LTL shipping is provably marginal. The online appendix is available at https://doi.org/10.1287/opre.2016.1586 .

A new Bi-objective model for a Two-echelon Capacitated Vehicle Routing Problem for Perishable Products with the Environmental Factor

In multi-echelon distribution strategy freight is delivered to customers via intermediate depots. Rather than using direct shipments, this strategy is an increasingly popular one in urban logistics. This is primarily to alleviate the environmental (e.g., energy usage and congestion) and social (e.g., traffic-related air pollution, accidents and noise) consequences of logistics operations. This paper represents a two-echelon capacitated vehicle routing problem (2-ECVRP) in which customers' satisfaction and environmental issues are considered for perishable goods delivery for the first time. The paper proposes a novel bi-objective model that minimizes: 1) total customers waiting time, and 2) total travel cost. A restriction on maximum allowable carbon dioxide (CO2) emissions from transport in each route is considered as environmental issue in the problem. The proposed model is solved by simple additive weighting (SAW) method. Finally, the proposed model is applied to a real world problem in a supermarket chain. The results achieved by GAMS optimization software confirm the validity and high performance of the model in respect to the importance of the each objective function. Furthermore, the sensitivity analysis performed on the model reveals that less restrictive policies on carbon emissions lead to more total emissions but less total travel cost and customers waiting time.

PERANCANGAN APLIKASI SUPPLY CHAIN MANAGEMENT PADA DISTRIBUSI AYAM POTONG PT. NUJ / MTS DENGAN MENGGUNAKAN METODE DISTRIBUTOR REQUIRMENT PLANNING (DRP)

A company will be faced with problems relating to the distribution system. Thisproblem arises because consumers are in geographically dispersed locations, this resulted in the importance of holding inventories at several location. PT.Nuj / Mts is the distribution company, this company manufactures the product. PT.Nuj / Mts not yet have a good distribution planning. Planning distribution company run by less effective and has few weaknesses. Given these problems, then conducted research with the method of Distribution Requirements Planning (DRP) in the hope to be distributing the product from factory to each of city in an optimal distribution. Distribution Requirements Planning is a method to handle the procurement of supplies in a multi-echelon distribution network. The purpose of the Distribution Requirements Planning (DRP), which make a planning and scheduling activities of a good distribution, so that success in meeting customer demand will be more optimal, increase performance in fulfilling orders in a timely and appropriate amount so that distribution costs can be reduced minimized. Ofthe results, themethod ofdistribution planningcompany, the Total Cost of distribution including product demand data, product price, booking fees, storage fees, shipping costs and lead time data. By using DRP method, in order be expected to more profitable company in the future. Keywords : Distribution, Distribution Requirements Planning (DRP)

The two-echelon time-constrained vehicle routing problem in linehaul-delivery systems considering carbon dioxide emissions

Multi-echelon distribution strategy is primarily to alleviate the environmental (e.g., energy consumption and emissions) consequence of logistics operations. Differing from the long-term strategic problems (e.g., the two-echelon vehicle routing problem (2E-VRP), the two-echelon location routing problem (2E-LRP) and the truck and trailer routing problem (TTRP)) that make location decisions in depots or satellites, the paper introduces a short-term tactical problem named the two-echelon time-constrained vehicle routing problem in linehaul-delivery systems (2E-TVRP) considering carbon dioxide (CO2) emissions. The linehaul level and the delivery level are linked through city distribution centers (CDCs). The 2E-TVRP, which takes CO2 emissions per ton-kilometer as the objective, has inter-CDC linehaul on the 1st level and delivery from CDCs to satellites on the 2nd level. The Clarke and Wright savings heuristic algorithm (CW) improved by a local search phase is put forward. The case study shows the applicability of the model to real-life problems. The results suggest that the vehicle scheduling provided by the 2E-TVRP is promising to reduce the CO2 emissions per ton-kilometer of the linehaul-delivery system. Adjusting the central depot location or developing the loaded-semitrailer demand among O-D pairs to eliminate empty-running of tractors will contribute to reduce the CO2 emission factor.

Carbon-capped Distribution Planning: A JIT Perspective

Products distribution and transportation is one of the largest sources of CO2 emission in supply chains. To date, a number of researchers have argued that intensive transportation activities through popular distribution strategies such as Just-In-Time (JIT) could significantly increase carbon emissions within logistics chains. However, a systematic understanding of how JIT distribution affects carbon emissions is still lacking in current literature. In this study, we develop a bi-objective optimization model for a carbon-capped JIT distribution of multiple products in a multi-period and multi-echelon distribution network. The aims are to jointly minimize total logistics cost and to minimize the maximum carbon quota allowed per period (carbon cap). The considered problem is investigated under three different carbon emission constraints namely periodic, cumulative and global. Since the studied problem is NP-Hard, a non-dominated sorting genetic algorithm-II (NSGA-II) is developed and its parameters are tuned by Taguchi method. For further quality improvement of the developed solution approach, a novel local search approach called modified firefly algorithm incorporates NSGA-II. Different sizes of the problem are considered to compare the performances of the proposed hybrid NSGA-II and the classical one. Finally, the results are presented along with some policy and managerial insights. For policy makers, the findings show the impact of varying the carbon emission cap on total cost and total emissions under JIT distribution concept. From managerial perspectives, we analyze the relationships between average inventory holding and backlog level per period which can assist mangers to identify critical decisions for JIT distribution of products in carbon-capped environment.

Stochastic Dynamic Programming for Three-Echelon Inventory System of Limited Shelf Life Products

Coordination of inventory decisions within the supply chain is one of the major determinants of its competitiveness in the global market. Products with limited shelf life impose additional challenges in managing the inventory across the supply chain because of the additional wastage costs incurred in case of being stored beyond product’s useful life. This paper presents a stochastic dynamic programming model for inventory replenishment in a serial multi-echelon distribution supply chain. The model considers uncertain stationary discrete demand at the retailer and zero lead time. The objective is to minimize expected total costs across the supply chain echelons, while maintaining a preset service level. The results illustrate that a cost saving of around 17% is achievable due to coordinating inventory decisions across the supply chain.

A Hybrid Approach to the Optimization of Multiechelon Systems

In freight transportation there are two main distribution strategies: direct shipping and multiechelon distribution. In the direct shipping, vehicles, starting from a depot, bring their freight directly to the destination, while in the multiechelon systems, freight is delivered from the depot to the customers through an intermediate points. Multiechelon systems are particularly useful for logistic issues in a competitive environment. The paper presents a concept and application of a hybrid approach to modeling and optimization of the Multi-Echelon Capacitated Vehicle Routing Problem. Two ways of mathematical programming (MP) and constraint logic programming (CLP) are integrated in one environment. The strengths of MP and CLP in which constraints are treated in a different way and different methods are implemented and combined to use the strengths of both. The proposed approach is particularly important for the discrete decision models with an objective function and many discrete decision variables added up in multiple constraints. An implementation of hybrid approach in theECLiPSesystem using Eplex library is presented. The Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP) and its variants are shown as an illustrative example of the hybrid approach. The presented hybrid approach will be compared with classical mathematical programming on the same benchmark data sets.

강화학습 기반의 다단계 공급망 분배계획

강화학습 기반의 다단계 공급망 분배계획

The time-dependent two-echelon capacitated vehicle routing problem with environmental considerations

Multi-echelon distribution strategy in which freight is delivered to customers via intermediate depots rather than using direct shipments is an increasingly popular strategy in urban logistics. This is primarily to alleviate the environmental (e.g., energy usage and congestion) and social (e.g., traffic-related air pollution, accidents and noise) consequences of logistics operations. This paper presents a comprehensive MILP formulation for a time-dependent two-echelon capacitated vehicle routing problem (2E-CVRP) that accounts for vehicle type, traveled distance, vehicle speed, load, multiple time zones and emissions. A case study in a supermarket chain operating in the Netherlands shows the applicability of the model to a real-life problem. Several versions of the model, each differing with respect to the objective function, are tested to produce a number of selected key performance indicators (KPIs) relevant to distance, time, fuel consumption and cost. The paper offers insight on economies of environmentally friendly vehicle routing in two-echelon distribution systems. The results suggest that an environmentally friendly solution is obtained from the use of a two-echelon distribution system, whereas a single-echelon distribution system provides the least-cost solution.

Performance Evaluation of Complex Decentralized and Centralized Multi-Echelon Distribution Supply Chain

In this paper, we consider a multi-echelon multi-products distribution system, made up of a single supplier, and multiple distributors, wholesalers and retailers. Each participant places its orders to the upstream (previous) node according to a continuous review policy. To be in the real complexity of this system, we have presented demand, lead time and capacity as stochastic variables. In a multi-echelon distribution system, several decision centers interact to keep up the performance of this system. In this context, there are two control system architectures whereas each one differs from another to afford to the supply chain a better ability to meet the customers’ demand, namely, the centralized and the decentralized architecture. We first develop a generic discrete event simulator, which can be used as a support for simulation of both centralized and decentralized control system. Subsequently, the results of our model provide a performance analysis based on comparing local and global decision making.

A survey on two-echelon routing problems

The delivery of freight from its origin to its destination is often managed through one or more intermediate facilities where storing, merging and consolidation activities are performed. This type of distribution systems is commonly called multi-echelon, where each echelon refers to one level of the distribution network. Multi-echelon distribution systems are often considered by public administrations when implementing their transportation and traffic planning strategies as well as by private companies in their distribution networks. City logistics and multi-modal transportation systems are the most cited examples of multi-echelon distribution systems. Two-echelon distribution systems are a special case of multi-echelon systems where the distribution network comprises two levels. This latter type of distribution systems has inspired an ever growing body of literature in the last few years. This paper provides an overview of two-echelon distribution systems where routes are present at both levels. We consider three classes of problems: the two-echelon location routing problem, the two-echelon vehicle routing problem, and the truck and trailer routing problem. For each class we provide an introduction and survey the foremost related papers that have appeared in the operations research literature.

Simulating Demand Uncertainty and Inventory Control Variability of Multi-Echelon Distribution Supply Chain

Development of simulation models allows performance analysis of multi-echelon inventory networks, and this part of supply chain is not well-established research area. Moreover, by encapsulating all major characteristics of multi-echelon system, the simulation modeling does not allow only its study, but also it provides the means to achieve the desired system performance. This paper deals with a multi-echelon distribution inventory system made of suppliers, distributors and retailers. The retailer faces stochastic demands placed by customers and replenishing from its distributor, which in turn procures from its supplier at the upstream of the chain. The main purpose of this simulation model is to analyze the multi-echelon distribution system behavior according some key performance metrics such as cost and backorder, as well as customer service level at each echelon.

A novel integrated approach to the modelling and solving of the Two-Echelon Capacitated Vehicle Routing Problem

The paper presents a concept and implementation of a novel approach to the modelling and solving of the Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP). Multi-echelon distribution systems are quite common in supply-chain and logistic systems. Two environments, mathematical programming (MP) and constraint logic programming, in which constraints are treated in different ways and different methods are implemented, were combined to use the strengths of both. The proposed approach is particularly important for the decision models with an objective function and many discrete decision variables added up in multiple constraints. The 2E-CVRP is an extension of the classical Capacitated Vehicle Routing Problem (CVRP) where the delivery depot-customers pass through intermediate depots (called satellites). The 2E-CVRP was selected as a known and documented example for verification and evaluation of the effectiveness of the proposed approach. The presented approach will be compared with classical MP on the same data-sets in computational tests. The proposed approach will be used also for extensions of the modelled problem beyond the standard.