We consider a batch scheduling problem for a two-stage flow shop with fixed-position layout. In the first stage, a fixed number of jobs are assembled on a batch machine with a family batch setup time and a common proc...
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We consider a batch scheduling problem for a two-stage flow shop with fixed-position layout. In the first stage, a fixed number of jobs are assembled on a batch machine with a family batch setup time and a common processing time. In the second stage, the assembled jobs are individually performed for system integration on a discrete machine. The finished job is immediately packed and shipped if the payment has been made;otherwise, it is moved to a temporary storage area, incurring additional removal time. This study develops a mixed integer programming (MIP) to solve the problem of minimising the total completion time and proposes two heuristics for large-size problems. Computational results show that the proposed methods can be applied to resolve real-world problems similar to those in this study.
This article develops a multi-choice multi-objective linear programming model in order to solve an integrated production planning problem of a steel plant. The aim of the integrated production planning problem is to i...
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This article develops a multi-choice multi-objective linear programming model in order to solve an integrated production planning problem of a steel plant. The aim of the integrated production planning problem is to integrate the planning sub-functions into a single planning operation. The sub-functions are formulated by considering the capacity of different units of the plant, cost of raw materials from various territories, demands of customers in different geographical locations, time constraint for delivery the products, production cost and production rate at different stages of production process. Departure cost is also considered in the formulation of mathematical programming model. Some of the parameters are decided from a set of possible choices, therefore such parameters are considered as multi-choice type. Multi-choice mathematical programming problem cannot be solved directly. Therefore an equivalent multi-objective mathematical programming model is established in order to find the optimal solution of the problem. Computation of the mathematical programming model is performed with the practical production data of a plant to study the methodology.
The double row layout problem (DRLP) consists of arranging a number of rectangular machines of varying widths on either side of a corridor to minimize the total cost of material handling for products that move between...
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The double row layout problem (DRLP) consists of arranging a number of rectangular machines of varying widths on either side of a corridor to minimize the total cost of material handling for products that move between these machines. This problem arises in the context of many production environments, most notably semiconductor manufacturing. Because the DRLP contains both combinatorial and continuous aspects, traditional solution approaches are not well suited to obtain solutions within a reasonable time. Moreover, previous approaches to this problem did not consider asymmetric flows. In this paper, an effective local search procedure featuring linear programming is proposed for solving the DRLP with asymmetric flows (symmetric flows being a special case). This approach is compared against several constructive heuristics and solutions obtained by a commercial mixedinteger linear programming solver to evaluate its performance. Computational results show that the proposed heuristic is an effective approach, both in terms of solution quality and computational effort.
It is well known that freight consolidation is an effective way to improve the utilization of logistics resources. In fact today, this policy is locally and fragmentally implemented at the operational level. We propos...
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It is well known that freight consolidation is an effective way to improve the utilization of logistics resources. In fact today, this policy is locally and fragmentally implemented at the operational level. We propose here to explore the environmental impact of pooling of supply chains at the strategic level (merging supply chains). With real data from two main French retail chains and through an optimization model, we compute CO2 emissions for two transport modes, road and rail. As regards the general dependency of the emissions produced by the modes of transport on their loads, the emissions functions of the two modes are both piecewise linear and discontinuous functions. The supply network pooling proposed here is an efficient approach in reducing CO2 emissions. Even if the attention is focused on the emissions, the transportation costs are also studied and analyzed. (C) 2010 Elsevier B.V. All rights reserved.
This study used the concepts of set- and maximum-coverage to formulate capacitated multiple-recharging-station-location models, using a mixed integer programming method, based on a vehicle-refueling logic. The results...
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This study used the concepts of set- and maximum-coverage to formulate capacitated multiple-recharging-station-location models, using a mixed integer programming method, based on a vehicle-refueling logic. The results of the case study demonstrate that the use of mixed stations can achieve the optimal deployment for the planning area, with results that are better than those achieved with a single type of recharging stations. While in some paths the use of slow-recharging stations means that tours are not feasible, the deployment of mixed stations can provide an economical approach which ensures the completion of overall tours on each path. (c) 2013 Elsevier Ltd. All rights reserved.
Energy consumption of network operators can be minimized by the dynamic and smart relocation of networking resources. In this paper, we propose to take advantage of network virtualization to enable a smart energy awar...
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Energy consumption of network operators can be minimized by the dynamic and smart relocation of networking resources. In this paper, we propose to take advantage of network virtualization to enable a smart energy aware network provisioning. The virtualization of networking resources leads to the problem of mapping virtual demands to physical resources, known as Virtual Network Embedding (VNE). Our proposal modifies and improves exact existing energy aware VNE proposals where the objective is to switch off as many network nodes and interfaces as possible by allocating the virtual demands to a consolidated subset of active physical networking equipment. As exact energy efficient VNE approaches are hard to solve for large network sizes and have an adverse effect in the number of successful embeddings, an heuristic approach to reconfigure the allocation of already embedded virtual networks, minimizing the energy consumption, is also proposed. (c) 2013 Elsevier B.V. All rights reserved.
Efficient transport of timber for supplying industrial conversion and biomass power plants is a crucial factor for competitiveness in the forest industry. Throughout the recent years minimizing driving times has been ...
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Efficient transport of timber for supplying industrial conversion and biomass power plants is a crucial factor for competitiveness in the forest industry. Throughout the recent years minimizing driving times has been the main focus of optimizations in this field. In addition to this aim the objective of reducing environmental impacts, represented by carbon dioxide equivalent (CO(2)e) emissions, is discussed. The underlying problem is formulated as a multi-depot vehicle routing problem with pickup and delivery and time windows (MDVRPPDTW) and a new iterative solution method is proposed. For the numerical studies, real-life data are used to generate test instances of different scales concerning the supply chain of biomass power plants. Small ones are taken to validate the optimality of the new approach. Medium and large test instances are solved with respect to minimizing driving times and fuel consumptions separately. This study shows that the selection of the objective of minimizing fuel consumption leads to a significant reduction of CO(2)e emissions compared to a minimization of driving times.
This paper deals with the optimal selection of countermeasures in IT security planning to prevent or mitigate cyber-threats and a mixed integer programming approach is proposed for the decision making. Given a set of ...
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This paper deals with the optimal selection of countermeasures in IT security planning to prevent or mitigate cyber-threats and a mixed integer programming approach is proposed for the decision making. Given a set of potential threats and a set of available countermeasures, the decision maker needs to decide which counter-measure to implement under limited budget to minimize potential losses from successful cyber-attacks and mitigate the impact of disruptions caused by IT security incidents. The selection of countermeasures is based on their effectiveness of blocking different threats, implementation costs and probability of potential attack scenarios. The problem is formulated as a single- or bi-objective mixedinteger program and a conditional value-at-risk approach combined with scenario-based analysis is applied to control the risk of high losses due to operational disruptions and optimize worst-case performance of an IT system. The bi-objective trade-off model provides the decision maker with a simple tool for balancing expected and worst-case losses and for shaping of the resulting cost distribution through the selection of optimal subset of countermeasures for implementation, i.e., the selection of optimal countermeasure portfolio. The selected portfolio explicitly depends on preferred confidence level and cost/risk preference of the decision maker. Numerical examples are presented and some computational results are reported to compare the risk-averse solutions that minimize conditional value-at-risk with the risk-neutral ones that minimize expected cost. (C) 2013 Elsevier B.V. All rights reserved.
The Vehicle Routing Problem with Time Windows (VRPTW) requires to design minimum cost routes for a fleet of vehicles with identical capacities to serve a set of customers within given time windows. Each customer must ...
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The Vehicle Routing Problem with Time Windows (VRPTW) requires to design minimum cost routes for a fleet of vehicles with identical capacities to serve a set of customers within given time windows. Each customer must be visited exactly once and the load of a vehicle must not exceed its capacity. In this paper we introduce two new basic families of valid inequalities, called Lifted and Local Reachability Cuts, respectively, which extend the Reachability Cuts introduced by J. Lysgaard. Separation procedures for Lifted and Local Reachability Cuts have been implemented and embedded into a Branch-and-Cut framework to validate their computational effectiveness. They were tested on the Solomon and on the Gehring-Homberger benchmark instances (also known as the "Extended Solomon" instances) with 200 customers. Computational experiments show that the new cutting plane families can substantially improve the lower bounds returned by Lysgaard's Reachability Cuts. The Branch-and-Cut algorithm could also provide the optimal solution of three previously unsolved instances - C222, 025 and 026 - with large capacities and wide time windows and therefore difficult for exact algorithms. (C) 2013 Elsevier Ltd. All rights reserved.
- We investigate the problem of finding a maximal matching that has minimum total weight on a given edge-weighted graph. Although the minimum weight maximal matching problem is NP-hard in general, polynomial time exac...
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- We investigate the problem of finding a maximal matching that has minimum total weight on a given edge-weighted graph. Although the minimum weight maximal matching problem is NP-hard in general, polynomial time exact or approximation algorithms on several restricted graph classes are given in the literature. In this article, we propose an exact algorithm for solving several variants of the problem on general graphs. In particular, we develop integerprogramming (IP) formulations for the problem and devise a decomposition algorithm, which is based on a combination of IP techniques and combinatorial matching algorithms. Our computational tests on a large suite of randomly generated graphs show that our decomposition approach significantly improves the solvability of the problem compared to the underlying IP formulation. (c) 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 62(4), 273-287 2013
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