A Simulation Based Genetic Algorithm for Flowshop Scheduling Problem Considering Energy Cost under Uncertainty

A flowshop problem with objective functions of minimizing makespan and energy cost has been investigated. Reducing production costs is one of the goals that industries always have in mind. Increasing public awareness about the energy issues creates a new attitude toward minimizing energy costs. In order to make the problem more compatible with the real-world conditions, the problem is considered under uncertainty. An existing research gap inspired this study. It is assumed that machines can use the three slow, normal and fast speeds to process jobs. At high speeds, consumption rate increases and completion time decreases, and vice versa. The difference in machine processing speeds yields different and contradictory values in the objective functions. Therefore, a method should be proposed in which, in addition to the order of jobs, the speed of machines could be determined. A mathematical model is presented, and then a simulation-based genetic algorithm is used to solve the problem on a large scale. Simulation is used for each evaluation of the objective function in the genetic algorithm to consider the uncertainty of processing times. Due to the stochastic processing time, the expected value model is used to deal with uncertainty. The computational results indicate that the algorithm and approach show a good performance

Nurse Rostering Problem Considering Drect andIndirect Costs: Deferential Evolution Algorithm

The employee scheduling seeks to find an optimal schedule for employees according to the amount of demand (workload), employee availability, labor law, employment contracts, etc. The importance of this problem in improving the quality of service, health and satisfaction of employees and reducing costs, including in hospitals, military or service centers, has encouraged researchers to study. In this regard, nurse rostering problem is a scheduling that determines the number of nurses required with different skills and the time of their services on the planning horizon. In this research, by adding the nurses' shift preferences and number of consecutive working days constraints, an attempt has been made to make the problem more realistic. The objective function of the problem is to minimize the total cost of allocating work shifts to nurses, the cost of the number of nurses required to reserve, the cost of overtime from a particular shift, the cost of underemployment from a particular shift, the cost of overtime on the planning horizon, the cost of underemployment on the planning horizon and the cost of absence shift-working and non-working days preferred by nurses. To solve problem, after modeling the problem as a mixed-nteger program and due to the complexity of the problem, the differential evolutionary algorithm is used with innovation in its crossover operator. To validate the proposed algorithm, its output was compared with the genetic algorithm. The results show that the differential evolutionary algorithm has good performance in problem-solving.Keywords: Nurse Rostering Problem, Deferential Evolution Algorith

Location-Allocation and Scheduling of Inbound and Outbound Trucks in Multiple Cross-Dockings Considering Breakdown Trucks

This paper studies multiple cross-dockings where the loads are transferred from origins (suppliers) to destinations (customers) through cross-docking facilities. Products are no longer stored in intermediate depots and incoming shipments are consolidated based on customer demands and immediately delivered to them to their destinations. In this paper, each cross-docking has a covering radius that customers can be served by at least one cross-docking provided. In addition, this paper considers the breakdown of trucks. We present a two-stage model for the location of cross-docking centers and scheduling inbound and outbound trucks in multiple cross-dockings.We work on minimizing the transportation cost in a network by loading trucks in the supplier locations and route them to the customers via cross-docking facilities. The objective, in the first stage, is to minimize transportation cost of delivering products from suppliers to open cross-docks and cross-docks to the customers; in the second-stage, the objective is to minimize the makespans of open cross-dockings and the total weighted summation of completion time. Due to the difficulty of obtaining the optimum solution tomedium- and large-scale problems, we ‌propose four types of metaheuristic algorithms, i.e., genetic, simulated annealing, differential evolution, and hybrid algorithms.The result showed that simulated annealing is the best algorithm between the four algorithms

HAZARDOUS MATERIALS TRANSPORTATION WITH FOCUSING ON INTERMODAL TRANSPORTATION: A STATE-OF-THE-ART REVIEW

Transportation of hazardous materials (hazmat) is one of the most critical issues in transportation planning that involves multiple risks to the physical and social environments. Any improvements in it reduce not only environmental costs but also reduces external transport costs (e.g., reduces the risk of accidents which, in addition to environmental impact, also affects external costs). Besides, multi-modal transportation as a main part of transportation uses multiple modes (e.g., rail, ship, truck, air) to transport freight. If the containers carry hazmat, the government regulates their transportation due to the associated risks. Many researchers have studied the risk assessment of hazmat transportation to find ways for reducing hazardous material transportation risks. In this regard, the intermodal models and unimodal problems for hazmat transportation were studied by some researchers. In this study, after pointing out the importance of hazmat intermodal transportation and risks, the research related to hazmat intermodal transportation, including routing and scheduling, intermodal transportation, and location-routing problems. Then the reviewed literature is quantified and measured. Finally, the paper concludes by presenting some problems receiving less attention than the others and proposes several research opportunities in the field

Particle Swarm Optimization to Solve Competitive Production Routing Problem

The production routing problem (PRP) integrates vehicle routing and production planning problems. Generally, in PRPs, the impact of competitors has not been considered. Clearly, in the real world, it is no longer possible to have a monopoly market. In competitive environment, customers choose a supplier based on price and quality. So in this article as a definition of quality, providing quick access to customer needs and availability are determined as the requirements of a competitive environment. Therefore, the production routing problem has been modeled with knowing the earliest and latest time of competitor demand meeting. In this way, In case of delay in supplying customers demand, the market share is lost relative to the amount of delay. The problem is modeled and it has been solved by the GAMS software. Since particle swarm optimization has been successfully applied to a variety of problems, here, to solve the problem for the large-sized instances a particle swarm optimization algorithm is also presented. To evaluate the performance of the proposed algorithm, the results with small-sized instances were compared with solutions of GAMS

Optimizing Penalties of Total Lateness and Energy Costs for Heterogeneous Parallel Machines Scheduling Using Memetic Algorithm

In general, numerous studies have paid a special attention to machine planning, job allocating and job sequencing in scheduling problems to optimize makespan. Due to the relation among economy, energy and environmental concerns, energy use is one of the most important issues in different systems planning. In this paper, a scheduling of heterogeneous parallel machines is studied, in which the job process speed on every machine is settable. Since there is a direct link between used energy of machines and process speed, the purpose of the paper is to minimize total used energy and tardiness-related costs in delivering customers' demand. In order to optimizing the problem, two meta-heuristic algorithms, Memetic algorithm and Genetic algorithm, are developed, finally the results of both algorithms are analyzed and then compared to each other as well as to the results of the GAMS optimization software

A new heuristic algorithm based on minimum spanning tree for solving metric traveling salesman problem

Due to the many applications of the travelling salesman problem, solving this problem has been considered by many researchers. One of the subsets of the travelling salesman problem is the metric travelling salesman problem in which a triangular inequality is observed. This is a crucial problem in combinatorial optimization as it is used as a standard problem as a basis for proving complexity or providing solutions to other problems in this class. The solution is used usually in logistics, manufacturing and other areas for cost minimization. Since this is an NP-hard problem, heuristic and meta-heuristic algorithms seek near-optimal solutions in polynomial time as numerical solutions. For this purpose, in this paper, a heuristic algorithm based on the minimum spanning tree is presented to solve this problem. Then, by generating 20 instances, the efficiency of the proposed algorithm was compared with one of the most famous algorithms for solving the travelling salesman problem, namely the nearest neighbour algorithm and the ant colony optimization algorithm. The results show that the proposed algorithm has good convergence to the optimal solution. In general, the proposed algorithm has a balance between runtime and the solution found compared to the other two algorithms. So the nearest neighbour algorithm has a very good runtime to reach the solution but did not have the necessary convergence to the optimal solution, and vice versa, the ant colony algorithm converges very well to the optimal solution, but, its runtime solution is very longer than the proposed algorithm

Harmony Search Algorithm for Stochastic Operating Room Scheduling Considering Overhead Costs and Number of Surgeries

Given the increasing human need for health systems and the costs of using such systems, the problem of optimizing health-related systems has attracted the attention of many researchers. One of the most critical cases in this area is the operating room scheduling. Much of the cost of health systems is related to operating room costs. Therefore, planning and scheduling of operating rooms can play an essential role in increasing the efficiency of health systems as well as reducing costs. Given the uncertain factors involved in such matters, attention to uncertainty in this problem is one of the most critical factors in the results. In this study, the problem of the daily scheduling of the operating room with uncertain surgical time was investigated. For minimizing overhead costs and maximizing the number of surgeries to reduce patients' waiting time, after introducing a mathematical model, a chance-constrained programming approach is used to deal with its uncertainty. In this study, also, a harmony search algorithm is proposed to solve the model because of its NP-Hardness. By performing the numerical analysis and comparing the presented algorithm result with a genetic algorithm, the results show that the proposed algorithm has a better performance

A hyper-heuristic for distributed parallel machine scheduling with machine-dependent processing and sequence-dependent setup times

Today, because the market is scattered around the world, manufacturing activities are not limited to a single location and have spread globally. As a result, the discussion of scheduling the factory has changed from a classic single to a network scheduling as a need in the real world. In this regard, this study considers the scheduling of multiple factories by taking into account the job transportation time between factories. The main problem here is that each job would be assigned to which factory and machine. In this research, unrelated parallel machines are considered in which the processing time of jobs depends on the machine and setup time. To minimize the makespan, first, a mixed-integer linear model was proposed in which two types of modeling have been combined. Then, a hyper-heuristic algorithm (HHA) was designed to solve the problem in a reasonable time by choosing the best method among four low-level heuristic methods that are precisely designed according to the properties of the problem. Finally, the efficiency of the proposed algorithm has been compared with the imperialist competitive algorithm (ICA) by conducting experiments. The results show that the proposed algorithm performs very well compared to the ICA and, in more than 75% of the test problems, the proposed algorithm was superior. Also, based on the analysis, in comparing the proposed algorithm with the ICA, it can be concluded that there is a significant difference between the results, and in all cases, the HHA was remarkably better. Considering the challenges and rapid changes of today’s market that traditional centralized production planning does not have enough flexibility to respond to them, the results of this research are expected to be useful and attractive for planners in this field