This paper proposes a genetic algorithm (GA) for the inventory routing problem with lost sales under a vendor-managed inventory strategy in a two-echelon supply chain comprised of a single manufacturer and multiple re...
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This paper proposes a genetic algorithm (GA) for the inventory routing problem with lost sales under a vendor-managed inventory strategy in a two-echelon supply chain comprised of a single manufacturer and multiple retailers. The proposed GA is inspired by the solving mechanism of CPLEX for the optimization model of the problem. The proposed GA determines replenishment times and quantities and vehicle routes in a decoupled manner, while maximizing supply chain profits. The proposed GA is compared with the optimization model with respect to the effectiveness and efficiency in various test problems. The proposed GA finds solutions in a short computational time that are very close to those obtained with the optimization model for small problems and solutions that are within 3.2% of those for large problems. Furthermore, sensitivity analysis is conducted to investigate the effects of several problem parameters on the performance of the proposed GA and total profits. (c) 2016 Elsevier Ltd. All rights reserved.
In this study, a mixedintegerlinearprogrammingmodel of a distributed energy supply network integrated with electricity and hot water interchanges is presented minimizing the overall annual costs, which include ann...
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In this study, a mixedintegerlinearprogrammingmodel of a distributed energy supply network integrated with electricity and hot water interchanges is presented minimizing the overall annual costs, which include annualized initial investment cost, annual operation and maintenance cost as well as annualized total infrastructure cost while guaranteeing the resilience of energy demands of various consumers. The superstructure of the energy supply system is determined. Furthermore, the system size and operational strategies including energy interchanges are optimized simultaneously. As an illustrative example, a low-carbon community located in Tokyo, Japan, has been selected for study. The robustness of the optimization model as well as the performance of distributed energy supply network have been illustrated through comparative analysis of four scenarios and multiple sensitive analyses of various uncertainty parameters including community scale, energy prices, energy policies as well as energy generation efficiency. (C) 2015 Elsevier Ltd. All rights reserved.
This paper presents the development of a multi objective optimization explicit model for distribution systems restoration. The main goal of the methodology is to determine the combination of maneuvers devices to be op...
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ISBN:
(纸本)9781467366052
This paper presents the development of a multi objective optimization explicit model for distribution systems restoration. The main goal of the methodology is to determine the combination of maneuvers devices to be operated in order to maximize the number of customers with restored supply in the post-fault period and to minimize the time required by the process. The proposed formulation considers devices with arbitrary operation times, allowing the representation of automatic and manual maneuvers switches as well as, their effects on the duration of outages. In the proposed methodology, the supply restoration of distribution systems is formulated as a mixed-integer linear programming model. The proposed methodology was validated using a real distribution system. It is being implemented in one of the computational modules of the "SIRIs Platform" (Smart Grid Simulator), currently under development by NEO DOMINO and UFRGS in a research project supported by CELESC Distribuicao S.A., Brazil.
This paper proposes a method for designing a seamless centralized emergency supply network by integrating three sub-networks (shelter network, medical network, and distribution network) to support emergency logistics ...
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This paper proposes a method for designing a seamless centralized emergency supply network by integrating three sub-networks (shelter network, medical network, and distribution network) to support emergency logistics operations in response to large-scale natural disasters. The proposed method primarily involves three stage multi-objective (travel distance minimization, operational cost minimization, and psychological cost minimization), mixed-integer linear programming models. The three sub-networks are designed using the proposed programmingmodels. The distinctive features of the proposed method are as follows. (1) The proposed method is demand-driven. The order of the designed sub-networks is shelter, medical, and distribution, with the connections of the latter networks based on the arrangements for the former. (2) The objective functions of three stage programmingmodels include not only traditional objectives such as minimizing total travel distance and operational cost, which supply-side members focus on, but also minimizing the psychological cost experienced by demand-side members. model tests are conducted to demonstrate that the superiority of a centralized emergency supply network designed by the proposed method over a decentralized one, especially with regard to distribution network design. (C) 2014 Elsevier Ltd. All rights reserved.
Concerns over environmental degradation legislative requirements and growing business needs have fueled the growth of closed-loop supply chains (CLSCs). This paper addresses a bi-objective network design problem for m...
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Concerns over environmental degradation legislative requirements and growing business needs have fueled the growth of closed-loop supply chains (CLSCs). This paper addresses a bi-objective network design problem for multi-period, multi-product CLSC to minimize the total supply chain costs and to maximize the service efficiency of the warehouses and hybrid facilities. We develop a bi-objective mixedintegerlinearprogrammingmodel to assist decisions in (1) location/operating decisions for warehouses, hybrid facilities and manufacturing facilities and (2) production and distribution of products between stages in the supply chain. Goal programmingmodels and compromise programming techniques are used to solve the problem. An application of the model is demonstrated using a case study from the literature.
This paper proposes a liner ship fleet deployment (LSFD) problem with container transshipment operations. The proposed problem is formulated as a mixed-integer linear programming model which allows container transship...
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This paper proposes a liner ship fleet deployment (LSFD) problem with container transshipment operations. The proposed problem is formulated as a mixed-integer linear programming model which allows container transshipment operations at any port, any number of times, without explicitly defining the container transshipment variables. Experiments on the Asia-Europe-Oceania shipping network of a global liner shipping company show that more than one third (17-22 ports) of the total of 46 ports have transshipment throughputs. Computational studies based on randomly generated large-scale shipping networks demonstrate that the proposed model can be solved efficiently by CPLEX. (C) 2011 Elsevier Ltd. All rights reserved.
The object of this article is to develop a centralised replenishment, production, and distribution model for ceramic tile supply chains. These supply chains are assumed to be multi-item, multi-supplier, multi-facility...
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The object of this article is to develop a centralised replenishment, production, and distribution model for ceramic tile supply chains. These supply chains are assumed to be multi-item, multi-supplier, multi-facility, multi-type and multi-level distribution centres. The model deals with multi-period master planning where sourcing considerations for replenishments, production facilities, and distribution centres are important to maximise an objective function involving the total net profit. This model is deterministic and has been formulated as a mixed-integerlinearprogramming (MILP) model. An example based on modifications of real-world industrial problems is presented.
The production of active ingredients in the chemical-pharmaceutical industry involves numerous production stages with cumulative lead times of up to two years. Mainly because of rigorous purity requirements and the ne...
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The production of active ingredients in the chemical-pharmaceutical industry involves numerous production stages with cumulative lead times of up to two years. Mainly because of rigorous purity requirements and the need of extensive cleaning of the equipment units, production is carried out in campaigns, i.e. multiple batches of the same product type are produced successively before changing to another product type. Each campaign requires a specific configuration of equipment units according to the recipes of the particular chemical process. In the chemicalpharmaceutical industry, production stages are often assigned to different locations, even different countries. Hence the co-ordination of plant operations within the resulting multi-national supply network is of major importance. A key issue is the co-ordination of campaign schedules at different production stages in the various plants. In practice, it is almost impossible to determine exact optimal solutions to the corresponding complex supply network problem with respect to overall logistics costs. In order to reduce the required computational effort, we introduce several aggregation schemes and a novel MILP model formulation which is based on a continuous representation of time. Moreover, we propose an iterative near-optimal solution procedure which can be successfully applied to even exceptionally large real life problem instances. The applicability of the approach suggested is shown using a case study from industry.
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