Modeling Discrete Competitive Facility Location

PurposeFacility location and re-location decisions are critical managerial decisions in modern supply chains. Such decisions are difficult in this environment as managers encounter uncertainty and risks. The study investigates establishing or moving distribution facilities in the global supply chain by considering costs, fulfilment, trade uncertainties, risks under environmental trade-offs and disruptive technologies.Design/methodology/approachThis paper combines the possibilities and probabilistic scenarios for a supply chain network by proposing the novel Robust Optimisation and Mixed Integer Linear Programming (ROMILP) method developed under the potential uncertainty of demand while considering the costs associated with a four-tier supply chain network. ROMILP has been solved in a real-time logistics environment by applying a case study approach.FindingsThe solution is obtained using an exact solution approach and provides optimality in all tested market scenarios along the proposed global logistics corridor. A sensitivity analysis examines potential facility location scenarios in a global supply chain context.Research limitations/implicationsLogistics managers can apply the ROMILP model to test the cost-benefit trade-offs against their facility location and relocation decisions while operating under uncertainty. Future research is proposed to extend the literature by applying data from the OBOR logistics corridor.Originality/valueThis study is the first to examine sustainable dimensions along the global logistics corridor and investigate the global container traffic perspective. The study also adds value to the Middle East logistics corridor regarding facility location decisions.

Reliable Facility Location Under Event-Correlated Uncertainty

A stochastic multiproduct capacitated facility location problem involving a single supplier and multiple customers is investigated. Due to the stochastic demands, a reasonable amount of safety stock must be kept in the facilities to achieve suitable service levels, which results in increased inventory cost. Based on the assumption of normal distributed for all the stochastic demands, a nonlinear mixed-integer programming model is proposed, whose objective is to minimize the total cost, including transportation cost, inventory cost, operation cost, and setup cost. A combined simulated annealing (CSA) algorithm is presented to solve the model, in which the outer layer subalgorithm optimizes the facility location decision and the inner layer subalgorithm optimizes the demand allocation based on the determined facility location decision. The results obtained with this approach shown that the CSA is a robust and practical approach for solving a multiple product problem, which generates the suboptimal facility location decision and inventory policies. Meanwhile, we also found that the transportation cost and the demand deviation have the strongest influence on the optimal decision compared to the others.

Assalamu’alaikum Wr. Wb. and Greetings.I am pleased and honored to welcome you at the First International Conference on Computer Science and Applied Mathematic (ICCSAM 2018), which was held this year by AMIK and STIKOM Tunas Bangsa Pematangsiantar in collaboration with the Indonesian Computer and Information Professional Association (IPKIN), Indonesian Mathematical Society (INDOMS) and Bank Muamalat. As Chair of the Foundation, I would like to convey a greeting Welcome to Parapat City Lake Toba to presenters from various countries and especially those I respect for the presence of scientists in the field of Computer and Mathematical Sciences and industry practitioners in the 2018 ICCSAM event.I see that this activity is designed to enhance the exchange of knowledge and new discoveries in computer science and mathematics and related fields in industry. I hope that scientists in the field of computer science and mathematics and those who work in the industry can share knowledge and work together as a team that has mutual relations with each other. I am very pleased to say that the theme of this conference, “Advancing Computability Innovation” is very much in line with the objectives of AMIK and STIKOM conferences and missions, namely “to become a Science and Technology based Study Program that meets industrial needs and functions as a research center in Information Data Science. The conference was held in response to output and aoutcame which had a significant contribution in the field of computer science and mathematics as an inseparable unit of science towards the development of local and global industries. It is our happiness and honor to welcome distinguished professors present to convey their expertise at this conference.I hope this meeting will enable the development of productive dialogue between participants from various countries. They also provide invaluable opportunities for networking among participants, institutions and industries. I also hope that diversity in these fields can reveal more opportunities for researchers and practitioners from all over the world to start a lot of research related to the industry in the future.I would like to congratulate AMIK and STIKOM Tunas Bangsa for starting this conference with a synergistic contribution from dedicated partners namely the USU Faculty of Mathematics and Natural Sciences, USU’s Faculty of Computer and Information Technology, the Indonesian Computer and Informatics Professional Association (IPKIN), Indonesian Mathematical Society (INDOMS), Bank Muamalat, Malaysia Pahang University (UMP), University of Essex, United Kingdom, Ankara, Turkey and Institute of Applied Mathematics Middle East Technical University to all delegates for their full support, cooperation and contribution to the 2018 ICCSAM. I also want to thank the Organizing Committee for their perseverance and extraordinary efforts. Various sponsors are also rewarded for their contributions. I really hope that all participants will get a pleasant stay here at NIAGARA HOTEL and bring back unforgettable experiences and valuable knowledge from this conference.Thanks.H. Ahmad Ridwansyah Putra

Facility location is an integral part of the strategic planning process of almost every organization. Selecting the right location for software systems facilities involves considering various factors to ensure optimal performance, reliability, and cost-effectiveness. For business success, and competitive advantage there are some critical factors that very highly affect facility location. They are proximity to customers, infrastructure, labor quality, total cost, suppliers, etc. The criteria for selecting a facility location may be vaguely defined or open to interpretation. External factors such as economic conditions, political stability, and environmental risks may introduce vagueness and unpredictability into facility location decisions. These factors are often beyond the control of decision-makers but can have significant implications for the success of software systems. In this paper we apply fuzzy AHP-TOPSIS hybrid method for facility location in software systems. This offered model combines the fuzzy AHP and the fuzzy TOPSIS methods. Fuzzy AHP (Analytic Hierarchy Process) and fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) are both decision-making methods commonly used in facility location selection. AHP helps in structuring a decision problem into a hierarchy of criteria and alternatives, while TOPSIS ranks alternatives based on their distance to the ideal solution. In the first part of the facility location selection process, we use fuzzy AHP method for determining weights of criteria that are important in selection process. Then by using fuzzy TOPSIS we rank alternatives and select appropriate location for facility.

Humanitarian logistics and supply chain management have emerged as areas of research interest in the last decade, owing to increased frequencies and impacts of disasters. Facility location decisions are critical to the success of any humanitarian relief effort and thus have found significant attention from researchers and practitioners. This study aims to present current practices, trends and developments in facility location research. They are then used to identify and recommend areas for future research. A systematic review of existing literature is carried out over a period of 11 years (2005-2015) by classifying it exhaustively on basis of several dimensions such as disaster types, speed of occurrence, methodology adopted, model structure, objective functions, constraints and solution techniques. The literature is also categorised according to year of publication and journals. The paper establishes an agenda for future research in facility location domain of humanitarian logistics using an operational research perspective.

The multiple facility location problem (MFLP) is to locate certain facilities so as to serve optimally a given set of customers, whose locations and requirements are known. When the facility locations have to be selected from a given set of candidate locations, the corresponding location problem becomes a discrete multiple facility location problem (DMFLP). In this study we consider a special case of DMFLP where multiple facilities that are of different type are to be placed (location decision) and assign customers to these facilities (allocation or assignment). The case will be discussed based on with and without interactions among new facilities. We then propose new heuristic solution methods and branch-and-bound algorithms. Computational results on randomly generated data in comparison with optimal solutions indicate that the new methods are both accurate and efficient.

Optimizing Facility Location Decisions Considering Health Impact on Local Population

To model flexible objectives for discrete location problems, ordered median functions can be applied. These functions multiply a weight to the cost of fulfilling the demand of a customer which depends on the position of that cost relative to the costs of fulfilling the demand of the other customers. In this paper a reformulated and more compact version of a covering model for the discrete ordered median problem (DOMP) is considered. It is shown that by using this reformulation better solution times can be obtained. This is especially true for some objectives that are often employed in location theory. In addition, the covering model is extended so that ordered median functions with negative weights are feasible as well. This type of modeling weights has not been treated in the literature on the DOMP before. We show that several discrete location problems with equity objectives are particular cases of this model. As a result, a mixed-integer linear model for this type of problems is obtained for the first time.

Facility location and scale decision problem with customer preference

Facility location methods play a crucial role in specifying the optimum location options for various types of facilities. A question that arises is what makes a facility location decision a sustainable one? Facility location, also known as location analysis, is a known concept in the literature, but sustainable facility location is not. This requires appropriately defining the concept and framing the problem in order to address the relevant issues. Facility location models in the existing literature do not effectively include all the requirements of sustainable development. This paper serves as a discussion of the current literature concerning the sustainability aspects of the location problem. The aim is to conduct a comprehensive literature review to identify the characteristics of the sustainable facility location problem and propose a framework for classification of sustainability characteristics. The study shows that the location literature has steadily progressed toward considering not only economic but also social and environmental criteria in location decisions; but that many steps remain to be taken toward developing location models that integrate all three aspects of sustainability into decision making. The main motivation for the current study is to provide a foundation from which issues of sustainable development can be built into facility location and siting models.

Contributors

We consider a class of problems where a given number p of facility locations must be selected from a set of s potential locations so as to optimize a pre-determined fitness function. There exist outstanding location problems in this class, such as the p-median, max-covering, MAXCAP, and MAXPROFIT problems. These location problems may contain more than a single global optimal solution. Obtaining multiple global optimal solutions allows us to consider other characteristics in the process of selecting the preferred solution. In [B. Pelegrín, J. Redondo, P. Fernández, I. García, and P. Ortigosa, GASUB: Finding global optima to discrete location problems by a genetic-like algorithm, J. Glob. Optim. 38 (2007), pp. 249–264], a new ‘multimodal’ algorithm (gasub) was presented for solving the previously mentioned location problems. In the paper, the algorithm was fine-tuned to obtain a single global optimum (with 100% success) as fast as possible, and it was compared with two widely used techniques, that is, the standard optimizer Xpress-MP [Xpress-MP, Dashoptimization, 2004.] and the Multi Start Heuristic. In this paper, we propose a new set of parameter values so that gasub can explore the space more deeply and consequently find several alternative global optimal solutions. Moreover, two coarse-grain parallelizations of gasub are presented, cggasub and cggasub_mo. cggasub is able to reduce the computational time of gasub where solutions of the same quality are generated, while cggasub_mo finds more alternative global optima than the sequential version and spending similar computational time in the process. gasub, with the new parameter setting and also the parallel algorithms, will be evaluated using a comprehensive computational set of experiments. Additionally, some criteria can also be used to select one of the known global optimal solutions when several alternatives optimize the corresponding objective function.

On solving the discrete location problems when the facilities are prone to failure

This paper considers the problem of locating a path-shaped facility of a specific size on a weighted tree network. The criterion for optimality used in this paper is the minimax criterion in which the weighted distance to the farthest vertex from the facility is minimised. The minimax criterion gives acceptable results from the point of view of the fast response for the clients who are located far away from the facility. This location problem usually has applications in computer science, information science, and operations research. An O(n) time algorithm is proposed for finding the optimal location of a path-shaped facility of a bounded length on a weighted tree network, where n is the number of vertices in the tree.