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In this paper, we discuss discrete location problems where the objective is to locate a given number of facilities of different types in order to appropriately cover a given set of demand points. The coverage provided to each demand point is the result of the cooperation among the located facilities. The different types of facility refer to the coverage radius or quality of coverage that each type may provide. We present a non‐linear formulation of the problem where the objective is to maximize the percentage of demand points that are appropriately covered. We then show how the model can be linearized based on a representation of probabilities through a network structure. To address large instances of the problem, we introduce a genetic algorithm (GA) that is based on the representation of the solution by two chromosomes. Computational experiments over a wide range of randomly generated problems indicate the GA clearly outperforms CPLEX, especially in larger instances. [ABSTRACT FROM AUTHOR]

This paper presents a simple and easy-to-use methodology for designing the menu in Food and Beverage (F&B) enterprises over a period of time, considering that certain elements of the problem are subject to uncertainty. The methodology considers both nutritional and financial aspects and allows the decision makers to explore the effect of the uncertainty on the final solutions, according to their perception of risk. The proposed methodology is based on multi-objective mixed integer programming and in particular the Almost Robust Optimization (ARO) approach introduced by Baron et al. (2019). In contrast to conventional Robust Optimization techniques, the ARO approach is more flexible and offers the decision makers the possibility to express their attitude towards risk through appropriate parameters and obtain a series of solutions corresponding to different levels of risk. The proposed model is applied in a case study concerning F&B enterprises from the island of Crete, Greece, using real data that was collected in collaboration with nutritionists and managers employed in F&B enterprises. Decision-makers in F&B enterprises may use the proposed model as a decision support tool to incorporate the inherent uncertainty into the decision-making process. Through appropriate parameters, they may select optimal diets that are feasible for most realizations of the uncertain parameters, without incurring significant increases in cost. The model is flexible and produces a series of alternative solutions based on the decision makers' preferences and perception of risk. [ABSTRACT FROM AUTHOR]

An integrated system consisting of a geographical information system (GIS) module, a mathematical programming (MP) and a stimulation module for forest fire control are presented. The use of the GIS simplifies the computation of the distances between the various spatial points and the speed of the vehicles of different size and capacity and that the system can incorporate analytical models concerned with the prediction of fire behavior.

A multiobjective model was used to determine disposal or treatment facilities and transporting hazardous waste along the connection of a transportation network. Some points of this network may be population centers producing toxic waste which must be transported to the treatment facilities. The study considered four objectives including minimisation of total operating cost and minimisation of total perceived risk. A goal programming model was developed to solve the problem.

Byline: Ioannis Giannikos, Panagiotis Polychroniou This study introduces a fuzzy goal programming model for allocating tasks to employees in teamwork. Each task as well as each employee is represented in a multidimensional ability space on a team basis. The model also considers the possibility of improving employees' abilities through training and of using subcontracting or overtime, if necessary. The proposed model includes four fuzzy goals, which refer to: (1) total training cost, (2) total distance between tasks and employees in the ability space, (3) cost of idle time and (4) cost of overtime. We discuss the applicability of the model using real data from a major Greek bank. Some implications of the study are discussed, including the need for assigning tasks to employees on a team basis taking into account their abilities. Team managers reach better decisions by allocating tasks through a systematic process that includes in the analysis the objectives of the organisation as well as the interests of the employees. As a result, teamwork is encouraged as the objectives of task allocation become evident to all interested parties.