Plant Location Problem Research Articles

Optimizing sustainable biomass–coal co-firing power plant location problem under ambiguous supply

Promoting biomass–coal co-firing power generation technology in existing coal-fired power plants is a practical and relatively low-cost short-run choice for the coal-fired power industry to reduce carbon emissions and develop sustainably. However, the uncertainty of biomass supply complicates the location of co-firing power plants among existing coal-fired plants. To address the challenge that only limited historical data of uncertain supply are available, this paper proposes a new ambiguity distribution set to characterize the uncertain supply capacity, supply price, and supply quality (i.e., energy conversion coefficient). Accordingly, a novel bi-objective distributionally robust fuzzy optimization model is developed for the sustainable co-firing power plant location (SCPPL) problem to balance conflicting objectives of minimizing total cost and maximizing carbon emission reduction. Subsequently, the computationally tractable counterpart of the proposed model is derived against the worst-case distribution by reformulating the distributionally robust credibility objective and constraints as their solvable equivalent forms. Furthermore, the Pareto optimal solutions of the proposed bi-objective model are generated using the augmented ϵ-constraint method. Finally, a realistic case study of Heilongjiang Province in China is conducted to verify the feasibility and effectiveness of the proposed optimization method. The experimental results demonstrate that the proposed model can hedge against the distributional ambiguity of uncertain parameters and provides a robust co-firing power plant location strategy. By comparison with the nominal distribution model and analysis of the sensitivity of selected parameters, management insights are summarized for decision-makers.

The capacitated location problem of precast concrete component factory

The location and capacity of precast concrete component factories (PC component factories) are not only the key factors for manufacturers to gain competitive advantage, but also the important factors affecting the operational efficiency of the prefabricated construction supply chain. This paper takes the capacitated location problem of PC component factories as the research object. Drawing on the model of traditional capacitated plant location problem, the model of capacitated location problem of PC component factories is constructed by setting the optional production scale by stages. According to the characteristics of this model, the optimal strategy of location is determined by using the Tabu search algorithm. Taking the location problem of PC component factory in the Beijing-Tianjin-Hebei region as the object, the calculation example is designed, in which the influence of the distance parameters on the results of location problem is analyzed. The results can make the configuration of regional PC component factories more reasonable and balanced.

A semi-Lagrangian relaxation heuristic algorithm for the simple plant location problem with order

The Simple Plant Location Problem with Order (SPLPO) is a variant of the Simple Plant Location Problem (SPLP) where the customers have preferences over the facilities that will serve them. In particular, customers define their preferences by ranking each of the potential facilities from the most preferred to the least preferred. Even though the SPLP has been widely studied in the literature, the SPLPO, which is a harder model to deal with, has been studied much less and the size of instances that can be solved is very limited. In this article, we study a Lagrangian relaxation of the SPLPO model and we show that some properties previously studied and exploited to develop efficient SPLP solution procedures can be extended for their use in solving the SPLPO. We propose a heuristic method that takes a Lagrangian relaxation solution as the starting point of a semi-Lagrangian relaxation algorithm. Finally, we include some computational studies to illustrate the good performance of this method, which quite often ends with the optimal solution.

New variants of the simple plant location problem and applications

This work revisits novel variants of the Simple Plant Location Problem (SPLP), which were originally presented in the Ph.D. thesis Set packing, location and related problems (2019). They consist in considering different requirements on clients allocation to facilities, namely incompatibilities or co-location for some specific pairs of clients. These correspond to “cannot-link” and “must-link” constraints previously studied in the related topic of clustering. Set packing formulations of these new location problems are presented, together with families of valid inequalities and facets. Computational experience supporting the effectiveness of the resulting cuts into a branch and cut procedure is exposed. The comprehensive revision on these new variants of the SPLP is completed by illustrative applications, including the design of telecommunication networks and interesting connections in the field of Genetics, and also by the analysis of open questions and future research directions.

Optimization of plant location problem in interval domain via particle swarm optimization

Optimization of plant location problem in interval domain via particle swarm optimization

Learning management system‐integrated spreadsheet exercises for facility location modeling

AbstractSupply chain management students are introduced to a variety of modeling concepts. Yet learning facility location and capacity allocation modeling to the level of application and mastery proves difficult for the typical undergraduate business student. Spreadsheets facilitate the teaching of optimization modeling; however, providing feedback to students on spreadsheet assignments is an onerous and time‐consuming task for instructors. In this teaching brief, a set of learning management system (LMS)‐integrated spreadsheet exercises is presented which helps students learn spreadsheet modeling for the capacitated plant location problem. These exercises are also designed to reduce the instructor's grading burden while providing timely feedback and assessment of student performance. The effectiveness of the exercises is demonstrated by assessment data gathered from the LMS as well as postmodule student survey results.

A separation algorithm for the simple plant location problem

The Simple Plant Location Problem (SPLP) is a well-known NP-hard optimisation problem with applications in logistics. Although many families of facet-defining inequalities are known for the associated polyhedron, very little work has been done on separation algorithms. We present the first ever polynomial-time separation algorithm for the SPLP that separates exactly over an exponentially large family of facet-defining inequalities. We also present some promising computational results.

Solving Classic Discrete Facility Location Problems Using Excel Spreadsheets

There are a variety of discrete facility location models that have practical relevance for operations management and management science courses. Integer linear programming (ILP) is the standard technique for solving such problems. An alternative approach that is often conceptually appealing to students is to pose the problem as one of finding the best possible subset of p facilities out of n possible candidates. I developed an Excel workbook that allows students to interactively evaluate the quality of different subsets, to run a VBA macro that finds the optimal subset, or to solve an ILP formulation that finds the optimal subset. Spreadsheets are available for five classic discrete location models: (1) the location set-covering problem, (2) the maximal covering location problem, (3) the p-median problem, (4) the p-centers problem, and (5) the simple plant location problem. The results from an assignment in a master’s-level business analytics course indicate that the workbook facilitates a better conceptual understanding of the precise nature of the discrete facility location problems by showing that they can be solved via enumeration of all possible combinations of p subsets that can be drawn from n candidate locations. More important, students directly observe the superiority of ILP as a solution approach as n increases and as p approaches n/2.

Decision Support Technique Based on Spherical Fuzzy Yager Aggregation Operators and Their Application in Wind Power Plant Locations: A Case Study of Jhimpir, Pakistan

The problem of energy crisis and environmental pollution has been mitigated by the generation and use of wind power; however, the choice of locations for wind power plants is a difficult task because the decision-making process includes political, socioeconomic, and environmental aspects. Thus, several adverse consequences have been created by the choice of suboptimal locations. The objective of this paper is to address the integrated qualitative and quantitative multicriteria decision-making framework for the selection of wind power plant locations. Spherical fuzzy sets are the latest extension of the ordinary fuzzy sets. The main characteristic of the spherical fuzzy sets is satisfying the condition that the squared sum of the positive, neutral, and negative grades must be at least zero and at most one. In this research, we establish novel operational laws based on the Yager t-norm and t-conorm under spherical fuzzy environments (SFE). Furthermore, based on these Yager operational laws, we develop list of novel aggregation operators under SFE. In addition, we design an algorithm to tackle the uncertainty to investigating the best wind power plant selection in four potential locations in Pakistan. A numerical example of wind power plant location problem is considered to show the supremacy and effectiveness of the proposed study. Also, a detailed comparison is constructed to evaluate the performance and validity of the established technique.

Decision Support Technique Based on Neutrosophic Yager Aggregation Operators: Application in Solar Power Plant Locations—Case Study of Bahawalpur, Pakistan

The problem of energy crisis and environmental pollution has been mitigated by the generation and use of solar power; however, the choice of locations for solar power plants is a difficult task because the decision-making process includes political, socio-economic, and environmental aspects. Thus, several adverse consequences have been created by the choice of suboptimal locations. The objective of this paper is to address the integrated qualitative and quantitative multicriteria decision-making framework for the selection of solar power plant locations. Neutrosophic sets (NSs) are the latest extension of the ordinary fuzzy sets. The main characteristic of the neutrosophic sets is satisfying the condition that the sum of the truth, indeterminacy, and falsity grades must be at least zero and at most three. In this research, we establish novel operational laws based on the Yager t-norm and t-conorm under neutrosophic environments (NE). Furthermore, based on these Yager operational laws, we develop a list of novel aggregation operators under NE. In addition, we design an algorithm to tackle the uncertainty to investigating the best solar power plant selection in five potential locations in Pakistan. A numerical example of solar power plant location problem is considered to show the supremacy and effectiveness of the proposed study. Also, a detailed comparison is constructed to evaluate the performance and validity of the established technique.

Analysis of Covering Problem Models for Setting the Location of a Ready-Mixed Concrete Plant: Case Study of the Rayong Province, Thailand

Decision makers must prepare good plans when developing large-scale projects. The Thai government has been increasing economic growth in three provinces under the Eastern Economic Corridor Development Program (EEC). The project includes reconstruction and new construction. This paper aims to find locations for several ready-mixed concrete plants to cover all ready-mixed concrete needs in the Rayong province. The plant location problem could be solved using optimization modelling for a set covering location problem (SCLP) and a maximal covering location problem (MCLP). The results show that two ready-mixed concrete plants location in the Mueang Rayong district and Klaeng district could meet the problem solution.

The double-assignment plant location problem with co-location

In this paper, a new variant of the Simple Plant Location Problem is proposed. We consider additional conditions in the classic location-allocation problem for clients and facilities. Namely, some pairs have to be served by a common plant. The resulting problem can be addressed with existing models for the case of single assignment. However, to the best of our knowledge, the proposed setting when each client must be assigned to a couple of facilities is still unexplored. We examine the implications of adding such new constraints to standard formulations of the SPLP with double assignment. We compare the resulting formulations from a theoretical point of view. After that, we focus on the study of one of the models, which turned out to be a set packing problem. All the clique facets are identified and a separation algorithm is devised. Although the separation problem is proved to be NP-hard, our computational experience shows that the separation algorithm is effective and efficient, reducing computational times and duality gaps for all the instances tested.

Adding incompatibilities to the Simple Plant Location Problem: Formulation, facets and computational experience

We propose and study a new variant of the Simple Plant Location Problem (SPLP). The problem we tackle, which we call Simple Plant Location Problem with Incompatibilities (SPLPI), differs from the classic version in the fact that possible incompatibilities between clients are considered. This variant not only adds flexibility to the SPLP, but also gathers together other previously studied location models. Two clients are said to be incompatible if they cannot be served by the same plant. This circumstance adds a new family of set-packing constraints to the classic set-packing formulation of the SPLP. We study the integer polytope of this restricted formulation, P, which is a tighter version of the polytope of the classic formulation of the problem. All the clique facets of P are identified. Different facets originated by holes of the corresponding conflict graph are described. Separation algorithms for each kind of facet are designed and a computational study to test their performance is presented.

A Hybrid Hesitant Fuzzy Decision-Making Approach for Evaluating Solar Power Plant Location Sites

Investments in energy projects require careful consideration, since inappropriate decisions can lead investors to waste finances, time and other resources. This paper will present the position that decisions for energy projects should be made after considerable, thoughtful and appropriate decision research and analyses. Due to the different aspects related to investment decisions in energy projects, using multi-criteria decision-making approaches is deemed to be a sensible approach to better ensure an appropriate course of action. In this study, the decision regarding location of a solar power plant, a critical issue pertaining to solar energy investments, is considered. Since solar power plant location decisions consist of conflicting criteria and varying possible locations, the solar power plant location problem is considered in this study. The main aim of this study is to propose a decision-making approach for solar power plant location problem which is integrating “Analytic Hierarchy Process” and “Technique for Order Preference by Similarity to Ideal Solution” methods under hesitant fuzzy environment. The applicability of the proposed approach is then tested on a case study. Besides the innovative contributions to industry literature, the results of the case study demonstrate that the proposed approach is applicable for the challenge of determining plant locations.

Bi-objective dependent location quadratic assignment problem: Formulation and solution using a modified artificial bee colony algorithm

Combinatorial optimization problems arise from various real life situations and the quadratic assignment problem (QAP) to model a facility layout problem or a plant location problem is such an example. While examining the facility layout of a semi-automated bus body manufacturing unit, a bi-objective facility layout optimization problem is identified in which the solution space of the second objective function depends and changes upon the feasible solutions of the first objective function. In this paper, the said problem is first defined in the form of a bi-objective quadratic dependent location assignment problem (bi-d-QAP), a heuristic solution approach is then provided, and finally, a modified artificial bee colony algorithm is proposed while combining both the genetic and neighborhood search algorithms to solve the considered bi-d-QAP. The data obtained from the above-mentioned manufacturing unit are utilized to show how the proposed algorithm performs better in comparison to some of the popular state-of-the-art optimization algorithms.

New valid inequalities and facets for the Simple Plant Location Problem

The Simple Plant Location Problem is a well-known (and NP-hard) combinatorial optimisation problem, with applications in logistics. We present a new family of valid inequalities for the associated family of polyhedra, and show that it contains an exponentially large number of new facet-defining members. We also present a new procedure, called facility augmentation, which enables one to derive even more valid and facet-defining inequalities.

A capacitated plant location model for Reverse Logistics Activities

Product remanufacturing is one of the most profitable activities in reverse logistics. Running a business plan, in which companies take responsibility for the waste generated at their end-of-life products, involves making important strategic decisions. One of the challenges in planning the reverse flow of products is decide where installing the reprocessing facilities. This decision influences directly the transport variables costs and the facilities installation fixed costs. This paper proposes a model for the Capacitated Plant Location Problem in Reverse Logistics (CPL-RL), in which we assume that offered material in each collection center is aimed at a single facility for reprocessing. This restriction includes specific cases where there is no logistic availability in the network to send the collected material to different locations. The Mixed Integer Problem (MILP) is solved using an algorithm in two steps. In the first step, reduction tests are performed, which ones determine a priori which facilities are opened/closed. If all facilities are fixed opened or closed then the solution is optimal. Although not all facilities can have their status defined that way, the resultant problem has a less number of variables and it is solved using Benders method. The dataset was randomly generated and the results showed that the applied techniques are appropriate, achieving the optimal solution for all test problems.

A Lagrangian heuristic for concave cost facility location problems: the plant location and technology acquisition problem

We propose a Lagrangian heuristic for facility location problems with concave cost functions and apply it to solve the plant location and technology acquisition problem. The problem is decomposed into a mixed integer subproblem and a set of trivial single-variable concave minimization subproblems. We are able to give a closed-form expression for the optimal Lagrangian multipliers such that the Lagrangian bound is obtained in a single iteration. Since the solution of the first subproblem is feasible to the original problem, a feasible solution and an upper bound are readily available. The Lagrangian heuristic can be embedded in a branch-and-bound scheme to close the optimality gap. Computational results show that the approach is capable of reaching high quality solutions efficiently. The proposed approach can be tailored to solve many concave-cost facility location problems.

Location of Farmers Warehouse at Adaklu Traditional Area, Volta Region, Ghana

Postharvest loss is one major problem farmers in Adaklu Traditional Area that most Ghanaian farmers face. As a result, many farmers wallow in abject poverty. Warehouses are important facilities that help to reduce postharvest loss. In this research, Beresnev pseudo-Boolean Simple Plant Location Problem (SPLP) model is used to locate a warehouse at Adaklu Traditional Area, Volta Region, Ghana. This model was used because it gives a straightforward computation and produces no iteration as compared with other models. The SPLP is a problem of selecting a site from candidate sites to locate a plant so that customers can be supplied from the plant at a minimum cost. The model is made up of fixed cost and transportation cost. Location index ordering matrix was developed from the transportation cost matrix and we used it with the fixed cost and differences between variable costs to formulate the Beresnev function. Linear term developed from the function which was partial is pegged to obtain a complete solution. Of the 14 notable communities considered,Adaklu Wayais found most suitable for the setting of the warehouse. The total cost involved is Gh₵78,180.00.

Применение метода последовательных расчетов к одной нелинейной задаче размещения производства

Применение метода последовательных расчетов к одной нелинейной задаче размещения производства