Strategic Network Design Research Articles

Strategic network design of downstream petroleum supply chains: Single versus multi-entity participation

The petroleum supply chain (PSC) is a highly competitive system that motivates complex studies for decisions involving different problems such as the redesign aimed at optimizing existing distribution networks. This paper considers a multi-entity, multi-echelon, multi-product and multi-transportation downstream PSC network with shared installations, resource capacities, supply sources and demand requirements. A deterministic mixed integer linear program (MILP) is developed for strategic design and planning of the downstream PSC network that determines optimal depot locations, capacities, transportation modes, routes and network affectations for long term planning. The MILP maximizes the multi-echelon total profits for the petroleum companies along the supply, refining, distribution and retail stages. A multi-entity PSC network is considered, involving companies’ financial participation in refineries, transportation and storage depots. The MILP is tested with the real-case Portuguese PSC network involving production at local refineries and supply from a regional hub. Uni-entity networks as well as multi-entity networks with competitive or individualistic operation are modeled, presenting the current, grassroots and retrofit designs.

Sustainable development of global supply chains—part 1: sustainability optimization framework

In global industry supply chains, environmental sustainability optimization addresses the overall consumption of resources and energy, the reduction of carbon emissions and generated waste to name a few. In this paper, we propose a holistic sustainability optimization framework for strategic network design of industry supply chains under consideration of economic, social as well as ecologic objectives. The framework is flexible to incorporate multiple sustainability indicators, alternative sustainability optimization strategies as well as a variety of internal and external industry-specific factors which impact the sustainability of the entire industry supply chain in the long-term. The core of the framework is an end-to-end closed-loop value chain model consisting of process, transport and product-in-use modules. For the first time, the product-in-use impact (“use” vs. “make”) is integrated in one network design approach. In addition, the model fully closes the loop from sourcing of raw materials via manufacturing towards reverse value chain steps such as disposal and recycling. Finally, we propose the minimize-time-to-sustainability approach as new optimization strategy for long-term network design problems focusing on minimizing the time, industry supply chain structures need to transform into sustainability steady states for all defined sustainability indicators such as CO2e emissions, costs or social indicators based on defined target values. In part 2 of this paper the application of the optimization framework to the European automotive industry is shown.

Mixed fleet dispatching in truckload relay network design optimization

We propose a mathematical formulation for strategic relay network design and dispatching method selection for full truckload transportation. The proposed model minimizes total transportation and installation costs of a mixed fleet dispatching system combining relay network and point-to-point dispatching. Operational constraints such as maximum driver tour length and load circuity are considered within the variable definition using predefined templates to generate feasible routes. High quality solutions for largely-sized problem instances are obtained in reasonable times. Computational results are analyzed to develop insights about the mixed fleet dispatching system and quantify its benefits over relay network-only and point-to-point dispatching.

The bullwhip effect in capacitated supply chains with consideration for product life-cycle aspects

This paper presents an analysis of the bullwhip effect and net-stock amplification in a three-echelon supply chain considering step-changes in the production rates during a product's life-cycle demand. The analysis is focused around highly complex and engineered products (e.g., automobiles), that have relatively long production life-cycles and require significant capital investment in manufacturing. Using a simulation approach, we analyze three stages of the product life-cycle including low volumes during product introduction, peak demand, and eventual decline toward the end of the life-cycle. Parts of the simulation model have been adopted by a major North-American automotive OEM as part of a scenario analysis tool for strategic supply network design and analysis. The simulation results show that performance of a system as a whole deteriorates when there is a step-change in the life-cycle demand. While restriction in production capacity does not significantly impact the bullwhip effect, it increases the net stock amplification significantly for the supply chain setting under consideration. Furthermore, a number of important managerial insights are presented based on sensitivity analysis of interaction effect of capacity constraints with other supply chain parameters.

A simulation based approach for supply network control

We focus on the problem of strategic network design in the context of multi-site enterprises. We develop a simulation model for various supply network designs evaluation. The suggested model is generic in the sense that only input data modifications are needed to adapt the model to a given supply network structure, while the model's procedural logic stays unchanged. The model reproduces the dynamics of the totality of the supply chain activities. It takes into account control policies related to the inventory management and to the scheduling of production activities. It also carries out local optimisations by dynamically allocating the customers’ demands to the appropriate distribution site and the replenishment orders expressed by the distribution and production sites of the enterprise so as to minimise a global cost function. Simulation execution permits to compute various performance measures. These indicators inform the decision-maker about the profit of the enterprise and about its customer's service level. In this paper, we propose to detail the simulation model. Then, for a fictitious problem of supply network design based on two real life applications, we highlight the influence of the control policies on the performance of the studied enterprise.

Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty

Measuring and controlling emissions across the logistics network is an important challenge for today’s firms according to increasing concern about the environmental impact of business activities. This paper proposes a bi-objective credibility-based fuzzy mathematical programming model for designing the strategic configuration of a green logistics network under uncertain conditions. The model aims to minimize the environmental impacts and the total costs of network establishment simultaneously for the sake of providing a sensible balance between them. A popular but credible environmental impact assessment index, i.e., CO 2 equivalent index is used to model the environmental impact across the concerned logistics network. Since transportation mode and production technology play important roles on the concerned objectives, the proposed model integrates their respective decisions with those of strategic network design ones. In addition, to solve the proposed bi-objective fuzzy optimization model, an interactive fuzzy solution approach based upon credibility measure is developed. An industrial case study is also provided to show the applicability of the proposed model as well as the usefulness of its solution method.

A Benders decomposition approach for a distribution network design problem with consolidation and capacity considerations

We develop a model for a strategic freight-forwarding network design problem in which the design decisions involve the locations and capacities of consolidation and deconsolidation centers, and capacities on linehaul linkages as well as the shipment routes from origins to destinations through centers. We devise a solution approach based on Benders decomposition and conduct a computational study that illustrates the efficiency and the effectiveness of the approach.

Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges

The management of food distribution networks is receiving more and more attention, both in practice and in the scientific literature. In this paper, we review quantitative operations management approaches to food distribution management, and relate this to challenges faced by the industry. Here, our main focus is on three aspects: food quality, food safety, and sustainability. We discuss the literature on three decision levels: strategic network design, tactical network planning, and operational transportation planning. For each of these, we survey the research contributions, discuss the state of the art, and identify challenges for future research.

A possibilistic programming approach for closed-loop supply chain network design under uncertainty

The design of closed-loop supply chain networks has attracted more attention in recent years according to business and environmental factors. The significance of accounting for uncertainty and risk in such networks spurs an interest to develop appropriate decision making tools to cope with uncertain and imprecise parameters in closed-loop supply chain network design problems. This paper proposes a bi-objective possibilistic mixed integer programming model to deal with such issues. The proposed model integrates the network design decisions in both forward and reverse supply chain networks, and also incorporates the strategic network design decisions along with tactical material flow ones to avoid the sub-optimalities led from separated design in both parts. To solve the proposed possibilistic optimization model, an interactive fuzzy solution approach is developed by combining a number of efficient solution approaches from the recent literature. Numerical experiments are conducted to demonstrate the significance and applicability of the developed possibilistic model as well as the usefulness of the proposed solution approach.

Designing multi-echelon service parts networks with finite repair capacity

We propose an approach to model and solve the joint problem of facility location, inventory allocation and capacity investment in a two echelon, single-item, service parts supply chain with stochastic demand. The objective of the decision problem is to minimize the total expected costs associated with (1) opening repair facilities, (2) assigning each field service location to an opened facility, (3) determining capacity levels of the opened repair facilities, and (4) optimizing inventory allocation among the locations. Due to the size of the problem, computational efficiency is essential. The accuracy of the approximations and effectiveness of the approach are analyzed with two numerical studies. The approach provides optimal results in 90% of scenarios tested and was within 2% of optimal when it did not.We explore the impact of capacity utilization, inventory availability, and lead times on the performance of the approach. We show that including tactical considerations jointly with strategic network design resulted in additional cost savings from 3% to 12%. Our contribution is the development of a practical model and approach to support the decision making process of joint facility location and multi-echelon inventory optimization.

Multimarket Facility Network Design with Offshoring Applications

Moving production to low-wage countries may reduce manufacturing costs, but it increases logistics costs and is subject to foreign trade barriers, among others. This paper studies a manufacturer's multimarket facility network design problem and investigates the offshoring decision from a network capacity investment perspective. We analyze a firm that manufactures two products to serve two geographically separated markets using a common component and two localized final assemblies. The common part can be transported between the two markets that have different economic and demand characteristics. Two strategic network design questions arise naturally: (1) Should the common part be produced centrally or in two local facilities? (2) If a centralization strategy is adopted, in which market should the facility be located? We present a transportation cost threshold that captures costs, revenues, and demand risks, and below which centralization is optimal. The optimal location of commonality crucially depends on the relative magnitude of price and manufacturing cost differentials but also on demand size and uncertainty. Incorporating scale economies further enlarges the centralization's optimality region.

E-Work based collaborative optimization approach for strategic logistic network design problem

We propose a collaborative e-Work based optimization approach for assisting the strategic logistic network design problem, which considers fleet design and customer clustering decisions. Normally, fleet design and customer clustering decisions are made by mid-level logistics managers while network design decision is made independently by high-level logistics managers. In the proposed approach, strategic distribution network design is modeled as a Facility Location Problem, considering long term inventory control decisions. On the other hand, tactical fleet design and customer clustering decisions are modeled based on a Hub and Spoke cost structure. An e-Work based heuristic approach is proposed to solve collaboratively the network design problem at strategic and tactical levels. The collaborative solution approach results from a particular sequential decomposition of the problem, similarly to traditional location–allocation heuristics, modeling an information sharing strategy between decision makers involved at each organizational level. A numerical application of the approach with real data based instances shows significant benefits, when compared to a non-collaborative independent optimization, where the hierarchical levels share no dynamic information and base their decisions on static and independent optimization models. These results show evidence of the advantage of the e-collaborative approach to deal with logistic decisions at different hierarchical levels, organizational units, or companies, compared to non-integrated non-linear mixed integer programming models.

Strategic network design for reverse logistics and remanufacturing using new and old product modules

Establishment of reverse logistics (RL) networks for various original equipment manufacturers (OEM’s) is gaining significant importance. Various green legislations are forcing OEMs to take back their used, end-of-lease or end-of-life products, or products under warranty to minimize wastes and conserve resources. Therefore OEMs have turned to a better design of their products for maximum reuse and recycling and to retrieve back the used products through a network for reuse, remanufacture, recycle or disposal, so that maximum value can be achieved from their used products. However, designing of network points and assigning capacities to them depend not only on the volume of returned products but also on the demand for remanufactured products and the parts of used products. If OEMs are not able to add value to the used product, there would be no incentive to design a complex network. In this paper, a mathematical model for the design of a RL network is proposed. It is assumed that the returned products need to be consolidated in the warehouse before they are sent to reprocessing centres for inspection and dismantling. Dismantled parts are sent for remanufacturing or to the secondary market as spare parts. Recycling and disposal of these modules are also considered in the model. The use of the model is shown through its application in a numerical example.

Applying advanced planning systems for supply chain planning: three case studies

PurposeThe purpose of this paper is to explore how standardized advanced planning systems (APS) can be used for solving planning problems at tactical and strategic levels, and to identify the perceived effects of using APS.Design/methodology/approachMultiple case studies involving three cases using APS software for strategic network planning and master production scheduling are conducted. Comparative analysis explores how the planning situation, the model design and use of the APS impact the perceived planning effects.FindingsFindings show how APS support cost‐optimized strategic network design in one case and how efficiency, capacity utilization and delivery service problems were decreased in two cases using APS in global master planning processes. The cases show how APS supports cross‐functional integration and supply chain commitment to a common plan. Research directions are suggested about the feasibility of APS in situations with various planning complexities, how design of the optimization model creates complexity and affects the planning process, data gathering requirements when using APS, the role and design of the planning organization, and how to achieve positive planning effects, such as finding global optimum and single plan commitment.Research limitations/implicationsThe approach is descriptive and explorative. Only three cases are studied.Practical implicationsThe findings present experiences of APS usage and identify issues to consider when using APS and potential benefits to gain from usage.Originality/valueThe practical use of APS is low and the knowledge about how it impacts supply chain planning and performance is unexplored. The findings of this paper fill some of these gaps.

Strategic network design for multi-zone truckload shipments

In recent years, the truckload trucking industry has faced a serious problem in retaining drivers. The primary reason for this problem is the very-long driver tour lengths that keep drivers on the road for extended time periods. In this paper, we present a mathematical model for a multi-zone dispatching method that addresses this issue. In addition to reducing driver tour lengths to desirable levels, the model captures important aspects of other practical problems via unique constraints that address the perspectives of both the truckload company and the customers. Using this model, we first provide insights into various aspects of the problem as well as the nature of the interaction among the problem components that pertain to these perspectives. We later develop a construction heuristic and a tabu search framework, which considers driver tour length constraints (a major determinant of driver turnover rate) to solve the model. We provide computational results illustrating the excellent performance of the proposed solution procedure.

A Framework for Strategic Network Design Assessment, Decision Making, and Moral Imagination

This article presents a framework for practitioners who may be interested in maintaining adaptive stability of sociotechnical networks. The framework is developed from assembling several concepts that are useful for assessing and for drawing on appropriate moral reasoning strategies as sociotechnical networks are designed, constructed, and adapted. One such strategy involves the ability to assess degrees of perspective sharing and trading relationships in networks using moral imagination. The article uses the case of the design of an environmentally sustainable fabric to illustrate these strategic concepts. The framework also draws on other examples of cases in which sociotechnical networks have become destabilized to illustrate instances in which the framework may have been useful for decreasing a tendency for destabilization.

Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand

Abstract In this paper, we propose a simultaneous approach to incorporate inventory control decisions––such as economic order quantity and safety stock decisions––into typical facility location models, which are used to solve the distribution network design problem. A simultaneous model is developed considering a stochastic demand, modeling also the risk pooling phenomenon. We present a non-linear-mixed-integer model and a heuristic solution approach, based on Lagrangian relaxation and the sub-gradient method. In a numerical application, we found that the potential cost reduction, compared to the traditional approach, increases when the holding costs and/or the variability of demand are higher.

Multidepot Vehicle Routing with Uncertain Demands: Chance Constrained Approach

Distribution system design is a relevant problem that has been widely studied by researchers and practitioners in logistics and supply chain management. A stochastic programming model used to design part of the distribution system of a firm with multiple depots that face stochastic demands is presented. It is assumed that there is a fixed set of depots that serve a fixed set of customers with uncertain demands. The problem consists of finding the number of vehicles and assigning customers to capacitated vehicle routes such that the total system cost is minimum. The vehicle capacity is treated stochastically through chance constraint programming, in which the model finds a solution that presents a probability of exceeding capacity constraints lower than a fixed probability. The customers are characterized by the means and variances of their demands. The problem is modeled on the basis of a hub-and-spokes network topology, in contrast to the standard objective function of the vehicle routing problem. This modeling is considered to be included in a strategic distribution network design problem, which is typically solved by using facility location problems. Unlike previous work found in the literature, this approach simultaneously considers multiple depots and stochastic constraints. A heuristic procedure is presented along with an algorithm to find the objective function's lower bound, which allows the computation of an upper bound of the error of the heuristic solutions.

Emerging Supply Chain Management Trends and Their Impact on Spatial Organization of Logistical Networks

Although the development of supply chain management trends has been the subject of substantial research, little attention has been devoted to examining the impact of the emerging supply chain management (SCM) trends on the spatial organization of the supply and distribution system. Specifically, the literature lacks an integrated methodological framework for examining the impact of emerging SCM trends on decision making relative to the spatial organization of logistical networks. This analysis has three purposes—( a) develop a conceptual framework for analyzing how decisions are made on the spatial organization of logistical networks, ( b) test the proposed framework via case studies, and ( c) develop a preliminary set of guidelines on the essential elements in making strategic logistical network design decisions. An interesting finding in implementing the proposed methodological framework, which was further supported by the limited empirical research, is that decision-making requirements vary substantially among the industrial and service sectors. This variance reflects differences in objectives and the extent of integration and collaboration of business entities along the respective supply chains.

Strategic Service Network Design for DHL Hong Kong

We developed a two-phase network planning methodology to design a service network for DHL(HK) based on two measures of service performance: service coverage and service reliability. The methodology consists of first using an optimization model to determine a least-cost distribution network, which forms the basis of a simulation model for analyzing the network's operational characteristics. It takes into consideration such design issues as inbound and outbound shipment, equivalent shipment, cutoff time, demand fluctuation, and random behaviors. DHL decision makers can now examine trade-offs between coverage and reliability by adjusting workforce, changing cutoff time, or redesigning the service network.