The Share-a-Ride Problem with Flexible Compartments (SARPFC) is an extension of the Share-a-Ride Problem (SARP) where both passenger and freight transport are serviced by a single taxi network. The aim of SARPFC is to increase profit by introducing flexible compartments into the SARP model. SARPFC allows taxis to adjust their compartment size within the lower and upper bounds while maintaining the same total capacity permitting them to service more parcels while simultaneously serving at most one passenger. The main contribution of this study is that we formulated a new mathematical model for the problem and proposed a new variant of the Simulated Annealing (SA) algorithm called Simulated Annealing with Mutation Strategy (SAMS) to solve SARPFC. The mutation strategy is an intensification approach to improve the solution based on slack time, which is activated in the later stage of the algorithm. The proposed SAMS was tested on SARP benchmark instances, and the result shows that it outperforms existing algorithms. Several computational studies have also been conducted on the SARPFC instances. The analysis of the effects of compartment size and the portion of package requests to the total profit showed that, on average, utilizing flexible compartments as in SARPFC brings in more profit than using a fixed-size compartment as in SARP.

中文翻译：

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具有灵活隔间的共享乘车问题的变异策略模拟退火

灵活车厢拼车问题 (SARPFC) 是拼车问题 (SARP) 的延伸，其中客运和货运均由单一出租车网络提供服务。SARPFC 的目标是通过在 SARP 模型中引入灵活的隔间来增加利润。SARPFC 允许出租车在上下限范围内调整其车厢大小，同时保持相同的总容量，从而允许它们在最多为一名乘客提供服务的同时为更多包裹提供服务。这项研究的主要贡献是我们为该问题制定了一个新的数学模型，并提出了模拟退火 (SA) 算法的一种新变体，称为具有突变策略的模拟退火 (SAMS) 来解决 SARPFC。变异策略是一种基于松弛时间改进解的强化方法，它在算法的后期阶段被激活。所提出的 SAMS 在 SARP 基准实例上进行了测试，结果表明它优于现有算法。还对 SARPFC 实例进行了多项计算研究。对隔间尺寸和包裹请求部分对总利润的影响的分析表明，平均而言，在 SARPFC 中使用灵活隔间比在 SARP 中使用固定尺寸隔间带来更多利润。