Hybrid Genetic-simulated Annealing Algorithm for Optimal Weapon Allocation in Multilayer Defence Scenario

Simulated annealing is one of the several heuristic optimisation techniques, that has been studied in the past to determine the most effective mix of weapons and their allocation to enemytargets in a multilayer defence scenario. Simulated annealing is a general stochastic search algorithm. It is usually employed as an optimisation method to find a near-optimal solution forhard combinatorial optimisation problems, but it is very difficult to give the accuracy of the  solution found. To find a better solution, aji often used strategy is to run the algorithm byapplying the existing best solution from the population space as the initial starting point. Giving many passes of genetic algorithm can generate the best start-point solution. This paper describes a new hybrid optimisation method, named genetic-simulated annealing, that combines the global crossover operators from genetic algorithm and the local stochastic hill-climbing features from simulated annealing, to arrive at an improved solution with reduced computational time. The basic idea is to use the genetic operators of genetic algorithm to quickly converge the search  to a near-global minima/maxima, that will further be refined to a near-optimum solution by simulated anneling using annealing process. The new hybrid algorithm has been applied to optimal weapon allocation in multilayer defence scenario problem to arrive at a better solution than produced by genetic algorithm or simulated annealing alone

Genetic algorithm for optimal weapon allocation in multilayer defence scenario

Several heuristic optimisation techniques have been studied in the past to determine the most effective mix of weapons and their allocation to enemy targets in a multilayer defence scenario. This paper discusses a genetic algorithm approach to arrive at improved solutions with reduced computational time. The most important aspect of the new approach is the mapping of the nonlinear optimisation problem into a discrete problem. The results demonstrate that if the problem mapping is correct, even a primitive algorithm can yield high quality results to a complex optimisation problem

Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art

Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP

Nurse Rostering: A Tabu Search Technique With Embedded Nurse Preferences

The decision making in assigning all nursing staffs to shift duties in a hospital unit must be done appropriately because it is a crucial task due to various requirements and constraints that need to be fulfilled. The shift assignment or also known as roster has a great impact on the nurses’ operational circumstances which are strongly related to the intensity of quality of health care. The head nurse usually spends a substantial amount of time developing manual rosters, especially when there are many staff requests. Yet, sometimes she could not ensure that all constraints are met. Therefore, this research identified the relevant constraints being imposed in solving the nurse rostering problem (NRP) and examined the efficient method to generate the nurse roster based on constraints involved. Subsequently, as part of this research, we develop a Tabu Search (TS) model to solve a particular NRP. There are two aspects of enhancement in the proposed TS model. The first aspect is in the initialization phase of the TS model, where we introduced a semi-random initialization method to produce an initial solution. The advantage of using this initialization method is that it avoids the violation of hard constraints at any time in the TS process. The second aspect is in the neighbourhood generation phase, where several neighbours need to be generated as part of the TS approach. In this phase, we introduced two different neighbourhood generation methods, which are specific to the NRP. The proposed TS model is evaluated for its efficiency, where 30 samples of rosters generated were taken for analysis. The feasible solutions (i.e. the roster) were evaluated based on their minimum penalty values. The penalty values were given based on different violations of hard and soft constraints. The TS model is able to produce efficient rosters which do not violate any hard constraints and at the same time, fulfill the soft constraints as much as possible. The performance of the model is certainly better than the manually generated model and also comparable to the existing similar nurse rostering model

Alternatívy k MILP pre rozvrhovanie dávkových banských procesov

Cie¾om príspevku je navrhnú monos, nahradenia optimalizaèných metód na báze zmieaného celoèíselného programovania pouitím pribliných metód rozhodovania (heuristika) v oblasti plánovania a riadenia banských procesov. Výsledkom tohto poèítaèom podporaovaného plánovania sú detailné výrobné rozvrhy vytvorené pod¾a poiadaviek ako sú vysoká efektivita výroby, alebo zníená redukcia odpadov. Tieto problémy patria medzi zloité, NP-úplné problémy, èie ich rieenie je v súèasnosti pod¾a náho názoru pre reálne problémy efektívnejie s pomocou heuristík. Heuristiky uvedené v príspevku sú: simulované íhanie, tabu search a genetické algoritmy. Tabu search(metóda zakázaného preh¾adávania), aj keï sa jedná o heuristiku, je v tandardnej verzii deterministicky stachastická. V prispevku sú zhrnuté hlavné výhody heuristík v porovnaní s MILP, predovetkým ich rýchlos a jednoduchos a teda aj niie nároky na výpoètovú techniku a software, ako aj kvalita poskytovaných výsledkov. Èlánok uvádza struèný popis rieených problémov a základy matematického popisu týchto problémov, zhrnuté sú aj rôzne ciele optimalizácie reprezentované rôznymi cie¾ovými kritériami. Príspevok ïalej popisuje jednotlivé heuristiky, ich princípy, ich vlastnosti a ich monosti, poiadavky ktoré musí úloha splòova, aby bolo moné algoritmus poui. Prekadú z uvedených heuristík uvádzame aj slovný popis jednotlivých èastí algoritmu. Èlánok uvádza jednotlivé výsledky porovnania výkonov týchto heuristík oproti MILP, uvedené sú aj výsledky aplikácie týchto algoritmov na iné podobné problémy v chemickom priemysle. Vzh¾adom k zámerom tohto príspevku text obsahuje aj odkazy na ïaliu literatúru zaoberajúcu sa touto problematikou

An interactive product development model in remanufacturing environment: a chaos-based artificial bee colony approach

This research presents an interactive product development model in re-manufacturing environment. The product development model defined a quantitative value model considering product design and development tasks and their value attributes responsible to describe functions of the product. At the last stage of the product development process, re-manufacturing feasibility of used components is incorporated. The consummate feature of this consideration lies in considering variability in cost, weight, and size of the constituted components depending on its types and physical states. Further, this research focuses on reverse logistics paradigm to drive environmental management and economic concerns of the manufacturing industry after the product launching and selling in the market. Moreover, the model is extended by integrating it with RFID technology. This RFID embedded model is aimed at analyzing the economical impact on the account of having advantage of a real time system with reduced inventory shrinkage, reduced processing time, reduced labor cost, process accuracy, and other directly measurable benefits. Consideration the computational complexity involved in product development process reverse logistics, this research proposes; Self-Guided Algorithms & Control (S-CAG) approach for the product development model, and Chaos-based Interactive Artificial Bee Colony (CI-ABC) approach for re-manufacturing model. Illustrative Examples has been presented to test the efficacy of the models. Numerical results from using the S-CAG and CI-ABC for optimal performance are presented and analyzed. The results clearly reveal the efficacy of proposed algorithms when applied to the underlying problems. --Abstract, page iv

Optimization of manpower allocation by considering customer relationship management criteria and uncertainty conditions in car dealerships

Purpose A mathematical mixed integer model was used in this research in order to optimize manpower allocation in car industry. The objective function of proposed model subjected to minimization of the maximum waiting time for customers in service queue and limitations included manpower allocation and time calculation for each service in each center. Methodology: Therefore, mathematical optimization methods were employed in this research. To solve the problem at small dimensions, BARON solver was used through GAMS software. Metaheuristic algorithms were used to solve the large dimensions of problem due to NP-hard nature of allocation problem. However, these algorithms have been designed based on the natural elements; hence, a stochastic procedure is applied to generate initial responses and to improve the process to obtained the final response. Therefore, proper comparisons should be done to make sure of accurate performance of such procedure. To this end, three metaheuristic algorithms of Genetic, Harmony Search and Gray Wolf were used to solve the final problem. Findings: According to the obtained computational results, gray wolf algorithm had the highest performance efficiency compared to other algorithms so it is more practical in solving the real numerical samples. Originality/Value: The objective function of proposed model subjected to minimization of the maximum waiting time for customers in service queue and limitations included manpower allocation and time calculation for each service in each center. We used three metaheuristic algorithms, Genetic, Harmony Search and Gray Wolf, to solve the final problem