A bilevel fixed charge location model for facilities under imminent attack

We investigate a bilevel fixed charge facility location problem for a system planner (the defender) who has to provide public service to customers. The defender cannot dictate customer-facility assignments since the customers pick their facility of choice according to its proximity. Thus, each facility must have sufficient capacity installed to accommodate all customers for whom it is the closest one. Facilities can be opened either in the protected or unprotected mode. Protection immunizes against an attacker who is capable of destroying at most r unprotected facilities in the worst-case scenario. Partial protection or interdiction is not possible. The defender selects facility sites from m candidate locations which have different costs. The attacker is assumed to know the unprotected facilities with certainty. He makes his interdiction plan so as to maximize the total post-attack cost incurred by the defender. If a facility has been interdicted, its customers are reallocated to the closest available facilities making capacity expansion necessary. The problem is formulated as a static Stackelberg game between the defender (leader) and the attacker (follower). Two solution methods are proposed. The first is a tabu search heuristic where a hash function calculates and records the hash values of all visited solutions for the purpose of avoiding cycling. The second is a sequential method in which the location and protection decisions are separated. Both methods are tested on 60 randomly generated instances in which m ranges from 10 to 30, and r varies between 1 and 3. The solutions are further validated by means of an exhaustive search algorithm. Test results show that the defender's facility opening plan is sensitive to the protection and distance costs.     

Applying two efficient hybrid heuristics for hub location problem with fully interconnected backbone and access networks

This paper considers the design of two-layered networks with fully interconnected backbone and access networks. The problem, a specific application of hub location to network design, is known as fully interconnected network design problem (FINDP). A novel mathematical programming formulation advantageous over an earlier formulation is presented to model the problem. Two hybrid heuristics are proposed to solve the problem, namely SA_VNS and TS_VNS which incorporate a variable neighborhood search (VNS) algorithm into the framework of simulated annealing (SA) and tabu search (TS). The proposed algorithms are able to easily obtain the optimal solutions for 24 small instances existing in the literature in addition to efficiently solve new generated medium and large instances. Results indicate that the proposed algorithms generate high quality solutions in a quite short CPU time.     

The optimization of warehouse location and resources distribution for emergency rescue under uncertainty

China is one of the countries that suffer the most natural disasters in the world. The situation of emergency response and rescue is extremely tough. Establishing the emergency warehouse is one of the important ways to cope with rapid-onset disasters. In this paper, a mixed integer programming (MIP) model based on time cost under uncertainty is proposed, which help solve the emergency warehouse location and distribution problem. Comprehensive consideration of factors such as time cost, penalty cost for lack of resources, alternative origins of resources from both suppliers and emergency warehouses, different means of transportation and multiple resources types are involved in our study. We also introduce uncertain scenarios to describe the severity of the disaster. Particle swarm optimization (PSO) and variable neighborhood search (VNS) are designed to solve the MIP model of different scales of instances. Numerous examples have been tested to compare two heuristics with commercial solver (CPLEX). Both of two algorithms can obtain the exact solution same as CPLEX in small-scale instances while show great performance on larger instances with 10 candidate warehouses, 25 disasters and 50 scenarios.     

A hybrid variable neighborhood search algorithm for solving the limited-buffer permutation flow shop scheduling problem with the makespan criterion

This paper investigates the limited-buffer permutation flow shop scheduling problem (LBPFSP) with the makespan criterion. A hybrid variable neighborhood search (HVNS) algorithm hybridized with the simulated annealing algorithm is used to solve the problem. A method is also developed to decrease the computational effort needed to implement different types of local search approaches used in the HVNS algorithm. Computational results show the higher efficiency of the HVNS algorithm as compared with the state-of-the-art algorithms. In addition, the HVNS algorithm is competitive with the algorithms proposed in the literature for solving the blocking flow shop scheduling problem (i.e., LBPFSP with zero-capacity buffers), and finds 54 new upper bounds for the Taillard's benchmark instances.     

A decision model on human-robot collaborative routing for automatic logistics

This paper studies an integrated human-robot routing problem arising in collaborative order picking systems. Travelling routes of pickers and robots interact with each other and simultaneously determine the start time of tasks. A mixed-integer programming model is proposed which aims to minimize the total task completion time by optimizing both pickers’ and robots’ routes. Moreover, a variable neighborhood search and dynamic programming -based heuristic algorithm is developed for solving the problem in large-scale environments. Numerical experiments are conducted to prove the necessity of the proposed model and also validate the efficiency of the proposed heuristic algorithm. The results indicate the methodology can yield solutions with smaller optimality gap within a reasonable period of time, in comparison with an intuitive but common decision rule. A sensitivity analysis is also conducted to derive a number of managerial implications regarding the configuration of pickers and robots and robots’ speed setting.     

A linearized polynomial mixed integer programming model for the integration of process planning and scheduling

This paper presents a linearized polynomial mixed-integer programming model (PMIPM) for the integration of process planning and scheduling problem. First, the integration problem is modeled as a PMIPM in which some of the terms are of products of up to three variables, of both binary and continuous in nature. Then, an equivalent linearized model is derived from the polynomial model by applying certain linearization techniques. Although the linearized models have more variables and constraints than their polynomial counterparts, they are potentially solvable to the optimum in comparison to their equivalent polynomial models. Experiments show that the linearized model possesses certain characteristics that are absent from other models in the literature, and provides a fundamental framework for further research in this area.