Applications of simulation and optimization techniques in optimizing room and pillar mining systems

The goal of this research was to apply simulation and optimization techniques in solving mine design and production sequencing problems in room and pillar mines (R&P). The specific objectives were to: (1) apply Discrete Event Simulation (DES) to determine the optimal width of coal R&P panels under specific mining conditions; (2) investigate if the shuttle car fleet size used to mine a particular panel width is optimal in different segments of the panel; (3) test the hypothesis that binary integer linear programming (BILP) can be used to account for mining risk in R&P long range mine production sequencing; and (4) test the hypothesis that heuristic pre-processing can be used to increase the computational efficiency of branch and cut solutions to the BILP problem of R&P mine sequencing. A DES model of an existing R&P mine was built, that is capable of evaluating the effect of variable panel width on the unit cost and productivity of the mining system. For the system and operating conditions evaluated, the result showed that a 17-entry panel is optimal. The result also showed that, for the 17-entry panel studied, four shuttle cars per continuous miner is optimal for 80% of the defined mining segments with three shuttle cars optimal for the other 20%. The research successfully incorporated risk management into the R&P production sequencing problem, modeling the problem as BILP with block aggregation to minimize computational complexity. Three pre-processing algorithms based on generating problem-specific cutting planes were developed and used to investigate whether heuristic pre-processing can increase computational efficiency. Although, in some instances, the implemented pre-processing algorithms improved computational efficiency, the overall computational times were higher due to the high cost of generating the cutting planes --Abstract, page iii

Multiobjective Optimal Formulations for Bus Fleet Size of Public Transit under Headway-Based Holding Control

In recent years, with the development of advanced technologies for data collection, real-time bus control strategies have been implemented to improve the daily operation of transit systems, especially headway-based holding control which is a proven strategy to reduce bus bunching and improve service reliability for high-frequency bus routes, with the concept of regulating headways between successive buses. This hot topic has inspired the reconsideration of the traditional issue of fleet size optimization and the integrated bus holding control strategy. The traditional headway-based control method only focused on the regulation of bus headways, without considering the number of buses on the route. The number of buses is usually assumed as a given in advance and the task of the control method is to regulate the headways between successive buses. They did not consider the bus fleet size problem integrated with headway-based holding control method. Therefore, this work has presented a set of optimal control formulations to minimize the costs for the passengers and the bus company through calculating the optimal number of buses and the dynamic holding time, taking into account the randomness of passenger arrivals. A set of equations were formulated to obtain the operation of the buses with headway-based holding control or the schedule-based control method. The objective was to minimize the total cost for the passengers and the bus company in the system, and a Monte Carlo simulation based solution method was subsequently designed to solve the optimization model. The effects of this optimization method were tested under different operational settings. A comparison of the total costs was conducted between the headway-based holding control and the schedule-based holding control. It was found that the model was capable of reducing the costs of the bus company and passengers through utilizing headway-based bus holding control combined with optimization of the bus fleet size. The proposed optimization model could minimize the number of buses on the route for a guaranteed service level, alleviating the problem of redundant bus fleet sizes caused by bus bunching in the traditional schedule-based control method. Document type: Articl

Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection

Mobile collection of waste electrical and electronic equipment is a collection method that is convenient for residents and companies. New opportunities to use mobile apps and internet applications facilitate the ordering of waste pickups from households and preparation of a collection plan for a waste collection company. It improves the secondary raw materials collection in a circular economy approach after recycling waste equipment. This study presents a combined methodology for improving the efficiency of e-waste collection. An online ewaste collection supporting systemuses a Harmony Search algorithm for route optimization of waste collection vehicles. The results of the optimization are better compared to other artificial intelligence algorithms presented in the literature and the number of visited collection points is higher from1.2%–6.6% depending on the compared algorithm. To increase the efficiency ofwaste loading and packing, a novel collection vehicle body construction is presented. The design includes the convenient loading of waste from both sides of the vehicle and the rear side being equippedwith a hydraulic lift. The proposed vehiclemodel can be used for e-waste collection in placeswith limited parking spaces or where the parking time is limited, such as in densely populated city centers. The waste equipment packing efficiency increases and eliminates the necessity of including a container loading problem in the algorithm and allows increasing waste equipment number loaded in a collection vehicle

Determination of vehicle requirements of AGV system based on discrete event simulation and response surface methodology

The determination of AGV vehicle requirements in a manufacturing system has a great impact on the system performance. This paper first defines the AGV vehicle requirement determination as a general optimization problem, and secondly develops a new AGV vehicle requirement determination method capable of effective solving the problem. This method features with the combination of discrete event simulation (DES), sensitivity analysis, fractional factorial design (FFD) and response surface methodology (RSM). Tests and comparisons with other simulation based methods have shown that the proposed method combining the simulation method with analytical method, can make full use of their respective advantages and overcome the defects of existing methods. It is more practical

Reverse Logistics and Urban Logistics: Making a Link

5684This work is aimed at analyzing potential links between reverse logistics and urban logistics and describing opportunities for collaboration between both areas of research. A description of the current state-of-the-art is provided in order to highlight the main challenges faced by both disciplines. For example, regarding reverse logistics, new recovery options, marketing strategies for recovered products, and legislation issues on the return of products in specific contexts; in regards to urban logistics, long-term planning, stakeholders’ engagement, information management, efficiency, reliability and safety, and new business models are some of such key challenges. Despite the growing interest shown in both logistics areas and their relevance for companies and consumers, reverse logistics and urban logistics are two concepts that are still somewhat unknown and, above all, treated as being relatively separated. However, there exist some aspectswhere the two disciplines converge and thatmay represent opportunities for collaboration, for example, the proper treatment and management of urban waste, and the efficient management of commercial refunds and returns. In addition, other key issues, such as land use, city typology, infrastructures, and stakeholders’ engagement should be further analyzed in order to keep advancing in the description of links between both areas.S

A decision model for a strategic closed-loop supply chain to reclaim End-of-Life Vehicles

Closed-loop supply chain strategies for End-of-Life (EOL) products and their logistics operations have received greater attention in recent years from supply chain research community. These strategies include warranty–based acquisition, quantity–based acquisition, quality–based acquisition, centrally coordinated logistics operations and third-party logistics (3PL) operations. The proposed research integrates two important aspects of an automobile's closed-loop supply chain strategy. The first aspect is optimal transportation planning for raw material parts, newly manufactured and EOL products in a closed-loop supply chain, using demand, collection rate and capacity of associated facilities in the network as functional parameters. We formulated a mixed integer mathematical model for the closed-loop supply chain network with a multi-echelon inventory, multi-period planning and multi-product scenario, which are used to compute the maximum contribution margin generated through different strategies. The second aspect pertains to using the output of the proposed model in first stage to handle the sequential form of a cooperative game. The proposed two–phase decision model analyzes the realization times and delivery limits of different products as an indicator of swapping different strategies. We analyze three instances to understand and validate the applicability of the model. In these scenarios, sensitivity analysis has been performed to demonstrate the robustness of the proposed model. We present managerial insights, leading to flexibility in decision making. It is observed that the demand, collection rate and capacity of network facilities create highly sensitive trilogy for the contribution margin of proposed network. The outcome of this research firstly confers optimal amounts of mass flows in the closed loop supply chain network from a state of the end product (new products, recycled products and non–recycled used products) to a state of the raw material (ferrous metal, non-ferrous metal and shredder fluff). Secondly, authors culminated a confound dichotomy among all reintegration strategies (conveyance, acquisition and cannibalization) by distinct enumeration and quantification (regarding realization times and delivery limits) of each one to forge a robust planning horizon for original equipment manufacturer

Redesigning Large-Scale Multimodal Transit Networks with Shared Autonomous Mobility Services

Public transit systems have faced challenges and opportunities from emerging Shared Autonomous Mobility Services (SAMS). This study addresses a city-scale multimodal transit network design problem, with shared autonomous vehicles as both transit feeders and a direct interzonal mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and designs transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, while satisfying existing transit origin-destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each clustered zone are characterized with continuous approximation. Afterward, the decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. To demonstrate the framework efficiency, this study uses transit demand from the Chicago metropolitan area to redesign a multimodal transit network. The computational results present savings in travelers' journey time and operators' costs, demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.Comment: 44 pages, 15 figures, under review for the 25th International Symposium on Transportation and Traffic Theory (ISTTT25

Fleets of robots for environmentally-safe pest control in agriculture

Feeding the growing global population requires an annual increase in food production. This requirement suggests an increase in the use of pesticides, which represents an unsustainable chemical load for the environment. To reduce pesticide input and preserve the environment while maintaining the necessary level of food production, the efficiency of relevant processes must be drastically improved. Within this context, this research strived to design, develop, test and assess a new generation of automatic and robotic systems for effective weed and pest control aimed at diminishing the use of agricultural chemical inputs, increasing crop quality and improving the health and safety of production operators. To achieve this overall objective, a fleet of heterogeneous ground and aerial robots was developed and equipped with innovative sensors, enhanced end-effectors and improved decision control algorithms to cover a large variety of agricultural situations. This article describes the scientific and technical objectives, challenges and outcomes achieved in three common crops

Transportation-mission-based Optimization of Heterogeneous Heavy-vehicle Fleet Including Electrified Propulsion

Commercial-vehicle manufacturers design vehicles to operate over a wide range of transportation tasks and driving cycles. However, certain possibilities of reducing emissions, manufacturing and operational costs from end vehicles are neglected if the target range of transportation tasks is narrow and known in advance, especially in case of electrified propulsion. Apart from real-time energy optimization, vehicle hardware can be meticulously tailored to best fit a known transportation task. As proposed in this study, a heterogeneous fleet of heavy-vehicles can be designed in a more cost- and energy-efficient manner, if the coupling between vehicle hardware, transportation mission, and infrastructure is considered during initial conceptual-design stages. To this end, a rather large optimization problem was defined and solved to minimize the total cost of fleet ownership in an integrated manner for a real-world case study. In the said case-study, design variables of optimization problem included mission, recharging infrastructure, loading--unloading scheme, number of vehicles of each type, number of trips, vehicle-loading capacity, selection between conventional, fully electric, and hybrid powertrains, size of internal-combustion engines and electric motors, number of axles being powered, and type and size of battery packs. This study demonstrated that by means of integrated fleet customization, battery-electric heavy-vehicles could strongly compete against their conventional combustion-powered counterparts. Primary focus has been put on optimizing vehicle propulsion, transport mission, infrastructure and fleet size rather than routing