In this work we assess the benefits of an integrated planning approach for the supply of raw material and the subsequent production process. The supply part is concerned with the collection of raw materials from geogr...
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In this work we assess the benefits of an integrated planning approach for the supply of raw material and the subsequent production process. The supply part is concerned with the collection of raw materials from geographically dispersed suppliers, while the production planning part addresses the conversion of those raw materials into final products to satisfy customer demand. The proposed model is an extension of the model introduced by Kuhn and Liske (2011) considering dynamic demand and general structures of the bill of materials. We investigate two scenarios: one including raw material inventories at the production site, and the other supposing just-in-time (JIT) supply. Numerical experiments show that substantial cost savings are possible with an integrated planning approach compared to a classical sequential approach. The JIT scenario and situations with a rather low utilization in the production system benefit most from the integration. The proposed supply vehicle routing and production planning problem has a reverse structure unlike the well-studied production-distribution systems. Surprisingly, a sensitivity analysis on the dependency of the cost savings on different parameters show a quite similar behavior for both types of planning problems. (C) 2016 Elsevier B.V. All rights reserved.
In this paper, we propose a new formulation based on benders decomposition approach to solve the thermal unit commitment (UC) problem. In the proposed approach, the UC problem is decomposed into a master problem, whic...
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In this paper, we propose a new formulation based on benders decomposition approach to solve the thermal unit commitment (UC) problem. In the proposed approach, the UC problem is decomposed into a master problem, which is an integer optimization problem, and a subproblem, which is a nonlinear optimization problem. The proper on/off states of the generating units are found by solving the master problem using the mixed-integer programming method. The subproblem utilizes the solution of the master problem to form appropriate cuts and returns the cuts to the master problem for running the next iteration of the UC problem. In both optimization problems, corresponding constraints are exactly modeled. To demonstrate the effectiveness of the proposed approach, simulation results are compared with the results obtained by other methods. (C) 2009 Elsevier Ltd. All rights reserved.
This article introduces a new Cost Management and Decision Support System (DSS) applicable to Order Management. This model is better fit and compatible with today's competitive, and constantly changing, business e...
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This article introduces a new Cost Management and Decision Support System (DSS) applicable to Order Management. This model is better fit and compatible with today's competitive, and constantly changing, business environment. The presented Profitable-To-Promise (PTP) approach is a novel modeling approach which integrates System Dynamics (SD) simulation with mixed-integer programming (MIP). This Order Management model incorporates Activity-Based Costing and Management (ABC/M) as a link to merge the two models. MIP and SD. This combination is introduced as a hybrid Decision Support System. Such a system can evaluate the profitability of each Order Fulfillment policy and generate valuable cost information. Unlike existing optimization-based DSS models, the presented hybrid modeling approach can perform on-time cost analysis. This will lead to better business decisions based on the updated information. (C) 2011 Elsevier B.V. All rights reserved.
It is well known that linear prices supporting a competitive equilibrium exist in the case of convex markets, however, in the presence of integralities this is open and hard to decide in general. We present necessary ...
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It is well known that linear prices supporting a competitive equilibrium exist in the case of convex markets, however, in the presence of integralities this is open and hard to decide in general. We present necessary and sufficient conditions for the existence of such prices for decentralized market problems where market participants have integral decision variables and their feasible sets are given in complete linear description. We utilize total unimodularity and the aforementioned conditions to show that such linear prices exist and present some applications. Furthermore, we compute competitive equilibria for two classes of decentralized market problems arising in energy markets and show that competitive equilibria may exist regardless of integralities.
The main purpose of the paper is to introduce a mixed-integer programming model for the diet problem with glycemic load (GL) values of foods as objective function parameters. It is assumed that the glycemic load value...
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The main purpose of the paper is to introduce a mixed-integer programming model for the diet problem with glycemic load (GL) values of foods as objective function parameters. It is assumed that the glycemic load values are subject to uncertainty. The diet problem with minimum cost function is well-known in the literature. However, the diet problem with minimum total daily GL values of foods that satisfies the daily nutritional and serving size requirements has not been proposed. Robust optimization approach is used to account for uncertainty in the GL values of foods. The decision maker is flexible to tune the degree of uncertainty rather than assuming a worst-case scenario. An experimental analysis with a total of 177 foods is performed based on the nutritional and serving size requirements and the basic food groups recommended by the U.S. Department of Health and Human Services & U.S. Department of Agriculture (USDA). The results of the experimental analysis with different scenarios give different solutions for different degrees of uncertainty. However, some foods are frequently found to be in the optimum solutions. These foods are in good agreement with the literature advising them as a part of a daily diet for attaining low level of blood glucose levels. Although we believe that the proposed diet problem with minimum total GL has contributions for satisfying the daily nutritional and serving size requirements with a minimum level of effect on blood glucose levels, it has several limitations. It is a basic diet problem, and assumes that the overall GL is a linear combination of number of serving sizes with the GL values of foods. It also does not consider any other factors such as several combinations of foods and their varying effects on blood glucose levels. These factors should be considered for the next research. (C) 2014 Elsevier Inc. All rights reserved.
In this paper we consider a generalization of the p-center problem called the r-all-neighbor p-center problem (RANPCP). The objective of the RANPCP is to minimize the maximum distance from a demand point to its rth-cl...
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In this paper we consider a generalization of the p-center problem called the r-all-neighbor p-center problem (RANPCP). The objective of the RANPCP is to minimize the maximum distance from a demand point to its rth-closest located facility. The RANPCP is applicable to facility location with disruptions because it considers the maximum transportation distance after (r - 1) facilities are disrupted. While this problem has been studied from a single-objective perspective, this paper studies two bi-objective versions. The main contributions of this paper are (1) algorithms for computing the Pareto-efficient sets for two pairs of objectives (closest distance vs rth-closest distance and cost vs. rth-closest distance) and (2) an empirical analysis that gives several useful insights into the RANPCP. Based on the empirical results, the RANPCP produces solutions that not only minimize vulnerability but also perform reasonably well when disruptions do not occur. In contrast, if disruptions are not considered when locating facilities, the consequence due to facility disruptions is much higher, on average, than if disruptions had been considered. Thus, our results show the importance of optimizing for vulnerability. Therefore, we recommend a bi-objective analysis. (C) 2014 Elsevier Ltd. All rights reserved.
Environmental integrated production and recycling planning is of great importance for the competitive position of production enterprises. Due to increasing disposal costs for industrial byproducts and waste as well as...
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Environmental integrated production and recycling planning is of great importance for the competitive position of production enterprises. Due to increasing disposal costs for industrial byproducts and waste as well as stronger emission standards. companies will be required to setup and control advanced, environmental friendly production technologies, so that emissions and byproducts will be reduced drastically. Nonavoidable byproducts and used products at the end of their lifetime have to be recycled by the producers. The complexity of the resulting decision problems requires adequate. operations research methods. The following paper deals with the development of sophisticated operations research models for two selected planning problems: recycling of industrial byproducts and dismantling and recycling of products at the end of their lifetime. The models have been applied successfully to large industrial problems in practice in the fields of recycling of demolition waste in a German-French region and byproduct management in the steel industry. The presentations of these two applications follow a case study point of view.
In the literature, various discrete-time and continuous-time mixed-integer linear programming (MIP) formulations for project scheduling problems have been proposed. The performance of these formulations has been analy...
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In the literature, various discrete-time and continuous-time mixed-integer linear programming (MIP) formulations for project scheduling problems have been proposed. The performance of these formulations has been analyzed based on generic test instances. The objective of this study is to analyze the performance of discrete-time and continuous-time MIP formulations for a real-life application of project scheduling in human resource management. We consider the problem of scheduling assessment centers. In an assessment center, candidates for job positions perform different tasks while being observed and evaluated by assessors. Because these assessors are highly qualified and expensive personnel, the duration of the assessment center should be minimized. Complex rules for assigning assessors to candidates distinguish this problem from other scheduling problems discussed in the literature. We develop two discrete-time and three continuous-time MIP formulations, and we present problem-specific lower bounds. In a comparative study, we analyze the performance of the five MIP formulations on four real-life instances and a set of 240 instances derived from real-life data. The results indicate that good or optimal solutions are obtained for all instances within short computational time. In particular, one of the real-life instances is solved to optimality. Surprisingly, the continuous-time formulations outperform the discrete-time formulations in terms of solution quality.
mixedintegerprogramming (MIP) was used to formulate a unit commitment and economic dispatch (UCED) algorithm that included two models for simulating the dynamic performance of compressed-air energy storage (CAES) un...
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mixedintegerprogramming (MIP) was used to formulate a unit commitment and economic dispatch (UCED) algorithm that included two models for simulating the dynamic performance of compressed-air energy storage (CAES) units. The first model assumes CAES operating with fixed efficiencies (FEs) on both the compression and expansion side, similar to models commonly used in industry and academia. The second model is a detailed thermoeconomic (TE) model, which uses power curves obtained by CAES manufacturers to model the effect of cavern pressure on operation limits of both the compressor and expander. The UCED and CAES models were combined to identify the contribution of CAES on minimizing the impacts of wind integration into the Irish power sector in 2020. The results of the UCED-TE model show that the addition of a CAES unit in the North Ireland grid can reduce wind curtailment, CO2 emissions, and system costs. The cost reductions result mainly from reductions in wind curtailment and coal-fired generation. The FE model shows much higher reductions of wind curtailment, CO2 emissions, and total system costs. However, both models show that the benefit of CAES on the system increases with variable renewable energy and CAES installed capacity. The difference in the results of the two models is reduced in scenarios of high CAES capacity. The main conclusions of the study are that existing fixed-efficiencies CAES models overestimate reductions on wind curtailment whereas both models show the system-wide economic benefits of CAES grow considerably as wind production grows.
Scheduling problems on which constraints are imposed with regard to the temporal distances between successive executions of the same task have numerous applications, ranging from task scheduling in real-time systems t...
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Scheduling problems on which constraints are imposed with regard to the temporal distances between successive executions of the same task have numerous applications, ranging from task scheduling in real-time systems to automobile production on a mixed-model assembly line. This paper introduces a new NP-hard optimization problem belonging to this class of problems, namely the Weighted Fair Sequences Problem (WFSP). We present a mathematical formulation for the WFSP based on mixed-integer linear programming (MILP) as well as a series of cuts to improve its resolution via exact methods. Finally, we propose a heuristic solution method that works with much less variables of the WFSP formulation. The reported computational experiments show that, for a given time horizon, the proposed MILP-based heuristic increases the size of WFSP instances that can be tackled in practice. Moreover, its results should be considered as optimal whether a presented conjecture on the WFSP problem is proved true in the future. (C) 2017 Elsevier Ltd. All rights reserved.
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