As a special Travelling Salesman Problem (TSP), the Single-Picker Routing Problem (SPRP) in warehouses is of important theoretical and practical significance. In manual order picking, the items are usually picked up b...
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As a special Travelling Salesman Problem (TSP), the Single-Picker Routing Problem (SPRP) in warehouses is of important theoretical and practical significance. In manual order picking, the items are usually picked up by a picker with a cart. To speed up the picking process, the picker is allowed to stop the cart to pick up the items individually and return to the location of the cart. This paper proposes a mixedintegerlinearprogramming formulation for an order picking problem with the decoupling of the picker and the cart in a multi-block warehouse, where the picker capacity is at most four. And based on this model, order batching problem is also analysed. Through numerical experiments, the cost of above decoupling problems is computed under different settings of the speed ratio and the capacity of the picker while he/she is travelling alone. The results indicate how the decoupling of picker and cart leads to cost reduction. Our model shows good performance for orders of small and medium scale and hence has great potential to improve the order picking operation which is executed by decoupling containers and robots/vehicles in modern digital warehouse systems.
The heavy-haul flexible traction power supply system (HFTPSS), integrated with an energy storage system (ESS) and power flow controller (PFC), offers significant potential for improving energy efficiency and reducing ...
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The heavy-haul flexible traction power supply system (HFTPSS), integrated with an energy storage system (ESS) and power flow controller (PFC), offers significant potential for improving energy efficiency and reducing costs. However, the state of ESS capacity and the uncertainty of traction power significantly affect HFTPSS operation, creating challenges in fully utilizing flexibility to achieve economic system operation. To address this challenge, a classical scenario generation approach combining long short-term memory (LSTM), Latin hypercube sampling (LHS), and fuzzy c-means (FCM) is proposed to quantitatively characterize traction power uncertainty. Based on the generated scenarios, and considering the energy balance and safe operation constraints of HFTPSS, a stochastic optimal energy dispatch model is developed. The model aims to minimize the operational cost for heavy-haul electrified railways (HERs) while accounting for the impact of online ESS capacity degradation on the energy scheduling process. Finally, the effectiveness of the proposed strategy and model is validated using operational data from a real HER system.
This paper proposes a mathematical model for the green forward and reverse logistics network (LN), examining the impact of epidemics such as coronavirus (COVID-19) and human metapneumovirus (HMPV) on this network. Alo...
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This paper proposes a mathematical model for the green forward and reverse logistics network (LN), examining the impact of epidemics such as coronavirus (COVID-19) and human metapneumovirus (HMPV) on this network. Alongside managing the network, a new support center and dedicated infectious waste recycling and disposal facilities have been established. A mixed-integer linear programming (MOMILP) approach is employed for modeling a green forward and reverse LN during epidemics. This study presents two problem-solving techniques: the LP-metric method for small problems and the whale optimization algorithm (WOA) for medium and largescale issues. The positive and negative effects of epidemics on environmental and economic aspects of the objective functions were assessed. This study's contribution and novelty, compared to previous research, lie in the introduction of backup supply centers, particularly waste disposal centers, and the comparison of normal and epidemic conditions for disaster management using the IoT approach.
A hydrogen compressed air energy storage power plant with an integrated electrolyzer is ideal for large-scale,long-term energy storage because of the emission-free operation and the possibility to offer multiple ancil...
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A hydrogen compressed air energy storage power plant with an integrated electrolyzer is ideal for large-scale,long-term energy storage because of the emission-free operation and the possibility to offer multiple ancillary services on the German energy *** paper defines analyzes such a storage concept and conducts an extensive comparison with four additional storage concepts based on various *** results show that the combination of storing compressed air and hydrogen offers a higher efficiency than storing only hydrogen and lower specific investment costs than storing only compressed *** result is confirmed with analysis of the optimal sizing of each power plant component for simultaneous participation on multiple energy markets with a linear optimization dispatch *** hydrogen compressed air energy storage(HCAES)power plant can utilize more revenue possibilities than a hydrogen energy storage because of the higher round-trip efficiency and the combination of the air compressor and the integrated electrolyzer during charging *** integration of the electrolyzer,however,offers a couple of challenges itself because of the highly flexible operation mode.A new concept for the controllable 24-pulse diode-thyristor rectifier of the electrolyzer is presented,that uses mostly common components while offering little to no grid harmonics and a long *** flexible integrated electrolyzer allows for the 4-quadrant operation of the storage power plant.
We consider the two-machine permutation flow shop scheduling problem with uncertain job processing time, which is sampled from no specific distribution type. For the ease of discussion, an ambiguity set with a priori ...
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We consider the two-machine permutation flow shop scheduling problem with uncertain job processing time, which is sampled from no specific distribution type. For the ease of discussion, an ambiguity set with a priori mean and support set information is constructed. We then introduce a distributionally robust optimization (DRO) perspective to handle the uncertainty. To the best of our knowledge, this is the first time that a DRO method is applied to this problem setting. Given that the original DRO model is nonlinear and intractable in nature, we first reformulate the inner maximization problem into a linearprogramming model with a fixed sequence, based on the duality theory and optimality conditions. By including the sequence decision, we further transform it into an equivalent mixed-integer linear programming (MILP) problem via incorporating the valid lower and upper bounds and McCormick inequalities. The obtained MILP could be solved to optimality with the off-the-shelf commercial solvers. In the numerical study, it is demonstrated that the DRO-based model could effectively solve the large scale instances with up to 100 jobs optimally within 30 s. Compared with the SLP, DRO model always triumphs on the worst-case indicator. And as the problem scale increases, the DRO model gradually outperforms the SLP in terms of the Up-90% and Up-75% indicators. Furthermore, the optimal sequence obtained by the deterministic model is less stable than the DRO model, which can enhance the robustness of the manufacturing system against processing uncertainty.
The purpose of this paper is to introduce a new rich source of ideas and techniques that could be used to build new algorithms capable to solve numerous encountered optimisation problems in different fields of science...
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The purpose of this paper is to introduce a new rich source of ideas and techniques that could be used to build new algorithms capable to solve numerous encountered optimisation problems in different fields of science and engineering. The art of war is one of the most rich disciplines in terms of already experimented strategies and tactics that can inspire researchers to design new powerful and efficient metaheuristics. The framework of the proposed method are inspired by the main war phases and contains seven components: initialization, intelligence, conception, suppression, advance, assault and exploitation. The basic fire and manoeuvre tactic is adopted in the suppression and advance phases. The proposed fire and manoeuvre algorithm (FMA) is a hybridization of a greedy algorithm with a multi-neighbouring search procedure. The developed algorithm has been employed to minimize makespan of the classical flow shop scheduling problem. A mathematical model is presented to describe the studied optimisation problem. Comparative experiments on Taillard's data set confirmed that the (FMA) results are more accurate than already published data. A comparison between the FMA and other popular nature-inspired algorithms has been conducted. It was revealed that the proposed metaheuristic outperforms the classical genetic algorithm, the migrating birds optimisation and the whale optimisation algorithm.
PurposeDue to its longer length compared to other construction materials and distinctive stacking patterns, obtaining construction steel bars in congested construction sites with limited storage capacity becomes chall...
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PurposeDue to its longer length compared to other construction materials and distinctive stacking patterns, obtaining construction steel bars in congested construction sites with limited storage capacity becomes challenging. Lack of storage space in crowded places prompts the need for building steel bar storage choice optimization. Therefore, this study aims to optimize the construction steel bar procurement plan by providing when and how much rebar to order and how to stack different sizes of rebar considering limited storage ***/methodology/approachA novel approach has been presented in this paper by integrating 4D building information modelling (BIM) and mixed-integer linear programming (MILP). This technique uses BIM to retrieve material quantities, including rebar, during the design phase. Following that, activities are scheduled depending on the duration determined by crew productivity data and material quantity. Then, based on the prior price, the price of each unit of rebar is projected for the duration of construction using the exponential smoothing method. After that, the MILP approach is used to generate an optimal steel bar procurement plan for limited storage space following the scheduled rebar-related *** developed strategy minimizes overall procurement costs and ensures the storage of rebar as per standard guidelines. An optimal rebar procurement and storage plan to construct a six-storied RC frame has been presented in this paper as a demonstrative example to show the effectiveness of the proposed ***/valueThis work partially satisfies a long-sought research need for establishing a comprehensive construction steel bar procurement system, making it a very useful source of information for practitioners and researchers. The proposed method can be used to minimize a key performance limitation that the conventional rebar procurement practice for crowded building sites may experience.
In this paper, an integrated harvest and production planning problem in the olive oil industry is addressed. The aim of the paper is to develop and optimize a mathematical model that integrates both olive harvest and ...
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In this paper, an integrated harvest and production planning problem in the olive oil industry is addressed. The aim of the paper is to develop and optimize a mathematical model that integrates both olive harvest and olive oil production process. The objective is to maximize the total profit while determining quantity of olives harvested from several olive groves, quantity of olives purchased from external farmers, quantity of olive oil produced, and by-product management to handle hazardous effects of olive oil production. The problem is formulated as a mixedintegerlinearprogramming model (MILP). Maximization of profit consists of two components;total sales revenue and total cost including harvesting, purchasing, fixed and variable processing costs. Constraints on the system include harvest planning, harvest capacity, production planning, and processing constraints. The proposed MILP model incorporates several distinguishing characteristics of the problem such as ripeness of olives, olive oil quality, organic and conventional farming, and by-product management. A numerical experiment based on a real-world case study was presented to verify the effectiveness of the developed model. The results show that simultaneously considering harvesting and production processes can significantly assist the profitability of the olive oil supply chain. A scenario analysis is conducted by extending the base model to explore olive loss in the olive groves which can occur due to the severe climatic conditions.
Proper scheduling of jobs is essential for modern production systems to work effectively. The hybrid flow shop scheduling problem is a scheduling problem with many applications in the industry. The problem has also at...
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Proper scheduling of jobs is essential for modern production systems to work effectively. The hybrid flow shop scheduling problem is a scheduling problem with many applications in the industry. The problem has also attracted much attention from researchers due to its complexity. This study addresses the hybrid flow shop scheduling problem (HFSP), which considers unrelated parallel machines at each stage and the machine eligibility constraints. HFSP is a well-known NP-hard problem with the aim of minimizing the makespan. Owing to the complexity of the problem, this study develops constraint programming (CP) models for the HFSP and its extensions: the no-wait HFSP, the blocking HFSP, the HFSP with sequence-dependent setup times, the no-wait HFSP with sequence-dependent setup times, and the blocking HFSP with sequence-dependent setup times. We also propose two mixed-integer linear programming models (MILP) for no-wait and blocking HSFPs with sequence-dependent setup times. The performances of the CP models were tested against their MILP counterparts using randomly generated instances and benchmark instances from the literature. The computational results indicated that the proposed CP model outperformed the best MILP solutions for benchmark instances. It is also more effective for finding high-quality solutions for larger problem instances.
In smart factories, transfer vehicles streamline the material handling of machines, leading to the complex job shop scheduling problem with transportation (JSSPT). Establishing a concise and effective mixed-integer li...
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In smart factories, transfer vehicles streamline the material handling of machines, leading to the complex job shop scheduling problem with transportation (JSSPT). Establishing a concise and effective mixed-integer linear programming (MILP) model for the JSSPT is challenging due to the intricate interaction of job scheduling and vehicle routing. To address the challenge, this paper described the JSSPT as a synchronised asymmetric multiple travelling salesman problem based on a disjunctive directed graph. A MILP model is subsequently developed, followed by an analysis of its inherent properties. Numerical experiments demonstrate that the proposed MILP model outperforms several published heuristic algorithms and MILP models on standard instances. Further analysis reveals that under specific parameters, the optimisation objectives of makespan and exit time align. Additionally, a heuristic strategy of scheduling vehicles prior to scheduling jobs yields better lower and upper bounds for the optimal solution compared to scheduling jobs first.
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