Integrated supply chain planning under uncertainty using an improved stochastic approach

This paper proposes an integrated model and a modified solution method for solving supply chain network design problems under uncertainty. The stochastic supply chain network design model is provided as a two-stage stochastic program where the two stages in the decision-making process correspond to the strategic and tactical decisions. The uncertainties are mostly found in the tactical stage because most tactical parameters are not fully known when the strategic decisions have to be made. The main uncertain parameters are the operational costs, the customer demand and capacity of the facilities. In the improved solution method, the sample average approximation technique is integrated with the accelerated Benders’ decomposition approach to improvement of the mixed integer linear programming solution phase. The surrogate constraints method will be utilized to acceleration of the decomposition algorithm. A computational study on randomly generated data sets is presented to highlight the efficiency of the proposed solution method. The computational results show that the modified sample average approximation method effectively expedites the computational procedure in comparison with the original approach.     

Integrated methodology to design and manage work-in-process buffers in repetitive building projects

An integrated methodology to design and manage buffers in repetitive building projects using work-in-process at conceptual level is proposed. The buffer design framework employs the Multiobjective Analytic Model and Simulation-Optimisation techniques, applied at strategic and tactical scheduling levels. The buffer management framework uses a statistical model to predict work progress, the Reliable Commitment Model, applied at operational scheduling level. The integrated methodology provides a new buffering approach for scheduling repetitive building projects, which considers: (1) a general production framework covering all the production levels from top to bottom; (2) a general modelling structure suitable to any type of repetitive building project; and (3) a sound theoretical and practical framework describing different production scenarios. The benefits of using this methodology are illustrated through a hypothetical project application.     

Supply chain design under uncertainty using sample average approximation and dual decomposition

We present a supply chain design problem modeled as a sequence of splitting and combining processes. We formulate the problem as a two-stage stochastic program. The first-stage decisions are strategic location decisions, whereas the second stage consists of operational decisions. The objective is to minimize the sum of investment costs and expected costs of operating the supply chain. In particular the model emphasizes the importance of operational flexibility when making strategic decisions. For that reason short-term uncertainty is considered as well as long-term uncertainty. The real-world case used to illustrate the model is from the Norwegian meat industry. We solve the problem by sample average approximation in combination with dual decomposition. Computational results are presented for different sample sizes and different levels of data aggregation in the second stage.     

Risk-Based Integration of Strategic and Tactical Capacity Planning

We consider capacity management with a long-term strategic choice, such as the number of production lines to install before demand is known, and short-term tactical decisions relating to production, inventory, and subcontracting (recourse actions made after demand is known). We present an integrated scenario-based mathematical modeling and solution framework. For a single-product environment, we examine properties of total profit as a function of demand and the long-term capacity z. We investigate two measures of risk (profit variance and mean downside risk) and their corresponding profit-risk frontiers. Computational experiments are used to illustrate parameter sensitivity results obtained from the model.     

An integrated optimization model for managing the global value chain of a chemical commodities manufacturer

The realization of supply chain management concepts goes along with the introduction of comprehensive software systems for supporting decisions at the strategic, tactical, and operational planning level. Moreover, in industry the focus has shifted from a pure logistics-oriented view towards the integration of pricing and revenue issues into cross-functional value chain planning models. This paper presents a practical decision support tool for global value chain planning in the production of chemical commodities. The proposed linear optimization model consists of various modules that reflect sales, distribution, production, and procurement activities within a company-internal value chain. The objective of the model is to maximize profit by coordinating all activities within the supply chain. The model formulation is related to a real industry case. It is shown how the model can be used to support decision making from sales to procurement by volume and value.     

An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations

This study develops a mathematical model for helicopter mission planning during a disaster relief operation. The decisions inherent in the problem decompose hierarchically into two sub-problems where tactical decisions are made at the top level, and the operational routing and loading decisions are made at the base level. Consistency between the decomposed problems is achieved with an iterative coordination procedure which transfers anticipated information from the base level to improve the top level decisions. The existence of conflicting multiple objectives in this hierarchical structure requires the development of a multi-criteria analysis, and an interactive procedure is designed with the top level decision-maker to assess the preference of alternative non-dominated solutions.     

Characterization of facility assignment costs for a location-inventory model under truckload distribution

We consider a two-stage distribution system, where the first stage consists of potential distribution centres (DCs) and the second stage consists of geographically dispersed existing retailers. Our goal is to determine the set of open DCs and assignment of open DCs to retailers simultaneously with inventory decisions of retailers. In addition to the DC-specific fixed facility location costs, we explicitly model the inventory replenishment and holding costs at the retailers and truckload transportation costs between the DCs and the retailers. The transportation costs are subject to truck/cargo capacity, leading to an integrated location-inventory problem with explicit cargo costs. We develop a mixed-integer nonlinear model and analyse its structural properties leading to exact expressions for the so-called implied facility assignment costs and imputed per-unit per-mile transportation costs. These expressions analytically demonstrate the interplay between strategic location and tactical inventory/transportation decisions in terms of resulting operational costs. Although both the theory and practice of integrated logistics have recognized the fact that strategic and tactical decisions are interrelated, to the best of our knowledge, our paper is the first to offer closed-form results demonstrating the relationship explicitly. We propose an efficient solution approach utilizing the implied facility assignment costs and we demonstrate that significant savings are realizable when the inventory decisions and cargo costs are modelled explicitly for facility location purposes.     

Investment Decisions under Uncertainty and Variability — Some Practical Experiences of Using Forecasts and Probabilities

This paper illustrates by three practical examples the use of forecasts and probabilities in making actual investment decisions. In each case the use of a simple forecast would lead to a wrong decision. The emphasis throughout is on the interaction between management and the operational research scientist. The three examples discussed in the paper are: (a) Study of future production capacity, where future demand is uncertain; (b) Study of storage facilities, where demand is variable and there is a risk of losing custom; (c) Study of future pipe-line capacity, where throughput is variable and there was uncertainty when to invest. The paper concludes with a discussion on the effects of variability, uncertainty and risk on the decision criterion, and on the decisions made.     

On capacity expansion planning under strategic and operational uncertainties based on stochastic dominance risk averse management

A new scheme for dealing with uncertainty in scenario trees is presented for dynamic mixed 0–1 optimization problems with strategic and operational stochastic parameters. Let us generically name this type of problems as capacity expansion planning (CEP) in a given system, e.g., supply chain, production, rapid transit network, energy generation and transmission network, etc. The strategic scenario tree is usually a multistage one, and the replicas of the strategic nodes root structures in the form of either a special scenario graph or a two-stage scenario tree, depending on the type of operational activity in the system. Those operational scenario structures impact in the constraints of the model and, thus, in the decomposition methodology for solving usually large-scale problems. This work presents the modeling framework for some of the risk neutral and risk averse measures to consider for CEP problem solving. Two types of risk averse measures are considered. The first one is a time-inconsistent mixture of the chance-constrained and second-order stochastic dominance (SSD) functionals of the value of a given set of functions up to the strategic nodes in selected stages along the time horizon, The second type is a strategic node-based time-consistent SSD functional for the set of operational scenarios in the strategic nodes at selected stages. A specialization of the nested stochastic decomposition methodology for that problem solving is outlined. Its advantages and drawbacks as well as the framework for some schemes to, at least, partially avoid those drawbacks are also presented.     

Multi-horizon stochastic programming

Infrastructure-planning models are challenging because of their combination of different time scales: while planning and building the infrastructure involves strategic decisions with time horizons of many years, one needs an operational time scale to get a proper picture of the infrastructure’s performance and profitability. In addition, both the strategic and operational levels are typically subject to significant uncertainty, which has to be taken into account. This combination of uncertainties on two different time scales creates problems for the traditional multistage stochastic-programming formulation of the problem due to the exponential growth in model size. In this paper, we present an alternative formulation of the problem that combines the two time scales, using what we call a multi-horizon approach, and illustrate it on a stylized optimization model. We show that the new approach drastically reduces the model size compared to the traditional formulation and present two real-life applications from energy planning.     

Raising the bar: strategic multi-criteria decision analysis

This paper discusses the use of multi-criteria decision analysis for supporting strategic decision making in organisations. It begins by exploring the notions of strategic decisions and the strategic decision-making process. We suggest that structuring strategic objectives, dealing with high levels of uncertainty about the future, as well as considering the interconnectedness of strategic options and their long-term consequences are key aspects of strategic decision making support. We then consider the discursive nature of the processes within which strategic decisions are created and negotiated. Our exploration of these concepts leads us to propose a number of adaptations to the standard multi-criteria decision analysis approach, if it were to provide effective strategic decision support, particularly in strategy workshops. We make suggestions on how to implement these proposals, and illustrate their potential with examples drawn from real-world interventions in which we have provided strategic decision support.     

Forest planning at the tactical level

Forest management and planning can involve large tracts of land involving numerous areal units. One approach to plan activities for a forested region over decades involves breaking down decision making into three components: strategic, tactical, and operational. Each level of the management hierarchy can involve the development and application of optimization models. These models typically aid in exploring management alternatives as well as multi-objective tradeoffs. Even though a strategic model can provide support for long term management at a broad scale, solutions may not be feasible at an operational level. Tactical level modeling helps to bridge solutions reached at a strategic level using operational planning information. This paper presents several tactical level planning models that have been developed as a part of a research effort supported by the US Forest Service. These models have been utilized in land use management and planning by the US Forest Service through a specially developed spatial decision support system.     

An Approach for Strategic Supply Chain Planning under Uncertainty based on Stochastic 0-1 Programming

We present a two-stage stochastic 0-1 modeling and a related algorithmic approach for Supply Chain Management under uncertainty, whose goal consists of determining the production topology, plant sizing, product selection, product allocation among plants and vendor selection for raw materials. The objective is the maximization of the expected benefit given by the product net profit over the time horizon minus the investment depreciation and operations costs. The main uncertain parameters are the product net price and demand, the raw material supply cost and the production cost. The first stage is included by the strategic decisions. The second stage is included by the tactical decisions. A tight 0-1 model for the deterministic version is presented. A splitting variable mathematical representation via scenario is presented for the stochastic version of the model. A two-stage version of a Branch and Fix Coordination (BFC) algorithmic approach is proposed for stochastic 0-1 program solving, and some computational experience is reported for cases with dozens of thousands of constraints and continuous variables and hundreds of 0-1 variables.     

Multimodal freight transportation planning: A literature review

Multimodal transportation offers an advanced platform for more efficient, reliable, flexible, and sustainable freight transportation. Planning such a complicated system provides interesting areas in Operations Research. This paper presents a structured overview of the multimodal transportation literature from 2005 onward. We focus on the traditional strategic, tactical, and operational levels of planning, where we present the relevant models and their developed solution techniques. We conclude our review paper with an outlook to future research directions.     

Inner and outer loop optimization in semiconductor manufacturing supply chain management

Supply chain management (SCM) in semiconductor manufacturing differs from many other SCM applications in that it has to simultaneously consider both long and short time scale stochasticity and nonlinearity. We present a two-level hierarchical structure for SCM motivated by these considerations. A linear programming (LP)-based strategic planning module forms the outer loop which makes long timescale decisions on the starts of factories. A model predictive control (MPC) based tactical execution module forms the inner loop which generates short timescale decisions on the starts of factories by considering the stochasticity and nonlinearity on both supply and demand sides. Two representative case studies are examined under diverse realistic conditions with this integrated framework. It is demonstrated that given conditions of stochasticity, nonlinearity, and forecast error this hierarchical decision structure can be tuned to manage representative semiconductor manufacturing supply chains in a manner appealing to operations. This work was supported by grants from the Intel Research Council and the National Science Foundation (CMMI-0432429).     

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 stochastic bi-objective location model for strategic reverse logistics

In this paper we propose a comprehensive model for reverse logistics planning where many real-world features are considered such as the existence of several facility echelons, multiple commodities, choice of technology and stochasticity associated with transportation costs and waste generation. Moreover, we adopt a bi-objective model for the problem. First, the cost for building and operating the network is to be minimized. Second, the obnoxious effect caused by the reverse network facilities is also to be minimized. A two-stage stochastic bi-objective mixed-integer programming formulation is proposed, in which the strategic decisions are considered in the first stage and the tactical/operational decisions in the second one. A set of different scenarios is considered, and the extensive form of the deterministic equivalent problem is presented. This model is tested with a case study based on some data from the Spanish province of Cordoba. Nondominated solutions are obtained by combining the two different objectives and by using a general solver.     

Planning logistics operations in the oil industry

In this paper we apply stochastic programming modelling and solution techniques to planning problems for a consortium of oil companies. A multiperiod supply, transformation and distribution scheduling problem—the Depot and Refinery Optimization Problem (DROP)—is formulated for strategic or tactical level planning of the consortium's activities. This deterministic model is used as a basis for implementing a stochastic programming formulation with uncertainty in the product demands and spot supply costs (DROPS), whose solution process utilizes the deterministic equivalent linear programming problem. We employ our STOCHGEN general purpose stochastic problem generator to ‘recreate’ the decision (scenario) tree for the unfolding future as this deterministic equivalent. To project random demands for oil products at different spatial locations into the future and to generate random fluctuations in their future prices/costs a stochastic input data simulator is developed and calibrated to historical industry data. The models are written in the modelling language XPRESS-MP and solved by the XPRESS suite of linear programming solvers. From the viewpoint of implementation of large-scale stochastic programming models this study involves decisions in both space and time and careful revision of the original deterministic formulation. The first part of the paper treats the specification, generation and solution of the deterministic DROP model. The stochastic version of the model (DROPS) and its implementation are studied in detail in the second part and a number of related research questions and implications discussed.     

基于时间的随机需求二级分销网络物流系统集成优化研究

分销网络设计包括设施选址、库存控制、运输等方面的设计与优化,但以往只是从战略层、战术层、运作层来分别进行各自的研究。实际上,这三个层次的决策要素之间存在着复杂的互动关系,并存在着广泛的效益悖反关系,这些在变化的环境下显得尤为突出。本文充分考虑时间因素的重要性,从物流系统的集成优化高度出发,研究建立需求随机的多分销中心多顾客的设施选址———运输路线安排———库存控制问题(ILRIP)的模型,对此设计了一个两层粒子群优化(PSO)算法,并给出了计算实例。研究结果有助于供应链分销网络的集成优化,缩短商品流转周期,提高顾客服务水平,提升竞争力。     

Finding reliable solutions: event-driven probabilistic constraint programming

Real-life management decisions are usually made in uncertain environments, and decision support systems that ignore this uncertainty are unlikely to provide realistic guidance. We show that previous approaches fail to provide appropriate support for reasoning about reliability under uncertainty. We propose a new framework that addresses this issue by allowing logical dependencies between constraints. Reliability is then defined in terms of key constraints called “events”, which are related to other constraints via these dependencies. We illustrate our approach on three problems, contrast it with existing frameworks, and discuss future developments.