Earthquakes occurring in urban areas constitute an important concern for emergency management and rescue services. Emergency service location problems may be formulated in discrete space or by restricting the potential location(s) to a specified finite set of points in continuous space. We propose a Multicriteria Spatial Decision Support System to identify shelters and emergency service locations in urban evacuation planning. The proposed system has emerged as an integration of the geographical information systems (GIS) and the multicriteria Decision-Making method of Preference Ranking Organization Method for Enrichment Evaluation IV (PROMETHEE IV). This system incorporates multiple and often conflicting criteria and decision-makers' preferences into a spatial decision model. We consider three standard structural attributes (i.e., durability density, population density, and oldness density) in the form of spatial maps to determine the zones most vulnerable to an earthquake. The information on these spatial maps is then entered into the ArcGIS software to define the relevant scores for each point with regards to the aforementioned attributes. These scores will be used to compute the preference functions in PROMETHEE IV, whose net flow outranking for each alternative will be inputted in ArcGIS to determine the zones that are most vulnerable to an earthquake. The final scores obtained are integrated into a mathematical programming model designed to find the most suitable locations for the construction of emergency service stations. We demonstrate the applicability of the proposed method and the efficacy of the procedures and algorithms in an earthquake emergency service station planning case study in the city of Tehran.
Purpose This paper aims to minimize the total carbon emissions and costs and also maximize the total social benefits. Design/methodology/approach The present study develops a mathematical model for a closed-loop supply chain network of perishable products so that considers the vital aspects of sustainability across the life cycle of the supply chain network. To evaluate carbon emissions, two different regulating policies are studied. Findings According to the obtained results, increasing the lifetime of the perishable products improves the incorporated objective function (IOF) in both the carbon cap-and-trade model and the model with a strict cap on carbon emission while the solving time increases in both models. Moreover, the computational efficiency of the carbon cap-and-trade model is higher than that of the model with a strict cap, but its value of the IOF is worse. Results indicate that efficient policies for carbon management will support planners to achieve sustainability in a cost-effectively manner. Originality/value This research proposes a mathematical model for the sustainable closed-loop supply chain of perishable products that applies the significant aspects of sustainability across the life cycle of the supply chain network. Regional economic value, regional development, unemployment rate and the number of job opportunities created in the regions are considered as the social dimension.
Production scheduling and reliability of machinery are prominent issues in flexible manufacturing systems that are led to decreasing of production costs and increasing of system efficiency. In this paper, multiobjective optimization of stochastic failureprone job shop scheduling problem is sought wherein that job processing time seems to be controllable. It endeavours to determine the best sequence of jobs, optimal production rate, and optimum preventive maintenance period for simultaneous optimization of three criteria of sum of earliness and tardiness, system reliability, and energy consumption. First, a new mixed integer programming model is proposed to formulate the problem. Then, by combining of simulation and NSGA-II algorithm, a new algorithm is put forward for solving the problem. A set of Pareto optimal solutions is achieved through this algorithm. The stochastic failure-prone job shop with controllable processing times has not been investigated in the earlier research, and for the first time, a new hedging point policy is presented. The computational results reveal that the proposed metaheuristic algorithm converges into optimal or near-optimal solution. To end, results and managerial insights for the problem are presented. KEYWORDScontrollable processing times, failure-prone manufacturing system, modified hedging point policy, Pareto optimal solutions, stochastic job shop scheduling | INTRODUCTIONFailure-prone manufacturing systems (FPMSs) can be defined as a subcategory of flexible systems with stochastic breakdown and maintenance of machines. Indeed, the major goal is to identify the production rate; accordingly, holding, shortage, and maintenance costs could be reduced in a long-run planning horizon. FPMSs are models for studying manufacturing systems considering system ambiguities. Buffers in manufacturing systems play the role of decreased impact of machine breakdown on meeting demand. Thus, it seems indispensable to identify the optimal level of buffers to reduce holding and shortage costs. Seeing as machines in flexible manufacturing systems can produce with various speeds, identifying the optimal speed is particularly significant and is defined as the hedging point policy (HPP) in FPMS. Job shop problem is an NP-hard problem in terms of complexity (Garey, Tarjan, & Wilfong, 1988). Given that, the stochastic job shop problem is an NP-hard problem, as well, considering machine breakdown and variable processing speed.
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