Chase C. Murray

A corrected formulation for the double row layout problem

This paper presents a corrected formulation to the mixed integer programming model of the double-row layout problem (DRLP), first proposed by Chung and Tanchoco (2010, The double row layout problem. International Journal of Production Research, 48 (3), 709–727). In the DRLP, machines are placed along two rows of a corridor, where the objective is to minimise the total cost of material handling for products that move between these machines. We highlight the errors in the original formulation, propose corrections to the formulation, and provide an analytical validation of the corrections.

Solving an Extended Double Row Layout Problem Using Multiobjective Tabu Search and Linear Programming

Facility layout problems have drawn much attention over the years, as evidenced by many different versions and formulations in the manufacturing context. This paper is motivated by semiconductor manufacturing, where the floor space is highly expensive (such as in a cleanroom environment) but there is also considerable material handling amongst machines. This is an integrated optimization task that considers both material movement and manufacturing area. Specifically, a new approach combining multiobjective tabu search with linear programming is proposed for an extended double row layout problem, in which the objective is to determine exact locations of machines in both rows to minimize material handling cost and layout area where material flows are asymmetric. First, a formulation of this layout problem is established. Second, an optimization framework is proposed that utilizes multiobjective tabu search and linear programming to determine a set of non-dominated solutions, which includes both sequences and positions of machines. This framework is applied to various manufacturing situations, and compared with an exact approach and a popular multiobjective genetic algorithm optimization algorithm. Experimental results show that the proposed approach is able to obtain sets of Pareto solutions that are far better than those obtained by the alternative approaches.

Lane Change and Merge Maneuvers for Connected and Automated Vehicles: A Survey

Intelligence in vehicles has developed through the years as self-driving expectations and capabilities have increased. To date, the majority of the literature has focused on longitudinal control topics (e.g. Adaptive Cruise Control (ACC), Cooperative ACC (CACC), etc.). To a lesser extent, there have been a variety of research articles specifically dealing with lateral control, e.g., maneuvers such as lane changes and merging. This paper provides a survey of this particular area of vehicle automation. The key topics addressed are control systems, positioning systems, communication systems, simulation modeling, field tests, surroundings vehicles, and human factors. Overall, there has been some successful research and field testing in lane change and merge maneuvers; however, there is a strong need for standardization and even more research to enable comprehensive field testing of these lateral maneuvers, so that commercial implementation of automated vehicles can be realized.

Incorporating Human Factor Considerations in Unmanned Aerial Vehicle Routing

Unmanned aerial vehicles (UAVs) have become increasingly valuable military assets, and reliance upon them will continue to increase. Despite lacking an onboard pilot, UAVs require crews of up to three human operators. These crews are already experiencing high workload levels, which is a problem that will be likely compounded as the military envisions a future where a single operator controls multiple UAVs. To accomplish this goal, effective scheduling of UAVs and human operators is crucial to future mission success. We present a mathematical model for simultaneously routing UAVs and scheduling human operators, subject to operator workload considerations. This model is thought to be the first of its kind. Numerical examples demonstrate the dangers of ignoring the human element in UAV routing and scheduling.

An efficient local search heuristic for the double row layout problem with asymmetric material flow

Abstract The double row layout problem (DRLP) consists of arranging a number of rectangular machines of varying widths on either side of a corridor to minimize the total cost of material handling for products that move between these machines. This problem arises in the context of many production environments, most notably semiconductor manufacturing. Because the DRLP contains both combinatorial and continuous aspects, traditional solution approaches are not well suited to obtain solutions within a reasonable time. Moreover, previous approaches to this problem did not consider asymmetric flows. In this paper, an effective local search procedure featuring linear programming is proposed for solving the DRLP with asymmetric flows (symmetric flows being a special case). This approach is compared against several constructive heuristics and solutions obtained by a commercial mixed integer linear programming solver to evaluate its performance. Computational results show that the proposed heuristic is an effective approach, both in terms of solution quality and computational effort.

Vehicle routing and resource distribution in postdisaster humanitarian relief operations

After a disaster, supplies must be efficiently and equitably distributed to those in need, wounded persons must be evacuated to triage centers, and relief workers must be transported to affected areas. This complex humanitarian relief problem requires the coordination of numerous vehicles of varying capacities to transport goods, disaster victims, and volunteer workers through a network of roads, some of which may be impassable. To address this problem, a detailed mathematical programming model is presented. Owing to the complexity of this formulation, only small-scale problem instances may be solved optimally via commercial solver software. Therefore, a new heuristic approach is proposed to solve problems of practical size within acceptable time restrictions. The performance of the heuristic is evaluated for numerous representative test instances.

Two-layer simulated annealing and tabu search heuristics for a vehicle routing problem with cross docks and split deliveries

Abstract Cross docking plays an increasingly important role in improving the efficiency of large-scale distribution networks. Unlike traditional warehouses, cross docks hold little or no inventory. Instead, goods from incoming trucks are unloaded and immediately transferred through the cross dock to outgoing trucks. Thus, cross docks serve to reduce inventory holding costs and shorten lead times from suppliers to retailers. However, to fully realize these benefits, trucks must be effectively coordinated at each cross dock. Such coordination brings a challenging extension to vehicle routing problems. In this paper a new vehicle routing problem with cross docks and split deliveries is proposed. A mixed-integer linear programming formulation is established for this problem, along with solution methodologies combining a constructive heuristic with two-layer simulated annealing and tabu search. The constructive heuristic creates a solution which is further improved by two-layer variants of simulated annealing or tabu search. The first layer optimizes the allocation of trucks to cross docks while the second layer optimizes the visitation order to suppliers and retailers for trucks assigned to each cross dock. Experimental results demonstrate that the proposed approach effectively solves large-size problems within a reasonable computational time.

Sharing clearances to improve machine layout

This paper considers a double-row layout problem with shared clearances in the context of semiconductor manufacturing. By sharing some clearances, reductions in both layout area and material handling cost of approximately 7–10% are achieved. Along with minimal clearances for separating adjacent machines, clearances that can be shared by adjacent machines are considered. The shared clearances may be located on either or both sides of machines. A mixed integer linear programming formulation of this problem is established, with the objective to minimise both material flow cost and layout area. A hybrid approach combining multi-objective tabu search and heuristic rules is proposed to solve it. Computational results show that the hybrid approach is very effective for this problem and finds machine layouts with reduced areas and handling costs by exploiting shared clearances.

A tabu search heuristic for the single row layout problem with shared clearances

The single row layout problem is a common and well-studied practical facility layout problem. The problem seeks the arrangement of a fixed number of facilities along one row that minimizes the objective of total material handling cost. In this paper, a single row layout problem with shared clearance between facilities is proposed. The shared additional clearance may be considered on one or both sides of each facility. To solve this problem tabu search is combined with a heuristic rule to solve problems of realistic size. Tabu search is used to find the sequence of facilities while the heuristic rule is determines the additional clearance for each facility. The proposed solution approach is applied to several problem instances involving 10, 20 and 30 facilities, and is compared against a popular mathematical programming solver (CPLEX). Computational results show that our approach is able to obtain high quality solutions and outperforms CPLEX under limited computational time for problems of realistic sizes.

The Double-Bay Layout Problem

The layout of a semiconductor manufacturing facility can play a significant role in reducing operating costs and improving productivity. This paper introduces the double-bay layout problem that is encountered in semiconductor fabrication. This new problem not only considers the assignment and relative ordering of machines in two bays but also determines the exact location of each machine, and incorporates two objectives of minimizing material handling cost and layout area. A hybrid methodology combining a multi-objective genetic algorithm with linear programming is proposed to solve this problem. The genetic algorithm effectively identifies the set of non-dominated machine sequences, while the linear program uses the relative assignments obtained by the genetic algorithm to determine optimal absolute machine locations. Experimental results indicate that the proposed hybrid methodology finds Pareto-optimal solutions for small-scale problems and high-quality solutions for problems of practical size.