Dezentrale vernetzte Energiebewirtschaftung (DEZENT) im Netz der Zukunft

Given the sharply rising costs for traditional energy (based on coal, fossil oil or gas etc.) over the past few years renewable energy sources such as wind, sun, water, or seed oil have favorably come into the picture as economically desirable and ecologically clean alternatives. The corresponding power facilities (e.g. wind power stations, solar panels, block heat & power plants) are widely distributed and are quite heterogeneous regarding their productivity (capacity and reliability) yet at the same time transport paths and costs are at a minimal level. (In the near future it will be possible to cover all needs from such sources.) For guaranteeing both the adaptive integration of the diverse facilities and a balanced level of supply under (locally) highly unpredictable energy production we present a bottom-up power grid management architecture. A key novelty of our approach is a completely decentralized management for negotiating the available power supply and needs. This is realized through a safety-critical, real-time multi-agent system where bids and offers are negotiated on the (electrically) shortest time basis of 0.5 sec. While the system is secure against malicious attacks it exhibits a high amount of fault tolerance where the latter corresponds well to the exceptional production safety of the widely distributed facilities. Beyond the high supply reliability under the decentralized management of distributed facilities we demonstrate that both operation costs and consumer prices could be assumed lower than under a centralized management and architecture.     

A model to assess risk,equity and efficiency in facility location and transportation of hazardous materials

In recent years there has been increased public and governmental concern regarding hazardous materials management, and a concomitant increase in activities associated with designing and using hazardous material management systems. To be effective these systems must consider the costs and risks associated with the transport of hazardous materials as well as those associated with the facilities that generate, process, or dispose of such materials. To date, most of the modeling literature has considered these two aspects of the problem separately, either addressing the location of facilities with respect to fixed transportation patterns or the routing of materials to or from facilities at fixed locations. These two aspects of the problem are closely interrelated, as facility locations directly influence the routing options available and available routes affect the location of potential facility sites. In this paper we propose a multiobjective model to assist decision makers in the location of facilities that handle hazardous materials, and in the routing of hazardous materials to these facilities. In the model costs are comparised of per unit transportation costs, and fixed and variable costs at facilities. Risks and equity are spatially determined and are addressed on both an aggregate level, through minisum objectives, and at the individual level, through minimax objectives.     

A GIS-Based Optimization Framework for Competitive Multi-Facility Location-Routing Problem

In a dynamic market setting, firms need to quickly respond to shifting demographics and economic conditions. In this paper, we investigate the problem of determining the optimum set of locations for a firm, which operates a chain of facilities under competition. We consider the objective of maximizing profit, defined as gross profit margin minus logistics costs. We propose a location-routing model where revenue is realized according to probabilistic patronization of customers and routing costs are incurred due to vehicles serving the open facilities from a central depot. We propose a hybrid heuristic optimization methodology for solving this model. The optimal locations are searched for by a Genetic Algorithm while an integrated Tabu Search algorithm is employed for solving the underlying vehicle routing problem. The solution approach is tested on a real dataset of a supermarket chain. The results show that the location decisions made by the proposed methodology lead to increased market share and profit margin, while keeping logistics costs virtually unchanged. Finally, we present a GIS-based framework that can be used to store, analyze and visualize all data as well as model solutions in geographic format.     

一个关于求解k-种产品选址问题的近似算法

对于k-种产品工厂选址问题,有如下描述：存在一组客户和一组可以建立工厂的厂址。现在有k种不同的产品,要求每一个客户必须由k个不同的工厂来提供k种不同的产品,其中每个工厂都只能为客户提供唯一的一种产品。在该问题中,假定建厂费用以及任意两个结点之间的运输费用都为非负,并且任意两个结点之间的运输费用都满足对称和三角不等式关系的性质。问题的要求是要从若干厂址中选择一组厂址来建立工厂,给每个工厂指定一种需要生产的产品,并且给每一个客户提供一组指派使每个客户都能有k个工厂来为其供应这k种不同的产品。对于此类问题,优化目标是最小化建厂费用以及运输费用。论文在假设建厂费用为零的前提下,提出了求解该类问题的一种最坏性能比为3k/2-1的近似算法。     

Modeling and solving a capacitated stochastic location-allocation problem using sub-sources

We study a capacitated multi-facility location-allocation problem in which the customers have stochastic demands based on Bernoulli distribution function. We consider the capacitated sub-sources of facilities to satisfy demands of customers. In the discrete stochastic problem, the goal is to find optimal locations of facilities among candidate locations and optimal allocations of existing customers to operating facilities so that the total sum of fixed costs of operating facilities, allocation cost of the customers, expected values of servicing and outsourcing costs is minimized. The model is formulated as a mixed-integer nonlinear programming problem. Since finding an optimal solution may require an excessive amount of time depending on nonlinear constraints, we transform the nonlinear constraints of the problem to linear ones to arrive at a simple formulation of the model. Numerical results show that the LINGO 9.0 software is capable of solving small size problems. For medium and large-size problems, we propose two meta-heuristic algorithms, namely a genetic algorithm and a discrete version of colonial competitive algorithm. Computational results show that the proposed algorithms efficiently obtain effective solutions.     

Sequential versus simultaneous approach in the location and design of two new facilities using planar Huff-like models

Companies frequently decide on the location and design for new facilities in a sequential way. However, for a fixed number of new facilities, the company might be able to improve its profit by taking its decisions for all the facilities simultaneously. In this paper we compare three different strategies: simultaneous location and independent design of two facilities in the plane, the same with equal designs, and the sequential approach of determining each facility in turn. The basic model is profit maximization for the chain, taking market share, location costs and design costs into account. The market share captured by each facility depends on the distance to the customers (location) and its quality (design), through a probabilistic Huff-like model. Recent research on this type of models was aimed at finding global optima for a single new facility, holding quality fixed or variable, but no exact algorithm has been proposed to find optimal solutions for more than one facility. We develop such an exact interval branch-and-bound algorithm to solve both simultaneous location and design two-facility problems. Then, we present computational results and exhibit the differences in locations and qualities of the optimal solutions one may obtain by the sequential and simultaneous approaches.     

A bounded dynamic programming approach to schedule operations in a cross docking platform

Cross docking is a logistic technique employed to reduce the inventory holding, order picking, transportation costs as well as the delivery time. Products arriving to the cross dock are unloaded from inbound trailers, possibly reconsolidated with other products arriving from different destinations and loaded into outbound trailers within less than 24 h. In this study, we consider a multiple receiving and shipping door cross dock environment. The objective is to find optimal (for reasonably small cross docks) or near optimal (for larger cross docking facilities) scheduling policies which minimizes the total costs related to the transshipment operations at the facility.     

A cross decomposition procedure for the facility location problem with a choice of facility type

Consider a capacitated facility location problem in which each customer is assumed to have a unit demand, and each facility capacity has to be chosen from the given set of admissible levels. Under the restriction that each customer's unit demand be met by exactly one facility, the objective is to select a set of facilities to open, along with their capacities, and to assign customer's demand to them so as to minimize the total cost which includes fixed costs of opening facilities as well as variable assignment costs. The problem is modelled as a pure 0–1 program which extends the scope of applicability significantly over that by conventional location models. Based on Cross Decomposition recently developed by Van Roy, a solution procedure is proposed, when exploits the special structure of the problem. Computational results with a set of test problems shows the superiority of our solution procedure to other related ones.     

An exact algorithm for the modular hub location problem with single assignments

A key feature of hub-and-spoke networks is the consolidation of flows at hub facilities. The bundling of flows allows reduction in the transportation costs, which is frequently modeled using a constant discount factor that is applied to the flow cost associated with all interhub links. In this paper, we study the modular hub location problem, which explicitly models the flow-dependent transportation costs using modular arc costs. It neither assumes a full interconnection between hub nodes nor a particular topological structure, instead it considers link activation decisions as part of the design. We propose a branch-and-bound algorithm that uses a Lagrangean relaxation to obtain lower and upper bounds at the nodes of the enumeration tree. Numerical results are reported for benchmark instances with up to 75 nodes.     

An artificial intelligence approach for fuzzy possibilistic-stochastic multi-objective logistics network design

In this paper, a logistics network is investigated which includes multi-suppliers, collection centers, transfer stations, treatment stations, and products. For this purpose, a multi-objective mathematical programming model is proposed that minimizes the total costs including the fixed costs for opening facilities and transportation costs between facilities, minimizes the distance between each waste-generating facilities and transfer stations, maximizing the distance between treatment and disposal stations and customer zones, and maximizes the sum of the reliability of coverage for the potential facilities which will be open. In order to make the results of this paper more realistic, a case study in the iron and steel industry has been investigated. Besides, a new solution approach is proposed by combining fuzzy possibilistic programming, stochastic programming, and fuzzy multi-objective programming. Moreover, an imperialist competitive algorithm is proposed to obtain near optimal solution in comparison with other evolutionary algorithms. Finally, computational experiments are provided to demonstrate the applicability and suitability of the proposed model and solution approaches.     

Optimal facility location with random throughput costs

In this paper we consider the optimal location and size of facilities where the throughput costs for each facility are random. Given a set of origins and a set of destinations, we want to determine the optimal location and size of a set of intermediate facilities in order to minimize the expected total generalized transportation cost. The generalized transportation cost of a freight unit from an origin to a destination passing through a facility is the sum of two terms: the transportation cost from the origin to the destination through the facility and the throughput cost of the facility. While the first term is deterministic, the second one is stochastic with a Gumbel probability distribution. Looking for the expected value of the optimal solution, a mixed deterministic nonlinear problem for the optimal location of the facilities is derived. Two heuristics, which give very good approximations to the optimum, are proposed.     

Local search heuristics for the mobile facility location problem

In the mobile facility location problem (MFLP), one seeks to relocate (or move) a set of existing facilities and assign clients to these facilities so that the sum of facility movement costs and the client travel costs (each to its assigned facility) is minimized. This paper studies formulations and develops local search heuristics for the MFLP. First, we develop an integer programming (IP) formulation for the MFLP by observing that for a given set of facility destinations the problem may be decomposed into two polynomially solvable subproblems. This IP formulation is quite compact in terms of the number of nonzero coefficients in the constraint matrix and the number of integer variables; and allows for the solution of large-scale MFLP instances. Using the decomposition observation, we propose two local search neighborhoods for the MFLP. We report on extensive computational tests of the new IP formulation and local search heuristics on a large range of instances. These tests demonstrate that the proposed formulation and local search heuristics significantly outperform the existing formulation and a previously developed local search heuristic for the problem.     

A decomposition approach for the p-median problem on disconnected graphs

The p-median problem seeks for the location of p facilities on the vertices (customers) of a graph to minimize the sum of transportation costs for satisfying the demands of the customers from the facilities. In many real applications of the p-median problem the underlying graph is disconnected. That is the case of p-median problem defined over split administrative regions or regions geographically apart (e.g. archipelagos), and the case of problems coming from industry such as the optimal diversity management problem. In such cases the problem can be decomposed into smaller p-median problems which are solved in each component k for different feasible values of p_k, and the global solution is obtained by finding the best combination of p_k medians. This approach has the advantage that it permits to solve larger instances since only the sizes of the connected components are important and not the size of the whole graph. However, since the optimal number of facilities to select from each component is not known, it is necessary to solve p-median problems for every feasible number of facilities on each component. In this paper we give a decomposition algorithm that uses a procedure to reduce the number of subproblems to solve. Computational tests on real instances of the optimal diversity management problem and on simulated instances are reported showing that the reduction of subproblems is significant, and that optimal solutions were found within reasonable time.     

Stochastic facility location with general long-run costs and convex short-run costs

This paper addresses the problem of minimizing the expected cost of locating a number of single product facilities and allocating uncertain customer demand to these facilities. The total costs consist of two components: firstly linear transportation cost and secondly the costs of investing in a facility as well as maintaining and operating it. These facility costs are general and non-linear in shape and could express both changing economies of scale and diseconomies of scale. We formulate the problem as a two-stage stochastic programming model where both demand and short-run costs may be uncertain at the investment time. We use a solution method based on Lagrangean relaxation, and show computational results for a slaughterhouse location case from the Norwegian meat industry.     

A Game Theoretical Approach for Improving the Operational Efficiencies of Less-than-truckload Carriers Through Load Exchanges

Less-than-truckload (LTL) transportation offers fast, flexible and relatively low-cost transportation services to shippers. In order to cope with the effects of economic recessions, the LTL industry implemented ideas such as reducing excess capacity and increasing revenues through better yield management. In this paper, we extend these initiatives beyond the reach of individual carriers and propose a collaborative framework that facilitates load exchanges to reduce the operational costs. Even though collective solutions are proven to provide benefits to the participants by reducing the inefficiencies using a system-wide perspective, such solutions are often not attainable in real-life as the negotiating parties are seeking to maximize their individual profits rather than the overall profit and also they are unwilling to share confidential information. Therefore, a mechanism that enables collaboration among the carriers should account for the rationality of the individual participants and should require minimal information transfer between participants. Having this in mind, we propose a mechanism that facilities collaboration through a series of load exchange iterations and identifies an equilibrium among selfish carriers with limited information transfer among the participants. Our time-efficient mechanism can handle large instances with thousands of loads as well as provide significant benefits over the non-collaborative management of LTL networks.

     

Beam search heuristics for the single and multi-commodity capacitated Multi-facility Weber Problems

The Multi-facility Weber Problem (MWP) is concerned with locating I uncapacitated facilities in the plane to satisfy the demand of J customers with the minimum total transportation cost of a single commodity. It is a non-convex optimization problem and difficult to solve. In this work, we focus on the capacitated extensions of the MWP which are Capacitated MWP (CMWP) and multi-commodity CMWP (MCMWP). Both the CMWP and MCMWP impose capacity restrictions on facilities. Indeed, the MCMWP is a natural extension of the CMWP and considers the situation where K distinct commodities are shipped subject to limitations on the total amount of goods sent from facilities to the customers. Customer locations, demands and capacities for each commodity are known a priori. The transportation costs, which depend on the commodity type, are proportional to the distance between customers and facilities. We first introduce branch and bound algorithms for both the CMWP and the MCMWP then we propose beam search heuristics for these problems. According to our computational experiments on standard and randomly generated test instances, we can say that the new heuristics perform very well.     

Developing an integrated solid waste management system in western Greece: a dynamic location analysis

We study the problem of the simultaneous design of a distribution network with central treatment facilities, transfer stations and landfills, and the coordination of waste flows within this network for a long-term planning horizon. We apply these concepts in a research project for the development of a solid waste management system for a specific region in Greece. We then formulate a dynamic mixed integer programming model that minimizes the sum of costs for opening facilities and variable transportation costs related to solid waste flows. Finally, we analyze the results of the model and evaluate the various technologies that may be implemented in the treatment facilities.     

Two-echelon, multi-commodity supply chain network design with mode selection, lead-times and inventory costs

Designing distribution networks - as one of the most important strategic issues in supply chain management - has become the focus of research attention in recent years. This paper deals with a two-echelon supply chain network design problem in deterministic, single-period, multi-commodity contexts. The problem involves both strategic and tactical levels of supply chain planning including locating and sizing manufacturing plants and distribution warehouses, assigning the retailers' demands to the warehouses, and the warehouses to the plants, as well as selecting transportation modes.We have formulated the problem as a mixed integer programming model, which integrates the above mentioned decisions and intends to minimize total costs of the network including transportation, lead-times, and inventory holding costs for products, as well as opening and operating costs for facilities. Moreover, we have developed an efficient Lagrangian based heuristic solution algorithm for solving the real-sized problems in reasonable computational time.     

Hub network design problem in the presence of disruptions

The main issue in p-hub median problem is locating hub facilities and allocating spokes to those hubs in order to minimize the total transportation cost. However hub facilities may fail occasionally due to some disruptions which could lead to excessive costs. One of the most effective ways to hedge against disruptions especially intentional disruptions is designing more reliable hub networks. In this paper, we formulate the multiple allocation p-hub median problem under intentional disruptions by a bi-level model with two objective functions at the upper level and a single objective at the lower level. In this model, the leader aims at identifying the location of hubs so that minimize normal and worst-case transportation costs. Worst-case scenario is modeled in the lower level where the follower’s objective is to identify the hubs that if lost, it would mostly increase the transportation cost. We develop two multi-objective metaheuristics based on simulated annealing and tabu search to solve the problem. Computational results indicate the viability and effectiveness of the proposed algorithms for exploring the non-dominated solutions.     

Model for Facilities or Vendors Location in a Global Scale Considering Several Echelons in the Chain

The facilities location problem for companies with global operations is very complex and not well explored in the literature. This work proposes a MILP model that solves the problem through minimization of the total logistic cost. Main contributions of the model are the pioneer carrying cost calculation, the treatment given to the take-or-pay costs and to the international tax benefits such as drawback and added value taxes in Brazil. The model was successfully applied to a real case of a chemical industry with industrial plants and sales all over the world. The model application recommended a totally new sourcing model for the company.