Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services

Simulation is a powerful tool for modeling complex systems with intricate relationships between various entities and resources. Simulation optimization refers to methods that search the design space (i.e., the set of all feasible system configurations) to find a system configuration (also called a design point) that gives the best performance. Since simulation is often time consuming, sampling as few design points from the design space as possible is desired. However, in the case of multiple objectives, traditional simulation optimization methods are ineffective to uncover the efficient frontier. We propose a framework for multi-objective simulation optimization that combines the power of genetic algorithm (GA), which can effectively search very large design spaces, with data envelopment analysis (DEA) used to evaluate the simulation results and guide the search process. In our framework, we use a design point's relative efficiency score from DEA as its fitness value in the selection operation of GA. We apply our algorithm to determine optimal resource levels in surgical services. Our numerical experiments show that our algorithm effectively furthers the frontier and identifies efficient design points.     

A note on special optimal batching structures to minimize total weighted completion time

A batch is a subset of jobs which must be processed jointly in either serial or parallel form. The algorithmic aspects of the batching scheduling problems have been extensively studied in the literature. This paper presents necessary and sufficient conditions of the existence of optimal batch sequences for the single machine, batching, total weighted completion time scheduling problems on two batching ways: (1) all jobs form one batch; (2) each batch contains a single job. This kind of conditions can help us to recognize some special optimal schedules quickly. Research supported by NSFC (10671183).     

A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns

Traditionally, product returns have been viewed as an unavoidable cost of doing business, forfeiting any chance of cost savings. As cost pressures continue to mount in this era of economic downturns, a growing number of firms have begun to explore the possibility of managing product returns in a more cost-efficient manner. However, few studies have addressed the problem of determining the number and location of centralized return centers (i.e., reverse consolidation points) where returned products from retailers or end-customers were collected, sorted, and consolidated into a large shipment destined for manufacturers’ or distributors’ repair facilities. To fill the void in such a line of research, this paper proposes a nonlinear mixed-integer programming model and a genetic algorithm that can solve the reverse logistics problem involving product returns. The usefulness of the proposed model and algorithm was validated by its application to an illustrative example dealing with products returned from online sales.     

A hybrid genetic algorithm for train sequencing in the Korean railway

This article addresses the train-sequencing problem encountered in the Korean railway. It first presents a mixed integer programming model for the problem, in which the mileage must be balanced for each train route, while various field constraints must be satisfied, including overnight stay capacity and maintenance allocation restrictions. Then, it proposes a hybrid genetic algorithm as a solution approach to the problem. The proposed algorithm utilizes a modified elite group technique along with two heuristic procedures based on the mixed integer programming model. Finally, the proposed solution approach is tested with real-world data from the Korean railway. Numerical experiments under different conditions indicate that the proposed solution approach to the train-sequencing problem is promising.     

A simulated annealing algorithm for job shop scheduling

In this paper, the job shop scheduling problem is considered with the objective of minimization of makespan time. We first reviewed the literature on job shop scheduling using meta-heuristics. Then a simulated annealing algorithm is presented for scheduling in a job shop. To create neighbourhoods, three perturbation schemes, viz. pairwise exchange, insertion, and random insertion are used, and the effect of them on the final schedule is also compared. The proposed simulated annealing algorithm is compared with existing genetic algorithms and the comparative results are presented. For comparative evaluation, a wide variety of data sets are used. The proposed algorithm is found to perform well for scheduling in the job shop.     

Comparing backpropagation with a genetic algorithm for neural network training

This article shows that the use of a genetic algorithm can provide better results for training a feedforward neural network than the traditional techniques of backpropagation. Using a chaotic time series as an illustration, we directly compare the genetic algorithm and backpropagation for effectiveness, ease-of-use, and efficiency for training neural networks.     

A genetic algorithm for thepart family formation problem

Recently, there has been a lot of interest in group technology (GT) from researchers as well as from practitioners. This interest is explained by the fact that GT supports new manufacturing philosophies. One of the main issues in GT is the part family formation problem which is concerned with grouping similar products into same families. Many researchers have tackled this problem and many algorithms have been proposed for it. In this paper, we present a genetic technique-based heuristic for the quadratic integer programming model of the part family formation problem which was formulated by Kusiak et al. (1986). The heuristic is tested on several problems from the literature, and preliminary results are very promising.     

Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems

Genetic algorithm (GA) approach is developed for solving the P-model of chance constrained data envelopment analysis (CCDEA) problems, which include the concept of “Satisficing”. Problems here include cases in which inputs and outputs are stochastic, as well as cases in which only the outputs are stochastic. The basic solution technique for the above has so far been deriving “deterministic equivalents”, which is difficult for all stochastic parameters as there are no compact methods available. In the proposed approach, the stochastic objective function and chance constraints are directly used within the genetic process. The feasibility of chance constraints are checked by stochastic simulation techniques. A case of Indian banking sector has been presented to illustrate the above approach.     

A hybrid genetic algorithm for the batch sequencing problem on identical parallel machines

The batch sequencing problem (BSP) has been the focus of much research thanks to its practical applicability and NP-hard nature. This study aims to solve an extension of the problem from single machine case to parallel machine case. A dispatching rule called the EDD (Earliest Due Date)-Greedy heuristic and a pure genetic algorithm (GA) are proposed, and then combine them through an insertion method called the Gene Bank Strategy. Several test cases of up to 12 machines and 60 jobs are randomly generated and solved. Those experiments show that the introduced hybrid GA can provide far better results than its two component algorithms that can also independently solve the problem.     

越库转运问题的自适应遗传算法研究

探讨一种固定运输模式下的越库转运问题--采用运输量不可拆分的单次运送方式以最小费用通过选择固定的运输路径将货物经过越库转运到目的地,其货物将可能在越库中停留甚至无法运到目的地,这将会导致库存成本和惩罚成本.文中证明了此类越库转运问题是强NP难题,因此本文针对该问题的特殊结构,提出一种采用了邻域搜索技术的自适应遗传算法(...     

A genetic algorithm for scheduling on a single machine with set-up times and due dates

This paper presents a genetic algorithm for a single machine-scheduling problem with the objective of minimizing total tardiness. Each job has its own due date and the set-up times are sequence dependent. The parameters of the genetic algorithm are determined by a statistical method. For small problems, the solutions given by the proposed method are compared with solutions provided by a commercial package, and for larger problems, with those obtained by a heuristic proposed in the literature.