The Share-a-Ride Problem with Adjustable Compartment (SARPAC) is a study conducted with an aim to reduce traffic congestion and taxi wasted space when servicing a passenger. It is an extension of the Share-a-ride Problem (SARP) where both passenger and freight transport is handled by a single taxi network. SARPAC allows a taxi to adjust its compartment size within its lower and upper bounds while maintaining the same total capacity, allowing it to service more packages while also service one passenger at the same time. This will allow taxies to fully utilize their space to maximize profit. We propose a Simulated Annealing (SA) algorithm to solve SARPAC. Furthermore, we study the effect of delaying slack time mechanism on our algorithm’s computational time and solution quality by activating mutation neighbourhood at a later stage of temperature reduction. The performance of our algorithm is benchmarked against CPLEX for small instances and the SARP instances for large instances. The objective function of SARPAC is to maximize total profit obtained from serving passenger and parcel requests simultaneously. The proposed algorithm obtains optimal solutions for small instances with reasonable computational time. When compared to SARP result on large instances, SARPAC model obtains an average of 61.80% more profit.

The Share-a-Ride Problem with Adjustable Compartment (SARPAC) is a study conducted with an aim to reduce traffic congestion and taxi wasted space when servicing a passenger. It is an extension of the Share-a-ride Problem (SARP) where both passenger and freight transport is handled by a single taxi network. SARPAC allows a taxi to adjust its compartment size within its lower and upper bounds while maintaining the same total capacity, allowing it to service more packages while also service one passenger at the same time. This will allow taxies to fully utilize their space to maximize profit. We propose a Simulated Annealing (SA) algorithm to solve SARPAC. Furthermore, we study the effect of delaying slack time mechanism on our algorithm’s computational time and solution quality by activating mutation neighbourhood at a later stage of temperature reduction. The performance of our algorithm is benchmarked against CPLEX for small instances and the SARP instances for large instances. The objective function of SARPAC is to maximize total profit obtained from serving passenger and parcel requests simultaneously. The proposed algorithm obtains optimal solutions for small instances with reasonable computational time. When compared to SARP result on large instances, SARPAC model obtains an average of 61.80% more profit.

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