Plants, distribution centers, and other facilities generally function for years or decades, during which time the environment in which they operate may change substantially. Costs, demands, travel times, and other inputs to classical facility location models may be highly uncertain. This has made the development of models for facility location under uncertainty a high priority for researchers in both the logistics and stochastic/robust optimization communities. Indeed, a large number of the approaches that have been proposed for optimization under uncertainty have been applied to facility location problems. This paper reviews the literature...

The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamental assumptions, mathematical complexity and computational performance. This paper reviews some of the contributions to the current state-of-the-art. In particular, continuous location models, network location models, mixed-integer programming models, and applications are summarized.

This paper reviews recent work on integrated analysis of production-distribution systems, and identifies important areas where further research is needed. By integrated analysis we understand analysis performed on models that integrate decisions of different production and distribution functions for a simultaneous optimization. We review work that explicitly considers the transportation system in the analysis, since we are interested in the following questions: (i) How have logistics aspects been included in the integrated analysis? and (ii) What competitive advantages, if any, have been obtained from the integration of the distribution function to other production functions within a company and among different companies? In our review we also mention whether the work has been done at the strategic level, i.e. if it concerns the design of the distribution system, or at the tactical level, i.e. if it concerns optimization problems for which the characteristics of the distribution system are provided.

Production-distribution planning is the most important activity in supply chain management (SCM). To solve this planning problem, either analytic or simulation approaches have been used. However these two approaches have their own demerits in problem solving. In this paper, we propose a hybrid approach which is a specific problem solving procedure combining analytic and simulation methods to solve production-distribution problems in supply chains. The machine capacity and distribution capacity constraints in the analytic model are considered as stochastic factors and are adjusted by the proposed specific process according to the results from an independently developed simulation model which includes general production-distribution characteristics.

large body of work exists in process industry supply chain optimisation. We describe the state of the art of research in infrastructure design, modelling and analysis and planning and scheduling, together with some industrial examples. We draw some conclusions about the degree to which different classes of problem have been solved, and discuss challenges for the future. © 2005 Published by Elsevier Ltd.

In recent years, researchers and practitioners alike have devoted a great deal of attention to supply chain management (SCM). The main focus of SCM is the need to integrate operations along the supply chain as part of an overall logistic support function. At the same time, the need for globalization requires that the solution of SCM problems be performed in an international context as part of what we refer to as Global Supply Chain Management (GSCM). In this paper we propose an approach to study GSCM problems using an artificial intelligence framework called reinforcement learning (RL). The RL framework allows the management of global supply chains under an integration perspective. The RL approach has remarkable similarities to that of an autonomous agent network (AAN); a similarity that we shall discuss. The RL approach is applied to a case example, namely a networked production system that spans several geographic areas and logistics stages. We discuss the results and provide guidelines and implications for practical applications.

The integrated production, inventory and distribution routing problem (PDRP) is concerned with coordinating the production, inventory and delivery operations to meet customer demand with an objective to minimize the cost. The particular PDRP that we consider in this study also involves heterogeneous transporters with non-instantaneous traveling times and many customer demand centers each with its own inventory capacities. Optimally solving such an integrated problem is in general not easy due to its combinatorial nature, especially when transporter routing is involved. In this paper, we propose a two-phase solution approach to this problem. Phase I solves a mixed integer programming model which includes all the constraints in the original model except the transporter routings are restricted to direct shipment between facilities and customer demand centers. The resulting optimal solution to the Phase I problem is always feasible to the original model. Phase II solves an associated consolidation problem to handle the potential inefficiency of direct shipment. The delivery consolidation problem is formulated as a capacitated transportation problem with additional constraints and is solved by an efficient heuristic routing algorithm. The main advantage of this proposed approach, over the classical decoupled approach, is its ability to simultaneously optimize the production, inventory and transportation operations (subject to restricted routing/direct shipments) without the needs for

This dissertation addresses the issue of performance standards in developing nations, focusing on the role of local content requirements. It proposes a theoretical framework to understand the impact of this policy on the decisions of firms and the welfare of the domestic economy, and offers a methodology to apply the analysis to the context of the automotive supply chain. The central conclusion of the thesis relates to the existence of a gap between private and social opportunity returns and costs, an aspect that has been overlooked by previous literature.

Abstract: This paper analyzes the organizational structure of the Global Manufacturing Virtual Network (GMVN) and the material flow relationship between its members. In this paper we utilize an input-output model to describe the supply network of the GMVN. In addition to this, we discuss the manufacturing costs of in-theplan and out-of the-plan for short life-cycle products. Based on the aforementioned analysis and considering the demand uncertainty of short life-cycle products, we propose what we call the ‘joint optimal output model ’ for members in GMVN for maximizing total profit of GMVN. We prove that the object function is concave and present the solution of optimal joint output.

In today's global marketplace, the success of a supply chain (SC) depends on its managerial ability to integrate and coordinate business relationships among SC members. Ultimately, effectively integrated supply management results in lower costs, higher quality, better customer service, and higher profits for the organization, their suppliers, and their distributors. Recent advances in information technology provide an opportunity to the firms to integrate the planning of the fundamental stages, procurement, production and distribution, of the SCs. This paper provides a multi-objective production-distribution planning model. To provide a more realistic modeling structure by treating the vagueness in the target values of the SC partners ' objectives, and to reduce the computational burden, fuzzy modeling approach is used in this paper. The model is implemented using linear programming (LP), goal programming (GP) and fuzzy goal programming (FGP) approaches. The application of these approaches to the integrated model is illustrated by means of a realistic numerical example and the results are compared.