•The scheduling of equipment under a sharing resource system is first studied.•The ALNS metaheuristics employing the idea of DE is developed to solve the problem.•The new destroy and repair methods ...have been developed as unique and effective.
This paper presents the ALNS metaheuristics, employing the idea of DE to solve the mechanical harvester assignment and routing problem with time windows (HARPTW) to maximize the total area serviced by a mechanical harvester under a sharing infield resource system. The effective ALNS is designed to solve large-scale problems integrating the mechanical harvester assignment problem (HAP) and the mechanical harvester routing problem (HRP). The newly developed destroy and repair methods are unique and effective. Additionally, four new formulas have been developed to calculate the probability to accept the worse solution using linear and parabola functions instead of the exponential function that is used mostly in the literature. The numerical results show that the parabola function, which uses the information about the solution quality, outperforms all other proposed heuristics. This demonstrates that the proposed heuristics are very efficient and are not only useful for reducing the infield operations costs of small growers, but also for efficient management of the inbound logistics equipment and machinery of the sugarcane supply system.
•The heterogeneous fleet with many tanks is considered to collect raw milk.•Raw milk from different centers cannot be transferred into the same compartment.•Both traveling costs and cleaning costs ...are considered as the objective function.•Five modified DEs were proposed including reincarnation and survival processes.•Skipped customer and multi-route processes were developed in the decoding process.
This paper focuses on determining routes for raw milk collection from collection centers to dairy factories with the objective of minimizing the total costs, considering fuel costs and costs of cleaning and sanitizing raw milk tanks on vehicles. This problem is considered to be a special case of the vehicle routing problem (VRP) but it is complex compared to the general VRP, especially since each vehicle contains more than one tank with heterogeneous capacity to collect raw milk and raw milk from different collection centers cannot be transferred into the same compartment. In this paper, a DE metaheuristic was used to solve the problem. In order to improve the solution quality, five modified DE algorithms with two additional steps, reincarnation and survival processes, were proposed. In addition, the skipped customer and multi-route attributes are also developed in the decoding process in order to obtain a shorter traveling distance and lower truck usage in the system, especially if they are used together with the reincarnation and survival processes. The computational results reveal that the modified DE algorithms yield higher relative improvement (RI) on the total costs and also the RI on the number of vehicles used.
Additive manufacturing (AM) became widespread through several organizations due to its benefits in providing design freedom, inventory improvement, cost reduction, and supply chain design. Process ...planning in AM involving various AM technologies is also complicated and scarce. Thus, this study proposed a decision-support tool that integrates production and distribution planning in AM involving material extrusion (ME), stereolithography (SLA), and selective laser sintering (SLS). A multi-objective optimization approach was used to schedule component batches to a network of AM printers. Next, the analytic hierarchy process (AHP) technique was used to analyze trade-offs among conflicting criteria. The developed model was then demonstrated in a decision-support system environment to enhance practitioners’ applications. Then, the developed model was verified through a case study using automotive and healthcare parts. Finally, an experimental design was conducted to evaluate the complexity of the model and computation time by varying the number of parts, printer types, and distribution locations.
•The hybrid PSO and ALNS is firstly developed for the ice transportation.•A software tool based on the hybrid PSO and ALNS is developed to decide schedules.•A mobile application is introduced to ...support the transportation decision-makers.•A mobile application can take the available demand data input to the software.•A mobile application provides user-friendly visualization & displaying route maps.
This paper addresses the vehicle routing problem with consideration of vehicle capacities, time windows, multiple products, fleet sizes, and fleet size limits on roads (VRPTWFS). The mathematical model is developed to find an optimal schedule with minimum transportation costs. Since the problem is NP-hard, metaheuristics are required to find a near-optimal solution for larger, more practical problems. Therefore, this paper presents the hybridization of PSO and ALNS, which is firstly developed, for solving the problem. To help the route planner to decide the best routes and schedules for ice transport, a software tool (IceApp) based on the hybridization of PSO and ALNS is designed and developed. The IceApp has an architecture made of software components, running on the cloud server, where the Web Application, Web Service and Database Management System are installed. To support the transportation decision-makers, by taking advantage of the available demand data input to the IceApp software, a mobile application for a vehicle driver has been introduced for application in the ice manufacturing industry. Knowing exact demand allows logistics staff and route planners to coordinate their logistics and distribution with the right number of vehicles, optimize routes and maintain appropriate inventory by improving the availability of real-time customer information, resulting in lower transportation and holding costs. The mobile application for ice transportation also provides more user-friendly visualization, displaying route maps and route flow obtained from the calculation.
•Various types of workforces with multiple teams per each workforce type are considered.•Multi-visit for different types of workforces is included.•Hybrid differential evolution and particle swarm ...optimization for workforce scheduling and routing problem is proposed.
This research proposed an optimization method (Hybrid Differential Evolution and Particle Swarm Optimization, HDEPSO) using a solution technique based on two well-known techniques, Differential Evolution (DE) and Particle Swarm Optimization (PSO), to tackle a multi-visit and multi-period workforce scheduling and routing problem (MMWSRP) in field service operation of a sugarcane mill company in Thailand. The HDEPSO can be used for planning of routes and maintenance work for each sugarcane harvester to be provided by service teams of mechanical, hydraulic, and electrical technicians. The members of the service teams will be determined according to their skills and skill levels and service routes for each individual service team so that the operation cost is minimized. At first, mixed integer programing was used to determine the best solution. This technique is, however, not suitable for large-size problems. A HDEPSO was therefore developed to solve the MMWSRP and then tested against the mixed integer programing for small-size problems and it was found that both methods were equally effective. However, for larger-size problems, shortcomings of the mixed-integer technique became obvious whereas the HDEPSO was much more advantageous. The HDEPSO was also tested against the DE and PSO. The computational results show that the objective value of the proposed method was decreased by 4.94% and 7.45% compared with the DE and the PSO, respectively.
•Tractor scheduling and routing problem with tool allocation constraint is focused.•This research is based on real Thailand sugarcane agricultural case study.•The new neighborhood search strategies ...are added to the Hybrid PSO (HPSO).•Two new formulae for neighborhood search are used to increase the performance of proposed methods.
This paper presents the Hybrid Particle Swarm Optimization and Neighborhood Strategy Search (HPSO-NS) to solve a tractor scheduling and routing problem with equipment allocation constraint in sugarcane field preparation, to help the growers catch the season and ensure advantageous production of sugar from sugarcane. This problem can be formulated as the flexible flow shop scheduling problem with machine eligibility, time windows, sequence dependent setup time (SDST), blocking, machine restriction and machine grouping (FFS |Ssmt,Mj, Grouping, block, 6-stage, Tool, Tw |∑iNRi). A mixed-integer programming model was developed to solve small-scale problems. The HPSO-NS was developed for large-scale problems, and three neighborhood strategies were added to the PSO procedure and developed. Moreover, two new formulae which were used to select the neighborhood strategy in HPSO-NS are presented in this paper to increase the performance of the proposed method. The computational results show that the HPSO-NS outperforms the original PSO and the lower bound obtained from the optimization software, while using 97% less computational time.
Abstract
Additive manufacturing (AM) or three-dimensional printing (3DP) refers to producing objects from digital information layer by layer. Despite recent advancements in AM, process planning in AM ...has not received much attention compared to subtractive manufacturing. One of the critical process planning issues in AM is deciding part orientation. In this research, the integrative framework of multicriteria decision making for part orientation analysis in AM is investigated. Initially, quantitative data are assessed using the data envelopment analysis (DEA) technique without preferences from a decision maker. In contrast, a decision maker’s preferences are qualitatively analysed using the analytic hierarchy process (AHP) technique. Then, the proposed framework combining explicit data as in DEA, implicit preference as in AHP, and linear normalization (LN) technique is used, which reflects both preference and objective data in supporting decision making for 3DP part orientation. Two particular AM technologies, namely Fused Deposition Modelling and Selective Laser Sintering, are used as a case study to illustrate the proposed algorithm, which is further verified with experts to improve process planning for AM.
Graphical Abstract
Graphical Abstract
This research aims to develop decision making on how to provide employees with a transportation service that is fast, cost effective and timely. This work is different from previous research in that ...it integrates the bus stop determination, bus assignment and the bus route design sub-problems of an employee transportation service for a large-scale company. The objective of this article is to minimize total travelling costs and the total rental costs of the heterogeneous fleet capacitated vehicles. A mathematical model is applied to solve for small-size problems. Additionally, for large-size problems, five modified differential evolution (MDE) algorithms are developed to solve the three sub-problems simultaneously. New rules used in the mutation process of the MDE algorithms and an advanced design of the encoding and decoding method are presented in this article. The results demonstrate that the MDE is an efficient transportation management system for any organization.
The purpose of this study is to address two major issues: (1) the spread of epidemics such as COVID-19 due to long waiting times caused by a large number of waiting for customers, and (2) excessive ...energy consumption resulting from the elevator patterns used by various customers. The first issue is addressed through the development of a mobile application, while the second issue is tackled by implementing two strategies: (1) determining optimal stopping strategies for elevators based on registered passengers and (2) assigning passengers to elevators in a way that minimizes the number of floors the elevators need to stop at. The mobile application serves as an input parameter for the optimization toolbox, which employs the exact method and multi-objective variable neighborhood strategy adaptive search (M-VaNSAS) to find the optimal plan for passenger assignment and elevator scheduling. The proposed method, which adopts an even-odd floor strategy, outperforms the currently practiced procedure and leads to a 42.44% reduction in waiting time and a 29.61% reduction in energy consumption. Computational results confirmed the effectiveness of the proposed approach.
This paper focuses on the dynamic workforce scheduling and routing problem for the maintenance work of harvesters in a sugarcane harvesting operation. Technician teams categorized as mechanical, ...hydraulic, and electrical teams are assumed to have different skills at different levels to perform services. The jobs are skill-constrained and have time windows. During a working day, a repair request from a sugarcane harvester may arrive, and as time passes, the harvester’s position may shift to other sugarcane fields. We formulated this problem as a multi-visit and multi-period dynamic workforce scheduling and routing problem (MMDWSRP) and our study is the first to address the workforce scheduling and routing problem (WSRP). A mixed-integer programming formulation and a hybrid particle swarm and whale optimization algorithm (HPSWOA) were firstly developed to solve the problem, with the objective of minimizing the total cost, including technician labor cost, penalty for late service, overtime, travel, and subcontracting costs. The HPSWOA was developed for route planning and maintenance work for each mechanical harvester to be provided by technician teams. The proposed algorithm (HPSWOA) was validated against Lingo computational software using numerical experiments in respect of static problems. It was also tested against the current practice, the traditional whale optimization algorithm (WOA), and traditional particle swarm optimization (PSO) in respect of dynamic problems. The computational results show that the HPSWOA yielded a solution with significantly better quality. The HPSWO was also tested against the traditional genetic algorithm (GA), bat algorithm (BA), WOA, and PSO to solve the well-known CEC 2017 benchmark functions. The computational results show that the HPSWOA achieved more superior performance in most cases compared to the GA, BA, WOA, and PSO algorithms.
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