•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.
•This is the first report of the sugarcane harvester path planning involving field accessibility and split harvesting constraints.•Multi-objective particle swarm optimization was applied.•A new ...particle encoding with four decoding schemes were constructed to solve the problem.•The seven movement strategies for Multi-objective particle swarm optimization were applied.
One of the important aspects to increasing sugarcane mechanical harvesting efficiency is the path planning of the harvester, involving direction and field accessibility constraints. Moreover, in real-life applications, the two objective functions pertaining to minimization of harvested distance and maximization of sugarcane yield are conflicting and must be considered simultaneously. This paper presents a multi-objective with the variant of the particle swarm optimization combined gbest, lbest and nbest social structures (MO-GLNPSO), to solve sugarcane mechanical harvester route planning (MHRP). A new particle encoding/decoding scheme has been devised for combining the path planning with the accessibility and split harvesting constraints. Numerical computation results on several networks with sugarcane field topologies illustrate the efficiency of the proposed MO-GLNPSO method for computation of MHRP, which is compared with other methods such as the traditional particle swarm optimization (PSO) and Non-dominated Sorting Genetic Algorithm-II (NSGAII) by the values of C˜ metric indicator. The solutions found in this work can offer a decision maker a choice of trade-off solutions, providing sufficient options to give planners the power to make an informed choice that balances the important objectives.
Smart cities need energy-efficient and low-emission transportation for people and goods. Most studies focus on sustainable urban-transportation systems for passengers. Freight transportation in ...cities has increased significantly during the COVID-19 pandemic, leading to greenhouse gases emissions and negative externalities, such as traffic congestion. The purpose of this paper is to identify through a systematic literature review which innovations (hardware and software) applied by logistics service providers (LSPs) in sustainable urban freight (SUF) are suitable to support the transition to energy-efficient smart cities. We propose to classify the existing innovations in last-mile delivery for SUF into categories: (1) urban freight consolidation and/or trans-shipment; (2) the Consumer as a Service Provider (CaaSP); (3) choice of transportation modes. We introduce the concept of CaaSP as an innovative solution in last-mile delivery (LMD), where customers take over some transport operations with the use of smart technologies, and thus reduce the energy demand. We consider the modes of transportation, such as: drones, autonomous delivery robots, autonomous vehicles, cargo bikes (including e-cargo bikes, e-tricycles), electric vehicles (mainly vans), and combined passenger-and-cargo transportation rapid-transit systems. From the analyzed dataset, we find that energy-efficiency in smart cities can be improved by the consolidation of parcels in micro-depots, parcel lockers, and mobile depots. We analyze smart technologies (the Internet of things, big data, artificial intelligence, and digital twins), which enable energy efficiency by reducing the energy demand (fuel) of SUF, due to better operational planning and infrastructure sharing by logistics service providers. We propose a new IEE matrix as an actionable tool for the classification of innovations applied by LSPs in SUF, according to the level of their interconnectivity and energy efficiency. Additionally, this paper contributes to the theory by exploring possible future research directions for SUF in energy-efficient smart cities.
•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.
•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.
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.
This paper presents the Hybrid and Self-Adaptive Differential Evolution algorithms (HSADE) to solve an egg distribution problem in Thailand. We introduce and formalize a model for a multi-product, ...multi-depot vehicle routing problem with a time window, a heterogeneous fleet and inventory restrictions. The goal of the problem is to minimize the total cost. The multiple products comprise customers’ demands with different egg sizes. This paper presents a Mixed Integer Linear Programming (MILP) model, an initial solution-based constructive heuristic, a new self-adaptive mutation strategy, and a neighborhood search structure with the probability to improve DE. The two measurements of criteria are the heuristic performance (HP) compared with the solution obtained by MILP and the relative improvement (RI) of the solution compared with Thailand’s current egg distribution practice. The computational results show that the performance of HSADE is better than the current practice, and HSADE can provide on average a 14.13% improvement in total cost. Additionally, our proposed algorithm can be applied to similar agriculture logistics in Thailand and worldwide.
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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