Aggregate production planning (APP) addresses matching supply to forecast demand, with varying customer orders over the intermediate planning horizon. In real-world APP problems, input data and related parameters are ...
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Aggregate production planning (APP) addresses matching supply to forecast demand, with varying customer orders over the intermediate planning horizon. In real-world APP problems, input data and related parameters are commonly imprecise because information is incomplete or unavailable, and the decision maker (DM) must simultaneously consider conflicting objectives. This study develops an interactive possibilistic linearprogramming (i-PLP) approach to solve multi-product and multi-time period APP problems with multiple imprecise objectives and cost coefficients by triangular possibility distributions in uncertain environments. The imprecise multi-objective APP model designed here seeks to minimise total production costs and changes in work-force level with reference to imprecise demand, cost coefficients, available resources and capacity. Additionally, the proposed i-PLP approach provides a systematic framework that helps the decision-making process to solve fuzzy multi-objective APP problems, enabling a DM to interactively modify the imprecise data and parameters until a set of satisfactory solutions is derived. An industrial case demonstrates the feasibility of applying the proposed approach to a practical multi-objective APP problem.
This study proposes a heuristic algorithm, called the multi-objective master planning algorithm (MOMPA), to solve master planning (MP) problems for a supply chain network with multiple finished products. MOMPA has thr...
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This study proposes a heuristic algorithm, called the multi-objective master planning algorithm (MOMPA), to solve master planning (MP) problems for a supply chain network with multiple finished products. MOMPA has three objectives: to minimize delay penalties, to minimize use of outsourcing capacity, and to minimize the costs of materials, production, processing, transportation, and inventory holding-all while respecting the capacity limitations and the demand deadlines of all those involved in a given supply chain network. MOMPA plans each demand, one by one, without backtracking, and sorts those demands using a sorting mechanism that is part of the algorithm. For each demand, the minimum production cost tree is determined within the limits of the time bucket for the demand deadline. The maximum available capacity of this tree is then computed for the "no delay" case. Following this calculation, the delay-or-not criterion is evaluated to determine whether or not further delay is necessary. MOMPA compares the results of these two procedures and allocates the appropriate capacities to the demand for all the nodes on the selected tree. If the minimum cost production tree has no available capacity, MOMPA adjusts the network and looks for a new tree. With complexity and computational analysis, MOMPA is shown to be very efficient in solving NIP problems, sometimes generating the same optimal solution as the LP model. (C) 2006 Elsevier Ltd. All rights reserved.
The goal and model inputs when applies any of conventional techniques to APP decisions are generally assumed to be deterministic/crisp, and the major limitation of these techniques is that they do not consider the tim...
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The goal and model inputs when applies any of conventional techniques to APP decisions are generally assumed to be deterministic/crisp, and the major limitation of these techniques is that they do not consider the time value of money for each of operating cost categories. This work presents an interactive possibilistic linearprogramming (PLP) method for the solving the aggregate production planning (APP) problems with imprecise goal, forecast demand, related capacities and operating costs in uncertain environments. The imprecise APP model designed here attempts to minimize total manufacturing costs with reference to inventory, labor, overtime, subcontracting and backordering levels, machine capacity, warehouse space, and the time value of money for each of the operating cost categories. The proposed PLP method achieves greater computational efficiency by employing the simplified triangular distribution to specify imprecise goal and related coefficients. The analytical results obtained by implementing a real industrial case indicate that the proposed PLP method is practically applicable for solving the practical APP problems in uncertain environments.
The analytic hierarchy process is combined with multi-objective mixed integer programming to determine the optimal allocation of a limited number of aircraft among a group of airlift users with varying levels of prior...
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The analytic hierarchy process is combined with multi-objective mixed integer programming to determine the optimal allocation of a limited number of aircraft among a group of airlift users with varying levels of priority and length of usage. Canadian Forces airlift planners typically encounter such a capacity planning problem. The solution to this problem requires the constrained assignment of n variable length missions (tasks) integrating hundreds of airlift requests from several users with many priorities to m airframes (parallel machines).
This study considers both the internal and external costs of the utility in deriving the avoided capacity cost (ACC) and avoided operating cost (AOC) induced in an electric utility caused by the implementation of a de...
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This study considers both the internal and external costs of the utility in deriving the avoided capacity cost (ACC) and avoided operating cost (AOC) induced in an electric utility caused by the implementation of a demand side management program (DSM). In calculating the ACC, a multiple objectivelinearprogramming model is developed. Meanwhile, the AOC is calculated by considering the differences between the total and specific time period energy consumption ratios before and after the implementation of the DSM program. This study also develops an economic analysis method using Net Present Value and Pay Back Year models to assess the economic profitability of implementing a DSM program from a participant's point of view. The design and construction of a partial load leveling eutectic salt Cooling Energy Storage (CES) air conditioning system in a target office building in Kaohsiung, Taiwan, is discussed in order to simulate the cost benefit of the CES system from the perspective of the utility and from that of the participant. The results confirm the effectiveness of the developed models in simulating the economic benefits of implementing a DSM program from the perspectives of both the utility and the participant. (c) 2005 Elsevier Ltd. All rights reserved.
The choice for radial projections of classic data envelopment analysis (DEA) models, resulting in a number of projections onto the Pareto-inefficient portion of the frontier, has been seen lately as a disadvantage in ...
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The choice for radial projections of classic data envelopment analysis (DEA) models, resulting in a number of projections onto the Pareto-inefficient portion of the frontier, has been seen lately as a disadvantage in DEA. The search for a non-radial projection method resulted in developments such as preference structure models. These models consider a priori preference incorporation, using weights in the search for the most preferred efficient target, although presenting some implementation difficulties. In this paper, we propose a multi-objective approach that determines the bases for a posteriori preference incorporation, through individual projections of each variable ( input or output) as an objective function, thus allowing one to obtain a target at every extreme-efficient point on the frontier. This multi-objective approach is shown to be equivalent to the preference structure models, yet presenting some advantages, such as the mapping of the possible weights, assigned to partial efficiencies of an observed unit, in order to reach a specific target.
This paper concerns the solution of fuzzy linearprogramming (FLP) problems which involve fuzzy numbers in coefficients of objective functions. Firstly, a number of concepts of optimal solutions to FLP problems are in...
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This paper concerns the solution of fuzzy linearprogramming (FLP) problems which involve fuzzy numbers in coefficients of objective functions. Firstly, a number of concepts of optimal solutions to FLP problems are introduced and investigated. Then, a number of theorems are developed so as to convert the FLP to a multi-objective optimization problem with four-objective functions. Finally, two illustrative examples are given to demonstrate the solution procedure. It also shows that our method of solution includes an existing method as a special case, (C) 2002 Elsevier Science Inc. All rights reserved.
The cost of purchasing raw materials and components accounts for a significant fraction in the production and manufacturing industry. Modeling of purchasing issues and hence using quantitative methods to study the pur...
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The cost of purchasing raw materials and components accounts for a significant fraction in the production and manufacturing industry. Modeling of purchasing issues and hence using quantitative methods to study the purchasing issues are significant for a purchasing decision-maker (DM) with comparable schemes. This paper is grounded on purchasing of bulk raw materials of a certain large-scale steel plant. It establishes a multi-objective linear programming model (MOLP) for the special issues of purchasing these raw materials, and indicates selecting items, selecting vendors and deciding ordering quantity as the key issue in optimizing purchasing policies. A kind of multi-objective optimal method, the point estimate weighted-sums, is used to solve this model. A group of really numeral computational results show that the model is effective and can take effect on the determination. of purchasing decision. In the end of this paper, the future research directions on purchasing of raw materials are pointed out. (C) 2003 Published by Elsevier Science B.V.
To reduce the environmental and economical impact of soil erosion resulting from improper management of land-use activities, a study was initiated by the Iranian Ministry of Construction on Syahrood, one of the sub-ba...
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To reduce the environmental and economical impact of soil erosion resulting from improper management of land-use activities, a study was initiated by the Iranian Ministry of Construction on Syahrood, one of the sub-basins of the Damavand watershed in Iran. Land-use optimization is one of the appropriate strategies for soil conservation. It can empower the decision maker or watershed manager to choose from different land-use scenarios to reach the best decision with the different combinations of variables. The output results of the sediment yield model, including the integration of the Modified Universal Soil Loss Equation (MUSLE) with Spatial Analysis System-Geographic Information System (SPANS-GIS), along with the net income of each land use were used as input in the land-use optimization model for minimizing the sediment yield and maximizing farm production of each land use. The multi-objective linear programming simplex method of Steuer (1995) was used to solve the problem. The optimization process allocated dryland farming, areas to rangelands if no changes were made to the current supporting practice system. The expected annual sediment yield from the entire sub-basin was reduced by 2420 tonnes/year (or by 5%) and the annual net farm income was increased by 3.99 billion Iranian Rial/year (or by 134%).
This paper presents a cooperative production planning method. The method is mainly based on a horizontal decomposition, every department of the plant cooperates in order to obtain a good global plan. Coordination is m...
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This paper presents a cooperative production planning method. The method is mainly based on a horizontal decomposition, every department of the plant cooperates in order to obtain a good global plan. Coordination is made by the plant managers. The departments will solve multi-objective linear programming problems. In this process a large number of goals is under attention, including goals expressed by expert knowledge. A production plan with high quality technical and economic features can be very fast constructed in order to extend through worldwide the processing business of petrochemical plants
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