Logistics Network Design Problem Research Articles

A Novel Reverse Logistics Network Design Considering Multi-Level Investments for Facility Reconstruction with Environmental Considerations

Reverse logistics is convincingly one of the most efficient solutions to reduce environmental pollution and waste of resources by capturing and recovering the values of the used products. Many studies have been developed for decision-making at tactical, practical, and operational levels of the reverse supply chain. However, many enterprises face a challenge that is how to design the reverse logistics networks into their existing forward logistics networks to account for both economic and environmental sustainability. In this case, it is necessary to design a novel reverse logistics network by reconstructing the facilities based on the existing forward logistics network. Multi-level investments are considered for facility reconstruction because more investment and more advanced remanufacturing technologies need to be applied to reduce the carbon emissions and improve facility capacities. Besides, uncertain elements include the demand for new products and return quantity of used products, making this problem challenging. To handle those uncertain elements, a bi-objective stochastic integer nonlinear programming model is proposed to facilitate this novel reverse logistics network design problem with economic and environmental objectives, where tactical decisions of facility locations, investment level choices, item flows, and vehicle assignments are involved. To show the applicability and computational efficiency of the proposed model, several numerical experiments with sensitivity analysis are provided. Finally, the trade-off between the profit and carbon emissions is presented and the sensitive analysis of changing several key input parameters is also discussed.

A Bi-Objective Reverse Logistics Network Design Under the Emission Trading Scheme

In general, reverse logistics network design has been driven by a need to reduce costs and to improve customer service without considering its environmental impact. In this paper, we address a reverse logistics network design problem regarding carbon emission. The problem is formulated as a bi-objective, mixed-integer, and nonlinear programming model under various operation technologies and transport modes in the truck tire remanufacturing industry. An improved non-dominated sorting genetic algorithm II (NSGA-II) solves this NP-hard problem with bi-objectives. The numerical cases demonstrate the validation of the proposed model and the advantage of improved NSGA-II over the basic NSGA-II. Furthermore, we conducted extensive sensitivity analysis, and several managerial insights are derived.

Optimization of Biomass-to-Bioenergy Logistics Network Design Problem: A Case Study

Abstract Increasing the emissions of greenhouse gases (GHG) due to fossil fuel consumption has led to problems such as global warming, climate change, loss of biodiversity, and urban pollutions. Bioethanol production especially from different biomass such as wheat straw has been specified as one of the sustainable solutions to deal with energy crisis. Bioethanol logistics network optimization will reduce total costs of supply chain management and improves its competency with fossil fuels. In this paper, a mixed-integer linear programming (MILP) model is proposed to integrate and optimize bioethanol logistics network design problem. The proposed model is a multi-period and multi-echelon including feedstock supply centers, collection centers, bio-refineries, and customer centers. The proposed model is applied in a real case in Iran. The results justify the applicability and performance of the model in efficient design of bioethanol logistics network problems.

A two-stage robust approach for the reliable logistics network design problem

This paper examines a three-echelon logistics network in which all supply and transshipment nodes are subject to disruption. We use uncertainty sets to describe the possible scenarios without depending on probabilistic information. We adopt a two-stage robust optimization approach where location decisions are made before and recourse decisions are made after the disruptions are known. We construct three two-stage robust models, which are solved exactly by a column-and-constraint-generation algorithm. Numerical tests demonstrate that the proposed algorithm outperforms the Benders decomposition method in both solution quality and computational time, and that the system’s reliability can be improved with only a slight increase in the normal cost.

A Novel Intensive Distribution Logistics Network Design and Profit Allocation Problem considering Sharing Economy

The rapid growth of logistics distribution highlights the problems including the imperfect infrastructure of logistics distribution network, the serious shortage of distribution capacity of each individual enterprise, and the high cost of distribution in China. While the development of sharing economy makes it possible to achieve the integration of whole social logistic resources, big data technology can grasp customer’s logistics demand accurately on the basis of analyzing the customer’s logistics distribution preference, which contributes to the integration and optimization of the whole logistics resources. This paper proposes a kind of intensive distribution logistics network considering sharing economy, which assumes that all the social logistics suppliers build a strategic alliance, and individual idle logistics resources are also used to deal with distribution needs. Analyzing customer shopping behavior by the big data technology to determine customer’s logistics preference on the basis of dividing the customer’s logistics preference into high speed, low cost, and low pollution and then constructing the corresponding objective function model according to different logistics preferences, we obtain the intensive distribution logistics network model and solve it with heuristic algorithm. Furthermore, this paper analyzes the mechanism of interest distribution of the participants in the distribution network and puts forward an improved interval Shapley value method considering both satisfaction and contribution, with case verifying the feasibility and effectiveness of the model. The results showed that, compared with the traditional Shapley method, distribution coefficient calculated by the improved model could be fairer, improve stakeholder satisfaction, and promote the sustainable development of the alliance as well.

Accelerated Benders’ Decomposition for Integrated Forward/Reverse Logistics Network Design under Uncertainty

In this paper, a two-stage stochastic programming modelling is proposed, to design a multi-period, multistage, and single-commodity integrated forward/reverse logistics network design problem under uncertainty. The problem involved both strategic and tactical decision levels. The first stage dealt with strategic decisions, which are the number, capacity, and location of forward and reverse facilities. In the second stage, tactical decisions, such as base stock level as an inventory policy, were determined. The generic introduced model consisted of suppliers, manufactures, and distribution centers in forward logistic and collection centers, remanufactures, redistribution, and disposal centers in reverse logistic. The strength of the proposed model is its applicability to various industries. The problem was formulated as a mixed-integer linear programming model and was solved by using Benders’ Decomposition (BD) approach. In order to accelerate the Benders’ decomposition, a number of valid inequalities were added to the master problem. The proposed accelerated BD was evaluated through small-, medium-, and large-sized test problems. Numerical results confirmed that the proposed solution algorithm improved the convergence of BD lower bound and the upper bound, enabling to reach an acceptable optimality gap in a convenient time.

Sustainable multi-period reverse logistics network design and planning under uncertainty utilizing conditional value at risk (CVaR) for recycling construction and demolition waste

Decreasing the environmental effect and increasing the social effect as well as the profit of organizations has gained a considerable attention in supply chain network problems. Construction and demolition wastes (C&D wastes) management poses serious challenges to the government and private sector in terms of the environmental damages and potential gains due to recycling these types of wastes. On the other hand, considering the triple bottom line in reverse logistics network design problems simultaneously is an issue that has gained little attention in previous studies. Therefore in this research we propose a multi-period multi-objective mixed integer linear programming to design and plan a network of reverse logistics under uncertainty for recycling C&D wastes in which the objectives are represented as profit and social impact maximization and environmental effect minimization. In this paper the network design problem with stochastic demand of recycled products and rate on investment is regarded. In order to cope with the uncertainty in the model, we consider a risk averse two-stage stochastic programming, where we specify the conditional value at risk (CVaR) as the risk measure. We also apply off-site and on-site separation as two common method for segregating C&D wastes. Appropriate numerical is conducted to learn the effects of crucial parameters on the model and gain managerial insights as well.

A Bi-objective Stochastic Optimization Model for Humanitarian Relief Chain by Using Evolutionary Algorithms

Due to the increasing amount of natural disasters such as earthquakes and floods and unnatural disasters such as war and terrorist attacks, Humanitarian Relief Chain (HRC) is taken into consideration of most countries. Besides, this paper aims to contribute humanitarian relief chains under uncertainty. In this paper, we address a humanitarian logistics network design problem including local distribution centers (LDCs) and multiple central warehouses (CWs) and develop a scenario-based stochastic programming (SBSP) approach. Also, the uncertainty associated with demand and supply information as well as the availability of the transportation network's routes level after an earthquake are considered by employing stochastic optimization. While the proposed model attempts to minimize the total costs of the relief chain, it implicitly minimize the maximum travel time between each pair of facility and the demand point of the items. Additionally, a data set derived from a real disaster case study in the Iran area, and to solve the proposed model a exact method called ɛ-constraint in low dimension along with some well-known evolutionary algorithms are applied. Also, to achieve good performance, the parameters of these algorithms are tuned by using Taguchi method. In addition, the proposed algorithms are compared via four multi-objective metrics and statistically method. Based on the results, it was shown that: NSGA-II shows better performances in terms of SNS and CPU time, meanwhile, for NPS and MID, MRGA has better performances. Finally, some comments for future researches are suggested.

Reverse logistics network redesign under uncertainty for wood waste in the CRD industry

This paper addresses the reverse logistics network (RLN) design problem under environmental policies targeting recycled wood materials from the construction, renovation and demolition (CRD) industry. The main objective is to determine the location and the capacities of the sorting facilities to ensure compliance with the new regulation and prevent the wood from being massively landfilled. We formulated the problem as a mixed-integer linear programming model (MILP) to minimise the total cost of the wood recycling process collected from CRD sites. The main contribution lies in the consideration of important uncertain factors such as supply sources locations, the available quantity of recycled wood at the collection sites, and the various quality grades of the collected wood. A scenario-based analysis is conducted to evaluate the impact of uncertainties on the RLN design. In addition, the proposed MILP model has been applied for a case study in the CRD industry within the province of Quebec, Canada. The results of this study show the adjustment of the reverse logistics network leads to the reduction of wood recycling cost due to the improved efficiency of sorting facilities and the economy of scale achieved under the new policy. Moreover, sorting facilities are now located near the CRD collection points and not close to landfilling site as for the actual situation. Finally, the study demonstrates that efforts to obtain accurate information about the supply sources locations and the expected wood quantity recovered from sorting facilities will guarantee a more efficient RLN redesign.

Biomass Logistics Network Design Under Price-Based Supply and Yield Uncertainty

We consider an integrated supply pricing and biomass logistics network design problem in which yield rates are uncertain. The base supply amount is modeled via a farmers’ decision model that estimates the amount of land dedicated to biomass production under a given biomass wholesale price. We develop a two-stage stochastic integer program for integrated design of a network as well as biomass pricing decisions under yield uncertainty. To efficiently solve our model, we suggest a Benders decomposition–based algorithm in which the Benders cuts, one for each scenario, are aggregated by utilizing a scheme that takes into account yield rates as well as the geographical nature of the underlying problem. With further strengthening of the cuts, we present a significantly improved approach for solving relatively large instances and illustrate its performance via a computational study. We further present an extensive case study in Texas using realistic data to test our model’s capabilities and to demonstrate its effectiveness in capturing the relationships between total cost, biomass wholesale price, network structure, and various input parameters. Finally, we examine the relationship between farmers’ decision model parameters, which dictate the risk profile of farmers, and the expected total system cost, as well as the biomass wholesale price. The online appendix is available at https://doi.org/10.1287/trsc.2017.0766 .

Designing a capacitated multi-configuration logistics network under disturbances and parameter uncertainty: a real-world case of a drug supply chain

This study investigates the reliable multi-configuration capacitated logistics network design problem (RMCLNDP) under system disturbances, which relates to locating facilities, establishing transportation links, and also allocating their limited capacities to the customers conducive to provide their demand on the minimum expected total cost (including locating costs, link constructing costs, and also expected costs in normal and disturbance conditions). In addition, two types of risks are considered; (I) uncertain environment, (II) system disturbances. A two-level mathematical model is proposed for formulating of the mentioned problem. Also, because of the uncertain parameters of the model, an efficacious possibilistic robust optimization approach is utilized. To evaluate the model, a drug supply chain design (SCN) is studied. Finally, an extensive sensitivity analysis was done on the critical parameters. The obtained results show that the efficiency of the proposed approach is suitable and is worthwhile for analyzing the real practical problems.

A two-phase heuristic algorithm for designing reliable capacitated logistics networks under disruptions

This paper considers the reliable capacitated logistics network design problem (RCLNDP) with system disruptions, which is concerned with locating facilities, constructing transportation links, and allocating their capacities to customers in order to satisfy the demand with minimum expected total cost. Both the facilities and the transportation links are subject to random disruptions, and the expected total cost accounts for the costs of facility location, link construction, and flows in both normal and disrupted conditions. We model this problem as a two-stage stochastic program in which the decision maker establishes plans for facility location and link construction in the first stage (before disruptions are realised) and may choose link flows in the second stage. This is a large-scale mixed-integer optimisation problem and is therefore difficult to solve. Hence, we propose an efficient two-phase heuristic with three possible initial solution-generation methods. [Received 1 November 2015; Revised 3 March 2016; Revised 22 May 2016; Revised 8 June 2016; Accepted 10 June 2016]

A profit-maximization location-capacity model for designing a service system with risk of service interruptions

This paper considers the design of an immobile service system in which each facility’s service process is subject to the risk of interruptions. The location-capacity decisions and allocations are simultaneously made to maximize the difference between the service provider’s profit and the sum of customers’ transportation and waiting costs. An efficient Lagrangian-based solution algorithm is developed, which solves large-sized instances with up to 50 service facilities and 500 customers in a few seconds. Several sensitivity analyses and managerial insights are presented. The model is also applied to a case study on a logistics network design problem in the zinc mining industry.

Reverse logistics network design: a water flow-like algorithm approach

This study investigates the reverse logistics network design problem, including collection/inspection, recovery and disposal centers that a mixed integer linear programming model is considered. In this network, returned products from customer zones are collected in collection/inspection centers and after quality inspection, and also after separation, recoverable products are shipped to recovery centers and scrapped ones are transported to disposal centers. NP-hardness of this problem is proved in many papers, so a novel meta-heuristic solution method aiming minimization of total costs comprised fixed opening cost of collection/inspection, recovery and disposal centers and transportation cost of products between opened centers using priority based encoding presentation is proposed. Comparison of outputs from this proposed algorithm and a modified genetic algorithm shows the excellence of this new solution method. Finally, some directions for future research are proposed.

Decomposition approach for integrated intermodal logistics network design

The integrated intermodal logistics network design problem consists of determining terminal locations and selecting regular routes and transportation modes for loads. This problem was formulated using a path-based formulation and a decomposition-based search algorithm has been proposed for its solution. Computational results show that this approach is able to obtain optimal solutions for non-trivial problem instances of up to 150 nodes in reasonable computational times. Previous studies have only been able to obtain approximate solutions for network problems of this size. A few general insights about the effects of design parameters on solution characteristics were also obtained.

An analysis of fully fuzzy linear programming with fuzzy decision variables through logistics network design problem

Recently, there is a growing attention by the researchers to solve and interpret the analysis of fully fuzzy linear programming problems in which all of the parameters as well as the decision variables are considered as fuzzy numbers. Under a fully uncertain environment where all of the data are stated as fuzzy, presenting the reasonable range of values for the decision variables may be comparatively better than the currently available crisp solutions so as to provide ranges of flexibility to decision makers. However, there is still a scarcity of solution methodologies on fuzzy mathematical programs with fuzzy decision variables. Based on this motivation, a new parametric method which is mainly based on α-cut representation of fuzzy intervals is proposed in this paper by incorporating the decision maker's attitude toward risk. In order to illustrate validity and practicality of the proposed method, it is applied to a generic reverse logistics network design model including fuzzy decision variables. To the best of our knowledge, this is the first study in the literature which presents fuzzy efficient solutions and analysis for a fully fuzzy reverse logistics network design problem with fuzzy decision variables. The provided solutions by the proposed method are also compared to the available solution methodologies from the literature in terms of computational efficiency, solution quality and ease of use. By using the proposed method, the decision makers can be supported by yielding fuzzy efficient solutions under different uncertainty levels and risk attitudes. The computational results have also shown that more reliable and necessarily precise solutions can be generated by the proposed method for a risk-averse decision maker.

Humanitarian logistics network design under mixed uncertainty

In this paper, we address a two-echelon humanitarian logistics network design problem involving multiple central warehouses (CWs) and local distribution centers (LDCs) and develop a novel two-stage scenario-based possibilistic-stochastic programming (SBPSP) approach. The research is motivated by the urgent need for designing a relief network in Tehran in preparation for potential earthquakes to cope with the main logistical problems in pre- and post-disaster phases. During the first stage, the locations for CWs and LDCs are determined along with the prepositioned inventory levels for the relief supplies. In this stage, inherent uncertainties in both supply and demand data as well as the availability level of the transportation network's routes after an earthquake are taken into account. In the second stage, a relief distribution plan is developed based on various disaster scenarios aiming to minimize: total distribution time, the maximum weighted distribution time for the critical items, total cost of unused inventories and weighted shortage cost of unmet demands. A tailored differential evolution (DE) algorithm is developed to find good enough feasible solutions within a reasonable CPU time. Computational results using real data reveal promising performance of the proposed SBPSP model in comparison with the existing relief network in Tehran. The paper contributes to the literature on optimization based design of relief networks under mixed possibilistic-stochastic uncertainty and supports informed decision making by local authorities in increasing resilience of urban areas to natural disasters.

Hub-and-spoke logistics network design for third party logistics service providers

Third Party Logistics service providers (3PLs) are playing an increasing role in the management of supply chains. 3PLs offer a wide range of logistics services, e.g. warehousing and transportation services. We consider a 3PL company that is responsible for managing the inbound logistics of raw materials and components from multiple suppliers to several manufacturing plants. In this paper, we consider the hub-and-spoke logistics network design problem for 3PL companies. Our purpose is to decide on the number, location and operation of hub facilities so as to minimize the total logistics costs for the network. We formulate the problem as a mixed integer nonlinear programming model and linearize it using a step-by-step approach. Linearization of the model makes it possible to solve it to optimality using a standard optimization package. Numerical results reveal substantial cost saving as a consequence of using the hub-and-spoke network configuration compared to a direct shipment strategy.

A hybrid spanning tree-based genetic/simulated annealing algorithm for a closed-loop logistics network design problem

Affecting the efficiency and responsiveness of the logistics, supply chain network design, in particular, closed-loop logistics has attracted more attention in recent years. The problem in this paper is to minimise the total cost of the cyclic logistic network model by determining the best locations of plants, distribution centres (DCs), and dismantlers. Due to the NP-hard nature of the problem, it is inevitable to appeal to metaheuristic procedures to achieve satisfactory solutions for large-size problems. To tackle such an NP-hard problem, this paper proposes a simulated annealing (SA), a modified genetic algorithm (MGA), and a hybrid algorithm, applying these two algorithms based on a revised spanning tree and determinant encoding representation. To evaluate the proposed algorithms, we compare them with the genetic algorithm (GA) taken from the recent literature. Finally, it is shown that the proposed hybrid algorithm outperforms other algorithms.

Developing scenario-based robust optimisation approaches for the reverse logistics network design problem under uncertain environments

In the last decade, economic benefits and environmental legislation have imposed reverse logistics activities, induced by various forms of return, to organisations. Reverse logistics network design is a major strategic issue. This paper discusses scenario–based stochastic programming method and robust optimisation approaches including minimisation conditional value–at–risk (CVaR), p–robust regret and min–max regret models to find the most appropriate method dealing with uncertain environment in designing reverse logistics network. Firstly, the reverse logistics network design model is developed by using two–stage stochastic programming approach integrating CVaR in its objective function as a robustness criterion ensuring that the amount of objective function is not worse than the CVaR value with specified probability (confidence level) under all realisations. Also, the advantages of stochastic programming method are investigated respect to deterministic model under all defined scenarios. Then, the scenario–based robust optimisation methods are compared with the stochastic and deterministic ones to disclose their advantages and disadvantages.